Research ArticleThe Complexity of Technological Innovation Decision-Making inEmerging Industries

Wenjing Li 12 Xue Guo 12 and Dan Cao 3

1School of Economics Wuhan Textile University Wuhan 430200 China2Center of Industrial Economy Wuhan Textile University Wuhan 430200 China3School of Business Administration Zhejiang Gongshang University Hangzhou 310018 China

Correspondence should be addressed to Dan Cao cdmailzjgsueducn

Received 14 May 2021 Revised 5 July 2021 Accepted 12 July 2021 Published 31 July 2021

Academic Editor Baogui Xin

Copyright copy 2021 Wenjing Li et al +is is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work isproperly cited

It is well known that innovation-driven emerging industries have gradually become the main driving force of global economicrecovery and growth Technological innovation decision-making is a complex and dynamic system which is affected by variousfactors inside and outside an enterprise In this dynamic system how to make the optimal technological innovation investmentdecisions is a key concern for enterprises and governments As an investment activity technological innovation largely dependson the amount of external financing obtained by enterprises However financial constraints have increasingly become an obstacleto enterprisesrsquo technological innovation At the same time technological innovation is also affected by the external political andeconomic environment such as changes in economic policy government subsidy policies and institutional environmentalpolicies Can these external environments reduce the negative impact of financing constraints on technological innovation In thisstudy based on the data of listed companies in Chinarsquos strategic emerging industries we adopt a panel negative binomialregression model to investigate the complexity of technological innovation decision-making from the perspective of financingconstraints Our main findings include the following First financing constraints significantly inhibit the input and output oftechnological innovation in emerging industries Second the inhibition effect on the output of substantive innovations is morepronounced than that on the output of strategic innovations +ird based on the analysis of enterprise heterogeneity in differentdimensions we show that this inhibition has a selective effect among different industries Finally we show that economic policyand marketization can help alleviate the inhibition effect of financing constraints on technological innovation

1 Introduction

Innovation is a key issue of economic development and hasbecome a driving force for high-quality development [1]Innovation can bring more efficient technologies and pro-mote social development [2] No high-quality developmentis possible without the important supporting role of tech-nological innovation Furthermore strategic emerging in-dustries is the key forces of technological innovation +ecultivation and development of strategic emerging industriesare of great practical significance for building a modern

economic system and realizing high-quality economic de-velopment [3 4]

As an important investment activity technologicalinnovation is inevitably affected by a variety of factorsinside and outside an enterprise One is from the internalperspective of the enterprise such as its nature propertyrights governance structure strategy equity incentivessize age investment strategies and financial constraints+e other is from the external perspective of the enter-prise such as economic policy uncertainty and market-ization process In fact technological innovation depends

HindawiComplexityVolume 2021 Article ID 3611921 14 pageshttpsdoiorg10115520213611921

to a large extent on the amount of external financing anenterprise can secure +e external political and economicenvironment influences its financing constraints and ul-timately affects its technological innovation decision-making How to effectively incorporate internal and externalfactors when studying the dynamic decision-making ofenterprise technological innovation will be the focus of thisarticle

Technological innovation activities cannot be sepa-rated from the support of financing +e RampD and in-novation activities of enterprises depend to a large extenton whether they can obtain sufficient external equity fi-nancing and debt financing [5 6] In the face of largeinvestments in technological innovation the biggest ob-stacle faced by enterprises is capital constraints Manytechnological innovation projects are subject to capitalconstraints and eventually are shelved In a perfect capitalmarket information is completely symmetrical and thereare no transaction costs and financing costs so companiescan easily raise the funds from the capital marketHowever there are often financing constraints in theactual capital market due to information asymmetrymoral hazard and adverse selection which increase thecost and difficulty of external financing for enterprisesCompared with general investment the informationasymmetry in technological innovation is more severeMoreover technological innovation requires a greateramount of investment and longer RampD cycle As a resultit is more difficult for enterprises to obtain external fi-nancing for technological innovation +is can causeserious financing constraints +en do financing con-straints affect the companyrsquos technological innovationdecisions Is this effect related to enterprise and industrycharacteristics How do economic policy environment andmarketization work under such effect In this study we aimto analyze these problems Our results can provide theo-retical support for building a financial system suitable fortechnological innovation and providing guidance for betterimplementing the innovation-driven developmentstrategy

In this study we exploit the listed companies in Chinarsquosstrategic emerging industries over the period 2009 to 2019 tostudy the decision-making of technological innovation inemerging industries We focus on strategic emerging in-dustries because these industries are the main forces intechnological innovation

Given that technological innovation is also affected bythe external political and economic environment such aseconomic policy fluctuations and institutional environ-mental policies whether these external environments canminimize the negative effects of financing constraints ontechnological innovation stimulate enterprisesrsquo investmentin technological innovation and enhance enterprisesrsquotechnological innovation capability is also a key aspect ofnational policy attention Hence we add economic policyuncertainty and marketization environment variables as themoderator to test whether they can alleviate financingconstraints on technological innovation Our findings doindicate economic policy uncertainty and marketization can

weaken the negative effects of financing constraints ontechnological innovation Specifically the higher the mar-ketization the smaller the inhibition effect and the greaterthe uncertainty of economic policy the smaller the inhi-bition effect of financing constraints on technologicalinnovation

In this study we make three major contributions Firstlyin the literature most scholars analyzed the financingconstraints through investment-cash flow sensitivity basedon the idea of Fazzri et al [7] However more recent studiesfound that the investment-cash flow sensitivity has its owndrawbacks In this study financing constraints index (re-ferred to as FC in the text) is constructed through a series offinancial indicators to measure the financing constraintswhich are then incorporated into the overall analyticalframework of technological innovation as an influencingfactor Secondly the influence of financing constraints ontechnological innovation is studied from both the input andoutput of technological innovation In particular we analyzethe impact of financing constraints on the innovation outputof different types (strategic innovation and substantive in-novation) It is found that the inhibitory effect of financingconstraints on substantive innovation such as inventionpatent is more pronounced than that of strategic innova-tion +irdly we reveal the roles of economic policy un-certainty and the marketization in regulating the negativecorrelation between financing constraints and technologicalinnovations +is helps to realize the connection of themicrobehaviour and macro policy and provide the micro-evidence at the enterprise level on how the macropolicydrives the investment

+e rest of the article is organized as follows Section 2presents a literature review Section 3 develops the researchhypotheses Section 4 discusses the data and methodologyincluding sample selecting procedure data sources andmodel specification and the empirical results and relateddiscussions are described in Section 6 Finally Section 2presents the conclusion and policy implications

2 Literature Review

Innovation and financial development are both core factorsaffecting sustainable development of economy In the 21stcentury technological innovation plays a more importantrole in the countryrsquos comprehensive competitiveness whilefinancial constraints also become an increasingly seriousobstacle in the process of enterprise development In thiscontext scholars began to focus on the impact of financialdevelopment on the technological innovation of enterprises+e literature generally affirmed the promoting effect offinancial development on technological innovation [8 9]However there was no consensus on the mechanism offinancial influence innovation One view held that financialdevelopment promotes technological innovation by allevi-ating financing constraints faced by enterprises that isreducing financing costs and broadening financing channels[10 11] +e other view maintains that financial develop-ment boosts technological innovation by improving the

2 Complexity

efficiency of capital allocation [12] +ese studies focused onthe relationship between financial development and tech-nological innovation from a macroperspective and do notexplore its micromechanism in more detail Howevertechnological innovation is always the behavior of enter-prises at the microlevel +e transmission mechanism offinancial development to technological innovation still needsto be investigated on the motivation and specific behavior ofenterprises

As information economics took off scholars began tostudy financial constraints under the condition of infor-mation asymmetry Since Fazzari Hubbard and Petersenfirst proposed to measure the financing constraints using theinvestment-cash flow sensitivity [7] this method was sub-sequently extended to research and development investmentand other investment fields to seek stronger theoreticalsupport of financing constraints from the perspective of cashflow On the basis of this theory a large number of scholarsdiscussed the relationship between financing constraints andtechnology innovation investment from the perspective ofthe cash flow However there were great differences in thedevelopment of capital markets and innovation investmentin various countries When different countries were used assamples to study the relationship between financing con-straints and technological innovation there were somedifferences in the conclusions Hall believed that it is pre-cisely because of the difficulties in raising funds from outsidefor innovation investment that companies mainly rely oninternal financing for innovation [13] Hall Himmelbergand Petersen and He studied American companies andfound a strong positive correlation between RampD invest-ment and cash flow sensitivity [14ndash16] Mulkay et al heldthat the sensitivity of innovation investment to cash flow inthe United States is more pronounced than that in Franceand Japan [17] Harhoff showed that there is a statisticallysignificant but weak relationship between RampD investmentand cash flow of German companies [18] Bond and Harhoffheld that there is no significant relationship between RampDinvestment and cash flow in German and the UK [19] Blochbelieved that RampD investment expenditures of Danishcompanies are significantly affected by internal cash flowand RampD investment has investment-cash flow sensitivity[20]

With the advancement of technology and the expansionof production scale it is difficult to meet the capital needs ofenterprises solely by relying on internal financing forprojects with large investment such as technological inno-vation and external financing has gradually become themain financing channel for technological innovation Re-searchers began to focus on the financing constraints oftechnological innovation from the perspective of externalfinancing External funds for RampD projects are believed to berelatively scarce mainly due to the serious informationasymmetry in the financing process +e uncertainty andstrategic nature of RampD may restrict managers from dis-closing relevant information about RampD projects to externalinvestors in order to prevent the information from beingknown to competitors [21ndash23]

Hall pointed out that despite the adoption of measuressuch as intellectual property protection government sub-sidies and tax incentives technological innovation invest-ment is still difficult to finance or the financing cost is high+ere is often a big gap between the rate of return requiredby enterprises to use their own capital and the rate of returnrequired by outside investors which leads to the high cost ofexternal capital and some innovation projects cannot evenbe financed from outside [24] Brown and Petersen believedthat the output of innovation activities is characterized bynonexclusive indivisibility and uncertainty leading to thesocial optimal investment level much higher than the privateoptimal level which seriously hinders the enterprises fromusing all the internal funds for RampD [25] Li held thatcompared with general investment RampD investment is moreinflexible and RampD-intensive companies facing financingconstraints are more likely to pause or cut off RampD projects+e risk of RampD-intensive companies increases with thedegree of financing constraints [26] Mina et al believed thatthe uncertainty of innovation activities have a negativeimpact on financial supply which is related to the expec-tation of enterprises to take risky projects leading to higherexternal capital costs and possible access to suboptimalexternal financial resources [27]

In addition to the internal characteristics of the enter-prise the external environment will inevitably have animpact on innovation decisions Relevant literature con-centrates on research from the perspective of economicpolicies government subsidies and marketization+ere aretwo views on the impact of economic policy uncertainty ontechnological innovation One view is that economic policyuncertainty can have an incentive effect on technologicalinnovation +is is because economic policy uncertaintyrepresents an opportunity to increase revenue in the future[28] Under the motive of pursuing profit uncertainty isbeneficial to enterprises to increase RampD investment [29]+e other view based on the analysis of the physical budgettheory believes that the uncertainty of economic policiesinhibits the technological innovation [30] Regarding theeffect of marketization most scholars have affirmed the roleof marketization in the process of technological innovationand believed that the improvement of marketization isgenerally conducive to promoting technological innovation[31ndash33] Nonetheless no consensus has been reached onwhich systems play a role in promoting technologicalinnovation

When studying the impact of financing constraints ontechnological innovation most scholars only focused on in-novation input and rarely paid close attention to the impact offinancing constraints on technological innovation outputHowever technological innovation output is the key to pro-mote the development of social productivity especially sub-stantive innovation (invention patents) +erefore the impactof financing constraints on technological innovation output isworth of social attention In view of the above considerationsthe impact of financing constraints on technological inno-vation should be extended from technological innovationinput to technological innovation output aiming to

Complexity 3

comprehensively analyze the impact of financing constraintson technological innovation activities in strategic emergingindustries At the same time most scholars rarely took theexternal political and economic environment of the firm intoaccount and there was a lack of research on the combiningfinancing constraints with environmental factors such aseconomic policy uncertainty and institutional environment Infact technological innovation depends to a large extent on theamount of external financing that a company obtains Eco-nomic policy uncertainty and institutional environment bothaffect the companyrsquos external financing environment and thenaffect the companyrsquos financing constraints and ultimatelyaffect the companyrsquos technological innovation activity

3 Research Hypotheses

Arrow believed that technological innovations have exter-nalities showing spillover effects and diffusion effects whichmake technological innovation show strong nonexclusiveand limited exclusivity +e diffusion of externalities enablesthe competitors to acquire technology at a lower cost thaninnovators in order to improve their productivity and in-novation [34] +e enterprises undertaking technologicalinnovation burden the high cost but they fail to access all thebenefits Under the spillover effect of technological inno-vation some of the profits are occupied by other enterprisesso the enterprises are less motivated to engage in techno-logical innovation and even have the idea of free rider

According to the theory of net present value theprinciple of optimal investment decision is that marginalrevenue equals marginal cost Financing constraints disablethe enterprise to make optimal investment decisions andinhibit technological innovation +ere are two main rea-sons One reason is that due to the limited internal fundsexternal financing is required However companies willweigh the cost of financing and their benefits [35] Once thecompany faces financing constraints the cost of externalfunds will be higher than the cost of internal funds whichincreases the cost of technological innovation When suchinvestment cost increases to the present value of the projectrsquosexpected cash flow the enterprise may cease its investmentin technological innovation thus delaying the innovationproject that could have been implemented +e other reasonis the difficulty of external financingwhich prevents en-terprises from obtaining the funds they need +en even ifthe marginal revenue of an enterprise is greater than itsmarginal cost technological innovation projects cannot beimplemented due to capital constraints

It can be seen that under the influence of the externalcharacteristics of technological innovation enterprisesrsquowillingness to undertake technological innovation is nothigh When the financing constraints become tighter theenterprisersquos investment cost in technological innovation willincrease correspondingly +en the willingness of enter-prises to undertake technological innovation activities willalso decline +erefore Hypothesis 1 is put forward

Hypothesis 1 Financing constraints negatively affect thetechnological innovation In other words financing con-straints have an inhibitory effect on the enterprisersquos tech-nological innovation

Strategic emerging industries consist of nine subin-dustries Since the characteristics of financing constraintsand technological innovation vary in different industries theimpact of financing constraints on technological innovationmust be different to some extent in certain industries+erefore Hypothesis 2 is put forward

Hypothesis 2 +e influence of financing constraint ondifferent subindustries of strategic emerging industries isdifferent

Due to the great difference between the output of themanufacturing industry and that of the nonmanufacturingindustry there are different characteristics in innovation+e manufacturing industry is dominated by technologicalinnovation while the nonmanufacturing industry is dom-inated by service innovations such as organizational in-novation normative innovation and characteristicinnovation +ese service innovations have little to do withtechnological innovation Meanwhile the strategic positionof technological innovation in enterprises also varies greatlyYuan pointed out that there is an obvious difference in thedependence of the manufacturing industry and service in-dustry on RampD +e manufacturing industry has a relativelylarge investment in technological innovation and RampDinvestment plays a decisive role Many companies even haveset up specialized RampD departments while the service in-dustry has a smaller dependence on RampD and the invest-ment in technological innovation is far lower than that of themanufacturing industry [36] +erefore the manufacturingindustry has a strong desire to improve the productivity ofenterprises through technological innovation It can then beinferred that the inhibitory effect of financing constraints onthe manufacturing industry is smaller than that on thenonmanufacturing industry +erefore Hypothesis 3 is putforward

Hypothesis 3 +e negative impact of financing constrainton manufacturing industry is less than that on nonmanu-facturing industry

Rajan and Zingales constructed the industryrsquos externalfinancing dependence which was used to measure theindustryrsquos dependence on external funds and they analyzed36 industries in US industrial companies [10] +ey came toa conclusion that traditional industries generally rely less onexternal financing while emerging industries mostly relymore on external financing+e top three industries with thehighest external financing dependence are drugs andpharmaceuticals plastics and computing +e three in-dustries with the lowest external financing dependence arethe tobacco pottery manufacturing and leather industriesAccording to the definition and scope of strategic emergingindustries in China it can be found that most strategic

4 Complexity

emerging industries are highly dependent on external fi-nancings such as the new generation of the informationtechnology industry and the biomedical industry Based onthe industryrsquos external financing dependence we put for-ward the following expectations the deterioration of thefinancing environment will have a greater impact on thecomputer industry and other industries with higher externalfinancing dependence than those with low dependence onexternal financing +erefore Hypothesis 4 is put forward

Hypothesis 4 +e negative impact of financing constraintson the industries with low external financing dependence isless than that on the industries with medium- and high-levelexternal financing dependence

For general investment scholars generally believe thatthe economic policy uncertainty inhibits enterprisesrsquo in-vestment but technological innovation differs from generalinvestment and thus the impact of economic policy un-certainty may vary correspondingly Technological inno-vations are full of high uncertainty which also means thatthere are many market opportunities in the future It is thesource of future profits for enterprises +e future profitopportunities will disappear if these uncertainties are re-moved while enterprises pursue high profits Uncertaintywill stimulate their innovation behaviors and encourageentrepreneurs to pursue greater profits From this per-spective the uncertainty of economic policy means not onlyrisks and challenges but also opportunities and benefits Forentrepreneurs with an innovative spirit they will make fulluse of the opportunities brought by the economic policyuncertainty avoid risks create profits for enterprises andenable the enterprises to develop and grow stronger Fur-thermore the adjustment cost of technical innovation ishigher than that of physical capital investment [24]+erefore enterprises can save a lot of ldquoadjustment costsrdquo bykeeping RampD investment stable +en in the face of eco-nomic policy uncertainty enterprises will tend to make useof the opportunities and keep the stable investment oftechnological innovation so that they can maintain con-tinuous investment in technological innovation +ereforeHypothesis 5 is put forward

Hypothesis 5 +e restraining effect of financing constraintsin periods of high economic policy uncertainty is less thanthat in periods of low economic policy uncertainty that isfinancing constraints have a more restraining effect ontechnological innovation in periods of low economic policyuncertainty

Generally speaking in the highly marketized regionsthere are more mature and effective laws and regulationsless government interventions a more effective financialsystem and an open and transparent information disclosurewhich provides a proper external political environment fortechnological innovation As for the reasons first themarket-oriented institutional environment helps to alleviatethe information asymmetry between enterprises and in-vestors reduce the cost of external financing and broadenthe financing channels of enterprises thereby optimizing theexternal financing structure of enterprises andmaking up for

the lack of funds for technological innovation [37] Secondthe market-oriented system enhances the function of themarket in resource allocation It not only reduces thegovernmentrsquos intervention and possibility of rent-seekingbut also improves the specificity of innovation stimulatesthe current RampD investment and enhances the innovationinitiative through a perfect patent protection system (Wuand Tang) [38] +ird the market-oriented system canprovide more sensitive price information for enterprises andmake the capital flow to the sectors with high-efficiencyinvestment thereby improving the allocation efficiency ofcapital and reducing the distortion of capital price and therisk premium of capital to a certain extent Fourth themarket-oriented system increases the labor remuneration oftechnical innovation personnel through income distributionand the rate of return on the technology of technical per-sonnel stimulates the innovation enthusiasm of technicalpersonnel and promotes the technological innovation Fifththe improvement of the market-oriented system can effec-tively promote Chinarsquos foreign trade +rough the importand export of commodities it is conducive to the intro-duction and absorption of technology by Chinese compa-nies Under the influence of technology spillover effects itcan promote innovation activities of Chinese enterprises (Liand Liu) [33] +erefore the market-oriented institutionalenvironment is conducive to alleviate the negative effect offinancial constraints on technological innovation+ereforeHypothesis 6 is put forward

Hypothesis 6 +e restraining effect of financing constraintson technological innovation in high-market-oriented areasis less than that in low-market-oriented areas that is it has amore restraining effect on enterprises in low-market-ori-ented areas

4 Data and Methodology

41 Model Specification In order to test Hypothesis 1 weconstruct two models from the perspectives of input andoutput of technological innovation +e model of techno-logical innovation input is as follows

Innovationgit +0 + α1Fconstraintsitminus1 + 1113944 θixitminus1

+ indit + θt + εit(1)

In view of the continuity of technological innovationactivities when setting the model in this study explanatoryvariables and control variables will lag by one period (exceptthe variable of company age) Innovationgit represents thetechnological innovation input of the company i in the yeart Fconstraintsitminus1 represents the variable of financingconstraints indicating the financing constraints of thecompany i in the year t minus 1 1113936 θixitminus1 represents the vectorcombination of control variables indit represents the in-dustrial effect θt represents the time effect and εit repre-sents the comprehensive error

Since the number of patent applications which is theexplained variable to measure the technological innovationoutput is a discrete variable and does not necessarily meet

Complexity 5

the assumption of linear regression we adopt the panelcounting model According to the distribution character-istics of the number of patent applications there is a hugedifference between the variance and the mean and thevariance is obviously greater than the mean +ere may beexcessive data dispersion and it is difficult to satisfy Poissonregression condition that expectation and variance are equal+erefore the negative binomial regression model of paneldata is adopted

It is assumed that the number of patents filed by anenterprise is Yit (the subscripted variable i represents theenterprise observed and the subscripted variable t repre-sents the year observed) and its conditional distributionfunction is as follows

P Yit yit|xit( 1113857 e

minusλitλyit

it

yit yit 0 1 2 ( 1113857 (2)

where λit gt 0 represents the average occurrence times of theevent In order to ensure that λit is a nonnegative figure it isassumed that

λit exp xitprime β + ui( 1113857 exp xit

prime β( 1113857exp ui( 1113857 equiv vi exp xitprime β( 1113857

(3)

where xit does not contain constant term vi equiv exp(ui)

represents the individual effect in the form of product In thecase that it satisfies the condition that v1 v2 middot middot middot vn itmeans that there is no individual effect More generally theexistence of individual effect is allowed In other wordsdifferent individuals have different values of vi +e prob-ability density of the negative binomial regression model is

f Yit|μit α( 1113857 Γ yit + αminus1

1113872 1113873

Γ yit + 1( 1113857Γ αminus11113872 1113873

αminus1

αminus1 + μit

1113888 1113889

αminus1

μit

αminus1 + μit

1113888 1113889

αminus1

yit 0 1 (4)

where Γ(middot) represents the gamma distribution function andα represents the overdispersion parameter E(Yit|xit) μit

and Var(Yit|xit) μit + αμ2it gt μit yit represent the numberof patents applied by the enterprise i in the year t μit

represents the average value of the patents applied by theenterprise After introducing relevant control variables thenegative binomial regression model of the technologicalinnovation output is

μit exp α1Fconstraintsitminus1 + 1113944 θixitminus1 + εit1113872 1113873 (5)

To test Hypothesis 2 to Hypothesis 4 we group thesamples according to their characteristics and compare themagnitude and significance of financing constraint coeffi-cient To test Hypothesis 5 and Hypothesis 6 we introduceeconomic policy uncertainty or marketization as the mod-erator based on model (1) and set the following econometricmodel

Innovationgit α0 + α1Fconstraintsitminus1 + 1113944 θixitminus1

+α2Mit + α3Mit times Fconstraintsitminus1 + indit + θt + εit

(6)

where Mit represents the economic policy uncertainty or themarketization If the estimate of the interaction coefficient α3 issignificantly positive the hypothesesH5 andH6 are supportedIf α3 is significantly negative it means the economic policyuncertainty or themarketization increases the negative effect offinancing constraints on technological innovation

42 Variable

421 Explained Variable +e explained variable is tech-nical innovation Technical innovation can be measuredfrom the perspectives of input and output For innovation

input we measure the companyrsquos technological innovationactivities by the RampD expense and use the RampD concen-tration (the proportion of RampD expenses to the total assets ofthe current period) and the RampD intensity (the proportion ofRampD expenses to current sales revenue) In terms of thetechnical innovation output following Hall and Harhoff[39] we measure the output of technological innovation bythe number of patent applications

422 Explanatory Variable +e core explanatory variable isthe financing constraints Following Owen [40] to calculateKZ index and considering the characteristics of listedcompanies in China the measurement method of FC indexof the Chinese listed companies financing constraint isestablished +e first step is to classify the samples by yearand calculate medians of the annual operating net cash flow(CF) cash dividends (DIV) cash holdinglast termrsquos assets(CashH) the leverage ratio (LEV) and Tobinrsquos Q (TobinQ)+en we assign a value to the variable CF DIV and CashHare expected to be negative correlation with financingconstraints while LEV and TobinQ are positively correlatedwith financing constraints +e values are determinedaccording to the following principles If CF DIV and CashHare lower than their medians that year the correspondingindicator value is taken as 1 otherwise it is taken as 0expressed as FC1 FC2 and FC3 If LEV and TobinQ arehigher than their medians that year the correspondingindicator value is taken as 1 otherwise it is taken as 0expressed as FC4 and FC5 +e third step is to calculate thevalue of FC according to the equation FC CF1 + FC2+FC3 + FC4 + FC5+e fourth step is to use the panel sortinglogistic regression model for regression +e dependentvariable is FC and the explanatory variables are the originalvalues of CF DIV CashH LEV and TobinQ+e regression

6 Complexity

coefficients of these explanatory variables are estimated andthe expression of the FC index is obtained In the end theactual values of CF DIV CashH LEV and TobinQ aresubstituted into the regression model and the predicted FCvalue of the enterprise is calculated which was the FC indexof the enterprise +e higher the FC index is the moreserious financing constraints is

According to the process above for calculating the FCindex the measurement result of the financing constraints isFC 1135CF minus 4572DIV minus 404CashH + 538LEV+016TobinQ Moreover the regression coefficients of thefive variables are all significant at the significance level of 1and the overall significance of the model also passes thestatistical test It is illustrated that the five variables namelyCF DIV CashH LEV and TobinQ are the effective proxyvariables affecting the financing constraints In addition CFDIV and CashH are negatively correlated with financingconstraints while LEV and TobinQ are positively correlatedwith the financing constraints It means that listed com-panies with high operating cash flow high cash holding highdividends low leverage ratio and low investment oppor-tunities face less serious financing constraints In contrastlisted companies with low operating cash flow low cashholding low dividends high leverage ratio and high in-vestment opportunities face more severe financingconstraints

Two other important variables in this study are eco-nomic policy uncertainty andmarketization+ere are manymeasurement indicators of economic policy uncertainty Weadopt the economic policy uncertainty index constructed byBaker et al [41] +is index has been widely used in recentmacropolicy literature and has been proved to be com-prehensive and objective to reflect the fluctuation of Chinarsquoseconomic policy Since the index is a monthly index we usethe monthly arithmetic average to convert the monthlyeconomic policy uncertainty index into an annual economicpolicy uncertainty index We use variable EPU to representthe economic uncertainty index Marketization is mostlymeasured by the marketization index proposed by Fan et alwhich consists of the relationship between the governmentand the market the development of the nonstate economythe development of the product market the development ofthe factor market and the development of market inter-mediary organizations [31] +e five aspects of the legalsystem environment can fully reflect all aspects of mar-ketization +erefore marketization index is used to mea-sure the level of marketization in each region Since thesample in this article is data of listed companies in strategicemerging industries it is not possible to directly obtain thecorresponding market index of each listed company +emethod is to find the market index of the correspondingprovince according to the province where each listedcompany is registered We use marketization variable torepresent the marketization index

423 Control Variable Control variables involved are Sizerepresenting the size of enterprises Age representing the ageof enterprises TobinQ Concentr representing equity

concentration Sgrowth representing the sales growth rateand Cflow representing the cash flow ratio

43 8e Data Our samples are the companies listed on themain board of China in strategic emerging enterprise fromover 2009 to 2019 As there are no strategic emerging in-dustries in the industry classification of the China SecuritiesRegulatory Commission it is impossible to obtain thesample of listed companies of strategic emerging industriesdirectly In order to gather the samples of listed companies instrategic emerging industries the following steps are fol-lowed+e initial sample is selected from the sample stocks ofChina Strategic Emerging Industries Composite Index(ldquoEmerging Composite Indexrdquo) released by China SecuritiesIndex Co Ltd and Shanghai Stock Exchange in 2017covering a total of 1117 companies listed as A-shares inShanghai and Shenzhen Stock Exchanges on the SMEsboard (Small and Medium Enterprise Board) the second-board market and the new OTC market (new over-the-counter market) Due to the small amount of data disclosedby listed companies on the new OTC market and the poorcomparability compared with other listed companies suchcompanies are removed from the sample frame Next somefilter conditions are set in the sample frame+e STand lowast STlisted companies within the sample period are deleted+enthe listed companies in the financial and insurance industryare deleted At last the samples with missing values of someindexes are deleted After the steps detailed above the finalsamples are 757 listed companies in the strategic emergingindustries over the period of 2009 to 2019

5 Empirical Results and Discussions

51 8e Inhibitory Effect of Financing Constraints We focuson the impact of financing constraints on technical inno-vation Columns 2 and 3 of Table 1 report the impact offinancing constraints on technological innovation input Asshown in Table 1 financing constraints negatively affect theconcentration and intensity of RampD at the significance levelof 5 +at is financing constraints inhibit technologicalinnovation input For listed companies technological in-novation is a very important investment decision for en-terprises involving large capital investment and a long timeof capital occupation When enterprises face serious fi-nancing constraints their internal and external funds cannotmeet the capital demand for technological innovation soenterprisesrsquo technological innovation activities will bepostponed

+e impact of financing constraints on technologicalinnovation output is also vital According He and Zhang [42]and Li and Zheng [43] the patent applications for measuringthe technological innovation are divided into two types Oneis the substantive innovation with a higher technologicallevel represented by invention patents aiming to promotesocial and technological progress and occupy a competitiveadvantage in the industry Andrea pointed out that financingconstraints have a significant negative effect on fundamentalinnovation [44] +e other is strategic innovations with low

Complexity 7

technical level represented by utility model patents anddesign patents most of which aim to obtain other benefits+en the influence of financing constraints on the differenttypes of patent applications is studied Columns 4 to 6 ofTable 2 respectively report the impact of financing con-straints on the number of patent applications inventionapplications and noninvention applications In order todetermine whether negative binomial regression is a fixedeffect or a random effect the Hausmann test is carried out+e results show that the p value is less than 001 whichstrongly rejected the negative binomial regression of randomeffect +erefore the fixed-effect negative binomial regres-sion is adopted Hilbe pointed out that in the countingmodel the marginal effect of variables is more significantthan that of the regression coefficient [45] We use themarginal effect to analyze the impact of the unit change ofthe explanatory variable on the explained variable Columns4 and 5 of Table 1 show that in terms of both the number ofpatent applications and the number of invention patentsfinancing constraints inhibit technological innovation out-put at the significance level of 10 Column 6 of Table 1shows that the influence of financing constraints on thenumber of nonpatent applications is negative but not sig-nificant +e above results indicate that therestraint of fi-nancing constraints on the inventionpatent is greater thanthat of noninvention patent mainly because of the differentnature of the two types of patents +e invention patent canimprove the production efficiency of enterprises enableenterprises to obtain competitive advantages in the industryand represent the core competitiveness of enterprisesHowever invention patents usually require a lot of in-vestment and last a long time before they come out

On the contrary the purpose of noninvention patents isto echo the innovation strategy of enterprises and nationalinnovation policies and the companies pay more attentionto the ldquospeedrdquo and ldquoquantityrdquo of innovation Noninventionpatents involve a relatively small investment a relativelyshort duration cycle and a relatively little influence bycapital Due to the essential differences between inventionpatents and noninvention patents enterprises have differentattitudes towards the two kinds of patents which ultimatelyleads to the difference in the degree of influence of financingconstraints on the two kinds of patents +is conclusion isconsistent with Khan et al [46]

+e results above indicate that financing constraints notonly inhibit the technological innovation input in emergingindustries but also inhibit the technological innovationoutput of the enterprise in emerging industries and thusHypothesis 1 has been supported

52 8e Heterogeneous Influence of Financing Constraints

521 Analysis by Industry Considering that differentsubindustries in strategic emerging industries have differentcharacteristics of financing constraints and technologicalinnovation the sensitivity of technical innovation to fi-nancing constraints may be heterogeneous We furtheranalyze by industry in order to find out the difference in theimpact of financial constraints on technical innovation ofvarious subindustries in strategic emerging industries Sincethere are many subindustries in the strategic emerging in-dustries only RampD intensity is used as the explained variableto analyze by industry

Table 1 Regression results of the influence of financing constraints on technical innovation

Variable RampDconcentration

RampDintensity

Number of patentapplications

Number of inventionpatents

Number of noninventionpatents

LFC minus0031lowastlowast minus0114lowastlowastlowast minus0024lowast minus0038lowastlowast minus0002(0014) (0032) (0012) (0015) (0014)

Lsize minus0287lowastlowastlowast minus0629lowastlowastlowast 0357lowastlowastlowast 0378lowastlowastlowast 0348lowastlowastlowast(0051) (0119) (0044) (0050) (0058)

LTobinQ minus0009 minus0029 0024lowastlowastlowast 0030lowastlowastlowast 0009(0013) (0029) (0006) (0010) (0010)

LConcentr minus0009lowastlowast minus0025lowastlowastlowast minus0000 0002 0002(0004) (0009) (0003) (0003) (0003)

LSgrowth minus0011 minus0165lowastlowastlowast minus0000 minus0000 0000(0024) (0054) (0000) (0000) (0000)

Age minus0037lowastlowast minus0160lowastlowastlowast 0045lowastlowastlowast 0071lowastlowastlowast 0024lowast(0015) (0036) (0011) (0010) (0014)

LCflow minus0003lowastlowast minus0005lowastlowast minus0000(0002) (0002) (0002)

Constant 9033lowastlowastlowast 17980lowastlowastlowast minus7436lowastlowastlowast minus8479lowastlowastlowast minus7302lowastlowastlowast(1203) (2798) (0922) (1058) (1203)

Time effect Controlled ControlledIndustryeffect Controlled Controlled

Sample size 757 757 628 625 608Notes lowastlowastlowast lowastlowast and lowast mean significant at the significance level of 1 5 and 10 respectively +e figures in parentheses are standard errors the same asbelow Lvariables represent variables with a lag of one period

8 Complexity

Columns 2 to 8 of Table 2 report the effects of financingconstraints in different subindustries in strategic emergingindustries on technological innovation It is found that thereare pronounced industry differences in the impact of fi-nancing constraints on technological innovation Except forthe biological industry and new energy industry the impactof financing constraints on technological innovation in-vestment in the other five industries is uniformly negative Inthe new generation information technology industry ad-vanced equipment manufacturing and new energy vehicleindustry with high RampD intensity the negative impact isstatistically significant but not in the new material industryand energy conservation and environmental protectionindustry with relatively low RampD intensity +is is becausethe higher the RampD intensity is the more the money forRampD investment will be and the stronger the influence offinancing constraints will be In the regression analysis of thebiological industry and the new energy industry the fi-nancing constraintsrsquo coefficient is not negative +e mainreason is that these two industries are industries with rel-atively small financing constraints and are less affected byfinancial factors +erefore financing constraints have noobvious inhibitory effect on technological innovation andthus Hypothesis 2 has been verified

522 Whether It Is a Manufacturing Enterprise We dividethe listed companies in strategic emerging industries intomanufacturing and nonmanufacturing industries accordingto the classification standards of Chinarsquos high-tech indus-tries +e regression results of Table 3 show that the in-hibitory effect of the financing constraints on thetechnological innovation input and output of themanufacturing industry is lower than that of the non-manufacturing industry +e possible reason is that Chinarsquoslabor costs have risen sharply which has seriously affected

the operating profits of enterprises in the labor-intensive andcapital-intensive manufacturing industries +erefore themanufacturing industry is more willing to improve laborproductivity through technological innovation and tech-nological innovation is of greater significance tomanufacturing enterprises Manufacturing companies investmore in RampD and have a longer cycle for capital recoveryOnce the technical innovation project is launched the fundsneed to be continually invested Otherwise the invested costwill become a sunk cost and cannot be recovered +echaracteristic of high adjustment cost of RampD input is moreobvious in the manufacturing industry and manufacturingenterprises will pay more attention to the sustainability ofRampD +erefore technological innovation will not react sosensitively to the capital situation Even if the financingconstraints are intensified the technological innovation inthe manufacturing industry will not be reduced rapidly butneed a long time to adjust However nonmanufacturingindustries are mostly asset-light enterprises which involverelatively less investment in technological innovation withlow RampD intensity and less dependence on technologicalinnovation +erefore in these enterprises technologicalinnovation is more sensitive to the financial situation andthus Hypothesis 3 has been verified

523 Level of the Dependence of Industry External Financing+e companies are divided into two groups according to theexternal financing dependence in the industry to testwhether the inhibitory effect of financing constraints ontechnological innovation is related to the external financingdependence of the industry One group are the companieswith low external financing dependence and the other arethe companies with medium- and high-level external fi-nancing dependence +e grouping results of the sample inTable 4 show that only a few strategic emerging enterprises

Table 2 Regression analysis of different subindustries in strategic emerging industries

VariableNext-generationinformationtechnology

Advancedequipment

manufacturing

Newmaterialindustry

Biologicalindustry

New energyautomobileindustry

Newenergyindustry

Environmentalprotection industry

LFC minus0238lowastlowastlowast minus0084lowast minus0011 0211 minus0126lowast 0178 minus0005(0066) minus0049 (0039) (0170) (0073) (0143) (0041)

Lsize minus0098 minus0586lowastlowastlowast minus0642lowastlowastlowast minus0579 minus0065 minus0077 minus1002lowastlowastlowast(0273) (0161) (0136) (0723) (0211) (0212) (0204)

LTobinQ 0025 0002 0082lowast 0004 0037 1011lowastlowast minus0263lowastlowastlowast(0054) (0060) (0042) (0149) (0134) (0496) (0058)

LConcentr minus0021 minus0025lowastlowast 0008 minus0075 minus0008 0006 0009(0020) (0012) (0009) (0055) (0014) (0013) (0008)

LSgrowth 0009lowastlowastlowast minus0000 0006lowastlowastlowast minus0002 minus0001 0008 minus0001(0002) (0001) (0002) (0007) (0003) (0006) (0001)

Age minus0276lowastlowastlowast minus0096lowast 0020 minus0249 minus0036 0041 minus0071lowast(0073) (0050) (0039) (0179) (0056) (0031) (0040)

Sample size 290 257 95 43 19 14 24Notes according to the Guiding Catalogue of Key Products and Services for Strategic Emerging Industries (2016 Edition) issued by the National Developmentand Reform Commission in 2017 the strategic emerging industries are divided into nine sectors (including related service industries) Considering the smallnumber of samples of digital cultural and creative industries and related service industries they are not separately analyzed by industry

Complexity 9

are in the industries with low-level external financing de-pendence with only 14 samples accounting for 19 of thetotal samples It indicates that most enterprises in thestrategic emerging enterprises are in the industries with highexternal financing dependence +e regression results inTable 4 show that the negative effect of financing constraintson the technical innovation input and output is not sig-nificant in the samples of industries with low external fi-nancing dependence However in the industry withmedium- and high-level external financing dependence theimpact of financing constraints on the technical innovationrsquosinput and output is significant at the 10 significance leveland thus Hypothesis 4 has been verified

53 8e Moderating Effect of the External Environment

531 8e Economic Uncertainty as the Moderator As anexternal factor affecting enterprisesrsquo technological innova-tion economic policy has an impact on the technologicalinnovation activities of enterprises from amacroperspectiveOnce a countryrsquos economic policy changes enterprisesrsquoinnovation activities will inevitably be affected by the policy

Columns 2 and 3 in Table 5 are the regression resultsafter adding the cross terms of economic policy uncertaintyand financing constraints to the regression model It showsthat the negative effect of financing constraints on tech-nological innovation is still significant and the coefficient ofthe cross term is significantly positive indicating thateconomic policy uncertainty alleviates the inhibitory effectof financing constraints on technological innovation Spe-cifically when the uncertainty of the economic policy en-vironment increases the inhibitory effect of financingconstraints on the technological innovation input may bereduced+ere are two main explanations On the one handthe increasing uncertainty of economic policy means thatenterprisesrsquo business environment will change significantly

+is is both an opportunity and a challenge and the survivalof the fittest among enterprises will be presented Only thoseenterprises with high production efficiency and strongcompetitiveness can finally survive in the industry Fur-thermore innovation ability is the key factor for enterprisesto enhance their core competitiveness Enterprises can makeuse of the opportunities brought by the economic policyuncertainty and improve their core competitiveness by in-creasing the input in technological innovation activities so asto boost their chances of winning in the future marketcompetition On the other hand the uncertainty of eco-nomic policies will then spread to the external financingmarket leading to the fluctuation of the capital market andthen affect the external financing of technological innova-tion According to the research of Li and Yang [47] it can beseen that the economic policy uncertainty inhibits generalinvestment activities +erefore when economic policyuncertainty increases ordinary investment activities will notbe active and there will be abundant capital in the capitalmarket However technological innovation was fraught withuncertainty and the future is full of opportunities When theeconomic policy uncertainty increases financing for tech-nological innovation is more likely compared with general

Table 4 +e classification test result of the level of the external financing dependence in the industry

Variable

RampD concentration RampD intensity Number of patent applicationsLow externalfinancing

dependence

Medium- and high-level external

financing dependence

Low externalfinancing

dependence

Medium- and high-level external

financing dependence

Low externalfinancing

dependence

Medium- and high-level external

financing dependence

LFC 0107 minus0034lowastlowast minus0060 minus0116lowastlowastlowast minus0003 minus0021lowast(0113) (0014) (0117) (0033) (0088) (0013)

Allcontrols Controlled Controlled Controlled Controlled Controlled Controlled

Samplesize 14 743 14 743 14 614

Table 5 +e moderating effect of economic policy uncertainty

Variable RampD concentration RampD intensityLFC minus0069lowastlowastlowast (0026) minus0286lowastlowastlowast (0060)Lsize minus0273lowastlowastlowast (0050) minus0592lowastlowastlowast (0120)LTobinQ minus0022 (0029)LConcertr minus0009lowastlowast (0004) minus0025lowastlowastlowast (0009)LSgrowth minus0000 (0000) minus0002lowastlowastlowast (0001)Age minus0037lowastlowast (0015) minus0160lowastlowastlowast (0036)EPUlowastLFC 0000lowast (0000) 0001lowastlowastlowast (0000)EPU 0003lowastlowastlowast (0001) 0013lowastlowastlowast (0002)Constant 8409lowastlowastlowast (1162) 1580lowastlowastlowast (2775)Sample size 757 757

Table 3 +e classification test results of manufacturing enterprises and nonmanufacturing ones

VariableRampD concentration RampD intensity Number of patent applications

Manufacturing Nonmanufacturing Manufacturing Nonmanufacturing Manufacturing Nonmanufacturing

LFC minus0015 minus0094lowastlowast minus0100lowastlowastlowast minus0151lowastlowast minus0021lowast minus0061lowast(0012) (0043) (0035) (0075) (0012) (0034)

All controls Controlled Controlled Controlled Controlled Controlled ControlledSample size 543 214 543 214 484 144

10 Complexity

investment activities +erefore economic policy uncer-tainty can ease the inhibitory effect of financing constraintson technological innovation and thus Hypothesis 5 hasbeen verified

532 8eMarketization as the Moderator Columns 2 and 3in Table 6 are the regression results after adding the crossterms of marketization and financing constraints to theregression model It shows that the coefficient of financingconstraints is significantly negative the coefficient of mar-ketization is significantly positive and the coefficient of across term is significantly positive indicating that themarketization can help to alleviate the inhibitory effect offinancing constraints on technological innovation Specifi-cally when the market environment improves the inhibi-tory effect of financing constraints on technologicalinnovation will be reduced +erefore the more the market-oriented the lower the inhibitory effect of financing con-straints on technological innovation and vice versa In theend the Matthew effect occurs when the strong get strongerand the weak get weaker Under such effect the differencesin technological innovation between regions are constantlywidened +e possible explanation is as followsIn highlymarket-oriented regions there are a sound legal system highinformation transparency a more robust financial systemsound and abundant financial resources Such a perfectinstitutional environment can ease the enterprisersquos financingconstraints provide a proper external financing environ-ment for technological innovation and better escort theenterprisersquos technological innovation which is conducive toencourage enterprises to carry out technological innovationIn regions with less market-oriented it is impossible toprovide a strong institutional guarantee for technologicalinnovation For an enterprise facing financing constraintsthis adverse institutional environment intensifies the in-hibitory effect of financing constraints on technologicalinnovation and is not conducive to the enterprisersquos tech-nological innovation and thus Hypothesis 6 has beenverified

54 Robustness Test

541 Solutions to Endogenous Problems Financial con-straints inhibit enterprisesrsquo technological innovation activ-ities and technological innovation may make financialconstraints of enterprises get more serious thus leading to abidirectional causal relationship between financial con-straints and technical innovation In order to avoid possiblereverse causality all control variables and the explanatoryvariables are delayed by one period Simultaneously in thestudy to avoid the endogenous problems caused by theomitted variables the industry and time fixed effects arecontrolled in the empirical analysis

542 Recalculation of Financing Constraint Index In theempirical analysis we use the FC index to measure the fi-nancing constraints However there are various methods tomeasure the constraints with different emphases In order to

ensure robust conclusions the SA index proposed byHadlock and Pierce [48] is used to remeasure the financingconstraints +at is SA minus0737 Size + 0043 Size2minus004Age +e above empirical process is then repeated tocheck whether the financing constraints have an inhibitoryeffect on technological innovation

+e SA index is generally negative +e greater the ab-solute value of SA is the more serious the financing con-straints faced by the enterprise will be+erefore the SA indexis negatively correlated with the level of financing constraints+e regression results in Table 7 show that the SA index ispositively correlated with technological innovation at thesignificance level of 5 indicating the more serious the fi-nancing constraints is the stronger the inhibitory effect ontechnological innovation activities will be +is is consistentwith the conclusion of the test based on the FC index whichonce again verifies that financing constraints have an inhib-itory effect on technological innovation

543 Recalculation of the Technological Innovation Output+e increment of intangible assets is adopted to remeasurethe technological innovation output following Ju et al [49]Moreover the ratio of the increment of intangible assets tothe total assets at the end of the period is used as the proxyvariable of the technological innovation output to remeasurethe technological innovation output Finally the empiricalresults of the influence of financing constraints on thetechnological innovation output are shown in Table 8

+e results in Table 8 show that financing constraintshave a significant inhibitory effect on the technologicalinnovation output regardless of if the FC index or SA indexis used It is consistent with the conclusion where thenumber of patent applications is adopted as the techno-logical innovation output

544 8e Ordinary Panel Model as the Counting Model+e panel counting model is used to study the influence offinancing constraints on the technical innovation outputwhich shows that financing constraints have a significantinhibitory effect on the technological innovation output Inorder to test the robustness of the results the number ofpatent applications as the explained variable is taken as acontinuous common variable and then the ordinary panelmodel is used for empirical analysis +e empirical resultsare shown in Table 9

As shown in Table 9 the conclusion obtained using theordinary panel model is that the financing constraints sig-nificantly inhibit the technological innovation output After

Table 6 +e moderating effect of the marketization

Variable RampD concentration RampD intensityLFC minus0036lowastlowastlowast (0012) minus0168lowastlowastlowast (0030)Marketization 0838lowastlowastlowast (0266) 1319lowastlowast (0658)MarketizationlowastLFC 0017lowastlowastlowast (0005) 0024lowast (0013)All controls Controlled ControlledSample size 757 757

Complexity 11

dividing the number of patent applications into inventionpatents and noninvention patents we find that the inhibi-tory effect of the financing constraints on the inventionpatents is significantly higher than that of the noninventionpatents which is consistent with the conclusion obtainedusing the negative binomial regression model of panel data

6 Conclusions and Implications

As the world economy enters a stage of innovation compe-tition the decision-making problem of technological innova-tion in emerging industries is a practical problem worthy ofresearch and attention Based on the panel data of the listedcompanies in Chinarsquos strategic emerging industries from 2009to 2019 we adopt the negative binomial regression model ofpanel data and empirically study the technological innovationdecision-making issues of emerging industries from the per-spective of financing constraints Our results show that thefinancial constraints in strategic emerging industries in Chinasignificantly inhibit technological innovation in terms oftechnological innovation inputs and outputsWhat ismore the

inhibitory effect of substantive innovation like the inventionpatent is more pronounced than that of strategic innovationlike the noninvention patent +e influence of financingconstraints on technological innovation varies significantlybetween industries within strategic emerging industriesmanufacturing industries and nonmanufacturing industriesand among industries with different external financing de-pendence However the economic policy uncertainty and themarketization can help to alleviate the inhibitory effect of fi-nancing constraints on technological innovation

Our research conclusions lead to the following policyimplications First of all the government should focus onsolving the financial constraints by speeding up the reformof the financial system Promoting the reform of the financialsupply side improving the efficiency of financial resourceallocation and reducing the financing cost should also beemphasized In addition it is necessary to expand thecoverage of financial services to develop financing platformsand tools exclusive to RampD intensive industries such asstrategic emerging industries and to encourage venturecapital and long-term capital to invest in technological

Table 9 Robustness test results of the ordinary panel model

Variable Number of patent applications Number of invention patents Number of noninvention patentsLFC minus0045lowastlowastlowast (0012) minus0059lowastlowastlowast (0012) minus0028lowastlowast (0013)LTobinQ minus0003 (0008) 0002 (0008) minus0018lowastlowast (0009)LConcertr 0005lowastlowast (0002) 0004 (0003) 0006lowastlowast (0003)LSgrowth minus0000 (0000) minus0000 (0000) minus0000 (0000)Llev 0017lowastlowastlowast (0002) 0017lowastlowastlowast (0002) 0016lowastlowastlowast (0002)Age 0003 (0009) 0012 (0010) minus0004 (0010)LCflow minus0004lowastlowast (0002) minus0005lowastlowastlowast (0002) minus0003 (0004)Sample size 691 691 691

Table 8 Robustness test results of intangible assets increment

Variable FC index SA indexLFC minus0042lowastlowast (0019) 0249lowastlowast (0108)Lsize 0000 (0041)LTobinQ 0039lowastlowast (0019) 0037lowast (0019)LConcentr 0000 (0003) 0002 (0003)LCflow minus0014lowastlowastlowast (0004) minus0012lowastlowastlowast (0001)LSgrowth minus0009 (0042) 0004 (0042)Age minus0039lowastlowastlowast (0008)Ltangibility 0000 (0003)Constant 0508 (1039) 0316 (0593)Sample size 757 757

Table 7 Regression results of the impact of financing constraints on technological innovation through the SA index

Variable RampD concentration RampD intensity Number of patent applicationsLSA 0351lowastlowastlowast (0135) 0995lowastlowastlowast (0316) 0300lowast (0182)LLEV minus0444lowast (0240) minus4102lowastlowastlowast (0549) minus0003 (0002)LTobinQ 0003 (0013) minus0039 (0030) minus0005 (0012)LConcentr minus0010lowastlowast (0004) minus0020lowastlowast (00098) minus0000 (00037)LCflow 0004lowast (0003) minus0004 (0006)Ltangibility 0011lowastlowastlowast (0003) 0002 (0006)LSgrowth minus0011 (0023) minus0118lowastlowast (0053)Constant 2159lowastlowastlowast (0592) 5283lowastlowastlowast (1360) minus9301lowastlowastlowast (1361)Sample size 757 757 628

12 Complexity

innovation projects A technological innovation financingsystem based on the market mechanism guided by gov-ernment investment dominated by enterprises and widelyparticipated by social funds should be established Morefinancial resources can flow to technological innovationprojects and to enterprises with technological innovationactivities

Second at present Chinarsquos economy is in a ldquonew nor-malrdquo that requires innovation In view of the selective effectof financing constraints on innovation activities relevantdepartments should make effort to build a good externalfinancing environment and use effective financial policiesand administrative methods to help enterprises release theirinnovative vitality more fully For example relevant de-partments should pay attention to building a good market-oriented system and increase institutional guarantees forinnovative activities so as to improve the operating con-ditions of enterprises and promote high-quality innovativeactivities When companies face financing constraints thesemeasures that are conducive to improving operating con-ditions will help to stimulate innovation

Finally since the financing constraints have a moreobvious restraining effect on enterprisesrsquo substantive inno-vation of high quality the government should encourageenterprises to increase RampD investment in substantive in-novation [50ndash53] while guiding them to pursue high-qualityinnovation so as to avoid such innovations that emphasizeonly the quantity and speed In addition the governmentalso should continue to increase financial investment inbasic research for making up for the lack of substantiveinnovation of enterprises and aiming to truly improve in-novation ability and quality of the enterprise

Data Availability

+e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

+e authors declare that there are no conflicts of interestregarding the publication of this study

Acknowledgments

+is research was partly supported by the Natural ScienceFoundation of Zhejiang Province (Grant no LQ19G030007)and Zhejiang Gongshang University Online and OfflineHybrid Teaching Reform Project (Grant no1010XJ2919103)

References

[1] H Lei and W Wang ldquo+e impact of network structure oninnovation efficiency an agent-based study in the context ofinnovation networksrdquo Complexity vol 21 no 2 pp 111ndash1222015

[2] W B Arthur and W Polak ldquo+e evolution of technologywithin a simple computer modelrdquo Complexity vol 11 no 5pp 23ndash31 2010

[3] J J Pang G Lin R L Yang et al ldquoPromote continuouseconomic development economic trendsrdquo Economic Dy-namics vol 7 pp 3ndash19 2019

[4] M Cai and S J Liu ldquo+e impact of government subsidies onthe leverage ratio of strategic emerging industriesrdquo Con-temporary Economic Research vol 8 pp 90ndash100 2020

[5] J R Brown S M Fazzari and B C Petersen ldquoFinancinginnovation and growth cash flow external equity and the1990s RampD boomrdquo Journal of Finance vol 64 pp 151ndash1852010

[6] J S Ang Y Cheng and C Wu ldquoDoes enforcement of in-tellectual property rights matter in China evidence fromfinancing and investment choices in the high-tech industryrdquoReview of Economics and Statistics vol 96 no 2 pp 332ndash3482014

[7] S M Fazzari R G Hubbard B C Petersen A S Blinder andJ M Poterba ldquoFinancing constraints and corporate invest-mentrdquo Brookings Papers on Economic Activity vol 1988 no 1206 pages 1988

[8] T Beck and R Levine ldquoIndustry growth and capital alloca-tionrdquo Journal of Financial Economics vol 64 no 2pp 147ndash180 2002

[9] M Gertler and S Gilchrist ldquo+e role of credit market im-perfections in the monetary transmission mechanism argu-ments and evidencerdquo8e Scandinavian Journal of Economicsvol 95 no 1 pp 43ndash64 1993

[10] R G Rajan and L Zingales ldquoFinancial dependence andgrowthrdquo Social Science Electronic Publishing vol 88 no 3pp 559ndash586 1998

[11] G Borisova and J R Brown ldquoRampD sensitivity to asset saleproceeds new evidence on financing constraints and intan-gible investmentrdquo Journal of Banking amp Finance vol 37 no 1pp 159ndash173 2013

[12] Y S Hwang H G Min and S H Han ldquo+e influence offinancial development on RampD activity cross-country evi-dencerdquo Review of Pacific Basin Financial Markets amp Policiesvol 13 no 3 pp 381ndash401 2010

[13] A Hall G Bockett S Taylor M V K Sivamohan andN Clark ldquoWhy research partnerships really matter inno-vation theory institutional arrangements and implications fordeveloping new technology for the poorrdquo World Develop-ment vol 29 no 5 pp 783ndash797 2001

[14] B H Hall ldquoInvestment and research and development at thefirm level does the source of financing matterrdquo NBERWorking Papers 1992

[15] C P Himmelberg and B C Petersen ldquoRampD and internalfinance a panel study of small firms in high-tech industriesrdquo8e Review of Economics and Statistics vol 76 pp 38ndash511994

[16] Z He and M B Wintoki ldquo+e cost of innovation RampD andhigh cash holdings in US firmsrdquo Journal of Corporate Financevol 41 pp 280ndash303 2016

[17] B Mulkay B H Hall and J Mairesse Firm Level Investmentand RampD in France and the United States A comparisonSocial Science Electronic Publishing Berlin Germany 2001

[18] D Harhoff Are there financing constraints for RampD and in-vestment in German manufacturing firms 8e Economics andEconometrics of Innovation Springer Berlin Germany 2000

[19] S Bond and D H V Harhoff ldquoInvestment RampD and fi-nancial constraints in Britain and Germanyrdquo AnnalesdrsquoEconomie et de Statistique vol 7980 pp 433ndash460 2005

[20] C Bloch ldquoRampD investment and internal finance the cash floweffectrdquo Economics of Innovation and New Technology vol 14no 3 pp 213ndash223 2005

Complexity 13

[21] G Scellato ldquoPatents firm size and financial constraints anempirical analysis for a panel of Italian manufacturing firmsrdquoCambridge Journal of Economics vol 31 pp 55ndash76 2007

[22] E Ughetto ldquoDoes internal finance matter for RampD newevidence from a panel of Italian firmsrdquo Cambridge Journal ofEconomics vol 32 no 6 pp 907ndash925 2008

[23] D Czarnitzki and H Hottenrott ldquoRampD investment and fi-nancing constraints of small and medium-sized firmsrdquo SmallBusiness Economics vol 36 no 1 pp 65ndash83 2011

[24] B H Hall ldquo+e financing of research and developmentrdquoOxford Review of Economic Policy vol 18 no 1 pp 35ndash512002

[25] J R Brown and B C Petersen ldquoCash holdings and RampDsmoothingrdquo Journal of Corporate Finance vol 17 no 3pp 694ndash709 2011

[26] D Li ldquoFinancial constraints RampD investment and stockreturnsrdquo Review of Financial Studies vol 24 no 9pp 2974ndash3007 2011

[27] A Mina H Lahr and A Hughes ldquo+e demand and supply ofexternal finance for innovative firmsrdquo Industrial and Cor-porate Change vol 22 no 4 pp 869ndash901 2013

[28] X Gu Y Chen and S Pan ldquoEconomic policy uncertainty andinnovation evidence from listed companies in Chinardquo Reviewof Financial Studies vol 2 pp 109ndash123 2018

[29] F Knight Risk Uncertainty and Profit Houghton MifflinCompany Boston MA USA 1921

[30] W Hao W Wei and J Wen ldquoHow does economic policyuncertainty affect corporate innovationmdashperspective ofmechanism of action of real option theoryrdquo EconomicManagement vol 38 pp 40ndash54 2016

[31] G Fan X Wang and G Ma ldquo+e contribution of Chinarsquosmarketization process to economic growthrdquo Economic Re-search vol 46 no 9 pp 4ndash16 2011

[32] K Z Dai and Y J Liu ldquo+e impact of marketization reformon RampD investment in Chinarsquos high-tech industryrdquo ScienceStudies vol 31 pp 50ndash57 2013

[33] P Li and X Liu ldquo+e impact of market-oriented institutionalchange on Chinarsquos technological progressmdashbased on theperspective of independent R amp D and technology intro-ductionrdquo Economic Perspectives vol 4 pp 42ndash50 2015

[34] K J Arrow ldquoEconomic welfare and the allocation of resourcesfor inventionrdquo 8e Rate and Direction of Inventive Activityvol 12 pp 609ndash626 1962

[35] P Kijkasiwat and P Phuensane ldquoInnovation and firm per-formance the moderating and mediating roles of firm sizeand small and medium enterprise financerdquo Journal of Riskand Financial Management vol 13 no 5 p 97 2020

[36] X N Yuan ldquoManufacturing innovation and service inno-vation comparison and convergencerdquo Finance and TradeResearch vol 20 pp 14ndash19 2009

[37] Y M Zhu and M E Jia ldquoMarketization process financialconstraints and corporate technology innovationrdquo Com-mercial ResearchInnovation Technology vol 1 pp 49ndash562017

[38] C P Wu and D Tang ldquoIntellectual property rights en-forcement corporate innovation and operating performanceevidence from Chinarsquos listed companiesrdquo Economic ResearchJournal vol 51 no 11 pp 125ndash139 2016

[39] B H Hall and D Harhoff ldquoRecent research on the economicsof patentsrdquo Annual Review of Economics vol 4 pp 541ndash5652012

[40] L Owen P Christopher and J Saaa-Requejo ldquoFinancialconstraints and stock returnsrdquo Review of Financial Studiesvol 14 pp 529ndash554 2001

[41] S R Baker N Bloom and S J Davis ldquoMeasuring economicpolicy uncertaintyrdquo 8e Quarterly Journal of Economicsvol 131 no 4 pp 1593ndash1636 2016

[42] J F He and X J Zhang ldquo+e influence of rising labor cost onenterprise innovationrdquo 8e Journal of Quantitative amp Tech-nical Economics vol 45 no 8 pp 56ndash73 2018

[43] W J Li and M N Zheng ldquoIs it substantive innovation orstrategic innovationmdashimpact of macroeconomic policies onmicro-enterprisesrsquo innovationrdquo Economic Research Journalvol 4 pp 60ndash73 2016

[44] C Andrea ldquoFinancing constraints radical versus incrementalinnovation and aggregate productivityrdquo American EconomicJournal Macroeconomics vol 11 no 2 pp 75ndash309 2019

[45] J M Hilbe Modeling Count Data Cambridge UniversityPress Cambridge UK 2014

[46] S U Khan A Shah and M F Rizwan ldquoDo financingconstraints matter for technological and non-technologicalinnovation a (re) examination of developing marketsrdquoEmerging Markets Finance and Trade vol 57 pp 1ndash28 2019

[47] F Y Li and M Z Yang ldquoCan economic policy uncertaintyinfluence corporate investment the empirical research byusing China economic policy uncertainty indexrdquo Journal ofFinancial Research vol 57 no 9 pp 2739ndash2766 2015

[48] C J Hadlock and J R Pierce ldquoNew evidence on measuringfinancial constraints moving beyond the KZ indexrdquo Review ofFinancial Studies vol 23 no 5 pp 1909ndash1940 2010

[49] X S Ju D Lu and Y H Yu ldquoFinancial constraints workingcapital management and the sustainability of firm innova-tionrdquo Economic Research Journal vol 48 pp 4ndash16 2013

[50] J Li J Liang V Shi and Q Wang ldquo+e benefit of manu-facturer encroachment considering consumerrsquos environ-mental awareness and product competitionrdquo Annals ofOperation Research vol 3 pp 1ndash19 2021

[51] J Li F Wang and Y He ldquoElectric vehicle routing problemwith battery swapping considering energy consumption andcarbon emissionsrdquo Sustainability vol 12 no 24 Article ID10537 2020

[52] J Li L Yi V Shi and X Chen ldquoSupplier encroachmentstrategy in the presence of retail strategic inventory cen-tralization or decentralizationrdquo Omega vol 98 Article ID102213 2021

[53] J Li Z Hu V Shi and Q Wang ldquoManufacturerrsquos en-croachment strategy with substitutable green productsrdquo In-ternational Journal of Production Economics vol 235 ArticleID 108102 2021

14 Complexity

efficiency of capital allocation [12] +ese studies focused onthe relationship between financial development and tech-nological innovation from a macroperspective and do notexplore its micromechanism in more detail Howevertechnological innovation is always the behavior of enter-prises at the microlevel +e transmission mechanism offinancial development to technological innovation still needsto be investigated on the motivation and specific behavior ofenterprises

As information economics took off scholars began tostudy financial constraints under the condition of infor-mation asymmetry Since Fazzari Hubbard and Petersenfirst proposed to measure the financing constraints using theinvestment-cash flow sensitivity [7] this method was sub-sequently extended to research and development investmentand other investment fields to seek stronger theoreticalsupport of financing constraints from the perspective of cashflow On the basis of this theory a large number of scholarsdiscussed the relationship between financing constraints andtechnology innovation investment from the perspective ofthe cash flow However there were great differences in thedevelopment of capital markets and innovation investmentin various countries When different countries were used assamples to study the relationship between financing con-straints and technological innovation there were somedifferences in the conclusions Hall believed that it is pre-cisely because of the difficulties in raising funds from outsidefor innovation investment that companies mainly rely oninternal financing for innovation [13] Hall Himmelbergand Petersen and He studied American companies andfound a strong positive correlation between RampD invest-ment and cash flow sensitivity [14ndash16] Mulkay et al heldthat the sensitivity of innovation investment to cash flow inthe United States is more pronounced than that in Franceand Japan [17] Harhoff showed that there is a statisticallysignificant but weak relationship between RampD investmentand cash flow of German companies [18] Bond and Harhoffheld that there is no significant relationship between RampDinvestment and cash flow in German and the UK [19] Blochbelieved that RampD investment expenditures of Danishcompanies are significantly affected by internal cash flowand RampD investment has investment-cash flow sensitivity[20]

With the advancement of technology and the expansionof production scale it is difficult to meet the capital needs ofenterprises solely by relying on internal financing forprojects with large investment such as technological inno-vation and external financing has gradually become themain financing channel for technological innovation Re-searchers began to focus on the financing constraints oftechnological innovation from the perspective of externalfinancing External funds for RampD projects are believed to berelatively scarce mainly due to the serious informationasymmetry in the financing process +e uncertainty andstrategic nature of RampD may restrict managers from dis-closing relevant information about RampD projects to externalinvestors in order to prevent the information from beingknown to competitors [21ndash23]

Hall pointed out that despite the adoption of measuressuch as intellectual property protection government sub-sidies and tax incentives technological innovation invest-ment is still difficult to finance or the financing cost is high+ere is often a big gap between the rate of return requiredby enterprises to use their own capital and the rate of returnrequired by outside investors which leads to the high cost ofexternal capital and some innovation projects cannot evenbe financed from outside [24] Brown and Petersen believedthat the output of innovation activities is characterized bynonexclusive indivisibility and uncertainty leading to thesocial optimal investment level much higher than the privateoptimal level which seriously hinders the enterprises fromusing all the internal funds for RampD [25] Li held thatcompared with general investment RampD investment is moreinflexible and RampD-intensive companies facing financingconstraints are more likely to pause or cut off RampD projects+e risk of RampD-intensive companies increases with thedegree of financing constraints [26] Mina et al believed thatthe uncertainty of innovation activities have a negativeimpact on financial supply which is related to the expec-tation of enterprises to take risky projects leading to higherexternal capital costs and possible access to suboptimalexternal financial resources [27]

In addition to the internal characteristics of the enter-prise the external environment will inevitably have animpact on innovation decisions Relevant literature con-centrates on research from the perspective of economicpolicies government subsidies and marketization+ere aretwo views on the impact of economic policy uncertainty ontechnological innovation One view is that economic policyuncertainty can have an incentive effect on technologicalinnovation +is is because economic policy uncertaintyrepresents an opportunity to increase revenue in the future[28] Under the motive of pursuing profit uncertainty isbeneficial to enterprises to increase RampD investment [29]+e other view based on the analysis of the physical budgettheory believes that the uncertainty of economic policiesinhibits the technological innovation [30] Regarding theeffect of marketization most scholars have affirmed the roleof marketization in the process of technological innovationand believed that the improvement of marketization isgenerally conducive to promoting technological innovation[31ndash33] Nonetheless no consensus has been reached onwhich systems play a role in promoting technologicalinnovation

When studying the impact of financing constraints ontechnological innovation most scholars only focused on in-novation input and rarely paid close attention to the impact offinancing constraints on technological innovation outputHowever technological innovation output is the key to pro-mote the development of social productivity especially sub-stantive innovation (invention patents) +erefore the impactof financing constraints on technological innovation output isworth of social attention In view of the above considerationsthe impact of financing constraints on technological inno-vation should be extended from technological innovationinput to technological innovation output aiming to

Complexity 3

comprehensively analyze the impact of financing constraintson technological innovation activities in strategic emergingindustries At the same time most scholars rarely took theexternal political and economic environment of the firm intoaccount and there was a lack of research on the combiningfinancing constraints with environmental factors such aseconomic policy uncertainty and institutional environment Infact technological innovation depends to a large extent on theamount of external financing that a company obtains Eco-nomic policy uncertainty and institutional environment bothaffect the companyrsquos external financing environment and thenaffect the companyrsquos financing constraints and ultimatelyaffect the companyrsquos technological innovation activity

3 Research Hypotheses

Arrow believed that technological innovations have exter-nalities showing spillover effects and diffusion effects whichmake technological innovation show strong nonexclusiveand limited exclusivity +e diffusion of externalities enablesthe competitors to acquire technology at a lower cost thaninnovators in order to improve their productivity and in-novation [34] +e enterprises undertaking technologicalinnovation burden the high cost but they fail to access all thebenefits Under the spillover effect of technological inno-vation some of the profits are occupied by other enterprisesso the enterprises are less motivated to engage in techno-logical innovation and even have the idea of free rider

According to the theory of net present value theprinciple of optimal investment decision is that marginalrevenue equals marginal cost Financing constraints disablethe enterprise to make optimal investment decisions andinhibit technological innovation +ere are two main rea-sons One reason is that due to the limited internal fundsexternal financing is required However companies willweigh the cost of financing and their benefits [35] Once thecompany faces financing constraints the cost of externalfunds will be higher than the cost of internal funds whichincreases the cost of technological innovation When suchinvestment cost increases to the present value of the projectrsquosexpected cash flow the enterprise may cease its investmentin technological innovation thus delaying the innovationproject that could have been implemented +e other reasonis the difficulty of external financingwhich prevents en-terprises from obtaining the funds they need +en even ifthe marginal revenue of an enterprise is greater than itsmarginal cost technological innovation projects cannot beimplemented due to capital constraints

It can be seen that under the influence of the externalcharacteristics of technological innovation enterprisesrsquowillingness to undertake technological innovation is nothigh When the financing constraints become tighter theenterprisersquos investment cost in technological innovation willincrease correspondingly +en the willingness of enter-prises to undertake technological innovation activities willalso decline +erefore Hypothesis 1 is put forward

Hypothesis 1 Financing constraints negatively affect thetechnological innovation In other words financing con-straints have an inhibitory effect on the enterprisersquos tech-nological innovation

Strategic emerging industries consist of nine subin-dustries Since the characteristics of financing constraintsand technological innovation vary in different industries theimpact of financing constraints on technological innovationmust be different to some extent in certain industries+erefore Hypothesis 2 is put forward

Hypothesis 2 +e influence of financing constraint ondifferent subindustries of strategic emerging industries isdifferent

Due to the great difference between the output of themanufacturing industry and that of the nonmanufacturingindustry there are different characteristics in innovation+e manufacturing industry is dominated by technologicalinnovation while the nonmanufacturing industry is dom-inated by service innovations such as organizational in-novation normative innovation and characteristicinnovation +ese service innovations have little to do withtechnological innovation Meanwhile the strategic positionof technological innovation in enterprises also varies greatlyYuan pointed out that there is an obvious difference in thedependence of the manufacturing industry and service in-dustry on RampD +e manufacturing industry has a relativelylarge investment in technological innovation and RampDinvestment plays a decisive role Many companies even haveset up specialized RampD departments while the service in-dustry has a smaller dependence on RampD and the invest-ment in technological innovation is far lower than that of themanufacturing industry [36] +erefore the manufacturingindustry has a strong desire to improve the productivity ofenterprises through technological innovation It can then beinferred that the inhibitory effect of financing constraints onthe manufacturing industry is smaller than that on thenonmanufacturing industry +erefore Hypothesis 3 is putforward

Hypothesis 3 +e negative impact of financing constrainton manufacturing industry is less than that on nonmanu-facturing industry

Rajan and Zingales constructed the industryrsquos externalfinancing dependence which was used to measure theindustryrsquos dependence on external funds and they analyzed36 industries in US industrial companies [10] +ey came toa conclusion that traditional industries generally rely less onexternal financing while emerging industries mostly relymore on external financing+e top three industries with thehighest external financing dependence are drugs andpharmaceuticals plastics and computing +e three in-dustries with the lowest external financing dependence arethe tobacco pottery manufacturing and leather industriesAccording to the definition and scope of strategic emergingindustries in China it can be found that most strategic

4 Complexity

emerging industries are highly dependent on external fi-nancings such as the new generation of the informationtechnology industry and the biomedical industry Based onthe industryrsquos external financing dependence we put for-ward the following expectations the deterioration of thefinancing environment will have a greater impact on thecomputer industry and other industries with higher externalfinancing dependence than those with low dependence onexternal financing +erefore Hypothesis 4 is put forward

Hypothesis 4 +e negative impact of financing constraintson the industries with low external financing dependence isless than that on the industries with medium- and high-levelexternal financing dependence

For general investment scholars generally believe thatthe economic policy uncertainty inhibits enterprisesrsquo in-vestment but technological innovation differs from generalinvestment and thus the impact of economic policy un-certainty may vary correspondingly Technological inno-vations are full of high uncertainty which also means thatthere are many market opportunities in the future It is thesource of future profits for enterprises +e future profitopportunities will disappear if these uncertainties are re-moved while enterprises pursue high profits Uncertaintywill stimulate their innovation behaviors and encourageentrepreneurs to pursue greater profits From this per-spective the uncertainty of economic policy means not onlyrisks and challenges but also opportunities and benefits Forentrepreneurs with an innovative spirit they will make fulluse of the opportunities brought by the economic policyuncertainty avoid risks create profits for enterprises andenable the enterprises to develop and grow stronger Fur-thermore the adjustment cost of technical innovation ishigher than that of physical capital investment [24]+erefore enterprises can save a lot of ldquoadjustment costsrdquo bykeeping RampD investment stable +en in the face of eco-nomic policy uncertainty enterprises will tend to make useof the opportunities and keep the stable investment oftechnological innovation so that they can maintain con-tinuous investment in technological innovation +ereforeHypothesis 5 is put forward

Hypothesis 5 +e restraining effect of financing constraintsin periods of high economic policy uncertainty is less thanthat in periods of low economic policy uncertainty that isfinancing constraints have a more restraining effect ontechnological innovation in periods of low economic policyuncertainty

Generally speaking in the highly marketized regionsthere are more mature and effective laws and regulationsless government interventions a more effective financialsystem and an open and transparent information disclosurewhich provides a proper external political environment fortechnological innovation As for the reasons first themarket-oriented institutional environment helps to alleviatethe information asymmetry between enterprises and in-vestors reduce the cost of external financing and broadenthe financing channels of enterprises thereby optimizing theexternal financing structure of enterprises andmaking up for

the lack of funds for technological innovation [37] Secondthe market-oriented system enhances the function of themarket in resource allocation It not only reduces thegovernmentrsquos intervention and possibility of rent-seekingbut also improves the specificity of innovation stimulatesthe current RampD investment and enhances the innovationinitiative through a perfect patent protection system (Wuand Tang) [38] +ird the market-oriented system canprovide more sensitive price information for enterprises andmake the capital flow to the sectors with high-efficiencyinvestment thereby improving the allocation efficiency ofcapital and reducing the distortion of capital price and therisk premium of capital to a certain extent Fourth themarket-oriented system increases the labor remuneration oftechnical innovation personnel through income distributionand the rate of return on the technology of technical per-sonnel stimulates the innovation enthusiasm of technicalpersonnel and promotes the technological innovation Fifththe improvement of the market-oriented system can effec-tively promote Chinarsquos foreign trade +rough the importand export of commodities it is conducive to the intro-duction and absorption of technology by Chinese compa-nies Under the influence of technology spillover effects itcan promote innovation activities of Chinese enterprises (Liand Liu) [33] +erefore the market-oriented institutionalenvironment is conducive to alleviate the negative effect offinancial constraints on technological innovation+ereforeHypothesis 6 is put forward

Hypothesis 6 +e restraining effect of financing constraintson technological innovation in high-market-oriented areasis less than that in low-market-oriented areas that is it has amore restraining effect on enterprises in low-market-ori-ented areas

4 Data and Methodology

41 Model Specification In order to test Hypothesis 1 weconstruct two models from the perspectives of input andoutput of technological innovation +e model of techno-logical innovation input is as follows

Innovationgit +0 + α1Fconstraintsitminus1 + 1113944 θixitminus1

+ indit + θt + εit(1)

In view of the continuity of technological innovationactivities when setting the model in this study explanatoryvariables and control variables will lag by one period (exceptthe variable of company age) Innovationgit represents thetechnological innovation input of the company i in the yeart Fconstraintsitminus1 represents the variable of financingconstraints indicating the financing constraints of thecompany i in the year t minus 1 1113936 θixitminus1 represents the vectorcombination of control variables indit represents the in-dustrial effect θt represents the time effect and εit repre-sents the comprehensive error

Since the number of patent applications which is theexplained variable to measure the technological innovationoutput is a discrete variable and does not necessarily meet

Complexity 5

the assumption of linear regression we adopt the panelcounting model According to the distribution character-istics of the number of patent applications there is a hugedifference between the variance and the mean and thevariance is obviously greater than the mean +ere may beexcessive data dispersion and it is difficult to satisfy Poissonregression condition that expectation and variance are equal+erefore the negative binomial regression model of paneldata is adopted

It is assumed that the number of patents filed by anenterprise is Yit (the subscripted variable i represents theenterprise observed and the subscripted variable t repre-sents the year observed) and its conditional distributionfunction is as follows

P Yit yit|xit( 1113857 e

minusλitλyit

it

yit yit 0 1 2 ( 1113857 (2)

where λit gt 0 represents the average occurrence times of theevent In order to ensure that λit is a nonnegative figure it isassumed that

λit exp xitprime β + ui( 1113857 exp xit

prime β( 1113857exp ui( 1113857 equiv vi exp xitprime β( 1113857

(3)

where xit does not contain constant term vi equiv exp(ui)

represents the individual effect in the form of product In thecase that it satisfies the condition that v1 v2 middot middot middot vn itmeans that there is no individual effect More generally theexistence of individual effect is allowed In other wordsdifferent individuals have different values of vi +e prob-ability density of the negative binomial regression model is

f Yit|μit α( 1113857 Γ yit + αminus1

1113872 1113873

Γ yit + 1( 1113857Γ αminus11113872 1113873

αminus1

αminus1 + μit

1113888 1113889

αminus1

μit

αminus1 + μit

1113888 1113889

αminus1

yit 0 1 (4)

where Γ(middot) represents the gamma distribution function andα represents the overdispersion parameter E(Yit|xit) μit

and Var(Yit|xit) μit + αμ2it gt μit yit represent the numberof patents applied by the enterprise i in the year t μit

represents the average value of the patents applied by theenterprise After introducing relevant control variables thenegative binomial regression model of the technologicalinnovation output is

μit exp α1Fconstraintsitminus1 + 1113944 θixitminus1 + εit1113872 1113873 (5)

To test Hypothesis 2 to Hypothesis 4 we group thesamples according to their characteristics and compare themagnitude and significance of financing constraint coeffi-cient To test Hypothesis 5 and Hypothesis 6 we introduceeconomic policy uncertainty or marketization as the mod-erator based on model (1) and set the following econometricmodel

Innovationgit α0 + α1Fconstraintsitminus1 + 1113944 θixitminus1

+α2Mit + α3Mit times Fconstraintsitminus1 + indit + θt + εit

(6)

where Mit represents the economic policy uncertainty or themarketization If the estimate of the interaction coefficient α3 issignificantly positive the hypothesesH5 andH6 are supportedIf α3 is significantly negative it means the economic policyuncertainty or themarketization increases the negative effect offinancing constraints on technological innovation

42 Variable

421 Explained Variable +e explained variable is tech-nical innovation Technical innovation can be measuredfrom the perspectives of input and output For innovation

input we measure the companyrsquos technological innovationactivities by the RampD expense and use the RampD concen-tration (the proportion of RampD expenses to the total assets ofthe current period) and the RampD intensity (the proportion ofRampD expenses to current sales revenue) In terms of thetechnical innovation output following Hall and Harhoff[39] we measure the output of technological innovation bythe number of patent applications

422 Explanatory Variable +e core explanatory variable isthe financing constraints Following Owen [40] to calculateKZ index and considering the characteristics of listedcompanies in China the measurement method of FC indexof the Chinese listed companies financing constraint isestablished +e first step is to classify the samples by yearand calculate medians of the annual operating net cash flow(CF) cash dividends (DIV) cash holdinglast termrsquos assets(CashH) the leverage ratio (LEV) and Tobinrsquos Q (TobinQ)+en we assign a value to the variable CF DIV and CashHare expected to be negative correlation with financingconstraints while LEV and TobinQ are positively correlatedwith financing constraints +e values are determinedaccording to the following principles If CF DIV and CashHare lower than their medians that year the correspondingindicator value is taken as 1 otherwise it is taken as 0expressed as FC1 FC2 and FC3 If LEV and TobinQ arehigher than their medians that year the correspondingindicator value is taken as 1 otherwise it is taken as 0expressed as FC4 and FC5 +e third step is to calculate thevalue of FC according to the equation FC CF1 + FC2+FC3 + FC4 + FC5+e fourth step is to use the panel sortinglogistic regression model for regression +e dependentvariable is FC and the explanatory variables are the originalvalues of CF DIV CashH LEV and TobinQ+e regression

6 Complexity

coefficients of these explanatory variables are estimated andthe expression of the FC index is obtained In the end theactual values of CF DIV CashH LEV and TobinQ aresubstituted into the regression model and the predicted FCvalue of the enterprise is calculated which was the FC indexof the enterprise +e higher the FC index is the moreserious financing constraints is

According to the process above for calculating the FCindex the measurement result of the financing constraints isFC 1135CF minus 4572DIV minus 404CashH + 538LEV+016TobinQ Moreover the regression coefficients of thefive variables are all significant at the significance level of 1and the overall significance of the model also passes thestatistical test It is illustrated that the five variables namelyCF DIV CashH LEV and TobinQ are the effective proxyvariables affecting the financing constraints In addition CFDIV and CashH are negatively correlated with financingconstraints while LEV and TobinQ are positively correlatedwith the financing constraints It means that listed com-panies with high operating cash flow high cash holding highdividends low leverage ratio and low investment oppor-tunities face less serious financing constraints In contrastlisted companies with low operating cash flow low cashholding low dividends high leverage ratio and high in-vestment opportunities face more severe financingconstraints

Two other important variables in this study are eco-nomic policy uncertainty andmarketization+ere are manymeasurement indicators of economic policy uncertainty Weadopt the economic policy uncertainty index constructed byBaker et al [41] +is index has been widely used in recentmacropolicy literature and has been proved to be com-prehensive and objective to reflect the fluctuation of Chinarsquoseconomic policy Since the index is a monthly index we usethe monthly arithmetic average to convert the monthlyeconomic policy uncertainty index into an annual economicpolicy uncertainty index We use variable EPU to representthe economic uncertainty index Marketization is mostlymeasured by the marketization index proposed by Fan et alwhich consists of the relationship between the governmentand the market the development of the nonstate economythe development of the product market the development ofthe factor market and the development of market inter-mediary organizations [31] +e five aspects of the legalsystem environment can fully reflect all aspects of mar-ketization +erefore marketization index is used to mea-sure the level of marketization in each region Since thesample in this article is data of listed companies in strategicemerging industries it is not possible to directly obtain thecorresponding market index of each listed company +emethod is to find the market index of the correspondingprovince according to the province where each listedcompany is registered We use marketization variable torepresent the marketization index

423 Control Variable Control variables involved are Sizerepresenting the size of enterprises Age representing the ageof enterprises TobinQ Concentr representing equity

concentration Sgrowth representing the sales growth rateand Cflow representing the cash flow ratio

43 8e Data Our samples are the companies listed on themain board of China in strategic emerging enterprise fromover 2009 to 2019 As there are no strategic emerging in-dustries in the industry classification of the China SecuritiesRegulatory Commission it is impossible to obtain thesample of listed companies of strategic emerging industriesdirectly In order to gather the samples of listed companies instrategic emerging industries the following steps are fol-lowed+e initial sample is selected from the sample stocks ofChina Strategic Emerging Industries Composite Index(ldquoEmerging Composite Indexrdquo) released by China SecuritiesIndex Co Ltd and Shanghai Stock Exchange in 2017covering a total of 1117 companies listed as A-shares inShanghai and Shenzhen Stock Exchanges on the SMEsboard (Small and Medium Enterprise Board) the second-board market and the new OTC market (new over-the-counter market) Due to the small amount of data disclosedby listed companies on the new OTC market and the poorcomparability compared with other listed companies suchcompanies are removed from the sample frame Next somefilter conditions are set in the sample frame+e STand lowast STlisted companies within the sample period are deleted+enthe listed companies in the financial and insurance industryare deleted At last the samples with missing values of someindexes are deleted After the steps detailed above the finalsamples are 757 listed companies in the strategic emergingindustries over the period of 2009 to 2019

5 Empirical Results and Discussions

51 8e Inhibitory Effect of Financing Constraints We focuson the impact of financing constraints on technical inno-vation Columns 2 and 3 of Table 1 report the impact offinancing constraints on technological innovation input Asshown in Table 1 financing constraints negatively affect theconcentration and intensity of RampD at the significance levelof 5 +at is financing constraints inhibit technologicalinnovation input For listed companies technological in-novation is a very important investment decision for en-terprises involving large capital investment and a long timeof capital occupation When enterprises face serious fi-nancing constraints their internal and external funds cannotmeet the capital demand for technological innovation soenterprisesrsquo technological innovation activities will bepostponed

+e impact of financing constraints on technologicalinnovation output is also vital According He and Zhang [42]and Li and Zheng [43] the patent applications for measuringthe technological innovation are divided into two types Oneis the substantive innovation with a higher technologicallevel represented by invention patents aiming to promotesocial and technological progress and occupy a competitiveadvantage in the industry Andrea pointed out that financingconstraints have a significant negative effect on fundamentalinnovation [44] +e other is strategic innovations with low

Complexity 7

technical level represented by utility model patents anddesign patents most of which aim to obtain other benefits+en the influence of financing constraints on the differenttypes of patent applications is studied Columns 4 to 6 ofTable 2 respectively report the impact of financing con-straints on the number of patent applications inventionapplications and noninvention applications In order todetermine whether negative binomial regression is a fixedeffect or a random effect the Hausmann test is carried out+e results show that the p value is less than 001 whichstrongly rejected the negative binomial regression of randomeffect +erefore the fixed-effect negative binomial regres-sion is adopted Hilbe pointed out that in the countingmodel the marginal effect of variables is more significantthan that of the regression coefficient [45] We use themarginal effect to analyze the impact of the unit change ofthe explanatory variable on the explained variable Columns4 and 5 of Table 1 show that in terms of both the number ofpatent applications and the number of invention patentsfinancing constraints inhibit technological innovation out-put at the significance level of 10 Column 6 of Table 1shows that the influence of financing constraints on thenumber of nonpatent applications is negative but not sig-nificant +e above results indicate that therestraint of fi-nancing constraints on the inventionpatent is greater thanthat of noninvention patent mainly because of the differentnature of the two types of patents +e invention patent canimprove the production efficiency of enterprises enableenterprises to obtain competitive advantages in the industryand represent the core competitiveness of enterprisesHowever invention patents usually require a lot of in-vestment and last a long time before they come out

On the contrary the purpose of noninvention patents isto echo the innovation strategy of enterprises and nationalinnovation policies and the companies pay more attentionto the ldquospeedrdquo and ldquoquantityrdquo of innovation Noninventionpatents involve a relatively small investment a relativelyshort duration cycle and a relatively little influence bycapital Due to the essential differences between inventionpatents and noninvention patents enterprises have differentattitudes towards the two kinds of patents which ultimatelyleads to the difference in the degree of influence of financingconstraints on the two kinds of patents +is conclusion isconsistent with Khan et al [46]

+e results above indicate that financing constraints notonly inhibit the technological innovation input in emergingindustries but also inhibit the technological innovationoutput of the enterprise in emerging industries and thusHypothesis 1 has been supported

52 8e Heterogeneous Influence of Financing Constraints

521 Analysis by Industry Considering that differentsubindustries in strategic emerging industries have differentcharacteristics of financing constraints and technologicalinnovation the sensitivity of technical innovation to fi-nancing constraints may be heterogeneous We furtheranalyze by industry in order to find out the difference in theimpact of financial constraints on technical innovation ofvarious subindustries in strategic emerging industries Sincethere are many subindustries in the strategic emerging in-dustries only RampD intensity is used as the explained variableto analyze by industry

Table 1 Regression results of the influence of financing constraints on technical innovation

Variable RampDconcentration

RampDintensity

Number of patentapplications

Number of inventionpatents

Number of noninventionpatents

LFC minus0031lowastlowast minus0114lowastlowastlowast minus0024lowast minus0038lowastlowast minus0002(0014) (0032) (0012) (0015) (0014)

Lsize minus0287lowastlowastlowast minus0629lowastlowastlowast 0357lowastlowastlowast 0378lowastlowastlowast 0348lowastlowastlowast(0051) (0119) (0044) (0050) (0058)

LTobinQ minus0009 minus0029 0024lowastlowastlowast 0030lowastlowastlowast 0009(0013) (0029) (0006) (0010) (0010)

LConcentr minus0009lowastlowast minus0025lowastlowastlowast minus0000 0002 0002(0004) (0009) (0003) (0003) (0003)

LSgrowth minus0011 minus0165lowastlowastlowast minus0000 minus0000 0000(0024) (0054) (0000) (0000) (0000)

Age minus0037lowastlowast minus0160lowastlowastlowast 0045lowastlowastlowast 0071lowastlowastlowast 0024lowast(0015) (0036) (0011) (0010) (0014)

LCflow minus0003lowastlowast minus0005lowastlowast minus0000(0002) (0002) (0002)

Constant 9033lowastlowastlowast 17980lowastlowastlowast minus7436lowastlowastlowast minus8479lowastlowastlowast minus7302lowastlowastlowast(1203) (2798) (0922) (1058) (1203)

Time effect Controlled ControlledIndustryeffect Controlled Controlled

Sample size 757 757 628 625 608Notes lowastlowastlowast lowastlowast and lowast mean significant at the significance level of 1 5 and 10 respectively +e figures in parentheses are standard errors the same asbelow Lvariables represent variables with a lag of one period

8 Complexity

Columns 2 to 8 of Table 2 report the effects of financingconstraints in different subindustries in strategic emergingindustries on technological innovation It is found that thereare pronounced industry differences in the impact of fi-nancing constraints on technological innovation Except forthe biological industry and new energy industry the impactof financing constraints on technological innovation in-vestment in the other five industries is uniformly negative Inthe new generation information technology industry ad-vanced equipment manufacturing and new energy vehicleindustry with high RampD intensity the negative impact isstatistically significant but not in the new material industryand energy conservation and environmental protectionindustry with relatively low RampD intensity +is is becausethe higher the RampD intensity is the more the money forRampD investment will be and the stronger the influence offinancing constraints will be In the regression analysis of thebiological industry and the new energy industry the fi-nancing constraintsrsquo coefficient is not negative +e mainreason is that these two industries are industries with rel-atively small financing constraints and are less affected byfinancial factors +erefore financing constraints have noobvious inhibitory effect on technological innovation andthus Hypothesis 2 has been verified

522 Whether It Is a Manufacturing Enterprise We dividethe listed companies in strategic emerging industries intomanufacturing and nonmanufacturing industries accordingto the classification standards of Chinarsquos high-tech indus-tries +e regression results of Table 3 show that the in-hibitory effect of the financing constraints on thetechnological innovation input and output of themanufacturing industry is lower than that of the non-manufacturing industry +e possible reason is that Chinarsquoslabor costs have risen sharply which has seriously affected

the operating profits of enterprises in the labor-intensive andcapital-intensive manufacturing industries +erefore themanufacturing industry is more willing to improve laborproductivity through technological innovation and tech-nological innovation is of greater significance tomanufacturing enterprises Manufacturing companies investmore in RampD and have a longer cycle for capital recoveryOnce the technical innovation project is launched the fundsneed to be continually invested Otherwise the invested costwill become a sunk cost and cannot be recovered +echaracteristic of high adjustment cost of RampD input is moreobvious in the manufacturing industry and manufacturingenterprises will pay more attention to the sustainability ofRampD +erefore technological innovation will not react sosensitively to the capital situation Even if the financingconstraints are intensified the technological innovation inthe manufacturing industry will not be reduced rapidly butneed a long time to adjust However nonmanufacturingindustries are mostly asset-light enterprises which involverelatively less investment in technological innovation withlow RampD intensity and less dependence on technologicalinnovation +erefore in these enterprises technologicalinnovation is more sensitive to the financial situation andthus Hypothesis 3 has been verified

523 Level of the Dependence of Industry External Financing+e companies are divided into two groups according to theexternal financing dependence in the industry to testwhether the inhibitory effect of financing constraints ontechnological innovation is related to the external financingdependence of the industry One group are the companieswith low external financing dependence and the other arethe companies with medium- and high-level external fi-nancing dependence +e grouping results of the sample inTable 4 show that only a few strategic emerging enterprises

Table 2 Regression analysis of different subindustries in strategic emerging industries

VariableNext-generationinformationtechnology

Advancedequipment

manufacturing

Newmaterialindustry

Biologicalindustry

New energyautomobileindustry

Newenergyindustry

Environmentalprotection industry

LFC minus0238lowastlowastlowast minus0084lowast minus0011 0211 minus0126lowast 0178 minus0005(0066) minus0049 (0039) (0170) (0073) (0143) (0041)

Lsize minus0098 minus0586lowastlowastlowast minus0642lowastlowastlowast minus0579 minus0065 minus0077 minus1002lowastlowastlowast(0273) (0161) (0136) (0723) (0211) (0212) (0204)

LTobinQ 0025 0002 0082lowast 0004 0037 1011lowastlowast minus0263lowastlowastlowast(0054) (0060) (0042) (0149) (0134) (0496) (0058)

LConcentr minus0021 minus0025lowastlowast 0008 minus0075 minus0008 0006 0009(0020) (0012) (0009) (0055) (0014) (0013) (0008)

LSgrowth 0009lowastlowastlowast minus0000 0006lowastlowastlowast minus0002 minus0001 0008 minus0001(0002) (0001) (0002) (0007) (0003) (0006) (0001)

Age minus0276lowastlowastlowast minus0096lowast 0020 minus0249 minus0036 0041 minus0071lowast(0073) (0050) (0039) (0179) (0056) (0031) (0040)

Sample size 290 257 95 43 19 14 24Notes according to the Guiding Catalogue of Key Products and Services for Strategic Emerging Industries (2016 Edition) issued by the National Developmentand Reform Commission in 2017 the strategic emerging industries are divided into nine sectors (including related service industries) Considering the smallnumber of samples of digital cultural and creative industries and related service industries they are not separately analyzed by industry

Complexity 9

are in the industries with low-level external financing de-pendence with only 14 samples accounting for 19 of thetotal samples It indicates that most enterprises in thestrategic emerging enterprises are in the industries with highexternal financing dependence +e regression results inTable 4 show that the negative effect of financing constraintson the technical innovation input and output is not sig-nificant in the samples of industries with low external fi-nancing dependence However in the industry withmedium- and high-level external financing dependence theimpact of financing constraints on the technical innovationrsquosinput and output is significant at the 10 significance leveland thus Hypothesis 4 has been verified

53 8e Moderating Effect of the External Environment

531 8e Economic Uncertainty as the Moderator As anexternal factor affecting enterprisesrsquo technological innova-tion economic policy has an impact on the technologicalinnovation activities of enterprises from amacroperspectiveOnce a countryrsquos economic policy changes enterprisesrsquoinnovation activities will inevitably be affected by the policy

Columns 2 and 3 in Table 5 are the regression resultsafter adding the cross terms of economic policy uncertaintyand financing constraints to the regression model It showsthat the negative effect of financing constraints on tech-nological innovation is still significant and the coefficient ofthe cross term is significantly positive indicating thateconomic policy uncertainty alleviates the inhibitory effectof financing constraints on technological innovation Spe-cifically when the uncertainty of the economic policy en-vironment increases the inhibitory effect of financingconstraints on the technological innovation input may bereduced+ere are two main explanations On the one handthe increasing uncertainty of economic policy means thatenterprisesrsquo business environment will change significantly

+is is both an opportunity and a challenge and the survivalof the fittest among enterprises will be presented Only thoseenterprises with high production efficiency and strongcompetitiveness can finally survive in the industry Fur-thermore innovation ability is the key factor for enterprisesto enhance their core competitiveness Enterprises can makeuse of the opportunities brought by the economic policyuncertainty and improve their core competitiveness by in-creasing the input in technological innovation activities so asto boost their chances of winning in the future marketcompetition On the other hand the uncertainty of eco-nomic policies will then spread to the external financingmarket leading to the fluctuation of the capital market andthen affect the external financing of technological innova-tion According to the research of Li and Yang [47] it can beseen that the economic policy uncertainty inhibits generalinvestment activities +erefore when economic policyuncertainty increases ordinary investment activities will notbe active and there will be abundant capital in the capitalmarket However technological innovation was fraught withuncertainty and the future is full of opportunities When theeconomic policy uncertainty increases financing for tech-nological innovation is more likely compared with general

Table 4 +e classification test result of the level of the external financing dependence in the industry

Variable

RampD concentration RampD intensity Number of patent applicationsLow externalfinancing

dependence

Medium- and high-level external

financing dependence

Low externalfinancing

dependence

Medium- and high-level external

financing dependence

Low externalfinancing

dependence

Medium- and high-level external

financing dependence

LFC 0107 minus0034lowastlowast minus0060 minus0116lowastlowastlowast minus0003 minus0021lowast(0113) (0014) (0117) (0033) (0088) (0013)

Allcontrols Controlled Controlled Controlled Controlled Controlled Controlled

Samplesize 14 743 14 743 14 614

Table 5 +e moderating effect of economic policy uncertainty

Variable RampD concentration RampD intensityLFC minus0069lowastlowastlowast (0026) minus0286lowastlowastlowast (0060)Lsize minus0273lowastlowastlowast (0050) minus0592lowastlowastlowast (0120)LTobinQ minus0022 (0029)LConcertr minus0009lowastlowast (0004) minus0025lowastlowastlowast (0009)LSgrowth minus0000 (0000) minus0002lowastlowastlowast (0001)Age minus0037lowastlowast (0015) minus0160lowastlowastlowast (0036)EPUlowastLFC 0000lowast (0000) 0001lowastlowastlowast (0000)EPU 0003lowastlowastlowast (0001) 0013lowastlowastlowast (0002)Constant 8409lowastlowastlowast (1162) 1580lowastlowastlowast (2775)Sample size 757 757

Table 3 +e classification test results of manufacturing enterprises and nonmanufacturing ones

VariableRampD concentration RampD intensity Number of patent applications

Manufacturing Nonmanufacturing Manufacturing Nonmanufacturing Manufacturing Nonmanufacturing

LFC minus0015 minus0094lowastlowast minus0100lowastlowastlowast minus0151lowastlowast minus0021lowast minus0061lowast(0012) (0043) (0035) (0075) (0012) (0034)

All controls Controlled Controlled Controlled Controlled Controlled ControlledSample size 543 214 543 214 484 144

10 Complexity

investment activities +erefore economic policy uncer-tainty can ease the inhibitory effect of financing constraintson technological innovation and thus Hypothesis 5 hasbeen verified

532 8eMarketization as the Moderator Columns 2 and 3in Table 6 are the regression results after adding the crossterms of marketization and financing constraints to theregression model It shows that the coefficient of financingconstraints is significantly negative the coefficient of mar-ketization is significantly positive and the coefficient of across term is significantly positive indicating that themarketization can help to alleviate the inhibitory effect offinancing constraints on technological innovation Specifi-cally when the market environment improves the inhibi-tory effect of financing constraints on technologicalinnovation will be reduced +erefore the more the market-oriented the lower the inhibitory effect of financing con-straints on technological innovation and vice versa In theend the Matthew effect occurs when the strong get strongerand the weak get weaker Under such effect the differencesin technological innovation between regions are constantlywidened +e possible explanation is as followsIn highlymarket-oriented regions there are a sound legal system highinformation transparency a more robust financial systemsound and abundant financial resources Such a perfectinstitutional environment can ease the enterprisersquos financingconstraints provide a proper external financing environ-ment for technological innovation and better escort theenterprisersquos technological innovation which is conducive toencourage enterprises to carry out technological innovationIn regions with less market-oriented it is impossible toprovide a strong institutional guarantee for technologicalinnovation For an enterprise facing financing constraintsthis adverse institutional environment intensifies the in-hibitory effect of financing constraints on technologicalinnovation and is not conducive to the enterprisersquos tech-nological innovation and thus Hypothesis 6 has beenverified

54 Robustness Test

541 Solutions to Endogenous Problems Financial con-straints inhibit enterprisesrsquo technological innovation activ-ities and technological innovation may make financialconstraints of enterprises get more serious thus leading to abidirectional causal relationship between financial con-straints and technical innovation In order to avoid possiblereverse causality all control variables and the explanatoryvariables are delayed by one period Simultaneously in thestudy to avoid the endogenous problems caused by theomitted variables the industry and time fixed effects arecontrolled in the empirical analysis

542 Recalculation of Financing Constraint Index In theempirical analysis we use the FC index to measure the fi-nancing constraints However there are various methods tomeasure the constraints with different emphases In order to

ensure robust conclusions the SA index proposed byHadlock and Pierce [48] is used to remeasure the financingconstraints +at is SA minus0737 Size + 0043 Size2minus004Age +e above empirical process is then repeated tocheck whether the financing constraints have an inhibitoryeffect on technological innovation

+e SA index is generally negative +e greater the ab-solute value of SA is the more serious the financing con-straints faced by the enterprise will be+erefore the SA indexis negatively correlated with the level of financing constraints+e regression results in Table 7 show that the SA index ispositively correlated with technological innovation at thesignificance level of 5 indicating the more serious the fi-nancing constraints is the stronger the inhibitory effect ontechnological innovation activities will be +is is consistentwith the conclusion of the test based on the FC index whichonce again verifies that financing constraints have an inhib-itory effect on technological innovation

543 Recalculation of the Technological Innovation Output+e increment of intangible assets is adopted to remeasurethe technological innovation output following Ju et al [49]Moreover the ratio of the increment of intangible assets tothe total assets at the end of the period is used as the proxyvariable of the technological innovation output to remeasurethe technological innovation output Finally the empiricalresults of the influence of financing constraints on thetechnological innovation output are shown in Table 8

+e results in Table 8 show that financing constraintshave a significant inhibitory effect on the technologicalinnovation output regardless of if the FC index or SA indexis used It is consistent with the conclusion where thenumber of patent applications is adopted as the techno-logical innovation output

544 8e Ordinary Panel Model as the Counting Model+e panel counting model is used to study the influence offinancing constraints on the technical innovation outputwhich shows that financing constraints have a significantinhibitory effect on the technological innovation output Inorder to test the robustness of the results the number ofpatent applications as the explained variable is taken as acontinuous common variable and then the ordinary panelmodel is used for empirical analysis +e empirical resultsare shown in Table 9

As shown in Table 9 the conclusion obtained using theordinary panel model is that the financing constraints sig-nificantly inhibit the technological innovation output After

Table 6 +e moderating effect of the marketization

Variable RampD concentration RampD intensityLFC minus0036lowastlowastlowast (0012) minus0168lowastlowastlowast (0030)Marketization 0838lowastlowastlowast (0266) 1319lowastlowast (0658)MarketizationlowastLFC 0017lowastlowastlowast (0005) 0024lowast (0013)All controls Controlled ControlledSample size 757 757

Complexity 11

dividing the number of patent applications into inventionpatents and noninvention patents we find that the inhibi-tory effect of the financing constraints on the inventionpatents is significantly higher than that of the noninventionpatents which is consistent with the conclusion obtainedusing the negative binomial regression model of panel data

6 Conclusions and Implications

As the world economy enters a stage of innovation compe-tition the decision-making problem of technological innova-tion in emerging industries is a practical problem worthy ofresearch and attention Based on the panel data of the listedcompanies in Chinarsquos strategic emerging industries from 2009to 2019 we adopt the negative binomial regression model ofpanel data and empirically study the technological innovationdecision-making issues of emerging industries from the per-spective of financing constraints Our results show that thefinancial constraints in strategic emerging industries in Chinasignificantly inhibit technological innovation in terms oftechnological innovation inputs and outputsWhat ismore the

inhibitory effect of substantive innovation like the inventionpatent is more pronounced than that of strategic innovationlike the noninvention patent +e influence of financingconstraints on technological innovation varies significantlybetween industries within strategic emerging industriesmanufacturing industries and nonmanufacturing industriesand among industries with different external financing de-pendence However the economic policy uncertainty and themarketization can help to alleviate the inhibitory effect of fi-nancing constraints on technological innovation

Our research conclusions lead to the following policyimplications First of all the government should focus onsolving the financial constraints by speeding up the reformof the financial system Promoting the reform of the financialsupply side improving the efficiency of financial resourceallocation and reducing the financing cost should also beemphasized In addition it is necessary to expand thecoverage of financial services to develop financing platformsand tools exclusive to RampD intensive industries such asstrategic emerging industries and to encourage venturecapital and long-term capital to invest in technological

Table 9 Robustness test results of the ordinary panel model

Variable Number of patent applications Number of invention patents Number of noninvention patentsLFC minus0045lowastlowastlowast (0012) minus0059lowastlowastlowast (0012) minus0028lowastlowast (0013)LTobinQ minus0003 (0008) 0002 (0008) minus0018lowastlowast (0009)LConcertr 0005lowastlowast (0002) 0004 (0003) 0006lowastlowast (0003)LSgrowth minus0000 (0000) minus0000 (0000) minus0000 (0000)Llev 0017lowastlowastlowast (0002) 0017lowastlowastlowast (0002) 0016lowastlowastlowast (0002)Age 0003 (0009) 0012 (0010) minus0004 (0010)LCflow minus0004lowastlowast (0002) minus0005lowastlowastlowast (0002) minus0003 (0004)Sample size 691 691 691

Table 8 Robustness test results of intangible assets increment

Variable FC index SA indexLFC minus0042lowastlowast (0019) 0249lowastlowast (0108)Lsize 0000 (0041)LTobinQ 0039lowastlowast (0019) 0037lowast (0019)LConcentr 0000 (0003) 0002 (0003)LCflow minus0014lowastlowastlowast (0004) minus0012lowastlowastlowast (0001)LSgrowth minus0009 (0042) 0004 (0042)Age minus0039lowastlowastlowast (0008)Ltangibility 0000 (0003)Constant 0508 (1039) 0316 (0593)Sample size 757 757

Table 7 Regression results of the impact of financing constraints on technological innovation through the SA index

Variable RampD concentration RampD intensity Number of patent applicationsLSA 0351lowastlowastlowast (0135) 0995lowastlowastlowast (0316) 0300lowast (0182)LLEV minus0444lowast (0240) minus4102lowastlowastlowast (0549) minus0003 (0002)LTobinQ 0003 (0013) minus0039 (0030) minus0005 (0012)LConcentr minus0010lowastlowast (0004) minus0020lowastlowast (00098) minus0000 (00037)LCflow 0004lowast (0003) minus0004 (0006)Ltangibility 0011lowastlowastlowast (0003) 0002 (0006)LSgrowth minus0011 (0023) minus0118lowastlowast (0053)Constant 2159lowastlowastlowast (0592) 5283lowastlowastlowast (1360) minus9301lowastlowastlowast (1361)Sample size 757 757 628

12 Complexity

innovation projects A technological innovation financingsystem based on the market mechanism guided by gov-ernment investment dominated by enterprises and widelyparticipated by social funds should be established Morefinancial resources can flow to technological innovationprojects and to enterprises with technological innovationactivities

Second at present Chinarsquos economy is in a ldquonew nor-malrdquo that requires innovation In view of the selective effectof financing constraints on innovation activities relevantdepartments should make effort to build a good externalfinancing environment and use effective financial policiesand administrative methods to help enterprises release theirinnovative vitality more fully For example relevant de-partments should pay attention to building a good market-oriented system and increase institutional guarantees forinnovative activities so as to improve the operating con-ditions of enterprises and promote high-quality innovativeactivities When companies face financing constraints thesemeasures that are conducive to improving operating con-ditions will help to stimulate innovation

Finally since the financing constraints have a moreobvious restraining effect on enterprisesrsquo substantive inno-vation of high quality the government should encourageenterprises to increase RampD investment in substantive in-novation [50ndash53] while guiding them to pursue high-qualityinnovation so as to avoid such innovations that emphasizeonly the quantity and speed In addition the governmentalso should continue to increase financial investment inbasic research for making up for the lack of substantiveinnovation of enterprises and aiming to truly improve in-novation ability and quality of the enterprise

Data Availability

+e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

+e authors declare that there are no conflicts of interestregarding the publication of this study

Acknowledgments

+is research was partly supported by the Natural ScienceFoundation of Zhejiang Province (Grant no LQ19G030007)and Zhejiang Gongshang University Online and OfflineHybrid Teaching Reform Project (Grant no1010XJ2919103)

References

[1] H Lei and W Wang ldquo+e impact of network structure oninnovation efficiency an agent-based study in the context ofinnovation networksrdquo Complexity vol 21 no 2 pp 111ndash1222015

[2] W B Arthur and W Polak ldquo+e evolution of technologywithin a simple computer modelrdquo Complexity vol 11 no 5pp 23ndash31 2010

[3] J J Pang G Lin R L Yang et al ldquoPromote continuouseconomic development economic trendsrdquo Economic Dy-namics vol 7 pp 3ndash19 2019

[4] M Cai and S J Liu ldquo+e impact of government subsidies onthe leverage ratio of strategic emerging industriesrdquo Con-temporary Economic Research vol 8 pp 90ndash100 2020

[5] J R Brown S M Fazzari and B C Petersen ldquoFinancinginnovation and growth cash flow external equity and the1990s RampD boomrdquo Journal of Finance vol 64 pp 151ndash1852010

[6] J S Ang Y Cheng and C Wu ldquoDoes enforcement of in-tellectual property rights matter in China evidence fromfinancing and investment choices in the high-tech industryrdquoReview of Economics and Statistics vol 96 no 2 pp 332ndash3482014

[7] S M Fazzari R G Hubbard B C Petersen A S Blinder andJ M Poterba ldquoFinancing constraints and corporate invest-mentrdquo Brookings Papers on Economic Activity vol 1988 no 1206 pages 1988

[8] T Beck and R Levine ldquoIndustry growth and capital alloca-tionrdquo Journal of Financial Economics vol 64 no 2pp 147ndash180 2002

[9] M Gertler and S Gilchrist ldquo+e role of credit market im-perfections in the monetary transmission mechanism argu-ments and evidencerdquo8e Scandinavian Journal of Economicsvol 95 no 1 pp 43ndash64 1993

[10] R G Rajan and L Zingales ldquoFinancial dependence andgrowthrdquo Social Science Electronic Publishing vol 88 no 3pp 559ndash586 1998

[11] G Borisova and J R Brown ldquoRampD sensitivity to asset saleproceeds new evidence on financing constraints and intan-gible investmentrdquo Journal of Banking amp Finance vol 37 no 1pp 159ndash173 2013

[12] Y S Hwang H G Min and S H Han ldquo+e influence offinancial development on RampD activity cross-country evi-dencerdquo Review of Pacific Basin Financial Markets amp Policiesvol 13 no 3 pp 381ndash401 2010

[13] A Hall G Bockett S Taylor M V K Sivamohan andN Clark ldquoWhy research partnerships really matter inno-vation theory institutional arrangements and implications fordeveloping new technology for the poorrdquo World Develop-ment vol 29 no 5 pp 783ndash797 2001

[14] B H Hall ldquoInvestment and research and development at thefirm level does the source of financing matterrdquo NBERWorking Papers 1992

[15] C P Himmelberg and B C Petersen ldquoRampD and internalfinance a panel study of small firms in high-tech industriesrdquo8e Review of Economics and Statistics vol 76 pp 38ndash511994

[16] Z He and M B Wintoki ldquo+e cost of innovation RampD andhigh cash holdings in US firmsrdquo Journal of Corporate Financevol 41 pp 280ndash303 2016

[17] B Mulkay B H Hall and J Mairesse Firm Level Investmentand RampD in France and the United States A comparisonSocial Science Electronic Publishing Berlin Germany 2001

[18] D Harhoff Are there financing constraints for RampD and in-vestment in German manufacturing firms 8e Economics andEconometrics of Innovation Springer Berlin Germany 2000

[19] S Bond and D H V Harhoff ldquoInvestment RampD and fi-nancial constraints in Britain and Germanyrdquo AnnalesdrsquoEconomie et de Statistique vol 7980 pp 433ndash460 2005

[20] C Bloch ldquoRampD investment and internal finance the cash floweffectrdquo Economics of Innovation and New Technology vol 14no 3 pp 213ndash223 2005

Complexity 13

[21] G Scellato ldquoPatents firm size and financial constraints anempirical analysis for a panel of Italian manufacturing firmsrdquoCambridge Journal of Economics vol 31 pp 55ndash76 2007

[22] E Ughetto ldquoDoes internal finance matter for RampD newevidence from a panel of Italian firmsrdquo Cambridge Journal ofEconomics vol 32 no 6 pp 907ndash925 2008

[23] D Czarnitzki and H Hottenrott ldquoRampD investment and fi-nancing constraints of small and medium-sized firmsrdquo SmallBusiness Economics vol 36 no 1 pp 65ndash83 2011

[24] B H Hall ldquo+e financing of research and developmentrdquoOxford Review of Economic Policy vol 18 no 1 pp 35ndash512002

[25] J R Brown and B C Petersen ldquoCash holdings and RampDsmoothingrdquo Journal of Corporate Finance vol 17 no 3pp 694ndash709 2011

[26] D Li ldquoFinancial constraints RampD investment and stockreturnsrdquo Review of Financial Studies vol 24 no 9pp 2974ndash3007 2011

[27] A Mina H Lahr and A Hughes ldquo+e demand and supply ofexternal finance for innovative firmsrdquo Industrial and Cor-porate Change vol 22 no 4 pp 869ndash901 2013

[28] X Gu Y Chen and S Pan ldquoEconomic policy uncertainty andinnovation evidence from listed companies in Chinardquo Reviewof Financial Studies vol 2 pp 109ndash123 2018

[29] F Knight Risk Uncertainty and Profit Houghton MifflinCompany Boston MA USA 1921

[30] W Hao W Wei and J Wen ldquoHow does economic policyuncertainty affect corporate innovationmdashperspective ofmechanism of action of real option theoryrdquo EconomicManagement vol 38 pp 40ndash54 2016

[31] G Fan X Wang and G Ma ldquo+e contribution of Chinarsquosmarketization process to economic growthrdquo Economic Re-search vol 46 no 9 pp 4ndash16 2011

[32] K Z Dai and Y J Liu ldquo+e impact of marketization reformon RampD investment in Chinarsquos high-tech industryrdquo ScienceStudies vol 31 pp 50ndash57 2013

[33] P Li and X Liu ldquo+e impact of market-oriented institutionalchange on Chinarsquos technological progressmdashbased on theperspective of independent R amp D and technology intro-ductionrdquo Economic Perspectives vol 4 pp 42ndash50 2015

[34] K J Arrow ldquoEconomic welfare and the allocation of resourcesfor inventionrdquo 8e Rate and Direction of Inventive Activityvol 12 pp 609ndash626 1962

[35] P Kijkasiwat and P Phuensane ldquoInnovation and firm per-formance the moderating and mediating roles of firm sizeand small and medium enterprise financerdquo Journal of Riskand Financial Management vol 13 no 5 p 97 2020

[36] X N Yuan ldquoManufacturing innovation and service inno-vation comparison and convergencerdquo Finance and TradeResearch vol 20 pp 14ndash19 2009

[37] Y M Zhu and M E Jia ldquoMarketization process financialconstraints and corporate technology innovationrdquo Com-mercial ResearchInnovation Technology vol 1 pp 49ndash562017

[38] C P Wu and D Tang ldquoIntellectual property rights en-forcement corporate innovation and operating performanceevidence from Chinarsquos listed companiesrdquo Economic ResearchJournal vol 51 no 11 pp 125ndash139 2016

[39] B H Hall and D Harhoff ldquoRecent research on the economicsof patentsrdquo Annual Review of Economics vol 4 pp 541ndash5652012

[40] L Owen P Christopher and J Saaa-Requejo ldquoFinancialconstraints and stock returnsrdquo Review of Financial Studiesvol 14 pp 529ndash554 2001

[41] S R Baker N Bloom and S J Davis ldquoMeasuring economicpolicy uncertaintyrdquo 8e Quarterly Journal of Economicsvol 131 no 4 pp 1593ndash1636 2016

[42] J F He and X J Zhang ldquo+e influence of rising labor cost onenterprise innovationrdquo 8e Journal of Quantitative amp Tech-nical Economics vol 45 no 8 pp 56ndash73 2018

[43] W J Li and M N Zheng ldquoIs it substantive innovation orstrategic innovationmdashimpact of macroeconomic policies onmicro-enterprisesrsquo innovationrdquo Economic Research Journalvol 4 pp 60ndash73 2016

[44] C Andrea ldquoFinancing constraints radical versus incrementalinnovation and aggregate productivityrdquo American EconomicJournal Macroeconomics vol 11 no 2 pp 75ndash309 2019

[45] J M Hilbe Modeling Count Data Cambridge UniversityPress Cambridge UK 2014

[46] S U Khan A Shah and M F Rizwan ldquoDo financingconstraints matter for technological and non-technologicalinnovation a (re) examination of developing marketsrdquoEmerging Markets Finance and Trade vol 57 pp 1ndash28 2019

[47] F Y Li and M Z Yang ldquoCan economic policy uncertaintyinfluence corporate investment the empirical research byusing China economic policy uncertainty indexrdquo Journal ofFinancial Research vol 57 no 9 pp 2739ndash2766 2015

[48] C J Hadlock and J R Pierce ldquoNew evidence on measuringfinancial constraints moving beyond the KZ indexrdquo Review ofFinancial Studies vol 23 no 5 pp 1909ndash1940 2010

[49] X S Ju D Lu and Y H Yu ldquoFinancial constraints workingcapital management and the sustainability of firm innova-tionrdquo Economic Research Journal vol 48 pp 4ndash16 2013

[50] J Li J Liang V Shi and Q Wang ldquo+e benefit of manu-facturer encroachment considering consumerrsquos environ-mental awareness and product competitionrdquo Annals ofOperation Research vol 3 pp 1ndash19 2021

[51] J Li F Wang and Y He ldquoElectric vehicle routing problemwith battery swapping considering energy consumption andcarbon emissionsrdquo Sustainability vol 12 no 24 Article ID10537 2020

[52] J Li L Yi V Shi and X Chen ldquoSupplier encroachmentstrategy in the presence of retail strategic inventory cen-tralization or decentralizationrdquo Omega vol 98 Article ID102213 2021

[53] J Li Z Hu V Shi and Q Wang ldquoManufacturerrsquos en-croachment strategy with substitutable green productsrdquo In-ternational Journal of Production Economics vol 235 ArticleID 108102 2021

14 Complexity

comprehensively analyze the impact of financing constraintson technological innovation activities in strategic emergingindustries At the same time most scholars rarely took theexternal political and economic environment of the firm intoaccount and there was a lack of research on the combiningfinancing constraints with environmental factors such aseconomic policy uncertainty and institutional environment Infact technological innovation depends to a large extent on theamount of external financing that a company obtains Eco-nomic policy uncertainty and institutional environment bothaffect the companyrsquos external financing environment and thenaffect the companyrsquos financing constraints and ultimatelyaffect the companyrsquos technological innovation activity

3 Research Hypotheses

Arrow believed that technological innovations have exter-nalities showing spillover effects and diffusion effects whichmake technological innovation show strong nonexclusiveand limited exclusivity +e diffusion of externalities enablesthe competitors to acquire technology at a lower cost thaninnovators in order to improve their productivity and in-novation [34] +e enterprises undertaking technologicalinnovation burden the high cost but they fail to access all thebenefits Under the spillover effect of technological inno-vation some of the profits are occupied by other enterprisesso the enterprises are less motivated to engage in techno-logical innovation and even have the idea of free rider

According to the theory of net present value theprinciple of optimal investment decision is that marginalrevenue equals marginal cost Financing constraints disablethe enterprise to make optimal investment decisions andinhibit technological innovation +ere are two main rea-sons One reason is that due to the limited internal fundsexternal financing is required However companies willweigh the cost of financing and their benefits [35] Once thecompany faces financing constraints the cost of externalfunds will be higher than the cost of internal funds whichincreases the cost of technological innovation When suchinvestment cost increases to the present value of the projectrsquosexpected cash flow the enterprise may cease its investmentin technological innovation thus delaying the innovationproject that could have been implemented +e other reasonis the difficulty of external financingwhich prevents en-terprises from obtaining the funds they need +en even ifthe marginal revenue of an enterprise is greater than itsmarginal cost technological innovation projects cannot beimplemented due to capital constraints

It can be seen that under the influence of the externalcharacteristics of technological innovation enterprisesrsquowillingness to undertake technological innovation is nothigh When the financing constraints become tighter theenterprisersquos investment cost in technological innovation willincrease correspondingly +en the willingness of enter-prises to undertake technological innovation activities willalso decline +erefore Hypothesis 1 is put forward

Hypothesis 1 Financing constraints negatively affect thetechnological innovation In other words financing con-straints have an inhibitory effect on the enterprisersquos tech-nological innovation

Strategic emerging industries consist of nine subin-dustries Since the characteristics of financing constraintsand technological innovation vary in different industries theimpact of financing constraints on technological innovationmust be different to some extent in certain industries+erefore Hypothesis 2 is put forward

Hypothesis 2 +e influence of financing constraint ondifferent subindustries of strategic emerging industries isdifferent

Due to the great difference between the output of themanufacturing industry and that of the nonmanufacturingindustry there are different characteristics in innovation+e manufacturing industry is dominated by technologicalinnovation while the nonmanufacturing industry is dom-inated by service innovations such as organizational in-novation normative innovation and characteristicinnovation +ese service innovations have little to do withtechnological innovation Meanwhile the strategic positionof technological innovation in enterprises also varies greatlyYuan pointed out that there is an obvious difference in thedependence of the manufacturing industry and service in-dustry on RampD +e manufacturing industry has a relativelylarge investment in technological innovation and RampDinvestment plays a decisive role Many companies even haveset up specialized RampD departments while the service in-dustry has a smaller dependence on RampD and the invest-ment in technological innovation is far lower than that of themanufacturing industry [36] +erefore the manufacturingindustry has a strong desire to improve the productivity ofenterprises through technological innovation It can then beinferred that the inhibitory effect of financing constraints onthe manufacturing industry is smaller than that on thenonmanufacturing industry +erefore Hypothesis 3 is putforward

Hypothesis 3 +e negative impact of financing constrainton manufacturing industry is less than that on nonmanu-facturing industry

Rajan and Zingales constructed the industryrsquos externalfinancing dependence which was used to measure theindustryrsquos dependence on external funds and they analyzed36 industries in US industrial companies [10] +ey came toa conclusion that traditional industries generally rely less onexternal financing while emerging industries mostly relymore on external financing+e top three industries with thehighest external financing dependence are drugs andpharmaceuticals plastics and computing +e three in-dustries with the lowest external financing dependence arethe tobacco pottery manufacturing and leather industriesAccording to the definition and scope of strategic emergingindustries in China it can be found that most strategic

4 Complexity

emerging industries are highly dependent on external fi-nancings such as the new generation of the informationtechnology industry and the biomedical industry Based onthe industryrsquos external financing dependence we put for-ward the following expectations the deterioration of thefinancing environment will have a greater impact on thecomputer industry and other industries with higher externalfinancing dependence than those with low dependence onexternal financing +erefore Hypothesis 4 is put forward

Hypothesis 4 +e negative impact of financing constraintson the industries with low external financing dependence isless than that on the industries with medium- and high-levelexternal financing dependence

For general investment scholars generally believe thatthe economic policy uncertainty inhibits enterprisesrsquo in-vestment but technological innovation differs from generalinvestment and thus the impact of economic policy un-certainty may vary correspondingly Technological inno-vations are full of high uncertainty which also means thatthere are many market opportunities in the future It is thesource of future profits for enterprises +e future profitopportunities will disappear if these uncertainties are re-moved while enterprises pursue high profits Uncertaintywill stimulate their innovation behaviors and encourageentrepreneurs to pursue greater profits From this per-spective the uncertainty of economic policy means not onlyrisks and challenges but also opportunities and benefits Forentrepreneurs with an innovative spirit they will make fulluse of the opportunities brought by the economic policyuncertainty avoid risks create profits for enterprises andenable the enterprises to develop and grow stronger Fur-thermore the adjustment cost of technical innovation ishigher than that of physical capital investment [24]+erefore enterprises can save a lot of ldquoadjustment costsrdquo bykeeping RampD investment stable +en in the face of eco-nomic policy uncertainty enterprises will tend to make useof the opportunities and keep the stable investment oftechnological innovation so that they can maintain con-tinuous investment in technological innovation +ereforeHypothesis 5 is put forward

Hypothesis 5 +e restraining effect of financing constraintsin periods of high economic policy uncertainty is less thanthat in periods of low economic policy uncertainty that isfinancing constraints have a more restraining effect ontechnological innovation in periods of low economic policyuncertainty

Generally speaking in the highly marketized regionsthere are more mature and effective laws and regulationsless government interventions a more effective financialsystem and an open and transparent information disclosurewhich provides a proper external political environment fortechnological innovation As for the reasons first themarket-oriented institutional environment helps to alleviatethe information asymmetry between enterprises and in-vestors reduce the cost of external financing and broadenthe financing channels of enterprises thereby optimizing theexternal financing structure of enterprises andmaking up for

the lack of funds for technological innovation [37] Secondthe market-oriented system enhances the function of themarket in resource allocation It not only reduces thegovernmentrsquos intervention and possibility of rent-seekingbut also improves the specificity of innovation stimulatesthe current RampD investment and enhances the innovationinitiative through a perfect patent protection system (Wuand Tang) [38] +ird the market-oriented system canprovide more sensitive price information for enterprises andmake the capital flow to the sectors with high-efficiencyinvestment thereby improving the allocation efficiency ofcapital and reducing the distortion of capital price and therisk premium of capital to a certain extent Fourth themarket-oriented system increases the labor remuneration oftechnical innovation personnel through income distributionand the rate of return on the technology of technical per-sonnel stimulates the innovation enthusiasm of technicalpersonnel and promotes the technological innovation Fifththe improvement of the market-oriented system can effec-tively promote Chinarsquos foreign trade +rough the importand export of commodities it is conducive to the intro-duction and absorption of technology by Chinese compa-nies Under the influence of technology spillover effects itcan promote innovation activities of Chinese enterprises (Liand Liu) [33] +erefore the market-oriented institutionalenvironment is conducive to alleviate the negative effect offinancial constraints on technological innovation+ereforeHypothesis 6 is put forward

Hypothesis 6 +e restraining effect of financing constraintson technological innovation in high-market-oriented areasis less than that in low-market-oriented areas that is it has amore restraining effect on enterprises in low-market-ori-ented areas

4 Data and Methodology

41 Model Specification In order to test Hypothesis 1 weconstruct two models from the perspectives of input andoutput of technological innovation +e model of techno-logical innovation input is as follows

Innovationgit +0 + α1Fconstraintsitminus1 + 1113944 θixitminus1

+ indit + θt + εit(1)

In view of the continuity of technological innovationactivities when setting the model in this study explanatoryvariables and control variables will lag by one period (exceptthe variable of company age) Innovationgit represents thetechnological innovation input of the company i in the yeart Fconstraintsitminus1 represents the variable of financingconstraints indicating the financing constraints of thecompany i in the year t minus 1 1113936 θixitminus1 represents the vectorcombination of control variables indit represents the in-dustrial effect θt represents the time effect and εit repre-sents the comprehensive error

Since the number of patent applications which is theexplained variable to measure the technological innovationoutput is a discrete variable and does not necessarily meet

Complexity 5

the assumption of linear regression we adopt the panelcounting model According to the distribution character-istics of the number of patent applications there is a hugedifference between the variance and the mean and thevariance is obviously greater than the mean +ere may beexcessive data dispersion and it is difficult to satisfy Poissonregression condition that expectation and variance are equal+erefore the negative binomial regression model of paneldata is adopted

It is assumed that the number of patents filed by anenterprise is Yit (the subscripted variable i represents theenterprise observed and the subscripted variable t repre-sents the year observed) and its conditional distributionfunction is as follows

P Yit yit|xit( 1113857 e

minusλitλyit

it

yit yit 0 1 2 ( 1113857 (2)

where λit gt 0 represents the average occurrence times of theevent In order to ensure that λit is a nonnegative figure it isassumed that

λit exp xitprime β + ui( 1113857 exp xit

prime β( 1113857exp ui( 1113857 equiv vi exp xitprime β( 1113857

(3)

where xit does not contain constant term vi equiv exp(ui)

represents the individual effect in the form of product In thecase that it satisfies the condition that v1 v2 middot middot middot vn itmeans that there is no individual effect More generally theexistence of individual effect is allowed In other wordsdifferent individuals have different values of vi +e prob-ability density of the negative binomial regression model is

f Yit|μit α( 1113857 Γ yit + αminus1

1113872 1113873

Γ yit + 1( 1113857Γ αminus11113872 1113873

αminus1

αminus1 + μit

1113888 1113889

αminus1

μit

αminus1 + μit

1113888 1113889

αminus1

yit 0 1 (4)

where Γ(middot) represents the gamma distribution function andα represents the overdispersion parameter E(Yit|xit) μit

and Var(Yit|xit) μit + αμ2it gt μit yit represent the numberof patents applied by the enterprise i in the year t μit

represents the average value of the patents applied by theenterprise After introducing relevant control variables thenegative binomial regression model of the technologicalinnovation output is

μit exp α1Fconstraintsitminus1 + 1113944 θixitminus1 + εit1113872 1113873 (5)

To test Hypothesis 2 to Hypothesis 4 we group thesamples according to their characteristics and compare themagnitude and significance of financing constraint coeffi-cient To test Hypothesis 5 and Hypothesis 6 we introduceeconomic policy uncertainty or marketization as the mod-erator based on model (1) and set the following econometricmodel

Innovationgit α0 + α1Fconstraintsitminus1 + 1113944 θixitminus1

+α2Mit + α3Mit times Fconstraintsitminus1 + indit + θt + εit

(6)

where Mit represents the economic policy uncertainty or themarketization If the estimate of the interaction coefficient α3 issignificantly positive the hypothesesH5 andH6 are supportedIf α3 is significantly negative it means the economic policyuncertainty or themarketization increases the negative effect offinancing constraints on technological innovation

42 Variable

421 Explained Variable +e explained variable is tech-nical innovation Technical innovation can be measuredfrom the perspectives of input and output For innovation

input we measure the companyrsquos technological innovationactivities by the RampD expense and use the RampD concen-tration (the proportion of RampD expenses to the total assets ofthe current period) and the RampD intensity (the proportion ofRampD expenses to current sales revenue) In terms of thetechnical innovation output following Hall and Harhoff[39] we measure the output of technological innovation bythe number of patent applications

422 Explanatory Variable +e core explanatory variable isthe financing constraints Following Owen [40] to calculateKZ index and considering the characteristics of listedcompanies in China the measurement method of FC indexof the Chinese listed companies financing constraint isestablished +e first step is to classify the samples by yearand calculate medians of the annual operating net cash flow(CF) cash dividends (DIV) cash holdinglast termrsquos assets(CashH) the leverage ratio (LEV) and Tobinrsquos Q (TobinQ)+en we assign a value to the variable CF DIV and CashHare expected to be negative correlation with financingconstraints while LEV and TobinQ are positively correlatedwith financing constraints +e values are determinedaccording to the following principles If CF DIV and CashHare lower than their medians that year the correspondingindicator value is taken as 1 otherwise it is taken as 0expressed as FC1 FC2 and FC3 If LEV and TobinQ arehigher than their medians that year the correspondingindicator value is taken as 1 otherwise it is taken as 0expressed as FC4 and FC5 +e third step is to calculate thevalue of FC according to the equation FC CF1 + FC2+FC3 + FC4 + FC5+e fourth step is to use the panel sortinglogistic regression model for regression +e dependentvariable is FC and the explanatory variables are the originalvalues of CF DIV CashH LEV and TobinQ+e regression

6 Complexity

coefficients of these explanatory variables are estimated andthe expression of the FC index is obtained In the end theactual values of CF DIV CashH LEV and TobinQ aresubstituted into the regression model and the predicted FCvalue of the enterprise is calculated which was the FC indexof the enterprise +e higher the FC index is the moreserious financing constraints is

According to the process above for calculating the FCindex the measurement result of the financing constraints isFC 1135CF minus 4572DIV minus 404CashH + 538LEV+016TobinQ Moreover the regression coefficients of thefive variables are all significant at the significance level of 1and the overall significance of the model also passes thestatistical test It is illustrated that the five variables namelyCF DIV CashH LEV and TobinQ are the effective proxyvariables affecting the financing constraints In addition CFDIV and CashH are negatively correlated with financingconstraints while LEV and TobinQ are positively correlatedwith the financing constraints It means that listed com-panies with high operating cash flow high cash holding highdividends low leverage ratio and low investment oppor-tunities face less serious financing constraints In contrastlisted companies with low operating cash flow low cashholding low dividends high leverage ratio and high in-vestment opportunities face more severe financingconstraints

Two other important variables in this study are eco-nomic policy uncertainty andmarketization+ere are manymeasurement indicators of economic policy uncertainty Weadopt the economic policy uncertainty index constructed byBaker et al [41] +is index has been widely used in recentmacropolicy literature and has been proved to be com-prehensive and objective to reflect the fluctuation of Chinarsquoseconomic policy Since the index is a monthly index we usethe monthly arithmetic average to convert the monthlyeconomic policy uncertainty index into an annual economicpolicy uncertainty index We use variable EPU to representthe economic uncertainty index Marketization is mostlymeasured by the marketization index proposed by Fan et alwhich consists of the relationship between the governmentand the market the development of the nonstate economythe development of the product market the development ofthe factor market and the development of market inter-mediary organizations [31] +e five aspects of the legalsystem environment can fully reflect all aspects of mar-ketization +erefore marketization index is used to mea-sure the level of marketization in each region Since thesample in this article is data of listed companies in strategicemerging industries it is not possible to directly obtain thecorresponding market index of each listed company +emethod is to find the market index of the correspondingprovince according to the province where each listedcompany is registered We use marketization variable torepresent the marketization index

423 Control Variable Control variables involved are Sizerepresenting the size of enterprises Age representing the ageof enterprises TobinQ Concentr representing equity

concentration Sgrowth representing the sales growth rateand Cflow representing the cash flow ratio

43 8e Data Our samples are the companies listed on themain board of China in strategic emerging enterprise fromover 2009 to 2019 As there are no strategic emerging in-dustries in the industry classification of the China SecuritiesRegulatory Commission it is impossible to obtain thesample of listed companies of strategic emerging industriesdirectly In order to gather the samples of listed companies instrategic emerging industries the following steps are fol-lowed+e initial sample is selected from the sample stocks ofChina Strategic Emerging Industries Composite Index(ldquoEmerging Composite Indexrdquo) released by China SecuritiesIndex Co Ltd and Shanghai Stock Exchange in 2017covering a total of 1117 companies listed as A-shares inShanghai and Shenzhen Stock Exchanges on the SMEsboard (Small and Medium Enterprise Board) the second-board market and the new OTC market (new over-the-counter market) Due to the small amount of data disclosedby listed companies on the new OTC market and the poorcomparability compared with other listed companies suchcompanies are removed from the sample frame Next somefilter conditions are set in the sample frame+e STand lowast STlisted companies within the sample period are deleted+enthe listed companies in the financial and insurance industryare deleted At last the samples with missing values of someindexes are deleted After the steps detailed above the finalsamples are 757 listed companies in the strategic emergingindustries over the period of 2009 to 2019

5 Empirical Results and Discussions

51 8e Inhibitory Effect of Financing Constraints We focuson the impact of financing constraints on technical inno-vation Columns 2 and 3 of Table 1 report the impact offinancing constraints on technological innovation input Asshown in Table 1 financing constraints negatively affect theconcentration and intensity of RampD at the significance levelof 5 +at is financing constraints inhibit technologicalinnovation input For listed companies technological in-novation is a very important investment decision for en-terprises involving large capital investment and a long timeof capital occupation When enterprises face serious fi-nancing constraints their internal and external funds cannotmeet the capital demand for technological innovation soenterprisesrsquo technological innovation activities will bepostponed

+e impact of financing constraints on technologicalinnovation output is also vital According He and Zhang [42]and Li and Zheng [43] the patent applications for measuringthe technological innovation are divided into two types Oneis the substantive innovation with a higher technologicallevel represented by invention patents aiming to promotesocial and technological progress and occupy a competitiveadvantage in the industry Andrea pointed out that financingconstraints have a significant negative effect on fundamentalinnovation [44] +e other is strategic innovations with low

Complexity 7

technical level represented by utility model patents anddesign patents most of which aim to obtain other benefits+en the influence of financing constraints on the differenttypes of patent applications is studied Columns 4 to 6 ofTable 2 respectively report the impact of financing con-straints on the number of patent applications inventionapplications and noninvention applications In order todetermine whether negative binomial regression is a fixedeffect or a random effect the Hausmann test is carried out+e results show that the p value is less than 001 whichstrongly rejected the negative binomial regression of randomeffect +erefore the fixed-effect negative binomial regres-sion is adopted Hilbe pointed out that in the countingmodel the marginal effect of variables is more significantthan that of the regression coefficient [45] We use themarginal effect to analyze the impact of the unit change ofthe explanatory variable on the explained variable Columns4 and 5 of Table 1 show that in terms of both the number ofpatent applications and the number of invention patentsfinancing constraints inhibit technological innovation out-put at the significance level of 10 Column 6 of Table 1shows that the influence of financing constraints on thenumber of nonpatent applications is negative but not sig-nificant +e above results indicate that therestraint of fi-nancing constraints on the inventionpatent is greater thanthat of noninvention patent mainly because of the differentnature of the two types of patents +e invention patent canimprove the production efficiency of enterprises enableenterprises to obtain competitive advantages in the industryand represent the core competitiveness of enterprisesHowever invention patents usually require a lot of in-vestment and last a long time before they come out

On the contrary the purpose of noninvention patents isto echo the innovation strategy of enterprises and nationalinnovation policies and the companies pay more attentionto the ldquospeedrdquo and ldquoquantityrdquo of innovation Noninventionpatents involve a relatively small investment a relativelyshort duration cycle and a relatively little influence bycapital Due to the essential differences between inventionpatents and noninvention patents enterprises have differentattitudes towards the two kinds of patents which ultimatelyleads to the difference in the degree of influence of financingconstraints on the two kinds of patents +is conclusion isconsistent with Khan et al [46]

+e results above indicate that financing constraints notonly inhibit the technological innovation input in emergingindustries but also inhibit the technological innovationoutput of the enterprise in emerging industries and thusHypothesis 1 has been supported

52 8e Heterogeneous Influence of Financing Constraints

521 Analysis by Industry Considering that differentsubindustries in strategic emerging industries have differentcharacteristics of financing constraints and technologicalinnovation the sensitivity of technical innovation to fi-nancing constraints may be heterogeneous We furtheranalyze by industry in order to find out the difference in theimpact of financial constraints on technical innovation ofvarious subindustries in strategic emerging industries Sincethere are many subindustries in the strategic emerging in-dustries only RampD intensity is used as the explained variableto analyze by industry

Table 1 Regression results of the influence of financing constraints on technical innovation

Variable RampDconcentration

RampDintensity

Number of patentapplications

Number of inventionpatents

Number of noninventionpatents

LFC minus0031lowastlowast minus0114lowastlowastlowast minus0024lowast minus0038lowastlowast minus0002(0014) (0032) (0012) (0015) (0014)

Lsize minus0287lowastlowastlowast minus0629lowastlowastlowast 0357lowastlowastlowast 0378lowastlowastlowast 0348lowastlowastlowast(0051) (0119) (0044) (0050) (0058)

LTobinQ minus0009 minus0029 0024lowastlowastlowast 0030lowastlowastlowast 0009(0013) (0029) (0006) (0010) (0010)

LConcentr minus0009lowastlowast minus0025lowastlowastlowast minus0000 0002 0002(0004) (0009) (0003) (0003) (0003)

LSgrowth minus0011 minus0165lowastlowastlowast minus0000 minus0000 0000(0024) (0054) (0000) (0000) (0000)

Age minus0037lowastlowast minus0160lowastlowastlowast 0045lowastlowastlowast 0071lowastlowastlowast 0024lowast(0015) (0036) (0011) (0010) (0014)

LCflow minus0003lowastlowast minus0005lowastlowast minus0000(0002) (0002) (0002)

Constant 9033lowastlowastlowast 17980lowastlowastlowast minus7436lowastlowastlowast minus8479lowastlowastlowast minus7302lowastlowastlowast(1203) (2798) (0922) (1058) (1203)

Time effect Controlled ControlledIndustryeffect Controlled Controlled

Sample size 757 757 628 625 608Notes lowastlowastlowast lowastlowast and lowast mean significant at the significance level of 1 5 and 10 respectively +e figures in parentheses are standard errors the same asbelow Lvariables represent variables with a lag of one period

8 Complexity

Columns 2 to 8 of Table 2 report the effects of financingconstraints in different subindustries in strategic emergingindustries on technological innovation It is found that thereare pronounced industry differences in the impact of fi-nancing constraints on technological innovation Except forthe biological industry and new energy industry the impactof financing constraints on technological innovation in-vestment in the other five industries is uniformly negative Inthe new generation information technology industry ad-vanced equipment manufacturing and new energy vehicleindustry with high RampD intensity the negative impact isstatistically significant but not in the new material industryand energy conservation and environmental protectionindustry with relatively low RampD intensity +is is becausethe higher the RampD intensity is the more the money forRampD investment will be and the stronger the influence offinancing constraints will be In the regression analysis of thebiological industry and the new energy industry the fi-nancing constraintsrsquo coefficient is not negative +e mainreason is that these two industries are industries with rel-atively small financing constraints and are less affected byfinancial factors +erefore financing constraints have noobvious inhibitory effect on technological innovation andthus Hypothesis 2 has been verified

522 Whether It Is a Manufacturing Enterprise We dividethe listed companies in strategic emerging industries intomanufacturing and nonmanufacturing industries accordingto the classification standards of Chinarsquos high-tech indus-tries +e regression results of Table 3 show that the in-hibitory effect of the financing constraints on thetechnological innovation input and output of themanufacturing industry is lower than that of the non-manufacturing industry +e possible reason is that Chinarsquoslabor costs have risen sharply which has seriously affected

the operating profits of enterprises in the labor-intensive andcapital-intensive manufacturing industries +erefore themanufacturing industry is more willing to improve laborproductivity through technological innovation and tech-nological innovation is of greater significance tomanufacturing enterprises Manufacturing companies investmore in RampD and have a longer cycle for capital recoveryOnce the technical innovation project is launched the fundsneed to be continually invested Otherwise the invested costwill become a sunk cost and cannot be recovered +echaracteristic of high adjustment cost of RampD input is moreobvious in the manufacturing industry and manufacturingenterprises will pay more attention to the sustainability ofRampD +erefore technological innovation will not react sosensitively to the capital situation Even if the financingconstraints are intensified the technological innovation inthe manufacturing industry will not be reduced rapidly butneed a long time to adjust However nonmanufacturingindustries are mostly asset-light enterprises which involverelatively less investment in technological innovation withlow RampD intensity and less dependence on technologicalinnovation +erefore in these enterprises technologicalinnovation is more sensitive to the financial situation andthus Hypothesis 3 has been verified

523 Level of the Dependence of Industry External Financing+e companies are divided into two groups according to theexternal financing dependence in the industry to testwhether the inhibitory effect of financing constraints ontechnological innovation is related to the external financingdependence of the industry One group are the companieswith low external financing dependence and the other arethe companies with medium- and high-level external fi-nancing dependence +e grouping results of the sample inTable 4 show that only a few strategic emerging enterprises

Table 2 Regression analysis of different subindustries in strategic emerging industries

VariableNext-generationinformationtechnology

Advancedequipment

manufacturing

Newmaterialindustry

Biologicalindustry

New energyautomobileindustry

Newenergyindustry

Environmentalprotection industry

LFC minus0238lowastlowastlowast minus0084lowast minus0011 0211 minus0126lowast 0178 minus0005(0066) minus0049 (0039) (0170) (0073) (0143) (0041)

Lsize minus0098 minus0586lowastlowastlowast minus0642lowastlowastlowast minus0579 minus0065 minus0077 minus1002lowastlowastlowast(0273) (0161) (0136) (0723) (0211) (0212) (0204)

LTobinQ 0025 0002 0082lowast 0004 0037 1011lowastlowast minus0263lowastlowastlowast(0054) (0060) (0042) (0149) (0134) (0496) (0058)

LConcentr minus0021 minus0025lowastlowast 0008 minus0075 minus0008 0006 0009(0020) (0012) (0009) (0055) (0014) (0013) (0008)

LSgrowth 0009lowastlowastlowast minus0000 0006lowastlowastlowast minus0002 minus0001 0008 minus0001(0002) (0001) (0002) (0007) (0003) (0006) (0001)

Age minus0276lowastlowastlowast minus0096lowast 0020 minus0249 minus0036 0041 minus0071lowast(0073) (0050) (0039) (0179) (0056) (0031) (0040)

Sample size 290 257 95 43 19 14 24Notes according to the Guiding Catalogue of Key Products and Services for Strategic Emerging Industries (2016 Edition) issued by the National Developmentand Reform Commission in 2017 the strategic emerging industries are divided into nine sectors (including related service industries) Considering the smallnumber of samples of digital cultural and creative industries and related service industries they are not separately analyzed by industry

Complexity 9

are in the industries with low-level external financing de-pendence with only 14 samples accounting for 19 of thetotal samples It indicates that most enterprises in thestrategic emerging enterprises are in the industries with highexternal financing dependence +e regression results inTable 4 show that the negative effect of financing constraintson the technical innovation input and output is not sig-nificant in the samples of industries with low external fi-nancing dependence However in the industry withmedium- and high-level external financing dependence theimpact of financing constraints on the technical innovationrsquosinput and output is significant at the 10 significance leveland thus Hypothesis 4 has been verified

53 8e Moderating Effect of the External Environment

531 8e Economic Uncertainty as the Moderator As anexternal factor affecting enterprisesrsquo technological innova-tion economic policy has an impact on the technologicalinnovation activities of enterprises from amacroperspectiveOnce a countryrsquos economic policy changes enterprisesrsquoinnovation activities will inevitably be affected by the policy

Columns 2 and 3 in Table 5 are the regression resultsafter adding the cross terms of economic policy uncertaintyand financing constraints to the regression model It showsthat the negative effect of financing constraints on tech-nological innovation is still significant and the coefficient ofthe cross term is significantly positive indicating thateconomic policy uncertainty alleviates the inhibitory effectof financing constraints on technological innovation Spe-cifically when the uncertainty of the economic policy en-vironment increases the inhibitory effect of financingconstraints on the technological innovation input may bereduced+ere are two main explanations On the one handthe increasing uncertainty of economic policy means thatenterprisesrsquo business environment will change significantly

+is is both an opportunity and a challenge and the survivalof the fittest among enterprises will be presented Only thoseenterprises with high production efficiency and strongcompetitiveness can finally survive in the industry Fur-thermore innovation ability is the key factor for enterprisesto enhance their core competitiveness Enterprises can makeuse of the opportunities brought by the economic policyuncertainty and improve their core competitiveness by in-creasing the input in technological innovation activities so asto boost their chances of winning in the future marketcompetition On the other hand the uncertainty of eco-nomic policies will then spread to the external financingmarket leading to the fluctuation of the capital market andthen affect the external financing of technological innova-tion According to the research of Li and Yang [47] it can beseen that the economic policy uncertainty inhibits generalinvestment activities +erefore when economic policyuncertainty increases ordinary investment activities will notbe active and there will be abundant capital in the capitalmarket However technological innovation was fraught withuncertainty and the future is full of opportunities When theeconomic policy uncertainty increases financing for tech-nological innovation is more likely compared with general

Table 4 +e classification test result of the level of the external financing dependence in the industry

Variable

RampD concentration RampD intensity Number of patent applicationsLow externalfinancing

dependence

Medium- and high-level external

financing dependence

Low externalfinancing

dependence

Medium- and high-level external

financing dependence

Low externalfinancing

dependence

Medium- and high-level external

financing dependence

LFC 0107 minus0034lowastlowast minus0060 minus0116lowastlowastlowast minus0003 minus0021lowast(0113) (0014) (0117) (0033) (0088) (0013)

Allcontrols Controlled Controlled Controlled Controlled Controlled Controlled

Samplesize 14 743 14 743 14 614

Table 5 +e moderating effect of economic policy uncertainty

Variable RampD concentration RampD intensityLFC minus0069lowastlowastlowast (0026) minus0286lowastlowastlowast (0060)Lsize minus0273lowastlowastlowast (0050) minus0592lowastlowastlowast (0120)LTobinQ minus0022 (0029)LConcertr minus0009lowastlowast (0004) minus0025lowastlowastlowast (0009)LSgrowth minus0000 (0000) minus0002lowastlowastlowast (0001)Age minus0037lowastlowast (0015) minus0160lowastlowastlowast (0036)EPUlowastLFC 0000lowast (0000) 0001lowastlowastlowast (0000)EPU 0003lowastlowastlowast (0001) 0013lowastlowastlowast (0002)Constant 8409lowastlowastlowast (1162) 1580lowastlowastlowast (2775)Sample size 757 757

Table 3 +e classification test results of manufacturing enterprises and nonmanufacturing ones

VariableRampD concentration RampD intensity Number of patent applications

Manufacturing Nonmanufacturing Manufacturing Nonmanufacturing Manufacturing Nonmanufacturing

LFC minus0015 minus0094lowastlowast minus0100lowastlowastlowast minus0151lowastlowast minus0021lowast minus0061lowast(0012) (0043) (0035) (0075) (0012) (0034)

All controls Controlled Controlled Controlled Controlled Controlled ControlledSample size 543 214 543 214 484 144

10 Complexity

investment activities +erefore economic policy uncer-tainty can ease the inhibitory effect of financing constraintson technological innovation and thus Hypothesis 5 hasbeen verified

532 8eMarketization as the Moderator Columns 2 and 3in Table 6 are the regression results after adding the crossterms of marketization and financing constraints to theregression model It shows that the coefficient of financingconstraints is significantly negative the coefficient of mar-ketization is significantly positive and the coefficient of across term is significantly positive indicating that themarketization can help to alleviate the inhibitory effect offinancing constraints on technological innovation Specifi-cally when the market environment improves the inhibi-tory effect of financing constraints on technologicalinnovation will be reduced +erefore the more the market-oriented the lower the inhibitory effect of financing con-straints on technological innovation and vice versa In theend the Matthew effect occurs when the strong get strongerand the weak get weaker Under such effect the differencesin technological innovation between regions are constantlywidened +e possible explanation is as followsIn highlymarket-oriented regions there are a sound legal system highinformation transparency a more robust financial systemsound and abundant financial resources Such a perfectinstitutional environment can ease the enterprisersquos financingconstraints provide a proper external financing environ-ment for technological innovation and better escort theenterprisersquos technological innovation which is conducive toencourage enterprises to carry out technological innovationIn regions with less market-oriented it is impossible toprovide a strong institutional guarantee for technologicalinnovation For an enterprise facing financing constraintsthis adverse institutional environment intensifies the in-hibitory effect of financing constraints on technologicalinnovation and is not conducive to the enterprisersquos tech-nological innovation and thus Hypothesis 6 has beenverified

54 Robustness Test

541 Solutions to Endogenous Problems Financial con-straints inhibit enterprisesrsquo technological innovation activ-ities and technological innovation may make financialconstraints of enterprises get more serious thus leading to abidirectional causal relationship between financial con-straints and technical innovation In order to avoid possiblereverse causality all control variables and the explanatoryvariables are delayed by one period Simultaneously in thestudy to avoid the endogenous problems caused by theomitted variables the industry and time fixed effects arecontrolled in the empirical analysis

542 Recalculation of Financing Constraint Index In theempirical analysis we use the FC index to measure the fi-nancing constraints However there are various methods tomeasure the constraints with different emphases In order to

ensure robust conclusions the SA index proposed byHadlock and Pierce [48] is used to remeasure the financingconstraints +at is SA minus0737 Size + 0043 Size2minus004Age +e above empirical process is then repeated tocheck whether the financing constraints have an inhibitoryeffect on technological innovation

+e SA index is generally negative +e greater the ab-solute value of SA is the more serious the financing con-straints faced by the enterprise will be+erefore the SA indexis negatively correlated with the level of financing constraints+e regression results in Table 7 show that the SA index ispositively correlated with technological innovation at thesignificance level of 5 indicating the more serious the fi-nancing constraints is the stronger the inhibitory effect ontechnological innovation activities will be +is is consistentwith the conclusion of the test based on the FC index whichonce again verifies that financing constraints have an inhib-itory effect on technological innovation

543 Recalculation of the Technological Innovation Output+e increment of intangible assets is adopted to remeasurethe technological innovation output following Ju et al [49]Moreover the ratio of the increment of intangible assets tothe total assets at the end of the period is used as the proxyvariable of the technological innovation output to remeasurethe technological innovation output Finally the empiricalresults of the influence of financing constraints on thetechnological innovation output are shown in Table 8

+e results in Table 8 show that financing constraintshave a significant inhibitory effect on the technologicalinnovation output regardless of if the FC index or SA indexis used It is consistent with the conclusion where thenumber of patent applications is adopted as the techno-logical innovation output

544 8e Ordinary Panel Model as the Counting Model+e panel counting model is used to study the influence offinancing constraints on the technical innovation outputwhich shows that financing constraints have a significantinhibitory effect on the technological innovation output Inorder to test the robustness of the results the number ofpatent applications as the explained variable is taken as acontinuous common variable and then the ordinary panelmodel is used for empirical analysis +e empirical resultsare shown in Table 9

As shown in Table 9 the conclusion obtained using theordinary panel model is that the financing constraints sig-nificantly inhibit the technological innovation output After

Table 6 +e moderating effect of the marketization

Variable RampD concentration RampD intensityLFC minus0036lowastlowastlowast (0012) minus0168lowastlowastlowast (0030)Marketization 0838lowastlowastlowast (0266) 1319lowastlowast (0658)MarketizationlowastLFC 0017lowastlowastlowast (0005) 0024lowast (0013)All controls Controlled ControlledSample size 757 757

Complexity 11

dividing the number of patent applications into inventionpatents and noninvention patents we find that the inhibi-tory effect of the financing constraints on the inventionpatents is significantly higher than that of the noninventionpatents which is consistent with the conclusion obtainedusing the negative binomial regression model of panel data

6 Conclusions and Implications

As the world economy enters a stage of innovation compe-tition the decision-making problem of technological innova-tion in emerging industries is a practical problem worthy ofresearch and attention Based on the panel data of the listedcompanies in Chinarsquos strategic emerging industries from 2009to 2019 we adopt the negative binomial regression model ofpanel data and empirically study the technological innovationdecision-making issues of emerging industries from the per-spective of financing constraints Our results show that thefinancial constraints in strategic emerging industries in Chinasignificantly inhibit technological innovation in terms oftechnological innovation inputs and outputsWhat ismore the

inhibitory effect of substantive innovation like the inventionpatent is more pronounced than that of strategic innovationlike the noninvention patent +e influence of financingconstraints on technological innovation varies significantlybetween industries within strategic emerging industriesmanufacturing industries and nonmanufacturing industriesand among industries with different external financing de-pendence However the economic policy uncertainty and themarketization can help to alleviate the inhibitory effect of fi-nancing constraints on technological innovation

Our research conclusions lead to the following policyimplications First of all the government should focus onsolving the financial constraints by speeding up the reformof the financial system Promoting the reform of the financialsupply side improving the efficiency of financial resourceallocation and reducing the financing cost should also beemphasized In addition it is necessary to expand thecoverage of financial services to develop financing platformsand tools exclusive to RampD intensive industries such asstrategic emerging industries and to encourage venturecapital and long-term capital to invest in technological

Table 9 Robustness test results of the ordinary panel model

Variable Number of patent applications Number of invention patents Number of noninvention patentsLFC minus0045lowastlowastlowast (0012) minus0059lowastlowastlowast (0012) minus0028lowastlowast (0013)LTobinQ minus0003 (0008) 0002 (0008) minus0018lowastlowast (0009)LConcertr 0005lowastlowast (0002) 0004 (0003) 0006lowastlowast (0003)LSgrowth minus0000 (0000) minus0000 (0000) minus0000 (0000)Llev 0017lowastlowastlowast (0002) 0017lowastlowastlowast (0002) 0016lowastlowastlowast (0002)Age 0003 (0009) 0012 (0010) minus0004 (0010)LCflow minus0004lowastlowast (0002) minus0005lowastlowastlowast (0002) minus0003 (0004)Sample size 691 691 691

Table 8 Robustness test results of intangible assets increment

Variable FC index SA indexLFC minus0042lowastlowast (0019) 0249lowastlowast (0108)Lsize 0000 (0041)LTobinQ 0039lowastlowast (0019) 0037lowast (0019)LConcentr 0000 (0003) 0002 (0003)LCflow minus0014lowastlowastlowast (0004) minus0012lowastlowastlowast (0001)LSgrowth minus0009 (0042) 0004 (0042)Age minus0039lowastlowastlowast (0008)Ltangibility 0000 (0003)Constant 0508 (1039) 0316 (0593)Sample size 757 757

Table 7 Regression results of the impact of financing constraints on technological innovation through the SA index

Variable RampD concentration RampD intensity Number of patent applicationsLSA 0351lowastlowastlowast (0135) 0995lowastlowastlowast (0316) 0300lowast (0182)LLEV minus0444lowast (0240) minus4102lowastlowastlowast (0549) minus0003 (0002)LTobinQ 0003 (0013) minus0039 (0030) minus0005 (0012)LConcentr minus0010lowastlowast (0004) minus0020lowastlowast (00098) minus0000 (00037)LCflow 0004lowast (0003) minus0004 (0006)Ltangibility 0011lowastlowastlowast (0003) 0002 (0006)LSgrowth minus0011 (0023) minus0118lowastlowast (0053)Constant 2159lowastlowastlowast (0592) 5283lowastlowastlowast (1360) minus9301lowastlowastlowast (1361)Sample size 757 757 628

12 Complexity

innovation projects A technological innovation financingsystem based on the market mechanism guided by gov-ernment investment dominated by enterprises and widelyparticipated by social funds should be established Morefinancial resources can flow to technological innovationprojects and to enterprises with technological innovationactivities

Second at present Chinarsquos economy is in a ldquonew nor-malrdquo that requires innovation In view of the selective effectof financing constraints on innovation activities relevantdepartments should make effort to build a good externalfinancing environment and use effective financial policiesand administrative methods to help enterprises release theirinnovative vitality more fully For example relevant de-partments should pay attention to building a good market-oriented system and increase institutional guarantees forinnovative activities so as to improve the operating con-ditions of enterprises and promote high-quality innovativeactivities When companies face financing constraints thesemeasures that are conducive to improving operating con-ditions will help to stimulate innovation

Finally since the financing constraints have a moreobvious restraining effect on enterprisesrsquo substantive inno-vation of high quality the government should encourageenterprises to increase RampD investment in substantive in-novation [50ndash53] while guiding them to pursue high-qualityinnovation so as to avoid such innovations that emphasizeonly the quantity and speed In addition the governmentalso should continue to increase financial investment inbasic research for making up for the lack of substantiveinnovation of enterprises and aiming to truly improve in-novation ability and quality of the enterprise

Data Availability

+e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

+e authors declare that there are no conflicts of interestregarding the publication of this study

Acknowledgments

+is research was partly supported by the Natural ScienceFoundation of Zhejiang Province (Grant no LQ19G030007)and Zhejiang Gongshang University Online and OfflineHybrid Teaching Reform Project (Grant no1010XJ2919103)

References

[1] H Lei and W Wang ldquo+e impact of network structure oninnovation efficiency an agent-based study in the context ofinnovation networksrdquo Complexity vol 21 no 2 pp 111ndash1222015

[2] W B Arthur and W Polak ldquo+e evolution of technologywithin a simple computer modelrdquo Complexity vol 11 no 5pp 23ndash31 2010

[3] J J Pang G Lin R L Yang et al ldquoPromote continuouseconomic development economic trendsrdquo Economic Dy-namics vol 7 pp 3ndash19 2019

[4] M Cai and S J Liu ldquo+e impact of government subsidies onthe leverage ratio of strategic emerging industriesrdquo Con-temporary Economic Research vol 8 pp 90ndash100 2020

[5] J R Brown S M Fazzari and B C Petersen ldquoFinancinginnovation and growth cash flow external equity and the1990s RampD boomrdquo Journal of Finance vol 64 pp 151ndash1852010

[6] J S Ang Y Cheng and C Wu ldquoDoes enforcement of in-tellectual property rights matter in China evidence fromfinancing and investment choices in the high-tech industryrdquoReview of Economics and Statistics vol 96 no 2 pp 332ndash3482014

[7] S M Fazzari R G Hubbard B C Petersen A S Blinder andJ M Poterba ldquoFinancing constraints and corporate invest-mentrdquo Brookings Papers on Economic Activity vol 1988 no 1206 pages 1988

[8] T Beck and R Levine ldquoIndustry growth and capital alloca-tionrdquo Journal of Financial Economics vol 64 no 2pp 147ndash180 2002

[9] M Gertler and S Gilchrist ldquo+e role of credit market im-perfections in the monetary transmission mechanism argu-ments and evidencerdquo8e Scandinavian Journal of Economicsvol 95 no 1 pp 43ndash64 1993

[10] R G Rajan and L Zingales ldquoFinancial dependence andgrowthrdquo Social Science Electronic Publishing vol 88 no 3pp 559ndash586 1998

[11] G Borisova and J R Brown ldquoRampD sensitivity to asset saleproceeds new evidence on financing constraints and intan-gible investmentrdquo Journal of Banking amp Finance vol 37 no 1pp 159ndash173 2013

[12] Y S Hwang H G Min and S H Han ldquo+e influence offinancial development on RampD activity cross-country evi-dencerdquo Review of Pacific Basin Financial Markets amp Policiesvol 13 no 3 pp 381ndash401 2010

[13] A Hall G Bockett S Taylor M V K Sivamohan andN Clark ldquoWhy research partnerships really matter inno-vation theory institutional arrangements and implications fordeveloping new technology for the poorrdquo World Develop-ment vol 29 no 5 pp 783ndash797 2001

[14] B H Hall ldquoInvestment and research and development at thefirm level does the source of financing matterrdquo NBERWorking Papers 1992

[15] C P Himmelberg and B C Petersen ldquoRampD and internalfinance a panel study of small firms in high-tech industriesrdquo8e Review of Economics and Statistics vol 76 pp 38ndash511994

[16] Z He and M B Wintoki ldquo+e cost of innovation RampD andhigh cash holdings in US firmsrdquo Journal of Corporate Financevol 41 pp 280ndash303 2016

[17] B Mulkay B H Hall and J Mairesse Firm Level Investmentand RampD in France and the United States A comparisonSocial Science Electronic Publishing Berlin Germany 2001

[18] D Harhoff Are there financing constraints for RampD and in-vestment in German manufacturing firms 8e Economics andEconometrics of Innovation Springer Berlin Germany 2000

[19] S Bond and D H V Harhoff ldquoInvestment RampD and fi-nancial constraints in Britain and Germanyrdquo AnnalesdrsquoEconomie et de Statistique vol 7980 pp 433ndash460 2005

[20] C Bloch ldquoRampD investment and internal finance the cash floweffectrdquo Economics of Innovation and New Technology vol 14no 3 pp 213ndash223 2005

Complexity 13

[21] G Scellato ldquoPatents firm size and financial constraints anempirical analysis for a panel of Italian manufacturing firmsrdquoCambridge Journal of Economics vol 31 pp 55ndash76 2007

[22] E Ughetto ldquoDoes internal finance matter for RampD newevidence from a panel of Italian firmsrdquo Cambridge Journal ofEconomics vol 32 no 6 pp 907ndash925 2008

[23] D Czarnitzki and H Hottenrott ldquoRampD investment and fi-nancing constraints of small and medium-sized firmsrdquo SmallBusiness Economics vol 36 no 1 pp 65ndash83 2011

[24] B H Hall ldquo+e financing of research and developmentrdquoOxford Review of Economic Policy vol 18 no 1 pp 35ndash512002

[25] J R Brown and B C Petersen ldquoCash holdings and RampDsmoothingrdquo Journal of Corporate Finance vol 17 no 3pp 694ndash709 2011

[26] D Li ldquoFinancial constraints RampD investment and stockreturnsrdquo Review of Financial Studies vol 24 no 9pp 2974ndash3007 2011

[27] A Mina H Lahr and A Hughes ldquo+e demand and supply ofexternal finance for innovative firmsrdquo Industrial and Cor-porate Change vol 22 no 4 pp 869ndash901 2013

[28] X Gu Y Chen and S Pan ldquoEconomic policy uncertainty andinnovation evidence from listed companies in Chinardquo Reviewof Financial Studies vol 2 pp 109ndash123 2018

[29] F Knight Risk Uncertainty and Profit Houghton MifflinCompany Boston MA USA 1921

[30] W Hao W Wei and J Wen ldquoHow does economic policyuncertainty affect corporate innovationmdashperspective ofmechanism of action of real option theoryrdquo EconomicManagement vol 38 pp 40ndash54 2016

[31] G Fan X Wang and G Ma ldquo+e contribution of Chinarsquosmarketization process to economic growthrdquo Economic Re-search vol 46 no 9 pp 4ndash16 2011

[32] K Z Dai and Y J Liu ldquo+e impact of marketization reformon RampD investment in Chinarsquos high-tech industryrdquo ScienceStudies vol 31 pp 50ndash57 2013

[33] P Li and X Liu ldquo+e impact of market-oriented institutionalchange on Chinarsquos technological progressmdashbased on theperspective of independent R amp D and technology intro-ductionrdquo Economic Perspectives vol 4 pp 42ndash50 2015

[34] K J Arrow ldquoEconomic welfare and the allocation of resourcesfor inventionrdquo 8e Rate and Direction of Inventive Activityvol 12 pp 609ndash626 1962

[35] P Kijkasiwat and P Phuensane ldquoInnovation and firm per-formance the moderating and mediating roles of firm sizeand small and medium enterprise financerdquo Journal of Riskand Financial Management vol 13 no 5 p 97 2020

[36] X N Yuan ldquoManufacturing innovation and service inno-vation comparison and convergencerdquo Finance and TradeResearch vol 20 pp 14ndash19 2009

[37] Y M Zhu and M E Jia ldquoMarketization process financialconstraints and corporate technology innovationrdquo Com-mercial ResearchInnovation Technology vol 1 pp 49ndash562017

[38] C P Wu and D Tang ldquoIntellectual property rights en-forcement corporate innovation and operating performanceevidence from Chinarsquos listed companiesrdquo Economic ResearchJournal vol 51 no 11 pp 125ndash139 2016

[39] B H Hall and D Harhoff ldquoRecent research on the economicsof patentsrdquo Annual Review of Economics vol 4 pp 541ndash5652012

[40] L Owen P Christopher and J Saaa-Requejo ldquoFinancialconstraints and stock returnsrdquo Review of Financial Studiesvol 14 pp 529ndash554 2001

[41] S R Baker N Bloom and S J Davis ldquoMeasuring economicpolicy uncertaintyrdquo 8e Quarterly Journal of Economicsvol 131 no 4 pp 1593ndash1636 2016

[42] J F He and X J Zhang ldquo+e influence of rising labor cost onenterprise innovationrdquo 8e Journal of Quantitative amp Tech-nical Economics vol 45 no 8 pp 56ndash73 2018

[43] W J Li and M N Zheng ldquoIs it substantive innovation orstrategic innovationmdashimpact of macroeconomic policies onmicro-enterprisesrsquo innovationrdquo Economic Research Journalvol 4 pp 60ndash73 2016

[44] C Andrea ldquoFinancing constraints radical versus incrementalinnovation and aggregate productivityrdquo American EconomicJournal Macroeconomics vol 11 no 2 pp 75ndash309 2019

[45] J M Hilbe Modeling Count Data Cambridge UniversityPress Cambridge UK 2014

[46] S U Khan A Shah and M F Rizwan ldquoDo financingconstraints matter for technological and non-technologicalinnovation a (re) examination of developing marketsrdquoEmerging Markets Finance and Trade vol 57 pp 1ndash28 2019

[47] F Y Li and M Z Yang ldquoCan economic policy uncertaintyinfluence corporate investment the empirical research byusing China economic policy uncertainty indexrdquo Journal ofFinancial Research vol 57 no 9 pp 2739ndash2766 2015

[48] C J Hadlock and J R Pierce ldquoNew evidence on measuringfinancial constraints moving beyond the KZ indexrdquo Review ofFinancial Studies vol 23 no 5 pp 1909ndash1940 2010

[49] X S Ju D Lu and Y H Yu ldquoFinancial constraints workingcapital management and the sustainability of firm innova-tionrdquo Economic Research Journal vol 48 pp 4ndash16 2013

[50] J Li J Liang V Shi and Q Wang ldquo+e benefit of manu-facturer encroachment considering consumerrsquos environ-mental awareness and product competitionrdquo Annals ofOperation Research vol 3 pp 1ndash19 2021

[51] J Li F Wang and Y He ldquoElectric vehicle routing problemwith battery swapping considering energy consumption andcarbon emissionsrdquo Sustainability vol 12 no 24 Article ID10537 2020

[52] J Li L Yi V Shi and X Chen ldquoSupplier encroachmentstrategy in the presence of retail strategic inventory cen-tralization or decentralizationrdquo Omega vol 98 Article ID102213 2021

[53] J Li Z Hu V Shi and Q Wang ldquoManufacturerrsquos en-croachment strategy with substitutable green productsrdquo In-ternational Journal of Production Economics vol 235 ArticleID 108102 2021

14 Complexity

emerging industries are highly dependent on external fi-nancings such as the new generation of the informationtechnology industry and the biomedical industry Based onthe industryrsquos external financing dependence we put for-ward the following expectations the deterioration of thefinancing environment will have a greater impact on thecomputer industry and other industries with higher externalfinancing dependence than those with low dependence onexternal financing +erefore Hypothesis 4 is put forward

Hypothesis 4 +e negative impact of financing constraintson the industries with low external financing dependence isless than that on the industries with medium- and high-levelexternal financing dependence

For general investment scholars generally believe thatthe economic policy uncertainty inhibits enterprisesrsquo in-vestment but technological innovation differs from generalinvestment and thus the impact of economic policy un-certainty may vary correspondingly Technological inno-vations are full of high uncertainty which also means thatthere are many market opportunities in the future It is thesource of future profits for enterprises +e future profitopportunities will disappear if these uncertainties are re-moved while enterprises pursue high profits Uncertaintywill stimulate their innovation behaviors and encourageentrepreneurs to pursue greater profits From this per-spective the uncertainty of economic policy means not onlyrisks and challenges but also opportunities and benefits Forentrepreneurs with an innovative spirit they will make fulluse of the opportunities brought by the economic policyuncertainty avoid risks create profits for enterprises andenable the enterprises to develop and grow stronger Fur-thermore the adjustment cost of technical innovation ishigher than that of physical capital investment [24]+erefore enterprises can save a lot of ldquoadjustment costsrdquo bykeeping RampD investment stable +en in the face of eco-nomic policy uncertainty enterprises will tend to make useof the opportunities and keep the stable investment oftechnological innovation so that they can maintain con-tinuous investment in technological innovation +ereforeHypothesis 5 is put forward

Hypothesis 5 +e restraining effect of financing constraintsin periods of high economic policy uncertainty is less thanthat in periods of low economic policy uncertainty that isfinancing constraints have a more restraining effect ontechnological innovation in periods of low economic policyuncertainty

Generally speaking in the highly marketized regionsthere are more mature and effective laws and regulationsless government interventions a more effective financialsystem and an open and transparent information disclosurewhich provides a proper external political environment fortechnological innovation As for the reasons first themarket-oriented institutional environment helps to alleviatethe information asymmetry between enterprises and in-vestors reduce the cost of external financing and broadenthe financing channels of enterprises thereby optimizing theexternal financing structure of enterprises andmaking up for

the lack of funds for technological innovation [37] Secondthe market-oriented system enhances the function of themarket in resource allocation It not only reduces thegovernmentrsquos intervention and possibility of rent-seekingbut also improves the specificity of innovation stimulatesthe current RampD investment and enhances the innovationinitiative through a perfect patent protection system (Wuand Tang) [38] +ird the market-oriented system canprovide more sensitive price information for enterprises andmake the capital flow to the sectors with high-efficiencyinvestment thereby improving the allocation efficiency ofcapital and reducing the distortion of capital price and therisk premium of capital to a certain extent Fourth themarket-oriented system increases the labor remuneration oftechnical innovation personnel through income distributionand the rate of return on the technology of technical per-sonnel stimulates the innovation enthusiasm of technicalpersonnel and promotes the technological innovation Fifththe improvement of the market-oriented system can effec-tively promote Chinarsquos foreign trade +rough the importand export of commodities it is conducive to the intro-duction and absorption of technology by Chinese compa-nies Under the influence of technology spillover effects itcan promote innovation activities of Chinese enterprises (Liand Liu) [33] +erefore the market-oriented institutionalenvironment is conducive to alleviate the negative effect offinancial constraints on technological innovation+ereforeHypothesis 6 is put forward

Hypothesis 6 +e restraining effect of financing constraintson technological innovation in high-market-oriented areasis less than that in low-market-oriented areas that is it has amore restraining effect on enterprises in low-market-ori-ented areas

4 Data and Methodology

41 Model Specification In order to test Hypothesis 1 weconstruct two models from the perspectives of input andoutput of technological innovation +e model of techno-logical innovation input is as follows

Innovationgit +0 + α1Fconstraintsitminus1 + 1113944 θixitminus1

+ indit + θt + εit(1)

In view of the continuity of technological innovationactivities when setting the model in this study explanatoryvariables and control variables will lag by one period (exceptthe variable of company age) Innovationgit represents thetechnological innovation input of the company i in the yeart Fconstraintsitminus1 represents the variable of financingconstraints indicating the financing constraints of thecompany i in the year t minus 1 1113936 θixitminus1 represents the vectorcombination of control variables indit represents the in-dustrial effect θt represents the time effect and εit repre-sents the comprehensive error

Since the number of patent applications which is theexplained variable to measure the technological innovationoutput is a discrete variable and does not necessarily meet

Complexity 5

the assumption of linear regression we adopt the panelcounting model According to the distribution character-istics of the number of patent applications there is a hugedifference between the variance and the mean and thevariance is obviously greater than the mean +ere may beexcessive data dispersion and it is difficult to satisfy Poissonregression condition that expectation and variance are equal+erefore the negative binomial regression model of paneldata is adopted

It is assumed that the number of patents filed by anenterprise is Yit (the subscripted variable i represents theenterprise observed and the subscripted variable t repre-sents the year observed) and its conditional distributionfunction is as follows

P Yit yit|xit( 1113857 e

minusλitλyit

it

yit yit 0 1 2 ( 1113857 (2)

where λit gt 0 represents the average occurrence times of theevent In order to ensure that λit is a nonnegative figure it isassumed that

λit exp xitprime β + ui( 1113857 exp xit

prime β( 1113857exp ui( 1113857 equiv vi exp xitprime β( 1113857

(3)

where xit does not contain constant term vi equiv exp(ui)

represents the individual effect in the form of product In thecase that it satisfies the condition that v1 v2 middot middot middot vn itmeans that there is no individual effect More generally theexistence of individual effect is allowed In other wordsdifferent individuals have different values of vi +e prob-ability density of the negative binomial regression model is

f Yit|μit α( 1113857 Γ yit + αminus1

1113872 1113873

Γ yit + 1( 1113857Γ αminus11113872 1113873

αminus1

αminus1 + μit

1113888 1113889

αminus1

μit

αminus1 + μit

1113888 1113889

αminus1

yit 0 1 (4)

where Γ(middot) represents the gamma distribution function andα represents the overdispersion parameter E(Yit|xit) μit

and Var(Yit|xit) μit + αμ2it gt μit yit represent the numberof patents applied by the enterprise i in the year t μit

represents the average value of the patents applied by theenterprise After introducing relevant control variables thenegative binomial regression model of the technologicalinnovation output is

μit exp α1Fconstraintsitminus1 + 1113944 θixitminus1 + εit1113872 1113873 (5)

To test Hypothesis 2 to Hypothesis 4 we group thesamples according to their characteristics and compare themagnitude and significance of financing constraint coeffi-cient To test Hypothesis 5 and Hypothesis 6 we introduceeconomic policy uncertainty or marketization as the mod-erator based on model (1) and set the following econometricmodel

Innovationgit α0 + α1Fconstraintsitminus1 + 1113944 θixitminus1

+α2Mit + α3Mit times Fconstraintsitminus1 + indit + θt + εit

(6)

where Mit represents the economic policy uncertainty or themarketization If the estimate of the interaction coefficient α3 issignificantly positive the hypothesesH5 andH6 are supportedIf α3 is significantly negative it means the economic policyuncertainty or themarketization increases the negative effect offinancing constraints on technological innovation

42 Variable

421 Explained Variable +e explained variable is tech-nical innovation Technical innovation can be measuredfrom the perspectives of input and output For innovation

input we measure the companyrsquos technological innovationactivities by the RampD expense and use the RampD concen-tration (the proportion of RampD expenses to the total assets ofthe current period) and the RampD intensity (the proportion ofRampD expenses to current sales revenue) In terms of thetechnical innovation output following Hall and Harhoff[39] we measure the output of technological innovation bythe number of patent applications

422 Explanatory Variable +e core explanatory variable isthe financing constraints Following Owen [40] to calculateKZ index and considering the characteristics of listedcompanies in China the measurement method of FC indexof the Chinese listed companies financing constraint isestablished +e first step is to classify the samples by yearand calculate medians of the annual operating net cash flow(CF) cash dividends (DIV) cash holdinglast termrsquos assets(CashH) the leverage ratio (LEV) and Tobinrsquos Q (TobinQ)+en we assign a value to the variable CF DIV and CashHare expected to be negative correlation with financingconstraints while LEV and TobinQ are positively correlatedwith financing constraints +e values are determinedaccording to the following principles If CF DIV and CashHare lower than their medians that year the correspondingindicator value is taken as 1 otherwise it is taken as 0expressed as FC1 FC2 and FC3 If LEV and TobinQ arehigher than their medians that year the correspondingindicator value is taken as 1 otherwise it is taken as 0expressed as FC4 and FC5 +e third step is to calculate thevalue of FC according to the equation FC CF1 + FC2+FC3 + FC4 + FC5+e fourth step is to use the panel sortinglogistic regression model for regression +e dependentvariable is FC and the explanatory variables are the originalvalues of CF DIV CashH LEV and TobinQ+e regression

6 Complexity

coefficients of these explanatory variables are estimated andthe expression of the FC index is obtained In the end theactual values of CF DIV CashH LEV and TobinQ aresubstituted into the regression model and the predicted FCvalue of the enterprise is calculated which was the FC indexof the enterprise +e higher the FC index is the moreserious financing constraints is

According to the process above for calculating the FCindex the measurement result of the financing constraints isFC 1135CF minus 4572DIV minus 404CashH + 538LEV+016TobinQ Moreover the regression coefficients of thefive variables are all significant at the significance level of 1and the overall significance of the model also passes thestatistical test It is illustrated that the five variables namelyCF DIV CashH LEV and TobinQ are the effective proxyvariables affecting the financing constraints In addition CFDIV and CashH are negatively correlated with financingconstraints while LEV and TobinQ are positively correlatedwith the financing constraints It means that listed com-panies with high operating cash flow high cash holding highdividends low leverage ratio and low investment oppor-tunities face less serious financing constraints In contrastlisted companies with low operating cash flow low cashholding low dividends high leverage ratio and high in-vestment opportunities face more severe financingconstraints

Two other important variables in this study are eco-nomic policy uncertainty andmarketization+ere are manymeasurement indicators of economic policy uncertainty Weadopt the economic policy uncertainty index constructed byBaker et al [41] +is index has been widely used in recentmacropolicy literature and has been proved to be com-prehensive and objective to reflect the fluctuation of Chinarsquoseconomic policy Since the index is a monthly index we usethe monthly arithmetic average to convert the monthlyeconomic policy uncertainty index into an annual economicpolicy uncertainty index We use variable EPU to representthe economic uncertainty index Marketization is mostlymeasured by the marketization index proposed by Fan et alwhich consists of the relationship between the governmentand the market the development of the nonstate economythe development of the product market the development ofthe factor market and the development of market inter-mediary organizations [31] +e five aspects of the legalsystem environment can fully reflect all aspects of mar-ketization +erefore marketization index is used to mea-sure the level of marketization in each region Since thesample in this article is data of listed companies in strategicemerging industries it is not possible to directly obtain thecorresponding market index of each listed company +emethod is to find the market index of the correspondingprovince according to the province where each listedcompany is registered We use marketization variable torepresent the marketization index

423 Control Variable Control variables involved are Sizerepresenting the size of enterprises Age representing the ageof enterprises TobinQ Concentr representing equity

concentration Sgrowth representing the sales growth rateand Cflow representing the cash flow ratio

43 8e Data Our samples are the companies listed on themain board of China in strategic emerging enterprise fromover 2009 to 2019 As there are no strategic emerging in-dustries in the industry classification of the China SecuritiesRegulatory Commission it is impossible to obtain thesample of listed companies of strategic emerging industriesdirectly In order to gather the samples of listed companies instrategic emerging industries the following steps are fol-lowed+e initial sample is selected from the sample stocks ofChina Strategic Emerging Industries Composite Index(ldquoEmerging Composite Indexrdquo) released by China SecuritiesIndex Co Ltd and Shanghai Stock Exchange in 2017covering a total of 1117 companies listed as A-shares inShanghai and Shenzhen Stock Exchanges on the SMEsboard (Small and Medium Enterprise Board) the second-board market and the new OTC market (new over-the-counter market) Due to the small amount of data disclosedby listed companies on the new OTC market and the poorcomparability compared with other listed companies suchcompanies are removed from the sample frame Next somefilter conditions are set in the sample frame+e STand lowast STlisted companies within the sample period are deleted+enthe listed companies in the financial and insurance industryare deleted At last the samples with missing values of someindexes are deleted After the steps detailed above the finalsamples are 757 listed companies in the strategic emergingindustries over the period of 2009 to 2019

5 Empirical Results and Discussions

51 8e Inhibitory Effect of Financing Constraints We focuson the impact of financing constraints on technical inno-vation Columns 2 and 3 of Table 1 report the impact offinancing constraints on technological innovation input Asshown in Table 1 financing constraints negatively affect theconcentration and intensity of RampD at the significance levelof 5 +at is financing constraints inhibit technologicalinnovation input For listed companies technological in-novation is a very important investment decision for en-terprises involving large capital investment and a long timeof capital occupation When enterprises face serious fi-nancing constraints their internal and external funds cannotmeet the capital demand for technological innovation soenterprisesrsquo technological innovation activities will bepostponed

+e impact of financing constraints on technologicalinnovation output is also vital According He and Zhang [42]and Li and Zheng [43] the patent applications for measuringthe technological innovation are divided into two types Oneis the substantive innovation with a higher technologicallevel represented by invention patents aiming to promotesocial and technological progress and occupy a competitiveadvantage in the industry Andrea pointed out that financingconstraints have a significant negative effect on fundamentalinnovation [44] +e other is strategic innovations with low

Complexity 7

technical level represented by utility model patents anddesign patents most of which aim to obtain other benefits+en the influence of financing constraints on the differenttypes of patent applications is studied Columns 4 to 6 ofTable 2 respectively report the impact of financing con-straints on the number of patent applications inventionapplications and noninvention applications In order todetermine whether negative binomial regression is a fixedeffect or a random effect the Hausmann test is carried out+e results show that the p value is less than 001 whichstrongly rejected the negative binomial regression of randomeffect +erefore the fixed-effect negative binomial regres-sion is adopted Hilbe pointed out that in the countingmodel the marginal effect of variables is more significantthan that of the regression coefficient [45] We use themarginal effect to analyze the impact of the unit change ofthe explanatory variable on the explained variable Columns4 and 5 of Table 1 show that in terms of both the number ofpatent applications and the number of invention patentsfinancing constraints inhibit technological innovation out-put at the significance level of 10 Column 6 of Table 1shows that the influence of financing constraints on thenumber of nonpatent applications is negative but not sig-nificant +e above results indicate that therestraint of fi-nancing constraints on the inventionpatent is greater thanthat of noninvention patent mainly because of the differentnature of the two types of patents +e invention patent canimprove the production efficiency of enterprises enableenterprises to obtain competitive advantages in the industryand represent the core competitiveness of enterprisesHowever invention patents usually require a lot of in-vestment and last a long time before they come out

On the contrary the purpose of noninvention patents isto echo the innovation strategy of enterprises and nationalinnovation policies and the companies pay more attentionto the ldquospeedrdquo and ldquoquantityrdquo of innovation Noninventionpatents involve a relatively small investment a relativelyshort duration cycle and a relatively little influence bycapital Due to the essential differences between inventionpatents and noninvention patents enterprises have differentattitudes towards the two kinds of patents which ultimatelyleads to the difference in the degree of influence of financingconstraints on the two kinds of patents +is conclusion isconsistent with Khan et al [46]

+e results above indicate that financing constraints notonly inhibit the technological innovation input in emergingindustries but also inhibit the technological innovationoutput of the enterprise in emerging industries and thusHypothesis 1 has been supported

52 8e Heterogeneous Influence of Financing Constraints

521 Analysis by Industry Considering that differentsubindustries in strategic emerging industries have differentcharacteristics of financing constraints and technologicalinnovation the sensitivity of technical innovation to fi-nancing constraints may be heterogeneous We furtheranalyze by industry in order to find out the difference in theimpact of financial constraints on technical innovation ofvarious subindustries in strategic emerging industries Sincethere are many subindustries in the strategic emerging in-dustries only RampD intensity is used as the explained variableto analyze by industry

Table 1 Regression results of the influence of financing constraints on technical innovation

Variable RampDconcentration

RampDintensity

Number of patentapplications

Number of inventionpatents

Number of noninventionpatents

LFC minus0031lowastlowast minus0114lowastlowastlowast minus0024lowast minus0038lowastlowast minus0002(0014) (0032) (0012) (0015) (0014)

Lsize minus0287lowastlowastlowast minus0629lowastlowastlowast 0357lowastlowastlowast 0378lowastlowastlowast 0348lowastlowastlowast(0051) (0119) (0044) (0050) (0058)

LTobinQ minus0009 minus0029 0024lowastlowastlowast 0030lowastlowastlowast 0009(0013) (0029) (0006) (0010) (0010)

LConcentr minus0009lowastlowast minus0025lowastlowastlowast minus0000 0002 0002(0004) (0009) (0003) (0003) (0003)

LSgrowth minus0011 minus0165lowastlowastlowast minus0000 minus0000 0000(0024) (0054) (0000) (0000) (0000)

Age minus0037lowastlowast minus0160lowastlowastlowast 0045lowastlowastlowast 0071lowastlowastlowast 0024lowast(0015) (0036) (0011) (0010) (0014)

LCflow minus0003lowastlowast minus0005lowastlowast minus0000(0002) (0002) (0002)

Constant 9033lowastlowastlowast 17980lowastlowastlowast minus7436lowastlowastlowast minus8479lowastlowastlowast minus7302lowastlowastlowast(1203) (2798) (0922) (1058) (1203)

Time effect Controlled ControlledIndustryeffect Controlled Controlled

Sample size 757 757 628 625 608Notes lowastlowastlowast lowastlowast and lowast mean significant at the significance level of 1 5 and 10 respectively +e figures in parentheses are standard errors the same asbelow Lvariables represent variables with a lag of one period

8 Complexity

Columns 2 to 8 of Table 2 report the effects of financingconstraints in different subindustries in strategic emergingindustries on technological innovation It is found that thereare pronounced industry differences in the impact of fi-nancing constraints on technological innovation Except forthe biological industry and new energy industry the impactof financing constraints on technological innovation in-vestment in the other five industries is uniformly negative Inthe new generation information technology industry ad-vanced equipment manufacturing and new energy vehicleindustry with high RampD intensity the negative impact isstatistically significant but not in the new material industryand energy conservation and environmental protectionindustry with relatively low RampD intensity +is is becausethe higher the RampD intensity is the more the money forRampD investment will be and the stronger the influence offinancing constraints will be In the regression analysis of thebiological industry and the new energy industry the fi-nancing constraintsrsquo coefficient is not negative +e mainreason is that these two industries are industries with rel-atively small financing constraints and are less affected byfinancial factors +erefore financing constraints have noobvious inhibitory effect on technological innovation andthus Hypothesis 2 has been verified

522 Whether It Is a Manufacturing Enterprise We dividethe listed companies in strategic emerging industries intomanufacturing and nonmanufacturing industries accordingto the classification standards of Chinarsquos high-tech indus-tries +e regression results of Table 3 show that the in-hibitory effect of the financing constraints on thetechnological innovation input and output of themanufacturing industry is lower than that of the non-manufacturing industry +e possible reason is that Chinarsquoslabor costs have risen sharply which has seriously affected

the operating profits of enterprises in the labor-intensive andcapital-intensive manufacturing industries +erefore themanufacturing industry is more willing to improve laborproductivity through technological innovation and tech-nological innovation is of greater significance tomanufacturing enterprises Manufacturing companies investmore in RampD and have a longer cycle for capital recoveryOnce the technical innovation project is launched the fundsneed to be continually invested Otherwise the invested costwill become a sunk cost and cannot be recovered +echaracteristic of high adjustment cost of RampD input is moreobvious in the manufacturing industry and manufacturingenterprises will pay more attention to the sustainability ofRampD +erefore technological innovation will not react sosensitively to the capital situation Even if the financingconstraints are intensified the technological innovation inthe manufacturing industry will not be reduced rapidly butneed a long time to adjust However nonmanufacturingindustries are mostly asset-light enterprises which involverelatively less investment in technological innovation withlow RampD intensity and less dependence on technologicalinnovation +erefore in these enterprises technologicalinnovation is more sensitive to the financial situation andthus Hypothesis 3 has been verified

523 Level of the Dependence of Industry External Financing+e companies are divided into two groups according to theexternal financing dependence in the industry to testwhether the inhibitory effect of financing constraints ontechnological innovation is related to the external financingdependence of the industry One group are the companieswith low external financing dependence and the other arethe companies with medium- and high-level external fi-nancing dependence +e grouping results of the sample inTable 4 show that only a few strategic emerging enterprises

Table 2 Regression analysis of different subindustries in strategic emerging industries

VariableNext-generationinformationtechnology

Advancedequipment

manufacturing

Newmaterialindustry

Biologicalindustry

New energyautomobileindustry

Newenergyindustry

Environmentalprotection industry

LFC minus0238lowastlowastlowast minus0084lowast minus0011 0211 minus0126lowast 0178 minus0005(0066) minus0049 (0039) (0170) (0073) (0143) (0041)

Lsize minus0098 minus0586lowastlowastlowast minus0642lowastlowastlowast minus0579 minus0065 minus0077 minus1002lowastlowastlowast(0273) (0161) (0136) (0723) (0211) (0212) (0204)

LTobinQ 0025 0002 0082lowast 0004 0037 1011lowastlowast minus0263lowastlowastlowast(0054) (0060) (0042) (0149) (0134) (0496) (0058)

LConcentr minus0021 minus0025lowastlowast 0008 minus0075 minus0008 0006 0009(0020) (0012) (0009) (0055) (0014) (0013) (0008)

LSgrowth 0009lowastlowastlowast minus0000 0006lowastlowastlowast minus0002 minus0001 0008 minus0001(0002) (0001) (0002) (0007) (0003) (0006) (0001)

Age minus0276lowastlowastlowast minus0096lowast 0020 minus0249 minus0036 0041 minus0071lowast(0073) (0050) (0039) (0179) (0056) (0031) (0040)

Sample size 290 257 95 43 19 14 24Notes according to the Guiding Catalogue of Key Products and Services for Strategic Emerging Industries (2016 Edition) issued by the National Developmentand Reform Commission in 2017 the strategic emerging industries are divided into nine sectors (including related service industries) Considering the smallnumber of samples of digital cultural and creative industries and related service industries they are not separately analyzed by industry

Complexity 9

are in the industries with low-level external financing de-pendence with only 14 samples accounting for 19 of thetotal samples It indicates that most enterprises in thestrategic emerging enterprises are in the industries with highexternal financing dependence +e regression results inTable 4 show that the negative effect of financing constraintson the technical innovation input and output is not sig-nificant in the samples of industries with low external fi-nancing dependence However in the industry withmedium- and high-level external financing dependence theimpact of financing constraints on the technical innovationrsquosinput and output is significant at the 10 significance leveland thus Hypothesis 4 has been verified

53 8e Moderating Effect of the External Environment

531 8e Economic Uncertainty as the Moderator As anexternal factor affecting enterprisesrsquo technological innova-tion economic policy has an impact on the technologicalinnovation activities of enterprises from amacroperspectiveOnce a countryrsquos economic policy changes enterprisesrsquoinnovation activities will inevitably be affected by the policy

Columns 2 and 3 in Table 5 are the regression resultsafter adding the cross terms of economic policy uncertaintyand financing constraints to the regression model It showsthat the negative effect of financing constraints on tech-nological innovation is still significant and the coefficient ofthe cross term is significantly positive indicating thateconomic policy uncertainty alleviates the inhibitory effectof financing constraints on technological innovation Spe-cifically when the uncertainty of the economic policy en-vironment increases the inhibitory effect of financingconstraints on the technological innovation input may bereduced+ere are two main explanations On the one handthe increasing uncertainty of economic policy means thatenterprisesrsquo business environment will change significantly

+is is both an opportunity and a challenge and the survivalof the fittest among enterprises will be presented Only thoseenterprises with high production efficiency and strongcompetitiveness can finally survive in the industry Fur-thermore innovation ability is the key factor for enterprisesto enhance their core competitiveness Enterprises can makeuse of the opportunities brought by the economic policyuncertainty and improve their core competitiveness by in-creasing the input in technological innovation activities so asto boost their chances of winning in the future marketcompetition On the other hand the uncertainty of eco-nomic policies will then spread to the external financingmarket leading to the fluctuation of the capital market andthen affect the external financing of technological innova-tion According to the research of Li and Yang [47] it can beseen that the economic policy uncertainty inhibits generalinvestment activities +erefore when economic policyuncertainty increases ordinary investment activities will notbe active and there will be abundant capital in the capitalmarket However technological innovation was fraught withuncertainty and the future is full of opportunities When theeconomic policy uncertainty increases financing for tech-nological innovation is more likely compared with general

Table 4 +e classification test result of the level of the external financing dependence in the industry

Variable

RampD concentration RampD intensity Number of patent applicationsLow externalfinancing

dependence

Medium- and high-level external

financing dependence

Low externalfinancing

dependence

Medium- and high-level external

financing dependence

Low externalfinancing

dependence

Medium- and high-level external

financing dependence

LFC 0107 minus0034lowastlowast minus0060 minus0116lowastlowastlowast minus0003 minus0021lowast(0113) (0014) (0117) (0033) (0088) (0013)

Allcontrols Controlled Controlled Controlled Controlled Controlled Controlled

Samplesize 14 743 14 743 14 614

Table 5 +e moderating effect of economic policy uncertainty

Variable RampD concentration RampD intensityLFC minus0069lowastlowastlowast (0026) minus0286lowastlowastlowast (0060)Lsize minus0273lowastlowastlowast (0050) minus0592lowastlowastlowast (0120)LTobinQ minus0022 (0029)LConcertr minus0009lowastlowast (0004) minus0025lowastlowastlowast (0009)LSgrowth minus0000 (0000) minus0002lowastlowastlowast (0001)Age minus0037lowastlowast (0015) minus0160lowastlowastlowast (0036)EPUlowastLFC 0000lowast (0000) 0001lowastlowastlowast (0000)EPU 0003lowastlowastlowast (0001) 0013lowastlowastlowast (0002)Constant 8409lowastlowastlowast (1162) 1580lowastlowastlowast (2775)Sample size 757 757

Table 3 +e classification test results of manufacturing enterprises and nonmanufacturing ones

VariableRampD concentration RampD intensity Number of patent applications

Manufacturing Nonmanufacturing Manufacturing Nonmanufacturing Manufacturing Nonmanufacturing

LFC minus0015 minus0094lowastlowast minus0100lowastlowastlowast minus0151lowastlowast minus0021lowast minus0061lowast(0012) (0043) (0035) (0075) (0012) (0034)

All controls Controlled Controlled Controlled Controlled Controlled ControlledSample size 543 214 543 214 484 144

10 Complexity

investment activities +erefore economic policy uncer-tainty can ease the inhibitory effect of financing constraintson technological innovation and thus Hypothesis 5 hasbeen verified

532 8eMarketization as the Moderator Columns 2 and 3in Table 6 are the regression results after adding the crossterms of marketization and financing constraints to theregression model It shows that the coefficient of financingconstraints is significantly negative the coefficient of mar-ketization is significantly positive and the coefficient of across term is significantly positive indicating that themarketization can help to alleviate the inhibitory effect offinancing constraints on technological innovation Specifi-cally when the market environment improves the inhibi-tory effect of financing constraints on technologicalinnovation will be reduced +erefore the more the market-oriented the lower the inhibitory effect of financing con-straints on technological innovation and vice versa In theend the Matthew effect occurs when the strong get strongerand the weak get weaker Under such effect the differencesin technological innovation between regions are constantlywidened +e possible explanation is as followsIn highlymarket-oriented regions there are a sound legal system highinformation transparency a more robust financial systemsound and abundant financial resources Such a perfectinstitutional environment can ease the enterprisersquos financingconstraints provide a proper external financing environ-ment for technological innovation and better escort theenterprisersquos technological innovation which is conducive toencourage enterprises to carry out technological innovationIn regions with less market-oriented it is impossible toprovide a strong institutional guarantee for technologicalinnovation For an enterprise facing financing constraintsthis adverse institutional environment intensifies the in-hibitory effect of financing constraints on technologicalinnovation and is not conducive to the enterprisersquos tech-nological innovation and thus Hypothesis 6 has beenverified

54 Robustness Test

541 Solutions to Endogenous Problems Financial con-straints inhibit enterprisesrsquo technological innovation activ-ities and technological innovation may make financialconstraints of enterprises get more serious thus leading to abidirectional causal relationship between financial con-straints and technical innovation In order to avoid possiblereverse causality all control variables and the explanatoryvariables are delayed by one period Simultaneously in thestudy to avoid the endogenous problems caused by theomitted variables the industry and time fixed effects arecontrolled in the empirical analysis

542 Recalculation of Financing Constraint Index In theempirical analysis we use the FC index to measure the fi-nancing constraints However there are various methods tomeasure the constraints with different emphases In order to

ensure robust conclusions the SA index proposed byHadlock and Pierce [48] is used to remeasure the financingconstraints +at is SA minus0737 Size + 0043 Size2minus004Age +e above empirical process is then repeated tocheck whether the financing constraints have an inhibitoryeffect on technological innovation

+e SA index is generally negative +e greater the ab-solute value of SA is the more serious the financing con-straints faced by the enterprise will be+erefore the SA indexis negatively correlated with the level of financing constraints+e regression results in Table 7 show that the SA index ispositively correlated with technological innovation at thesignificance level of 5 indicating the more serious the fi-nancing constraints is the stronger the inhibitory effect ontechnological innovation activities will be +is is consistentwith the conclusion of the test based on the FC index whichonce again verifies that financing constraints have an inhib-itory effect on technological innovation

543 Recalculation of the Technological Innovation Output+e increment of intangible assets is adopted to remeasurethe technological innovation output following Ju et al [49]Moreover the ratio of the increment of intangible assets tothe total assets at the end of the period is used as the proxyvariable of the technological innovation output to remeasurethe technological innovation output Finally the empiricalresults of the influence of financing constraints on thetechnological innovation output are shown in Table 8

+e results in Table 8 show that financing constraintshave a significant inhibitory effect on the technologicalinnovation output regardless of if the FC index or SA indexis used It is consistent with the conclusion where thenumber of patent applications is adopted as the techno-logical innovation output

544 8e Ordinary Panel Model as the Counting Model+e panel counting model is used to study the influence offinancing constraints on the technical innovation outputwhich shows that financing constraints have a significantinhibitory effect on the technological innovation output Inorder to test the robustness of the results the number ofpatent applications as the explained variable is taken as acontinuous common variable and then the ordinary panelmodel is used for empirical analysis +e empirical resultsare shown in Table 9

As shown in Table 9 the conclusion obtained using theordinary panel model is that the financing constraints sig-nificantly inhibit the technological innovation output After

Table 6 +e moderating effect of the marketization

Variable RampD concentration RampD intensityLFC minus0036lowastlowastlowast (0012) minus0168lowastlowastlowast (0030)Marketization 0838lowastlowastlowast (0266) 1319lowastlowast (0658)MarketizationlowastLFC 0017lowastlowastlowast (0005) 0024lowast (0013)All controls Controlled ControlledSample size 757 757

Complexity 11

dividing the number of patent applications into inventionpatents and noninvention patents we find that the inhibi-tory effect of the financing constraints on the inventionpatents is significantly higher than that of the noninventionpatents which is consistent with the conclusion obtainedusing the negative binomial regression model of panel data

6 Conclusions and Implications

As the world economy enters a stage of innovation compe-tition the decision-making problem of technological innova-tion in emerging industries is a practical problem worthy ofresearch and attention Based on the panel data of the listedcompanies in Chinarsquos strategic emerging industries from 2009to 2019 we adopt the negative binomial regression model ofpanel data and empirically study the technological innovationdecision-making issues of emerging industries from the per-spective of financing constraints Our results show that thefinancial constraints in strategic emerging industries in Chinasignificantly inhibit technological innovation in terms oftechnological innovation inputs and outputsWhat ismore the

inhibitory effect of substantive innovation like the inventionpatent is more pronounced than that of strategic innovationlike the noninvention patent +e influence of financingconstraints on technological innovation varies significantlybetween industries within strategic emerging industriesmanufacturing industries and nonmanufacturing industriesand among industries with different external financing de-pendence However the economic policy uncertainty and themarketization can help to alleviate the inhibitory effect of fi-nancing constraints on technological innovation

Our research conclusions lead to the following policyimplications First of all the government should focus onsolving the financial constraints by speeding up the reformof the financial system Promoting the reform of the financialsupply side improving the efficiency of financial resourceallocation and reducing the financing cost should also beemphasized In addition it is necessary to expand thecoverage of financial services to develop financing platformsand tools exclusive to RampD intensive industries such asstrategic emerging industries and to encourage venturecapital and long-term capital to invest in technological

Table 9 Robustness test results of the ordinary panel model

Variable Number of patent applications Number of invention patents Number of noninvention patentsLFC minus0045lowastlowastlowast (0012) minus0059lowastlowastlowast (0012) minus0028lowastlowast (0013)LTobinQ minus0003 (0008) 0002 (0008) minus0018lowastlowast (0009)LConcertr 0005lowastlowast (0002) 0004 (0003) 0006lowastlowast (0003)LSgrowth minus0000 (0000) minus0000 (0000) minus0000 (0000)Llev 0017lowastlowastlowast (0002) 0017lowastlowastlowast (0002) 0016lowastlowastlowast (0002)Age 0003 (0009) 0012 (0010) minus0004 (0010)LCflow minus0004lowastlowast (0002) minus0005lowastlowastlowast (0002) minus0003 (0004)Sample size 691 691 691

Table 8 Robustness test results of intangible assets increment

Variable FC index SA indexLFC minus0042lowastlowast (0019) 0249lowastlowast (0108)Lsize 0000 (0041)LTobinQ 0039lowastlowast (0019) 0037lowast (0019)LConcentr 0000 (0003) 0002 (0003)LCflow minus0014lowastlowastlowast (0004) minus0012lowastlowastlowast (0001)LSgrowth minus0009 (0042) 0004 (0042)Age minus0039lowastlowastlowast (0008)Ltangibility 0000 (0003)Constant 0508 (1039) 0316 (0593)Sample size 757 757

Table 7 Regression results of the impact of financing constraints on technological innovation through the SA index

Variable RampD concentration RampD intensity Number of patent applicationsLSA 0351lowastlowastlowast (0135) 0995lowastlowastlowast (0316) 0300lowast (0182)LLEV minus0444lowast (0240) minus4102lowastlowastlowast (0549) minus0003 (0002)LTobinQ 0003 (0013) minus0039 (0030) minus0005 (0012)LConcentr minus0010lowastlowast (0004) minus0020lowastlowast (00098) minus0000 (00037)LCflow 0004lowast (0003) minus0004 (0006)Ltangibility 0011lowastlowastlowast (0003) 0002 (0006)LSgrowth minus0011 (0023) minus0118lowastlowast (0053)Constant 2159lowastlowastlowast (0592) 5283lowastlowastlowast (1360) minus9301lowastlowastlowast (1361)Sample size 757 757 628

12 Complexity

innovation projects A technological innovation financingsystem based on the market mechanism guided by gov-ernment investment dominated by enterprises and widelyparticipated by social funds should be established Morefinancial resources can flow to technological innovationprojects and to enterprises with technological innovationactivities

Second at present Chinarsquos economy is in a ldquonew nor-malrdquo that requires innovation In view of the selective effectof financing constraints on innovation activities relevantdepartments should make effort to build a good externalfinancing environment and use effective financial policiesand administrative methods to help enterprises release theirinnovative vitality more fully For example relevant de-partments should pay attention to building a good market-oriented system and increase institutional guarantees forinnovative activities so as to improve the operating con-ditions of enterprises and promote high-quality innovativeactivities When companies face financing constraints thesemeasures that are conducive to improving operating con-ditions will help to stimulate innovation

Finally since the financing constraints have a moreobvious restraining effect on enterprisesrsquo substantive inno-vation of high quality the government should encourageenterprises to increase RampD investment in substantive in-novation [50ndash53] while guiding them to pursue high-qualityinnovation so as to avoid such innovations that emphasizeonly the quantity and speed In addition the governmentalso should continue to increase financial investment inbasic research for making up for the lack of substantiveinnovation of enterprises and aiming to truly improve in-novation ability and quality of the enterprise

Data Availability

+e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

+e authors declare that there are no conflicts of interestregarding the publication of this study

Acknowledgments

+is research was partly supported by the Natural ScienceFoundation of Zhejiang Province (Grant no LQ19G030007)and Zhejiang Gongshang University Online and OfflineHybrid Teaching Reform Project (Grant no1010XJ2919103)

References

[1] H Lei and W Wang ldquo+e impact of network structure oninnovation efficiency an agent-based study in the context ofinnovation networksrdquo Complexity vol 21 no 2 pp 111ndash1222015

[2] W B Arthur and W Polak ldquo+e evolution of technologywithin a simple computer modelrdquo Complexity vol 11 no 5pp 23ndash31 2010

[3] J J Pang G Lin R L Yang et al ldquoPromote continuouseconomic development economic trendsrdquo Economic Dy-namics vol 7 pp 3ndash19 2019

[4] M Cai and S J Liu ldquo+e impact of government subsidies onthe leverage ratio of strategic emerging industriesrdquo Con-temporary Economic Research vol 8 pp 90ndash100 2020

[5] J R Brown S M Fazzari and B C Petersen ldquoFinancinginnovation and growth cash flow external equity and the1990s RampD boomrdquo Journal of Finance vol 64 pp 151ndash1852010

[6] J S Ang Y Cheng and C Wu ldquoDoes enforcement of in-tellectual property rights matter in China evidence fromfinancing and investment choices in the high-tech industryrdquoReview of Economics and Statistics vol 96 no 2 pp 332ndash3482014

[7] S M Fazzari R G Hubbard B C Petersen A S Blinder andJ M Poterba ldquoFinancing constraints and corporate invest-mentrdquo Brookings Papers on Economic Activity vol 1988 no 1206 pages 1988

[8] T Beck and R Levine ldquoIndustry growth and capital alloca-tionrdquo Journal of Financial Economics vol 64 no 2pp 147ndash180 2002

[9] M Gertler and S Gilchrist ldquo+e role of credit market im-perfections in the monetary transmission mechanism argu-ments and evidencerdquo8e Scandinavian Journal of Economicsvol 95 no 1 pp 43ndash64 1993

[10] R G Rajan and L Zingales ldquoFinancial dependence andgrowthrdquo Social Science Electronic Publishing vol 88 no 3pp 559ndash586 1998

[11] G Borisova and J R Brown ldquoRampD sensitivity to asset saleproceeds new evidence on financing constraints and intan-gible investmentrdquo Journal of Banking amp Finance vol 37 no 1pp 159ndash173 2013

[12] Y S Hwang H G Min and S H Han ldquo+e influence offinancial development on RampD activity cross-country evi-dencerdquo Review of Pacific Basin Financial Markets amp Policiesvol 13 no 3 pp 381ndash401 2010

[13] A Hall G Bockett S Taylor M V K Sivamohan andN Clark ldquoWhy research partnerships really matter inno-vation theory institutional arrangements and implications fordeveloping new technology for the poorrdquo World Develop-ment vol 29 no 5 pp 783ndash797 2001

[14] B H Hall ldquoInvestment and research and development at thefirm level does the source of financing matterrdquo NBERWorking Papers 1992

[15] C P Himmelberg and B C Petersen ldquoRampD and internalfinance a panel study of small firms in high-tech industriesrdquo8e Review of Economics and Statistics vol 76 pp 38ndash511994

[16] Z He and M B Wintoki ldquo+e cost of innovation RampD andhigh cash holdings in US firmsrdquo Journal of Corporate Financevol 41 pp 280ndash303 2016

[17] B Mulkay B H Hall and J Mairesse Firm Level Investmentand RampD in France and the United States A comparisonSocial Science Electronic Publishing Berlin Germany 2001

[18] D Harhoff Are there financing constraints for RampD and in-vestment in German manufacturing firms 8e Economics andEconometrics of Innovation Springer Berlin Germany 2000

[19] S Bond and D H V Harhoff ldquoInvestment RampD and fi-nancial constraints in Britain and Germanyrdquo AnnalesdrsquoEconomie et de Statistique vol 7980 pp 433ndash460 2005

[20] C Bloch ldquoRampD investment and internal finance the cash floweffectrdquo Economics of Innovation and New Technology vol 14no 3 pp 213ndash223 2005

Complexity 13

[21] G Scellato ldquoPatents firm size and financial constraints anempirical analysis for a panel of Italian manufacturing firmsrdquoCambridge Journal of Economics vol 31 pp 55ndash76 2007

[22] E Ughetto ldquoDoes internal finance matter for RampD newevidence from a panel of Italian firmsrdquo Cambridge Journal ofEconomics vol 32 no 6 pp 907ndash925 2008

[23] D Czarnitzki and H Hottenrott ldquoRampD investment and fi-nancing constraints of small and medium-sized firmsrdquo SmallBusiness Economics vol 36 no 1 pp 65ndash83 2011

[24] B H Hall ldquo+e financing of research and developmentrdquoOxford Review of Economic Policy vol 18 no 1 pp 35ndash512002

[25] J R Brown and B C Petersen ldquoCash holdings and RampDsmoothingrdquo Journal of Corporate Finance vol 17 no 3pp 694ndash709 2011

[26] D Li ldquoFinancial constraints RampD investment and stockreturnsrdquo Review of Financial Studies vol 24 no 9pp 2974ndash3007 2011

[27] A Mina H Lahr and A Hughes ldquo+e demand and supply ofexternal finance for innovative firmsrdquo Industrial and Cor-porate Change vol 22 no 4 pp 869ndash901 2013

[28] X Gu Y Chen and S Pan ldquoEconomic policy uncertainty andinnovation evidence from listed companies in Chinardquo Reviewof Financial Studies vol 2 pp 109ndash123 2018

[29] F Knight Risk Uncertainty and Profit Houghton MifflinCompany Boston MA USA 1921

[30] W Hao W Wei and J Wen ldquoHow does economic policyuncertainty affect corporate innovationmdashperspective ofmechanism of action of real option theoryrdquo EconomicManagement vol 38 pp 40ndash54 2016

[31] G Fan X Wang and G Ma ldquo+e contribution of Chinarsquosmarketization process to economic growthrdquo Economic Re-search vol 46 no 9 pp 4ndash16 2011

[32] K Z Dai and Y J Liu ldquo+e impact of marketization reformon RampD investment in Chinarsquos high-tech industryrdquo ScienceStudies vol 31 pp 50ndash57 2013

[33] P Li and X Liu ldquo+e impact of market-oriented institutionalchange on Chinarsquos technological progressmdashbased on theperspective of independent R amp D and technology intro-ductionrdquo Economic Perspectives vol 4 pp 42ndash50 2015

[34] K J Arrow ldquoEconomic welfare and the allocation of resourcesfor inventionrdquo 8e Rate and Direction of Inventive Activityvol 12 pp 609ndash626 1962

[35] P Kijkasiwat and P Phuensane ldquoInnovation and firm per-formance the moderating and mediating roles of firm sizeand small and medium enterprise financerdquo Journal of Riskand Financial Management vol 13 no 5 p 97 2020

[36] X N Yuan ldquoManufacturing innovation and service inno-vation comparison and convergencerdquo Finance and TradeResearch vol 20 pp 14ndash19 2009

[37] Y M Zhu and M E Jia ldquoMarketization process financialconstraints and corporate technology innovationrdquo Com-mercial ResearchInnovation Technology vol 1 pp 49ndash562017

[38] C P Wu and D Tang ldquoIntellectual property rights en-forcement corporate innovation and operating performanceevidence from Chinarsquos listed companiesrdquo Economic ResearchJournal vol 51 no 11 pp 125ndash139 2016

[39] B H Hall and D Harhoff ldquoRecent research on the economicsof patentsrdquo Annual Review of Economics vol 4 pp 541ndash5652012

[40] L Owen P Christopher and J Saaa-Requejo ldquoFinancialconstraints and stock returnsrdquo Review of Financial Studiesvol 14 pp 529ndash554 2001

[41] S R Baker N Bloom and S J Davis ldquoMeasuring economicpolicy uncertaintyrdquo 8e Quarterly Journal of Economicsvol 131 no 4 pp 1593ndash1636 2016

[42] J F He and X J Zhang ldquo+e influence of rising labor cost onenterprise innovationrdquo 8e Journal of Quantitative amp Tech-nical Economics vol 45 no 8 pp 56ndash73 2018

[43] W J Li and M N Zheng ldquoIs it substantive innovation orstrategic innovationmdashimpact of macroeconomic policies onmicro-enterprisesrsquo innovationrdquo Economic Research Journalvol 4 pp 60ndash73 2016

[44] C Andrea ldquoFinancing constraints radical versus incrementalinnovation and aggregate productivityrdquo American EconomicJournal Macroeconomics vol 11 no 2 pp 75ndash309 2019

[45] J M Hilbe Modeling Count Data Cambridge UniversityPress Cambridge UK 2014

[46] S U Khan A Shah and M F Rizwan ldquoDo financingconstraints matter for technological and non-technologicalinnovation a (re) examination of developing marketsrdquoEmerging Markets Finance and Trade vol 57 pp 1ndash28 2019

[47] F Y Li and M Z Yang ldquoCan economic policy uncertaintyinfluence corporate investment the empirical research byusing China economic policy uncertainty indexrdquo Journal ofFinancial Research vol 57 no 9 pp 2739ndash2766 2015

[48] C J Hadlock and J R Pierce ldquoNew evidence on measuringfinancial constraints moving beyond the KZ indexrdquo Review ofFinancial Studies vol 23 no 5 pp 1909ndash1940 2010

[49] X S Ju D Lu and Y H Yu ldquoFinancial constraints workingcapital management and the sustainability of firm innova-tionrdquo Economic Research Journal vol 48 pp 4ndash16 2013

[50] J Li J Liang V Shi and Q Wang ldquo+e benefit of manu-facturer encroachment considering consumerrsquos environ-mental awareness and product competitionrdquo Annals ofOperation Research vol 3 pp 1ndash19 2021

[51] J Li F Wang and Y He ldquoElectric vehicle routing problemwith battery swapping considering energy consumption andcarbon emissionsrdquo Sustainability vol 12 no 24 Article ID10537 2020

[52] J Li L Yi V Shi and X Chen ldquoSupplier encroachmentstrategy in the presence of retail strategic inventory cen-tralization or decentralizationrdquo Omega vol 98 Article ID102213 2021

[53] J Li Z Hu V Shi and Q Wang ldquoManufacturerrsquos en-croachment strategy with substitutable green productsrdquo In-ternational Journal of Production Economics vol 235 ArticleID 108102 2021

14 Complexity

the assumption of linear regression we adopt the panelcounting model According to the distribution character-istics of the number of patent applications there is a hugedifference between the variance and the mean and thevariance is obviously greater than the mean +ere may beexcessive data dispersion and it is difficult to satisfy Poissonregression condition that expectation and variance are equal+erefore the negative binomial regression model of paneldata is adopted

It is assumed that the number of patents filed by anenterprise is Yit (the subscripted variable i represents theenterprise observed and the subscripted variable t repre-sents the year observed) and its conditional distributionfunction is as follows

P Yit yit|xit( 1113857 e

minusλitλyit

it

yit yit 0 1 2 ( 1113857 (2)

where λit gt 0 represents the average occurrence times of theevent In order to ensure that λit is a nonnegative figure it isassumed that

λit exp xitprime β + ui( 1113857 exp xit

prime β( 1113857exp ui( 1113857 equiv vi exp xitprime β( 1113857

(3)

where xit does not contain constant term vi equiv exp(ui)

represents the individual effect in the form of product In thecase that it satisfies the condition that v1 v2 middot middot middot vn itmeans that there is no individual effect More generally theexistence of individual effect is allowed In other wordsdifferent individuals have different values of vi +e prob-ability density of the negative binomial regression model is

f Yit|μit α( 1113857 Γ yit + αminus1

1113872 1113873

Γ yit + 1( 1113857Γ αminus11113872 1113873

αminus1

αminus1 + μit

1113888 1113889

αminus1

μit

αminus1 + μit

1113888 1113889

αminus1

yit 0 1 (4)

where Γ(middot) represents the gamma distribution function andα represents the overdispersion parameter E(Yit|xit) μit

and Var(Yit|xit) μit + αμ2it gt μit yit represent the numberof patents applied by the enterprise i in the year t μit

represents the average value of the patents applied by theenterprise After introducing relevant control variables thenegative binomial regression model of the technologicalinnovation output is

μit exp α1Fconstraintsitminus1 + 1113944 θixitminus1 + εit1113872 1113873 (5)

To test Hypothesis 2 to Hypothesis 4 we group thesamples according to their characteristics and compare themagnitude and significance of financing constraint coeffi-cient To test Hypothesis 5 and Hypothesis 6 we introduceeconomic policy uncertainty or marketization as the mod-erator based on model (1) and set the following econometricmodel

Innovationgit α0 + α1Fconstraintsitminus1 + 1113944 θixitminus1

+α2Mit + α3Mit times Fconstraintsitminus1 + indit + θt + εit

(6)

where Mit represents the economic policy uncertainty or themarketization If the estimate of the interaction coefficient α3 issignificantly positive the hypothesesH5 andH6 are supportedIf α3 is significantly negative it means the economic policyuncertainty or themarketization increases the negative effect offinancing constraints on technological innovation

42 Variable

421 Explained Variable +e explained variable is tech-nical innovation Technical innovation can be measuredfrom the perspectives of input and output For innovation

input we measure the companyrsquos technological innovationactivities by the RampD expense and use the RampD concen-tration (the proportion of RampD expenses to the total assets ofthe current period) and the RampD intensity (the proportion ofRampD expenses to current sales revenue) In terms of thetechnical innovation output following Hall and Harhoff[39] we measure the output of technological innovation bythe number of patent applications

422 Explanatory Variable +e core explanatory variable isthe financing constraints Following Owen [40] to calculateKZ index and considering the characteristics of listedcompanies in China the measurement method of FC indexof the Chinese listed companies financing constraint isestablished +e first step is to classify the samples by yearand calculate medians of the annual operating net cash flow(CF) cash dividends (DIV) cash holdinglast termrsquos assets(CashH) the leverage ratio (LEV) and Tobinrsquos Q (TobinQ)+en we assign a value to the variable CF DIV and CashHare expected to be negative correlation with financingconstraints while LEV and TobinQ are positively correlatedwith financing constraints +e values are determinedaccording to the following principles If CF DIV and CashHare lower than their medians that year the correspondingindicator value is taken as 1 otherwise it is taken as 0expressed as FC1 FC2 and FC3 If LEV and TobinQ arehigher than their medians that year the correspondingindicator value is taken as 1 otherwise it is taken as 0expressed as FC4 and FC5 +e third step is to calculate thevalue of FC according to the equation FC CF1 + FC2+FC3 + FC4 + FC5+e fourth step is to use the panel sortinglogistic regression model for regression +e dependentvariable is FC and the explanatory variables are the originalvalues of CF DIV CashH LEV and TobinQ+e regression

6 Complexity

coefficients of these explanatory variables are estimated andthe expression of the FC index is obtained In the end theactual values of CF DIV CashH LEV and TobinQ aresubstituted into the regression model and the predicted FCvalue of the enterprise is calculated which was the FC indexof the enterprise +e higher the FC index is the moreserious financing constraints is

According to the process above for calculating the FCindex the measurement result of the financing constraints isFC 1135CF minus 4572DIV minus 404CashH + 538LEV+016TobinQ Moreover the regression coefficients of thefive variables are all significant at the significance level of 1and the overall significance of the model also passes thestatistical test It is illustrated that the five variables namelyCF DIV CashH LEV and TobinQ are the effective proxyvariables affecting the financing constraints In addition CFDIV and CashH are negatively correlated with financingconstraints while LEV and TobinQ are positively correlatedwith the financing constraints It means that listed com-panies with high operating cash flow high cash holding highdividends low leverage ratio and low investment oppor-tunities face less serious financing constraints In contrastlisted companies with low operating cash flow low cashholding low dividends high leverage ratio and high in-vestment opportunities face more severe financingconstraints

Two other important variables in this study are eco-nomic policy uncertainty andmarketization+ere are manymeasurement indicators of economic policy uncertainty Weadopt the economic policy uncertainty index constructed byBaker et al [41] +is index has been widely used in recentmacropolicy literature and has been proved to be com-prehensive and objective to reflect the fluctuation of Chinarsquoseconomic policy Since the index is a monthly index we usethe monthly arithmetic average to convert the monthlyeconomic policy uncertainty index into an annual economicpolicy uncertainty index We use variable EPU to representthe economic uncertainty index Marketization is mostlymeasured by the marketization index proposed by Fan et alwhich consists of the relationship between the governmentand the market the development of the nonstate economythe development of the product market the development ofthe factor market and the development of market inter-mediary organizations [31] +e five aspects of the legalsystem environment can fully reflect all aspects of mar-ketization +erefore marketization index is used to mea-sure the level of marketization in each region Since thesample in this article is data of listed companies in strategicemerging industries it is not possible to directly obtain thecorresponding market index of each listed company +emethod is to find the market index of the correspondingprovince according to the province where each listedcompany is registered We use marketization variable torepresent the marketization index

423 Control Variable Control variables involved are Sizerepresenting the size of enterprises Age representing the ageof enterprises TobinQ Concentr representing equity

concentration Sgrowth representing the sales growth rateand Cflow representing the cash flow ratio

43 8e Data Our samples are the companies listed on themain board of China in strategic emerging enterprise fromover 2009 to 2019 As there are no strategic emerging in-dustries in the industry classification of the China SecuritiesRegulatory Commission it is impossible to obtain thesample of listed companies of strategic emerging industriesdirectly In order to gather the samples of listed companies instrategic emerging industries the following steps are fol-lowed+e initial sample is selected from the sample stocks ofChina Strategic Emerging Industries Composite Index(ldquoEmerging Composite Indexrdquo) released by China SecuritiesIndex Co Ltd and Shanghai Stock Exchange in 2017covering a total of 1117 companies listed as A-shares inShanghai and Shenzhen Stock Exchanges on the SMEsboard (Small and Medium Enterprise Board) the second-board market and the new OTC market (new over-the-counter market) Due to the small amount of data disclosedby listed companies on the new OTC market and the poorcomparability compared with other listed companies suchcompanies are removed from the sample frame Next somefilter conditions are set in the sample frame+e STand lowast STlisted companies within the sample period are deleted+enthe listed companies in the financial and insurance industryare deleted At last the samples with missing values of someindexes are deleted After the steps detailed above the finalsamples are 757 listed companies in the strategic emergingindustries over the period of 2009 to 2019

5 Empirical Results and Discussions

51 8e Inhibitory Effect of Financing Constraints We focuson the impact of financing constraints on technical inno-vation Columns 2 and 3 of Table 1 report the impact offinancing constraints on technological innovation input Asshown in Table 1 financing constraints negatively affect theconcentration and intensity of RampD at the significance levelof 5 +at is financing constraints inhibit technologicalinnovation input For listed companies technological in-novation is a very important investment decision for en-terprises involving large capital investment and a long timeof capital occupation When enterprises face serious fi-nancing constraints their internal and external funds cannotmeet the capital demand for technological innovation soenterprisesrsquo technological innovation activities will bepostponed

+e impact of financing constraints on technologicalinnovation output is also vital According He and Zhang [42]and Li and Zheng [43] the patent applications for measuringthe technological innovation are divided into two types Oneis the substantive innovation with a higher technologicallevel represented by invention patents aiming to promotesocial and technological progress and occupy a competitiveadvantage in the industry Andrea pointed out that financingconstraints have a significant negative effect on fundamentalinnovation [44] +e other is strategic innovations with low

Complexity 7

technical level represented by utility model patents anddesign patents most of which aim to obtain other benefits+en the influence of financing constraints on the differenttypes of patent applications is studied Columns 4 to 6 ofTable 2 respectively report the impact of financing con-straints on the number of patent applications inventionapplications and noninvention applications In order todetermine whether negative binomial regression is a fixedeffect or a random effect the Hausmann test is carried out+e results show that the p value is less than 001 whichstrongly rejected the negative binomial regression of randomeffect +erefore the fixed-effect negative binomial regres-sion is adopted Hilbe pointed out that in the countingmodel the marginal effect of variables is more significantthan that of the regression coefficient [45] We use themarginal effect to analyze the impact of the unit change ofthe explanatory variable on the explained variable Columns4 and 5 of Table 1 show that in terms of both the number ofpatent applications and the number of invention patentsfinancing constraints inhibit technological innovation out-put at the significance level of 10 Column 6 of Table 1shows that the influence of financing constraints on thenumber of nonpatent applications is negative but not sig-nificant +e above results indicate that therestraint of fi-nancing constraints on the inventionpatent is greater thanthat of noninvention patent mainly because of the differentnature of the two types of patents +e invention patent canimprove the production efficiency of enterprises enableenterprises to obtain competitive advantages in the industryand represent the core competitiveness of enterprisesHowever invention patents usually require a lot of in-vestment and last a long time before they come out

On the contrary the purpose of noninvention patents isto echo the innovation strategy of enterprises and nationalinnovation policies and the companies pay more attentionto the ldquospeedrdquo and ldquoquantityrdquo of innovation Noninventionpatents involve a relatively small investment a relativelyshort duration cycle and a relatively little influence bycapital Due to the essential differences between inventionpatents and noninvention patents enterprises have differentattitudes towards the two kinds of patents which ultimatelyleads to the difference in the degree of influence of financingconstraints on the two kinds of patents +is conclusion isconsistent with Khan et al [46]

+e results above indicate that financing constraints notonly inhibit the technological innovation input in emergingindustries but also inhibit the technological innovationoutput of the enterprise in emerging industries and thusHypothesis 1 has been supported

52 8e Heterogeneous Influence of Financing Constraints

521 Analysis by Industry Considering that differentsubindustries in strategic emerging industries have differentcharacteristics of financing constraints and technologicalinnovation the sensitivity of technical innovation to fi-nancing constraints may be heterogeneous We furtheranalyze by industry in order to find out the difference in theimpact of financial constraints on technical innovation ofvarious subindustries in strategic emerging industries Sincethere are many subindustries in the strategic emerging in-dustries only RampD intensity is used as the explained variableto analyze by industry

Table 1 Regression results of the influence of financing constraints on technical innovation

Variable RampDconcentration

RampDintensity

Number of patentapplications

Number of inventionpatents

Number of noninventionpatents

LFC minus0031lowastlowast minus0114lowastlowastlowast minus0024lowast minus0038lowastlowast minus0002(0014) (0032) (0012) (0015) (0014)

Lsize minus0287lowastlowastlowast minus0629lowastlowastlowast 0357lowastlowastlowast 0378lowastlowastlowast 0348lowastlowastlowast(0051) (0119) (0044) (0050) (0058)

LTobinQ minus0009 minus0029 0024lowastlowastlowast 0030lowastlowastlowast 0009(0013) (0029) (0006) (0010) (0010)

LConcentr minus0009lowastlowast minus0025lowastlowastlowast minus0000 0002 0002(0004) (0009) (0003) (0003) (0003)

LSgrowth minus0011 minus0165lowastlowastlowast minus0000 minus0000 0000(0024) (0054) (0000) (0000) (0000)

Age minus0037lowastlowast minus0160lowastlowastlowast 0045lowastlowastlowast 0071lowastlowastlowast 0024lowast(0015) (0036) (0011) (0010) (0014)

LCflow minus0003lowastlowast minus0005lowastlowast minus0000(0002) (0002) (0002)

Constant 9033lowastlowastlowast 17980lowastlowastlowast minus7436lowastlowastlowast minus8479lowastlowastlowast minus7302lowastlowastlowast(1203) (2798) (0922) (1058) (1203)

Time effect Controlled ControlledIndustryeffect Controlled Controlled

Sample size 757 757 628 625 608Notes lowastlowastlowast lowastlowast and lowast mean significant at the significance level of 1 5 and 10 respectively +e figures in parentheses are standard errors the same asbelow Lvariables represent variables with a lag of one period

8 Complexity

Columns 2 to 8 of Table 2 report the effects of financingconstraints in different subindustries in strategic emergingindustries on technological innovation It is found that thereare pronounced industry differences in the impact of fi-nancing constraints on technological innovation Except forthe biological industry and new energy industry the impactof financing constraints on technological innovation in-vestment in the other five industries is uniformly negative Inthe new generation information technology industry ad-vanced equipment manufacturing and new energy vehicleindustry with high RampD intensity the negative impact isstatistically significant but not in the new material industryand energy conservation and environmental protectionindustry with relatively low RampD intensity +is is becausethe higher the RampD intensity is the more the money forRampD investment will be and the stronger the influence offinancing constraints will be In the regression analysis of thebiological industry and the new energy industry the fi-nancing constraintsrsquo coefficient is not negative +e mainreason is that these two industries are industries with rel-atively small financing constraints and are less affected byfinancial factors +erefore financing constraints have noobvious inhibitory effect on technological innovation andthus Hypothesis 2 has been verified

522 Whether It Is a Manufacturing Enterprise We dividethe listed companies in strategic emerging industries intomanufacturing and nonmanufacturing industries accordingto the classification standards of Chinarsquos high-tech indus-tries +e regression results of Table 3 show that the in-hibitory effect of the financing constraints on thetechnological innovation input and output of themanufacturing industry is lower than that of the non-manufacturing industry +e possible reason is that Chinarsquoslabor costs have risen sharply which has seriously affected

the operating profits of enterprises in the labor-intensive andcapital-intensive manufacturing industries +erefore themanufacturing industry is more willing to improve laborproductivity through technological innovation and tech-nological innovation is of greater significance tomanufacturing enterprises Manufacturing companies investmore in RampD and have a longer cycle for capital recoveryOnce the technical innovation project is launched the fundsneed to be continually invested Otherwise the invested costwill become a sunk cost and cannot be recovered +echaracteristic of high adjustment cost of RampD input is moreobvious in the manufacturing industry and manufacturingenterprises will pay more attention to the sustainability ofRampD +erefore technological innovation will not react sosensitively to the capital situation Even if the financingconstraints are intensified the technological innovation inthe manufacturing industry will not be reduced rapidly butneed a long time to adjust However nonmanufacturingindustries are mostly asset-light enterprises which involverelatively less investment in technological innovation withlow RampD intensity and less dependence on technologicalinnovation +erefore in these enterprises technologicalinnovation is more sensitive to the financial situation andthus Hypothesis 3 has been verified

523 Level of the Dependence of Industry External Financing+e companies are divided into two groups according to theexternal financing dependence in the industry to testwhether the inhibitory effect of financing constraints ontechnological innovation is related to the external financingdependence of the industry One group are the companieswith low external financing dependence and the other arethe companies with medium- and high-level external fi-nancing dependence +e grouping results of the sample inTable 4 show that only a few strategic emerging enterprises

Table 2 Regression analysis of different subindustries in strategic emerging industries

VariableNext-generationinformationtechnology

Advancedequipment

manufacturing

Newmaterialindustry

Biologicalindustry

New energyautomobileindustry

Newenergyindustry

Environmentalprotection industry

LFC minus0238lowastlowastlowast minus0084lowast minus0011 0211 minus0126lowast 0178 minus0005(0066) minus0049 (0039) (0170) (0073) (0143) (0041)

Lsize minus0098 minus0586lowastlowastlowast minus0642lowastlowastlowast minus0579 minus0065 minus0077 minus1002lowastlowastlowast(0273) (0161) (0136) (0723) (0211) (0212) (0204)

LTobinQ 0025 0002 0082lowast 0004 0037 1011lowastlowast minus0263lowastlowastlowast(0054) (0060) (0042) (0149) (0134) (0496) (0058)

LConcentr minus0021 minus0025lowastlowast 0008 minus0075 minus0008 0006 0009(0020) (0012) (0009) (0055) (0014) (0013) (0008)

LSgrowth 0009lowastlowastlowast minus0000 0006lowastlowastlowast minus0002 minus0001 0008 minus0001(0002) (0001) (0002) (0007) (0003) (0006) (0001)

Age minus0276lowastlowastlowast minus0096lowast 0020 minus0249 minus0036 0041 minus0071lowast(0073) (0050) (0039) (0179) (0056) (0031) (0040)

Sample size 290 257 95 43 19 14 24Notes according to the Guiding Catalogue of Key Products and Services for Strategic Emerging Industries (2016 Edition) issued by the National Developmentand Reform Commission in 2017 the strategic emerging industries are divided into nine sectors (including related service industries) Considering the smallnumber of samples of digital cultural and creative industries and related service industries they are not separately analyzed by industry

Complexity 9

are in the industries with low-level external financing de-pendence with only 14 samples accounting for 19 of thetotal samples It indicates that most enterprises in thestrategic emerging enterprises are in the industries with highexternal financing dependence +e regression results inTable 4 show that the negative effect of financing constraintson the technical innovation input and output is not sig-nificant in the samples of industries with low external fi-nancing dependence However in the industry withmedium- and high-level external financing dependence theimpact of financing constraints on the technical innovationrsquosinput and output is significant at the 10 significance leveland thus Hypothesis 4 has been verified

53 8e Moderating Effect of the External Environment

531 8e Economic Uncertainty as the Moderator As anexternal factor affecting enterprisesrsquo technological innova-tion economic policy has an impact on the technologicalinnovation activities of enterprises from amacroperspectiveOnce a countryrsquos economic policy changes enterprisesrsquoinnovation activities will inevitably be affected by the policy

Columns 2 and 3 in Table 5 are the regression resultsafter adding the cross terms of economic policy uncertaintyand financing constraints to the regression model It showsthat the negative effect of financing constraints on tech-nological innovation is still significant and the coefficient ofthe cross term is significantly positive indicating thateconomic policy uncertainty alleviates the inhibitory effectof financing constraints on technological innovation Spe-cifically when the uncertainty of the economic policy en-vironment increases the inhibitory effect of financingconstraints on the technological innovation input may bereduced+ere are two main explanations On the one handthe increasing uncertainty of economic policy means thatenterprisesrsquo business environment will change significantly

+is is both an opportunity and a challenge and the survivalof the fittest among enterprises will be presented Only thoseenterprises with high production efficiency and strongcompetitiveness can finally survive in the industry Fur-thermore innovation ability is the key factor for enterprisesto enhance their core competitiveness Enterprises can makeuse of the opportunities brought by the economic policyuncertainty and improve their core competitiveness by in-creasing the input in technological innovation activities so asto boost their chances of winning in the future marketcompetition On the other hand the uncertainty of eco-nomic policies will then spread to the external financingmarket leading to the fluctuation of the capital market andthen affect the external financing of technological innova-tion According to the research of Li and Yang [47] it can beseen that the economic policy uncertainty inhibits generalinvestment activities +erefore when economic policyuncertainty increases ordinary investment activities will notbe active and there will be abundant capital in the capitalmarket However technological innovation was fraught withuncertainty and the future is full of opportunities When theeconomic policy uncertainty increases financing for tech-nological innovation is more likely compared with general

Table 4 +e classification test result of the level of the external financing dependence in the industry

Variable

RampD concentration RampD intensity Number of patent applicationsLow externalfinancing

dependence

Medium- and high-level external

financing dependence

Low externalfinancing

dependence

Medium- and high-level external

financing dependence

Low externalfinancing

dependence

Medium- and high-level external

financing dependence

LFC 0107 minus0034lowastlowast minus0060 minus0116lowastlowastlowast minus0003 minus0021lowast(0113) (0014) (0117) (0033) (0088) (0013)

Allcontrols Controlled Controlled Controlled Controlled Controlled Controlled

Samplesize 14 743 14 743 14 614

Table 5 +e moderating effect of economic policy uncertainty

Variable RampD concentration RampD intensityLFC minus0069lowastlowastlowast (0026) minus0286lowastlowastlowast (0060)Lsize minus0273lowastlowastlowast (0050) minus0592lowastlowastlowast (0120)LTobinQ minus0022 (0029)LConcertr minus0009lowastlowast (0004) minus0025lowastlowastlowast (0009)LSgrowth minus0000 (0000) minus0002lowastlowastlowast (0001)Age minus0037lowastlowast (0015) minus0160lowastlowastlowast (0036)EPUlowastLFC 0000lowast (0000) 0001lowastlowastlowast (0000)EPU 0003lowastlowastlowast (0001) 0013lowastlowastlowast (0002)Constant 8409lowastlowastlowast (1162) 1580lowastlowastlowast (2775)Sample size 757 757

Table 3 +e classification test results of manufacturing enterprises and nonmanufacturing ones

VariableRampD concentration RampD intensity Number of patent applications

Manufacturing Nonmanufacturing Manufacturing Nonmanufacturing Manufacturing Nonmanufacturing

LFC minus0015 minus0094lowastlowast minus0100lowastlowastlowast minus0151lowastlowast minus0021lowast minus0061lowast(0012) (0043) (0035) (0075) (0012) (0034)

All controls Controlled Controlled Controlled Controlled Controlled ControlledSample size 543 214 543 214 484 144

10 Complexity

investment activities +erefore economic policy uncer-tainty can ease the inhibitory effect of financing constraintson technological innovation and thus Hypothesis 5 hasbeen verified

532 8eMarketization as the Moderator Columns 2 and 3in Table 6 are the regression results after adding the crossterms of marketization and financing constraints to theregression model It shows that the coefficient of financingconstraints is significantly negative the coefficient of mar-ketization is significantly positive and the coefficient of across term is significantly positive indicating that themarketization can help to alleviate the inhibitory effect offinancing constraints on technological innovation Specifi-cally when the market environment improves the inhibi-tory effect of financing constraints on technologicalinnovation will be reduced +erefore the more the market-oriented the lower the inhibitory effect of financing con-straints on technological innovation and vice versa In theend the Matthew effect occurs when the strong get strongerand the weak get weaker Under such effect the differencesin technological innovation between regions are constantlywidened +e possible explanation is as followsIn highlymarket-oriented regions there are a sound legal system highinformation transparency a more robust financial systemsound and abundant financial resources Such a perfectinstitutional environment can ease the enterprisersquos financingconstraints provide a proper external financing environ-ment for technological innovation and better escort theenterprisersquos technological innovation which is conducive toencourage enterprises to carry out technological innovationIn regions with less market-oriented it is impossible toprovide a strong institutional guarantee for technologicalinnovation For an enterprise facing financing constraintsthis adverse institutional environment intensifies the in-hibitory effect of financing constraints on technologicalinnovation and is not conducive to the enterprisersquos tech-nological innovation and thus Hypothesis 6 has beenverified

54 Robustness Test

541 Solutions to Endogenous Problems Financial con-straints inhibit enterprisesrsquo technological innovation activ-ities and technological innovation may make financialconstraints of enterprises get more serious thus leading to abidirectional causal relationship between financial con-straints and technical innovation In order to avoid possiblereverse causality all control variables and the explanatoryvariables are delayed by one period Simultaneously in thestudy to avoid the endogenous problems caused by theomitted variables the industry and time fixed effects arecontrolled in the empirical analysis

542 Recalculation of Financing Constraint Index In theempirical analysis we use the FC index to measure the fi-nancing constraints However there are various methods tomeasure the constraints with different emphases In order to

ensure robust conclusions the SA index proposed byHadlock and Pierce [48] is used to remeasure the financingconstraints +at is SA minus0737 Size + 0043 Size2minus004Age +e above empirical process is then repeated tocheck whether the financing constraints have an inhibitoryeffect on technological innovation

+e SA index is generally negative +e greater the ab-solute value of SA is the more serious the financing con-straints faced by the enterprise will be+erefore the SA indexis negatively correlated with the level of financing constraints+e regression results in Table 7 show that the SA index ispositively correlated with technological innovation at thesignificance level of 5 indicating the more serious the fi-nancing constraints is the stronger the inhibitory effect ontechnological innovation activities will be +is is consistentwith the conclusion of the test based on the FC index whichonce again verifies that financing constraints have an inhib-itory effect on technological innovation

543 Recalculation of the Technological Innovation Output+e increment of intangible assets is adopted to remeasurethe technological innovation output following Ju et al [49]Moreover the ratio of the increment of intangible assets tothe total assets at the end of the period is used as the proxyvariable of the technological innovation output to remeasurethe technological innovation output Finally the empiricalresults of the influence of financing constraints on thetechnological innovation output are shown in Table 8

+e results in Table 8 show that financing constraintshave a significant inhibitory effect on the technologicalinnovation output regardless of if the FC index or SA indexis used It is consistent with the conclusion where thenumber of patent applications is adopted as the techno-logical innovation output

544 8e Ordinary Panel Model as the Counting Model+e panel counting model is used to study the influence offinancing constraints on the technical innovation outputwhich shows that financing constraints have a significantinhibitory effect on the technological innovation output Inorder to test the robustness of the results the number ofpatent applications as the explained variable is taken as acontinuous common variable and then the ordinary panelmodel is used for empirical analysis +e empirical resultsare shown in Table 9

As shown in Table 9 the conclusion obtained using theordinary panel model is that the financing constraints sig-nificantly inhibit the technological innovation output After

Table 6 +e moderating effect of the marketization

Variable RampD concentration RampD intensityLFC minus0036lowastlowastlowast (0012) minus0168lowastlowastlowast (0030)Marketization 0838lowastlowastlowast (0266) 1319lowastlowast (0658)MarketizationlowastLFC 0017lowastlowastlowast (0005) 0024lowast (0013)All controls Controlled ControlledSample size 757 757

Complexity 11

dividing the number of patent applications into inventionpatents and noninvention patents we find that the inhibi-tory effect of the financing constraints on the inventionpatents is significantly higher than that of the noninventionpatents which is consistent with the conclusion obtainedusing the negative binomial regression model of panel data

6 Conclusions and Implications

As the world economy enters a stage of innovation compe-tition the decision-making problem of technological innova-tion in emerging industries is a practical problem worthy ofresearch and attention Based on the panel data of the listedcompanies in Chinarsquos strategic emerging industries from 2009to 2019 we adopt the negative binomial regression model ofpanel data and empirically study the technological innovationdecision-making issues of emerging industries from the per-spective of financing constraints Our results show that thefinancial constraints in strategic emerging industries in Chinasignificantly inhibit technological innovation in terms oftechnological innovation inputs and outputsWhat ismore the

inhibitory effect of substantive innovation like the inventionpatent is more pronounced than that of strategic innovationlike the noninvention patent +e influence of financingconstraints on technological innovation varies significantlybetween industries within strategic emerging industriesmanufacturing industries and nonmanufacturing industriesand among industries with different external financing de-pendence However the economic policy uncertainty and themarketization can help to alleviate the inhibitory effect of fi-nancing constraints on technological innovation

Our research conclusions lead to the following policyimplications First of all the government should focus onsolving the financial constraints by speeding up the reformof the financial system Promoting the reform of the financialsupply side improving the efficiency of financial resourceallocation and reducing the financing cost should also beemphasized In addition it is necessary to expand thecoverage of financial services to develop financing platformsand tools exclusive to RampD intensive industries such asstrategic emerging industries and to encourage venturecapital and long-term capital to invest in technological

Table 9 Robustness test results of the ordinary panel model

Variable Number of patent applications Number of invention patents Number of noninvention patentsLFC minus0045lowastlowastlowast (0012) minus0059lowastlowastlowast (0012) minus0028lowastlowast (0013)LTobinQ minus0003 (0008) 0002 (0008) minus0018lowastlowast (0009)LConcertr 0005lowastlowast (0002) 0004 (0003) 0006lowastlowast (0003)LSgrowth minus0000 (0000) minus0000 (0000) minus0000 (0000)Llev 0017lowastlowastlowast (0002) 0017lowastlowastlowast (0002) 0016lowastlowastlowast (0002)Age 0003 (0009) 0012 (0010) minus0004 (0010)LCflow minus0004lowastlowast (0002) minus0005lowastlowastlowast (0002) minus0003 (0004)Sample size 691 691 691

Table 8 Robustness test results of intangible assets increment

Variable FC index SA indexLFC minus0042lowastlowast (0019) 0249lowastlowast (0108)Lsize 0000 (0041)LTobinQ 0039lowastlowast (0019) 0037lowast (0019)LConcentr 0000 (0003) 0002 (0003)LCflow minus0014lowastlowastlowast (0004) minus0012lowastlowastlowast (0001)LSgrowth minus0009 (0042) 0004 (0042)Age minus0039lowastlowastlowast (0008)Ltangibility 0000 (0003)Constant 0508 (1039) 0316 (0593)Sample size 757 757

Table 7 Regression results of the impact of financing constraints on technological innovation through the SA index

Variable RampD concentration RampD intensity Number of patent applicationsLSA 0351lowastlowastlowast (0135) 0995lowastlowastlowast (0316) 0300lowast (0182)LLEV minus0444lowast (0240) minus4102lowastlowastlowast (0549) minus0003 (0002)LTobinQ 0003 (0013) minus0039 (0030) minus0005 (0012)LConcentr minus0010lowastlowast (0004) minus0020lowastlowast (00098) minus0000 (00037)LCflow 0004lowast (0003) minus0004 (0006)Ltangibility 0011lowastlowastlowast (0003) 0002 (0006)LSgrowth minus0011 (0023) minus0118lowastlowast (0053)Constant 2159lowastlowastlowast (0592) 5283lowastlowastlowast (1360) minus9301lowastlowastlowast (1361)Sample size 757 757 628

12 Complexity

innovation projects A technological innovation financingsystem based on the market mechanism guided by gov-ernment investment dominated by enterprises and widelyparticipated by social funds should be established Morefinancial resources can flow to technological innovationprojects and to enterprises with technological innovationactivities

Second at present Chinarsquos economy is in a ldquonew nor-malrdquo that requires innovation In view of the selective effectof financing constraints on innovation activities relevantdepartments should make effort to build a good externalfinancing environment and use effective financial policiesand administrative methods to help enterprises release theirinnovative vitality more fully For example relevant de-partments should pay attention to building a good market-oriented system and increase institutional guarantees forinnovative activities so as to improve the operating con-ditions of enterprises and promote high-quality innovativeactivities When companies face financing constraints thesemeasures that are conducive to improving operating con-ditions will help to stimulate innovation

Finally since the financing constraints have a moreobvious restraining effect on enterprisesrsquo substantive inno-vation of high quality the government should encourageenterprises to increase RampD investment in substantive in-novation [50ndash53] while guiding them to pursue high-qualityinnovation so as to avoid such innovations that emphasizeonly the quantity and speed In addition the governmentalso should continue to increase financial investment inbasic research for making up for the lack of substantiveinnovation of enterprises and aiming to truly improve in-novation ability and quality of the enterprise

Data Availability

+e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

+e authors declare that there are no conflicts of interestregarding the publication of this study

Acknowledgments

+is research was partly supported by the Natural ScienceFoundation of Zhejiang Province (Grant no LQ19G030007)and Zhejiang Gongshang University Online and OfflineHybrid Teaching Reform Project (Grant no1010XJ2919103)

References

[1] H Lei and W Wang ldquo+e impact of network structure oninnovation efficiency an agent-based study in the context ofinnovation networksrdquo Complexity vol 21 no 2 pp 111ndash1222015

[2] W B Arthur and W Polak ldquo+e evolution of technologywithin a simple computer modelrdquo Complexity vol 11 no 5pp 23ndash31 2010

[3] J J Pang G Lin R L Yang et al ldquoPromote continuouseconomic development economic trendsrdquo Economic Dy-namics vol 7 pp 3ndash19 2019

[4] M Cai and S J Liu ldquo+e impact of government subsidies onthe leverage ratio of strategic emerging industriesrdquo Con-temporary Economic Research vol 8 pp 90ndash100 2020

[5] J R Brown S M Fazzari and B C Petersen ldquoFinancinginnovation and growth cash flow external equity and the1990s RampD boomrdquo Journal of Finance vol 64 pp 151ndash1852010

[6] J S Ang Y Cheng and C Wu ldquoDoes enforcement of in-tellectual property rights matter in China evidence fromfinancing and investment choices in the high-tech industryrdquoReview of Economics and Statistics vol 96 no 2 pp 332ndash3482014

[7] S M Fazzari R G Hubbard B C Petersen A S Blinder andJ M Poterba ldquoFinancing constraints and corporate invest-mentrdquo Brookings Papers on Economic Activity vol 1988 no 1206 pages 1988

[8] T Beck and R Levine ldquoIndustry growth and capital alloca-tionrdquo Journal of Financial Economics vol 64 no 2pp 147ndash180 2002

[9] M Gertler and S Gilchrist ldquo+e role of credit market im-perfections in the monetary transmission mechanism argu-ments and evidencerdquo8e Scandinavian Journal of Economicsvol 95 no 1 pp 43ndash64 1993

[10] R G Rajan and L Zingales ldquoFinancial dependence andgrowthrdquo Social Science Electronic Publishing vol 88 no 3pp 559ndash586 1998

[11] G Borisova and J R Brown ldquoRampD sensitivity to asset saleproceeds new evidence on financing constraints and intan-gible investmentrdquo Journal of Banking amp Finance vol 37 no 1pp 159ndash173 2013

[12] Y S Hwang H G Min and S H Han ldquo+e influence offinancial development on RampD activity cross-country evi-dencerdquo Review of Pacific Basin Financial Markets amp Policiesvol 13 no 3 pp 381ndash401 2010

[13] A Hall G Bockett S Taylor M V K Sivamohan andN Clark ldquoWhy research partnerships really matter inno-vation theory institutional arrangements and implications fordeveloping new technology for the poorrdquo World Develop-ment vol 29 no 5 pp 783ndash797 2001

[14] B H Hall ldquoInvestment and research and development at thefirm level does the source of financing matterrdquo NBERWorking Papers 1992

[15] C P Himmelberg and B C Petersen ldquoRampD and internalfinance a panel study of small firms in high-tech industriesrdquo8e Review of Economics and Statistics vol 76 pp 38ndash511994

[16] Z He and M B Wintoki ldquo+e cost of innovation RampD andhigh cash holdings in US firmsrdquo Journal of Corporate Financevol 41 pp 280ndash303 2016

[17] B Mulkay B H Hall and J Mairesse Firm Level Investmentand RampD in France and the United States A comparisonSocial Science Electronic Publishing Berlin Germany 2001

[18] D Harhoff Are there financing constraints for RampD and in-vestment in German manufacturing firms 8e Economics andEconometrics of Innovation Springer Berlin Germany 2000

[19] S Bond and D H V Harhoff ldquoInvestment RampD and fi-nancial constraints in Britain and Germanyrdquo AnnalesdrsquoEconomie et de Statistique vol 7980 pp 433ndash460 2005

[20] C Bloch ldquoRampD investment and internal finance the cash floweffectrdquo Economics of Innovation and New Technology vol 14no 3 pp 213ndash223 2005

Complexity 13

[21] G Scellato ldquoPatents firm size and financial constraints anempirical analysis for a panel of Italian manufacturing firmsrdquoCambridge Journal of Economics vol 31 pp 55ndash76 2007

[22] E Ughetto ldquoDoes internal finance matter for RampD newevidence from a panel of Italian firmsrdquo Cambridge Journal ofEconomics vol 32 no 6 pp 907ndash925 2008

[23] D Czarnitzki and H Hottenrott ldquoRampD investment and fi-nancing constraints of small and medium-sized firmsrdquo SmallBusiness Economics vol 36 no 1 pp 65ndash83 2011

[24] B H Hall ldquo+e financing of research and developmentrdquoOxford Review of Economic Policy vol 18 no 1 pp 35ndash512002

[25] J R Brown and B C Petersen ldquoCash holdings and RampDsmoothingrdquo Journal of Corporate Finance vol 17 no 3pp 694ndash709 2011

[26] D Li ldquoFinancial constraints RampD investment and stockreturnsrdquo Review of Financial Studies vol 24 no 9pp 2974ndash3007 2011

[27] A Mina H Lahr and A Hughes ldquo+e demand and supply ofexternal finance for innovative firmsrdquo Industrial and Cor-porate Change vol 22 no 4 pp 869ndash901 2013

[28] X Gu Y Chen and S Pan ldquoEconomic policy uncertainty andinnovation evidence from listed companies in Chinardquo Reviewof Financial Studies vol 2 pp 109ndash123 2018

[29] F Knight Risk Uncertainty and Profit Houghton MifflinCompany Boston MA USA 1921

[30] W Hao W Wei and J Wen ldquoHow does economic policyuncertainty affect corporate innovationmdashperspective ofmechanism of action of real option theoryrdquo EconomicManagement vol 38 pp 40ndash54 2016

[31] G Fan X Wang and G Ma ldquo+e contribution of Chinarsquosmarketization process to economic growthrdquo Economic Re-search vol 46 no 9 pp 4ndash16 2011

[32] K Z Dai and Y J Liu ldquo+e impact of marketization reformon RampD investment in Chinarsquos high-tech industryrdquo ScienceStudies vol 31 pp 50ndash57 2013

[33] P Li and X Liu ldquo+e impact of market-oriented institutionalchange on Chinarsquos technological progressmdashbased on theperspective of independent R amp D and technology intro-ductionrdquo Economic Perspectives vol 4 pp 42ndash50 2015

[34] K J Arrow ldquoEconomic welfare and the allocation of resourcesfor inventionrdquo 8e Rate and Direction of Inventive Activityvol 12 pp 609ndash626 1962

[35] P Kijkasiwat and P Phuensane ldquoInnovation and firm per-formance the moderating and mediating roles of firm sizeand small and medium enterprise financerdquo Journal of Riskand Financial Management vol 13 no 5 p 97 2020

[36] X N Yuan ldquoManufacturing innovation and service inno-vation comparison and convergencerdquo Finance and TradeResearch vol 20 pp 14ndash19 2009

[37] Y M Zhu and M E Jia ldquoMarketization process financialconstraints and corporate technology innovationrdquo Com-mercial ResearchInnovation Technology vol 1 pp 49ndash562017

[38] C P Wu and D Tang ldquoIntellectual property rights en-forcement corporate innovation and operating performanceevidence from Chinarsquos listed companiesrdquo Economic ResearchJournal vol 51 no 11 pp 125ndash139 2016

[39] B H Hall and D Harhoff ldquoRecent research on the economicsof patentsrdquo Annual Review of Economics vol 4 pp 541ndash5652012

[40] L Owen P Christopher and J Saaa-Requejo ldquoFinancialconstraints and stock returnsrdquo Review of Financial Studiesvol 14 pp 529ndash554 2001

[41] S R Baker N Bloom and S J Davis ldquoMeasuring economicpolicy uncertaintyrdquo 8e Quarterly Journal of Economicsvol 131 no 4 pp 1593ndash1636 2016

[42] J F He and X J Zhang ldquo+e influence of rising labor cost onenterprise innovationrdquo 8e Journal of Quantitative amp Tech-nical Economics vol 45 no 8 pp 56ndash73 2018

[43] W J Li and M N Zheng ldquoIs it substantive innovation orstrategic innovationmdashimpact of macroeconomic policies onmicro-enterprisesrsquo innovationrdquo Economic Research Journalvol 4 pp 60ndash73 2016

[44] C Andrea ldquoFinancing constraints radical versus incrementalinnovation and aggregate productivityrdquo American EconomicJournal Macroeconomics vol 11 no 2 pp 75ndash309 2019

[45] J M Hilbe Modeling Count Data Cambridge UniversityPress Cambridge UK 2014

[46] S U Khan A Shah and M F Rizwan ldquoDo financingconstraints matter for technological and non-technologicalinnovation a (re) examination of developing marketsrdquoEmerging Markets Finance and Trade vol 57 pp 1ndash28 2019

[47] F Y Li and M Z Yang ldquoCan economic policy uncertaintyinfluence corporate investment the empirical research byusing China economic policy uncertainty indexrdquo Journal ofFinancial Research vol 57 no 9 pp 2739ndash2766 2015

[48] C J Hadlock and J R Pierce ldquoNew evidence on measuringfinancial constraints moving beyond the KZ indexrdquo Review ofFinancial Studies vol 23 no 5 pp 1909ndash1940 2010

[49] X S Ju D Lu and Y H Yu ldquoFinancial constraints workingcapital management and the sustainability of firm innova-tionrdquo Economic Research Journal vol 48 pp 4ndash16 2013

[50] J Li J Liang V Shi and Q Wang ldquo+e benefit of manu-facturer encroachment considering consumerrsquos environ-mental awareness and product competitionrdquo Annals ofOperation Research vol 3 pp 1ndash19 2021

[51] J Li F Wang and Y He ldquoElectric vehicle routing problemwith battery swapping considering energy consumption andcarbon emissionsrdquo Sustainability vol 12 no 24 Article ID10537 2020

[52] J Li L Yi V Shi and X Chen ldquoSupplier encroachmentstrategy in the presence of retail strategic inventory cen-tralization or decentralizationrdquo Omega vol 98 Article ID102213 2021

[53] J Li Z Hu V Shi and Q Wang ldquoManufacturerrsquos en-croachment strategy with substitutable green productsrdquo In-ternational Journal of Production Economics vol 235 ArticleID 108102 2021

14 Complexity

coefficients of these explanatory variables are estimated andthe expression of the FC index is obtained In the end theactual values of CF DIV CashH LEV and TobinQ aresubstituted into the regression model and the predicted FCvalue of the enterprise is calculated which was the FC indexof the enterprise +e higher the FC index is the moreserious financing constraints is

According to the process above for calculating the FCindex the measurement result of the financing constraints isFC 1135CF minus 4572DIV minus 404CashH + 538LEV+016TobinQ Moreover the regression coefficients of thefive variables are all significant at the significance level of 1and the overall significance of the model also passes thestatistical test It is illustrated that the five variables namelyCF DIV CashH LEV and TobinQ are the effective proxyvariables affecting the financing constraints In addition CFDIV and CashH are negatively correlated with financingconstraints while LEV and TobinQ are positively correlatedwith the financing constraints It means that listed com-panies with high operating cash flow high cash holding highdividends low leverage ratio and low investment oppor-tunities face less serious financing constraints In contrastlisted companies with low operating cash flow low cashholding low dividends high leverage ratio and high in-vestment opportunities face more severe financingconstraints

Two other important variables in this study are eco-nomic policy uncertainty andmarketization+ere are manymeasurement indicators of economic policy uncertainty Weadopt the economic policy uncertainty index constructed byBaker et al [41] +is index has been widely used in recentmacropolicy literature and has been proved to be com-prehensive and objective to reflect the fluctuation of Chinarsquoseconomic policy Since the index is a monthly index we usethe monthly arithmetic average to convert the monthlyeconomic policy uncertainty index into an annual economicpolicy uncertainty index We use variable EPU to representthe economic uncertainty index Marketization is mostlymeasured by the marketization index proposed by Fan et alwhich consists of the relationship between the governmentand the market the development of the nonstate economythe development of the product market the development ofthe factor market and the development of market inter-mediary organizations [31] +e five aspects of the legalsystem environment can fully reflect all aspects of mar-ketization +erefore marketization index is used to mea-sure the level of marketization in each region Since thesample in this article is data of listed companies in strategicemerging industries it is not possible to directly obtain thecorresponding market index of each listed company +emethod is to find the market index of the correspondingprovince according to the province where each listedcompany is registered We use marketization variable torepresent the marketization index

423 Control Variable Control variables involved are Sizerepresenting the size of enterprises Age representing the ageof enterprises TobinQ Concentr representing equity

concentration Sgrowth representing the sales growth rateand Cflow representing the cash flow ratio

43 8e Data Our samples are the companies listed on themain board of China in strategic emerging enterprise fromover 2009 to 2019 As there are no strategic emerging in-dustries in the industry classification of the China SecuritiesRegulatory Commission it is impossible to obtain thesample of listed companies of strategic emerging industriesdirectly In order to gather the samples of listed companies instrategic emerging industries the following steps are fol-lowed+e initial sample is selected from the sample stocks ofChina Strategic Emerging Industries Composite Index(ldquoEmerging Composite Indexrdquo) released by China SecuritiesIndex Co Ltd and Shanghai Stock Exchange in 2017covering a total of 1117 companies listed as A-shares inShanghai and Shenzhen Stock Exchanges on the SMEsboard (Small and Medium Enterprise Board) the second-board market and the new OTC market (new over-the-counter market) Due to the small amount of data disclosedby listed companies on the new OTC market and the poorcomparability compared with other listed companies suchcompanies are removed from the sample frame Next somefilter conditions are set in the sample frame+e STand lowast STlisted companies within the sample period are deleted+enthe listed companies in the financial and insurance industryare deleted At last the samples with missing values of someindexes are deleted After the steps detailed above the finalsamples are 757 listed companies in the strategic emergingindustries over the period of 2009 to 2019

5 Empirical Results and Discussions

51 8e Inhibitory Effect of Financing Constraints We focuson the impact of financing constraints on technical inno-vation Columns 2 and 3 of Table 1 report the impact offinancing constraints on technological innovation input Asshown in Table 1 financing constraints negatively affect theconcentration and intensity of RampD at the significance levelof 5 +at is financing constraints inhibit technologicalinnovation input For listed companies technological in-novation is a very important investment decision for en-terprises involving large capital investment and a long timeof capital occupation When enterprises face serious fi-nancing constraints their internal and external funds cannotmeet the capital demand for technological innovation soenterprisesrsquo technological innovation activities will bepostponed

+e impact of financing constraints on technologicalinnovation output is also vital According He and Zhang [42]and Li and Zheng [43] the patent applications for measuringthe technological innovation are divided into two types Oneis the substantive innovation with a higher technologicallevel represented by invention patents aiming to promotesocial and technological progress and occupy a competitiveadvantage in the industry Andrea pointed out that financingconstraints have a significant negative effect on fundamentalinnovation [44] +e other is strategic innovations with low

Complexity 7

technical level represented by utility model patents anddesign patents most of which aim to obtain other benefits+en the influence of financing constraints on the differenttypes of patent applications is studied Columns 4 to 6 ofTable 2 respectively report the impact of financing con-straints on the number of patent applications inventionapplications and noninvention applications In order todetermine whether negative binomial regression is a fixedeffect or a random effect the Hausmann test is carried out+e results show that the p value is less than 001 whichstrongly rejected the negative binomial regression of randomeffect +erefore the fixed-effect negative binomial regres-sion is adopted Hilbe pointed out that in the countingmodel the marginal effect of variables is more significantthan that of the regression coefficient [45] We use themarginal effect to analyze the impact of the unit change ofthe explanatory variable on the explained variable Columns4 and 5 of Table 1 show that in terms of both the number ofpatent applications and the number of invention patentsfinancing constraints inhibit technological innovation out-put at the significance level of 10 Column 6 of Table 1shows that the influence of financing constraints on thenumber of nonpatent applications is negative but not sig-nificant +e above results indicate that therestraint of fi-nancing constraints on the inventionpatent is greater thanthat of noninvention patent mainly because of the differentnature of the two types of patents +e invention patent canimprove the production efficiency of enterprises enableenterprises to obtain competitive advantages in the industryand represent the core competitiveness of enterprisesHowever invention patents usually require a lot of in-vestment and last a long time before they come out

On the contrary the purpose of noninvention patents isto echo the innovation strategy of enterprises and nationalinnovation policies and the companies pay more attentionto the ldquospeedrdquo and ldquoquantityrdquo of innovation Noninventionpatents involve a relatively small investment a relativelyshort duration cycle and a relatively little influence bycapital Due to the essential differences between inventionpatents and noninvention patents enterprises have differentattitudes towards the two kinds of patents which ultimatelyleads to the difference in the degree of influence of financingconstraints on the two kinds of patents +is conclusion isconsistent with Khan et al [46]

+e results above indicate that financing constraints notonly inhibit the technological innovation input in emergingindustries but also inhibit the technological innovationoutput of the enterprise in emerging industries and thusHypothesis 1 has been supported

52 8e Heterogeneous Influence of Financing Constraints

521 Analysis by Industry Considering that differentsubindustries in strategic emerging industries have differentcharacteristics of financing constraints and technologicalinnovation the sensitivity of technical innovation to fi-nancing constraints may be heterogeneous We furtheranalyze by industry in order to find out the difference in theimpact of financial constraints on technical innovation ofvarious subindustries in strategic emerging industries Sincethere are many subindustries in the strategic emerging in-dustries only RampD intensity is used as the explained variableto analyze by industry

Table 1 Regression results of the influence of financing constraints on technical innovation

Variable RampDconcentration

RampDintensity

Number of patentapplications

Number of inventionpatents

Number of noninventionpatents

LFC minus0031lowastlowast minus0114lowastlowastlowast minus0024lowast minus0038lowastlowast minus0002(0014) (0032) (0012) (0015) (0014)

Lsize minus0287lowastlowastlowast minus0629lowastlowastlowast 0357lowastlowastlowast 0378lowastlowastlowast 0348lowastlowastlowast(0051) (0119) (0044) (0050) (0058)

LTobinQ minus0009 minus0029 0024lowastlowastlowast 0030lowastlowastlowast 0009(0013) (0029) (0006) (0010) (0010)

LConcentr minus0009lowastlowast minus0025lowastlowastlowast minus0000 0002 0002(0004) (0009) (0003) (0003) (0003)

LSgrowth minus0011 minus0165lowastlowastlowast minus0000 minus0000 0000(0024) (0054) (0000) (0000) (0000)

Age minus0037lowastlowast minus0160lowastlowastlowast 0045lowastlowastlowast 0071lowastlowastlowast 0024lowast(0015) (0036) (0011) (0010) (0014)

LCflow minus0003lowastlowast minus0005lowastlowast minus0000(0002) (0002) (0002)

Constant 9033lowastlowastlowast 17980lowastlowastlowast minus7436lowastlowastlowast minus8479lowastlowastlowast minus7302lowastlowastlowast(1203) (2798) (0922) (1058) (1203)

Time effect Controlled ControlledIndustryeffect Controlled Controlled

Sample size 757 757 628 625 608Notes lowastlowastlowast lowastlowast and lowast mean significant at the significance level of 1 5 and 10 respectively +e figures in parentheses are standard errors the same asbelow Lvariables represent variables with a lag of one period

8 Complexity

Columns 2 to 8 of Table 2 report the effects of financingconstraints in different subindustries in strategic emergingindustries on technological innovation It is found that thereare pronounced industry differences in the impact of fi-nancing constraints on technological innovation Except forthe biological industry and new energy industry the impactof financing constraints on technological innovation in-vestment in the other five industries is uniformly negative Inthe new generation information technology industry ad-vanced equipment manufacturing and new energy vehicleindustry with high RampD intensity the negative impact isstatistically significant but not in the new material industryand energy conservation and environmental protectionindustry with relatively low RampD intensity +is is becausethe higher the RampD intensity is the more the money forRampD investment will be and the stronger the influence offinancing constraints will be In the regression analysis of thebiological industry and the new energy industry the fi-nancing constraintsrsquo coefficient is not negative +e mainreason is that these two industries are industries with rel-atively small financing constraints and are less affected byfinancial factors +erefore financing constraints have noobvious inhibitory effect on technological innovation andthus Hypothesis 2 has been verified

522 Whether It Is a Manufacturing Enterprise We dividethe listed companies in strategic emerging industries intomanufacturing and nonmanufacturing industries accordingto the classification standards of Chinarsquos high-tech indus-tries +e regression results of Table 3 show that the in-hibitory effect of the financing constraints on thetechnological innovation input and output of themanufacturing industry is lower than that of the non-manufacturing industry +e possible reason is that Chinarsquoslabor costs have risen sharply which has seriously affected

the operating profits of enterprises in the labor-intensive andcapital-intensive manufacturing industries +erefore themanufacturing industry is more willing to improve laborproductivity through technological innovation and tech-nological innovation is of greater significance tomanufacturing enterprises Manufacturing companies investmore in RampD and have a longer cycle for capital recoveryOnce the technical innovation project is launched the fundsneed to be continually invested Otherwise the invested costwill become a sunk cost and cannot be recovered +echaracteristic of high adjustment cost of RampD input is moreobvious in the manufacturing industry and manufacturingenterprises will pay more attention to the sustainability ofRampD +erefore technological innovation will not react sosensitively to the capital situation Even if the financingconstraints are intensified the technological innovation inthe manufacturing industry will not be reduced rapidly butneed a long time to adjust However nonmanufacturingindustries are mostly asset-light enterprises which involverelatively less investment in technological innovation withlow RampD intensity and less dependence on technologicalinnovation +erefore in these enterprises technologicalinnovation is more sensitive to the financial situation andthus Hypothesis 3 has been verified

523 Level of the Dependence of Industry External Financing+e companies are divided into two groups according to theexternal financing dependence in the industry to testwhether the inhibitory effect of financing constraints ontechnological innovation is related to the external financingdependence of the industry One group are the companieswith low external financing dependence and the other arethe companies with medium- and high-level external fi-nancing dependence +e grouping results of the sample inTable 4 show that only a few strategic emerging enterprises

Table 2 Regression analysis of different subindustries in strategic emerging industries

VariableNext-generationinformationtechnology

Advancedequipment

manufacturing

Newmaterialindustry

Biologicalindustry

New energyautomobileindustry

Newenergyindustry

Environmentalprotection industry

LFC minus0238lowastlowastlowast minus0084lowast minus0011 0211 minus0126lowast 0178 minus0005(0066) minus0049 (0039) (0170) (0073) (0143) (0041)

Lsize minus0098 minus0586lowastlowastlowast minus0642lowastlowastlowast minus0579 minus0065 minus0077 minus1002lowastlowastlowast(0273) (0161) (0136) (0723) (0211) (0212) (0204)

LTobinQ 0025 0002 0082lowast 0004 0037 1011lowastlowast minus0263lowastlowastlowast(0054) (0060) (0042) (0149) (0134) (0496) (0058)

LConcentr minus0021 minus0025lowastlowast 0008 minus0075 minus0008 0006 0009(0020) (0012) (0009) (0055) (0014) (0013) (0008)

LSgrowth 0009lowastlowastlowast minus0000 0006lowastlowastlowast minus0002 minus0001 0008 minus0001(0002) (0001) (0002) (0007) (0003) (0006) (0001)

Age minus0276lowastlowastlowast minus0096lowast 0020 minus0249 minus0036 0041 minus0071lowast(0073) (0050) (0039) (0179) (0056) (0031) (0040)

Sample size 290 257 95 43 19 14 24Notes according to the Guiding Catalogue of Key Products and Services for Strategic Emerging Industries (2016 Edition) issued by the National Developmentand Reform Commission in 2017 the strategic emerging industries are divided into nine sectors (including related service industries) Considering the smallnumber of samples of digital cultural and creative industries and related service industries they are not separately analyzed by industry

Complexity 9

are in the industries with low-level external financing de-pendence with only 14 samples accounting for 19 of thetotal samples It indicates that most enterprises in thestrategic emerging enterprises are in the industries with highexternal financing dependence +e regression results inTable 4 show that the negative effect of financing constraintson the technical innovation input and output is not sig-nificant in the samples of industries with low external fi-nancing dependence However in the industry withmedium- and high-level external financing dependence theimpact of financing constraints on the technical innovationrsquosinput and output is significant at the 10 significance leveland thus Hypothesis 4 has been verified

53 8e Moderating Effect of the External Environment

531 8e Economic Uncertainty as the Moderator As anexternal factor affecting enterprisesrsquo technological innova-tion economic policy has an impact on the technologicalinnovation activities of enterprises from amacroperspectiveOnce a countryrsquos economic policy changes enterprisesrsquoinnovation activities will inevitably be affected by the policy

Columns 2 and 3 in Table 5 are the regression resultsafter adding the cross terms of economic policy uncertaintyand financing constraints to the regression model It showsthat the negative effect of financing constraints on tech-nological innovation is still significant and the coefficient ofthe cross term is significantly positive indicating thateconomic policy uncertainty alleviates the inhibitory effectof financing constraints on technological innovation Spe-cifically when the uncertainty of the economic policy en-vironment increases the inhibitory effect of financingconstraints on the technological innovation input may bereduced+ere are two main explanations On the one handthe increasing uncertainty of economic policy means thatenterprisesrsquo business environment will change significantly

+is is both an opportunity and a challenge and the survivalof the fittest among enterprises will be presented Only thoseenterprises with high production efficiency and strongcompetitiveness can finally survive in the industry Fur-thermore innovation ability is the key factor for enterprisesto enhance their core competitiveness Enterprises can makeuse of the opportunities brought by the economic policyuncertainty and improve their core competitiveness by in-creasing the input in technological innovation activities so asto boost their chances of winning in the future marketcompetition On the other hand the uncertainty of eco-nomic policies will then spread to the external financingmarket leading to the fluctuation of the capital market andthen affect the external financing of technological innova-tion According to the research of Li and Yang [47] it can beseen that the economic policy uncertainty inhibits generalinvestment activities +erefore when economic policyuncertainty increases ordinary investment activities will notbe active and there will be abundant capital in the capitalmarket However technological innovation was fraught withuncertainty and the future is full of opportunities When theeconomic policy uncertainty increases financing for tech-nological innovation is more likely compared with general

Table 4 +e classification test result of the level of the external financing dependence in the industry

Variable

RampD concentration RampD intensity Number of patent applicationsLow externalfinancing

dependence

Medium- and high-level external

financing dependence

Low externalfinancing

dependence

Medium- and high-level external

financing dependence

Low externalfinancing

dependence

Medium- and high-level external

financing dependence

LFC 0107 minus0034lowastlowast minus0060 minus0116lowastlowastlowast minus0003 minus0021lowast(0113) (0014) (0117) (0033) (0088) (0013)

Allcontrols Controlled Controlled Controlled Controlled Controlled Controlled

Samplesize 14 743 14 743 14 614

Table 5 +e moderating effect of economic policy uncertainty

Variable RampD concentration RampD intensityLFC minus0069lowastlowastlowast (0026) minus0286lowastlowastlowast (0060)Lsize minus0273lowastlowastlowast (0050) minus0592lowastlowastlowast (0120)LTobinQ minus0022 (0029)LConcertr minus0009lowastlowast (0004) minus0025lowastlowastlowast (0009)LSgrowth minus0000 (0000) minus0002lowastlowastlowast (0001)Age minus0037lowastlowast (0015) minus0160lowastlowastlowast (0036)EPUlowastLFC 0000lowast (0000) 0001lowastlowastlowast (0000)EPU 0003lowastlowastlowast (0001) 0013lowastlowastlowast (0002)Constant 8409lowastlowastlowast (1162) 1580lowastlowastlowast (2775)Sample size 757 757

Table 3 +e classification test results of manufacturing enterprises and nonmanufacturing ones

VariableRampD concentration RampD intensity Number of patent applications

Manufacturing Nonmanufacturing Manufacturing Nonmanufacturing Manufacturing Nonmanufacturing

LFC minus0015 minus0094lowastlowast minus0100lowastlowastlowast minus0151lowastlowast minus0021lowast minus0061lowast(0012) (0043) (0035) (0075) (0012) (0034)

All controls Controlled Controlled Controlled Controlled Controlled ControlledSample size 543 214 543 214 484 144

10 Complexity

investment activities +erefore economic policy uncer-tainty can ease the inhibitory effect of financing constraintson technological innovation and thus Hypothesis 5 hasbeen verified

532 8eMarketization as the Moderator Columns 2 and 3in Table 6 are the regression results after adding the crossterms of marketization and financing constraints to theregression model It shows that the coefficient of financingconstraints is significantly negative the coefficient of mar-ketization is significantly positive and the coefficient of across term is significantly positive indicating that themarketization can help to alleviate the inhibitory effect offinancing constraints on technological innovation Specifi-cally when the market environment improves the inhibi-tory effect of financing constraints on technologicalinnovation will be reduced +erefore the more the market-oriented the lower the inhibitory effect of financing con-straints on technological innovation and vice versa In theend the Matthew effect occurs when the strong get strongerand the weak get weaker Under such effect the differencesin technological innovation between regions are constantlywidened +e possible explanation is as followsIn highlymarket-oriented regions there are a sound legal system highinformation transparency a more robust financial systemsound and abundant financial resources Such a perfectinstitutional environment can ease the enterprisersquos financingconstraints provide a proper external financing environ-ment for technological innovation and better escort theenterprisersquos technological innovation which is conducive toencourage enterprises to carry out technological innovationIn regions with less market-oriented it is impossible toprovide a strong institutional guarantee for technologicalinnovation For an enterprise facing financing constraintsthis adverse institutional environment intensifies the in-hibitory effect of financing constraints on technologicalinnovation and is not conducive to the enterprisersquos tech-nological innovation and thus Hypothesis 6 has beenverified

54 Robustness Test

541 Solutions to Endogenous Problems Financial con-straints inhibit enterprisesrsquo technological innovation activ-ities and technological innovation may make financialconstraints of enterprises get more serious thus leading to abidirectional causal relationship between financial con-straints and technical innovation In order to avoid possiblereverse causality all control variables and the explanatoryvariables are delayed by one period Simultaneously in thestudy to avoid the endogenous problems caused by theomitted variables the industry and time fixed effects arecontrolled in the empirical analysis

542 Recalculation of Financing Constraint Index In theempirical analysis we use the FC index to measure the fi-nancing constraints However there are various methods tomeasure the constraints with different emphases In order to

ensure robust conclusions the SA index proposed byHadlock and Pierce [48] is used to remeasure the financingconstraints +at is SA minus0737 Size + 0043 Size2minus004Age +e above empirical process is then repeated tocheck whether the financing constraints have an inhibitoryeffect on technological innovation

+e SA index is generally negative +e greater the ab-solute value of SA is the more serious the financing con-straints faced by the enterprise will be+erefore the SA indexis negatively correlated with the level of financing constraints+e regression results in Table 7 show that the SA index ispositively correlated with technological innovation at thesignificance level of 5 indicating the more serious the fi-nancing constraints is the stronger the inhibitory effect ontechnological innovation activities will be +is is consistentwith the conclusion of the test based on the FC index whichonce again verifies that financing constraints have an inhib-itory effect on technological innovation

543 Recalculation of the Technological Innovation Output+e increment of intangible assets is adopted to remeasurethe technological innovation output following Ju et al [49]Moreover the ratio of the increment of intangible assets tothe total assets at the end of the period is used as the proxyvariable of the technological innovation output to remeasurethe technological innovation output Finally the empiricalresults of the influence of financing constraints on thetechnological innovation output are shown in Table 8

+e results in Table 8 show that financing constraintshave a significant inhibitory effect on the technologicalinnovation output regardless of if the FC index or SA indexis used It is consistent with the conclusion where thenumber of patent applications is adopted as the techno-logical innovation output

544 8e Ordinary Panel Model as the Counting Model+e panel counting model is used to study the influence offinancing constraints on the technical innovation outputwhich shows that financing constraints have a significantinhibitory effect on the technological innovation output Inorder to test the robustness of the results the number ofpatent applications as the explained variable is taken as acontinuous common variable and then the ordinary panelmodel is used for empirical analysis +e empirical resultsare shown in Table 9

As shown in Table 9 the conclusion obtained using theordinary panel model is that the financing constraints sig-nificantly inhibit the technological innovation output After

Table 6 +e moderating effect of the marketization

Variable RampD concentration RampD intensityLFC minus0036lowastlowastlowast (0012) minus0168lowastlowastlowast (0030)Marketization 0838lowastlowastlowast (0266) 1319lowastlowast (0658)MarketizationlowastLFC 0017lowastlowastlowast (0005) 0024lowast (0013)All controls Controlled ControlledSample size 757 757

Complexity 11

dividing the number of patent applications into inventionpatents and noninvention patents we find that the inhibi-tory effect of the financing constraints on the inventionpatents is significantly higher than that of the noninventionpatents which is consistent with the conclusion obtainedusing the negative binomial regression model of panel data

6 Conclusions and Implications

As the world economy enters a stage of innovation compe-tition the decision-making problem of technological innova-tion in emerging industries is a practical problem worthy ofresearch and attention Based on the panel data of the listedcompanies in Chinarsquos strategic emerging industries from 2009to 2019 we adopt the negative binomial regression model ofpanel data and empirically study the technological innovationdecision-making issues of emerging industries from the per-spective of financing constraints Our results show that thefinancial constraints in strategic emerging industries in Chinasignificantly inhibit technological innovation in terms oftechnological innovation inputs and outputsWhat ismore the

inhibitory effect of substantive innovation like the inventionpatent is more pronounced than that of strategic innovationlike the noninvention patent +e influence of financingconstraints on technological innovation varies significantlybetween industries within strategic emerging industriesmanufacturing industries and nonmanufacturing industriesand among industries with different external financing de-pendence However the economic policy uncertainty and themarketization can help to alleviate the inhibitory effect of fi-nancing constraints on technological innovation

Our research conclusions lead to the following policyimplications First of all the government should focus onsolving the financial constraints by speeding up the reformof the financial system Promoting the reform of the financialsupply side improving the efficiency of financial resourceallocation and reducing the financing cost should also beemphasized In addition it is necessary to expand thecoverage of financial services to develop financing platformsand tools exclusive to RampD intensive industries such asstrategic emerging industries and to encourage venturecapital and long-term capital to invest in technological

Table 9 Robustness test results of the ordinary panel model

Variable Number of patent applications Number of invention patents Number of noninvention patentsLFC minus0045lowastlowastlowast (0012) minus0059lowastlowastlowast (0012) minus0028lowastlowast (0013)LTobinQ minus0003 (0008) 0002 (0008) minus0018lowastlowast (0009)LConcertr 0005lowastlowast (0002) 0004 (0003) 0006lowastlowast (0003)LSgrowth minus0000 (0000) minus0000 (0000) minus0000 (0000)Llev 0017lowastlowastlowast (0002) 0017lowastlowastlowast (0002) 0016lowastlowastlowast (0002)Age 0003 (0009) 0012 (0010) minus0004 (0010)LCflow minus0004lowastlowast (0002) minus0005lowastlowastlowast (0002) minus0003 (0004)Sample size 691 691 691

Table 8 Robustness test results of intangible assets increment

Variable FC index SA indexLFC minus0042lowastlowast (0019) 0249lowastlowast (0108)Lsize 0000 (0041)LTobinQ 0039lowastlowast (0019) 0037lowast (0019)LConcentr 0000 (0003) 0002 (0003)LCflow minus0014lowastlowastlowast (0004) minus0012lowastlowastlowast (0001)LSgrowth minus0009 (0042) 0004 (0042)Age minus0039lowastlowastlowast (0008)Ltangibility 0000 (0003)Constant 0508 (1039) 0316 (0593)Sample size 757 757

Table 7 Regression results of the impact of financing constraints on technological innovation through the SA index

Variable RampD concentration RampD intensity Number of patent applicationsLSA 0351lowastlowastlowast (0135) 0995lowastlowastlowast (0316) 0300lowast (0182)LLEV minus0444lowast (0240) minus4102lowastlowastlowast (0549) minus0003 (0002)LTobinQ 0003 (0013) minus0039 (0030) minus0005 (0012)LConcentr minus0010lowastlowast (0004) minus0020lowastlowast (00098) minus0000 (00037)LCflow 0004lowast (0003) minus0004 (0006)Ltangibility 0011lowastlowastlowast (0003) 0002 (0006)LSgrowth minus0011 (0023) minus0118lowastlowast (0053)Constant 2159lowastlowastlowast (0592) 5283lowastlowastlowast (1360) minus9301lowastlowastlowast (1361)Sample size 757 757 628

12 Complexity

innovation projects A technological innovation financingsystem based on the market mechanism guided by gov-ernment investment dominated by enterprises and widelyparticipated by social funds should be established Morefinancial resources can flow to technological innovationprojects and to enterprises with technological innovationactivities

Second at present Chinarsquos economy is in a ldquonew nor-malrdquo that requires innovation In view of the selective effectof financing constraints on innovation activities relevantdepartments should make effort to build a good externalfinancing environment and use effective financial policiesand administrative methods to help enterprises release theirinnovative vitality more fully For example relevant de-partments should pay attention to building a good market-oriented system and increase institutional guarantees forinnovative activities so as to improve the operating con-ditions of enterprises and promote high-quality innovativeactivities When companies face financing constraints thesemeasures that are conducive to improving operating con-ditions will help to stimulate innovation

Finally since the financing constraints have a moreobvious restraining effect on enterprisesrsquo substantive inno-vation of high quality the government should encourageenterprises to increase RampD investment in substantive in-novation [50ndash53] while guiding them to pursue high-qualityinnovation so as to avoid such innovations that emphasizeonly the quantity and speed In addition the governmentalso should continue to increase financial investment inbasic research for making up for the lack of substantiveinnovation of enterprises and aiming to truly improve in-novation ability and quality of the enterprise

Data Availability

+e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

+e authors declare that there are no conflicts of interestregarding the publication of this study

Acknowledgments

+is research was partly supported by the Natural ScienceFoundation of Zhejiang Province (Grant no LQ19G030007)and Zhejiang Gongshang University Online and OfflineHybrid Teaching Reform Project (Grant no1010XJ2919103)

References

[1] H Lei and W Wang ldquo+e impact of network structure oninnovation efficiency an agent-based study in the context ofinnovation networksrdquo Complexity vol 21 no 2 pp 111ndash1222015

[2] W B Arthur and W Polak ldquo+e evolution of technologywithin a simple computer modelrdquo Complexity vol 11 no 5pp 23ndash31 2010

[3] J J Pang G Lin R L Yang et al ldquoPromote continuouseconomic development economic trendsrdquo Economic Dy-namics vol 7 pp 3ndash19 2019

[4] M Cai and S J Liu ldquo+e impact of government subsidies onthe leverage ratio of strategic emerging industriesrdquo Con-temporary Economic Research vol 8 pp 90ndash100 2020

[5] J R Brown S M Fazzari and B C Petersen ldquoFinancinginnovation and growth cash flow external equity and the1990s RampD boomrdquo Journal of Finance vol 64 pp 151ndash1852010

[6] J S Ang Y Cheng and C Wu ldquoDoes enforcement of in-tellectual property rights matter in China evidence fromfinancing and investment choices in the high-tech industryrdquoReview of Economics and Statistics vol 96 no 2 pp 332ndash3482014

[7] S M Fazzari R G Hubbard B C Petersen A S Blinder andJ M Poterba ldquoFinancing constraints and corporate invest-mentrdquo Brookings Papers on Economic Activity vol 1988 no 1206 pages 1988

[8] T Beck and R Levine ldquoIndustry growth and capital alloca-tionrdquo Journal of Financial Economics vol 64 no 2pp 147ndash180 2002

[9] M Gertler and S Gilchrist ldquo+e role of credit market im-perfections in the monetary transmission mechanism argu-ments and evidencerdquo8e Scandinavian Journal of Economicsvol 95 no 1 pp 43ndash64 1993

[10] R G Rajan and L Zingales ldquoFinancial dependence andgrowthrdquo Social Science Electronic Publishing vol 88 no 3pp 559ndash586 1998

[11] G Borisova and J R Brown ldquoRampD sensitivity to asset saleproceeds new evidence on financing constraints and intan-gible investmentrdquo Journal of Banking amp Finance vol 37 no 1pp 159ndash173 2013

[12] Y S Hwang H G Min and S H Han ldquo+e influence offinancial development on RampD activity cross-country evi-dencerdquo Review of Pacific Basin Financial Markets amp Policiesvol 13 no 3 pp 381ndash401 2010

[13] A Hall G Bockett S Taylor M V K Sivamohan andN Clark ldquoWhy research partnerships really matter inno-vation theory institutional arrangements and implications fordeveloping new technology for the poorrdquo World Develop-ment vol 29 no 5 pp 783ndash797 2001

[14] B H Hall ldquoInvestment and research and development at thefirm level does the source of financing matterrdquo NBERWorking Papers 1992

[15] C P Himmelberg and B C Petersen ldquoRampD and internalfinance a panel study of small firms in high-tech industriesrdquo8e Review of Economics and Statistics vol 76 pp 38ndash511994

[16] Z He and M B Wintoki ldquo+e cost of innovation RampD andhigh cash holdings in US firmsrdquo Journal of Corporate Financevol 41 pp 280ndash303 2016

[17] B Mulkay B H Hall and J Mairesse Firm Level Investmentand RampD in France and the United States A comparisonSocial Science Electronic Publishing Berlin Germany 2001

[18] D Harhoff Are there financing constraints for RampD and in-vestment in German manufacturing firms 8e Economics andEconometrics of Innovation Springer Berlin Germany 2000

[19] S Bond and D H V Harhoff ldquoInvestment RampD and fi-nancial constraints in Britain and Germanyrdquo AnnalesdrsquoEconomie et de Statistique vol 7980 pp 433ndash460 2005

[20] C Bloch ldquoRampD investment and internal finance the cash floweffectrdquo Economics of Innovation and New Technology vol 14no 3 pp 213ndash223 2005

Complexity 13

[21] G Scellato ldquoPatents firm size and financial constraints anempirical analysis for a panel of Italian manufacturing firmsrdquoCambridge Journal of Economics vol 31 pp 55ndash76 2007

[22] E Ughetto ldquoDoes internal finance matter for RampD newevidence from a panel of Italian firmsrdquo Cambridge Journal ofEconomics vol 32 no 6 pp 907ndash925 2008

[23] D Czarnitzki and H Hottenrott ldquoRampD investment and fi-nancing constraints of small and medium-sized firmsrdquo SmallBusiness Economics vol 36 no 1 pp 65ndash83 2011

[24] B H Hall ldquo+e financing of research and developmentrdquoOxford Review of Economic Policy vol 18 no 1 pp 35ndash512002

[25] J R Brown and B C Petersen ldquoCash holdings and RampDsmoothingrdquo Journal of Corporate Finance vol 17 no 3pp 694ndash709 2011

[26] D Li ldquoFinancial constraints RampD investment and stockreturnsrdquo Review of Financial Studies vol 24 no 9pp 2974ndash3007 2011

[27] A Mina H Lahr and A Hughes ldquo+e demand and supply ofexternal finance for innovative firmsrdquo Industrial and Cor-porate Change vol 22 no 4 pp 869ndash901 2013

[28] X Gu Y Chen and S Pan ldquoEconomic policy uncertainty andinnovation evidence from listed companies in Chinardquo Reviewof Financial Studies vol 2 pp 109ndash123 2018

[29] F Knight Risk Uncertainty and Profit Houghton MifflinCompany Boston MA USA 1921

[30] W Hao W Wei and J Wen ldquoHow does economic policyuncertainty affect corporate innovationmdashperspective ofmechanism of action of real option theoryrdquo EconomicManagement vol 38 pp 40ndash54 2016

[31] G Fan X Wang and G Ma ldquo+e contribution of Chinarsquosmarketization process to economic growthrdquo Economic Re-search vol 46 no 9 pp 4ndash16 2011

[32] K Z Dai and Y J Liu ldquo+e impact of marketization reformon RampD investment in Chinarsquos high-tech industryrdquo ScienceStudies vol 31 pp 50ndash57 2013

[33] P Li and X Liu ldquo+e impact of market-oriented institutionalchange on Chinarsquos technological progressmdashbased on theperspective of independent R amp D and technology intro-ductionrdquo Economic Perspectives vol 4 pp 42ndash50 2015

[34] K J Arrow ldquoEconomic welfare and the allocation of resourcesfor inventionrdquo 8e Rate and Direction of Inventive Activityvol 12 pp 609ndash626 1962

[35] P Kijkasiwat and P Phuensane ldquoInnovation and firm per-formance the moderating and mediating roles of firm sizeand small and medium enterprise financerdquo Journal of Riskand Financial Management vol 13 no 5 p 97 2020

[36] X N Yuan ldquoManufacturing innovation and service inno-vation comparison and convergencerdquo Finance and TradeResearch vol 20 pp 14ndash19 2009

[37] Y M Zhu and M E Jia ldquoMarketization process financialconstraints and corporate technology innovationrdquo Com-mercial ResearchInnovation Technology vol 1 pp 49ndash562017

[38] C P Wu and D Tang ldquoIntellectual property rights en-forcement corporate innovation and operating performanceevidence from Chinarsquos listed companiesrdquo Economic ResearchJournal vol 51 no 11 pp 125ndash139 2016

[39] B H Hall and D Harhoff ldquoRecent research on the economicsof patentsrdquo Annual Review of Economics vol 4 pp 541ndash5652012

[40] L Owen P Christopher and J Saaa-Requejo ldquoFinancialconstraints and stock returnsrdquo Review of Financial Studiesvol 14 pp 529ndash554 2001

[41] S R Baker N Bloom and S J Davis ldquoMeasuring economicpolicy uncertaintyrdquo 8e Quarterly Journal of Economicsvol 131 no 4 pp 1593ndash1636 2016

[42] J F He and X J Zhang ldquo+e influence of rising labor cost onenterprise innovationrdquo 8e Journal of Quantitative amp Tech-nical Economics vol 45 no 8 pp 56ndash73 2018

[43] W J Li and M N Zheng ldquoIs it substantive innovation orstrategic innovationmdashimpact of macroeconomic policies onmicro-enterprisesrsquo innovationrdquo Economic Research Journalvol 4 pp 60ndash73 2016

[44] C Andrea ldquoFinancing constraints radical versus incrementalinnovation and aggregate productivityrdquo American EconomicJournal Macroeconomics vol 11 no 2 pp 75ndash309 2019

[45] J M Hilbe Modeling Count Data Cambridge UniversityPress Cambridge UK 2014

[46] S U Khan A Shah and M F Rizwan ldquoDo financingconstraints matter for technological and non-technologicalinnovation a (re) examination of developing marketsrdquoEmerging Markets Finance and Trade vol 57 pp 1ndash28 2019

[47] F Y Li and M Z Yang ldquoCan economic policy uncertaintyinfluence corporate investment the empirical research byusing China economic policy uncertainty indexrdquo Journal ofFinancial Research vol 57 no 9 pp 2739ndash2766 2015

[48] C J Hadlock and J R Pierce ldquoNew evidence on measuringfinancial constraints moving beyond the KZ indexrdquo Review ofFinancial Studies vol 23 no 5 pp 1909ndash1940 2010

[49] X S Ju D Lu and Y H Yu ldquoFinancial constraints workingcapital management and the sustainability of firm innova-tionrdquo Economic Research Journal vol 48 pp 4ndash16 2013

[50] J Li J Liang V Shi and Q Wang ldquo+e benefit of manu-facturer encroachment considering consumerrsquos environ-mental awareness and product competitionrdquo Annals ofOperation Research vol 3 pp 1ndash19 2021

[51] J Li F Wang and Y He ldquoElectric vehicle routing problemwith battery swapping considering energy consumption andcarbon emissionsrdquo Sustainability vol 12 no 24 Article ID10537 2020

[52] J Li L Yi V Shi and X Chen ldquoSupplier encroachmentstrategy in the presence of retail strategic inventory cen-tralization or decentralizationrdquo Omega vol 98 Article ID102213 2021

[53] J Li Z Hu V Shi and Q Wang ldquoManufacturerrsquos en-croachment strategy with substitutable green productsrdquo In-ternational Journal of Production Economics vol 235 ArticleID 108102 2021

14 Complexity

technical level represented by utility model patents anddesign patents most of which aim to obtain other benefits+en the influence of financing constraints on the differenttypes of patent applications is studied Columns 4 to 6 ofTable 2 respectively report the impact of financing con-straints on the number of patent applications inventionapplications and noninvention applications In order todetermine whether negative binomial regression is a fixedeffect or a random effect the Hausmann test is carried out+e results show that the p value is less than 001 whichstrongly rejected the negative binomial regression of randomeffect +erefore the fixed-effect negative binomial regres-sion is adopted Hilbe pointed out that in the countingmodel the marginal effect of variables is more significantthan that of the regression coefficient [45] We use themarginal effect to analyze the impact of the unit change ofthe explanatory variable on the explained variable Columns4 and 5 of Table 1 show that in terms of both the number ofpatent applications and the number of invention patentsfinancing constraints inhibit technological innovation out-put at the significance level of 10 Column 6 of Table 1shows that the influence of financing constraints on thenumber of nonpatent applications is negative but not sig-nificant +e above results indicate that therestraint of fi-nancing constraints on the inventionpatent is greater thanthat of noninvention patent mainly because of the differentnature of the two types of patents +e invention patent canimprove the production efficiency of enterprises enableenterprises to obtain competitive advantages in the industryand represent the core competitiveness of enterprisesHowever invention patents usually require a lot of in-vestment and last a long time before they come out

On the contrary the purpose of noninvention patents isto echo the innovation strategy of enterprises and nationalinnovation policies and the companies pay more attentionto the ldquospeedrdquo and ldquoquantityrdquo of innovation Noninventionpatents involve a relatively small investment a relativelyshort duration cycle and a relatively little influence bycapital Due to the essential differences between inventionpatents and noninvention patents enterprises have differentattitudes towards the two kinds of patents which ultimatelyleads to the difference in the degree of influence of financingconstraints on the two kinds of patents +is conclusion isconsistent with Khan et al [46]

+e results above indicate that financing constraints notonly inhibit the technological innovation input in emergingindustries but also inhibit the technological innovationoutput of the enterprise in emerging industries and thusHypothesis 1 has been supported

52 8e Heterogeneous Influence of Financing Constraints

521 Analysis by Industry Considering that differentsubindustries in strategic emerging industries have differentcharacteristics of financing constraints and technologicalinnovation the sensitivity of technical innovation to fi-nancing constraints may be heterogeneous We furtheranalyze by industry in order to find out the difference in theimpact of financial constraints on technical innovation ofvarious subindustries in strategic emerging industries Sincethere are many subindustries in the strategic emerging in-dustries only RampD intensity is used as the explained variableto analyze by industry

Table 1 Regression results of the influence of financing constraints on technical innovation

Variable RampDconcentration

RampDintensity

Number of patentapplications

Number of inventionpatents

Number of noninventionpatents

LFC minus0031lowastlowast minus0114lowastlowastlowast minus0024lowast minus0038lowastlowast minus0002(0014) (0032) (0012) (0015) (0014)

Lsize minus0287lowastlowastlowast minus0629lowastlowastlowast 0357lowastlowastlowast 0378lowastlowastlowast 0348lowastlowastlowast(0051) (0119) (0044) (0050) (0058)

LTobinQ minus0009 minus0029 0024lowastlowastlowast 0030lowastlowastlowast 0009(0013) (0029) (0006) (0010) (0010)

LConcentr minus0009lowastlowast minus0025lowastlowastlowast minus0000 0002 0002(0004) (0009) (0003) (0003) (0003)

LSgrowth minus0011 minus0165lowastlowastlowast minus0000 minus0000 0000(0024) (0054) (0000) (0000) (0000)

Age minus0037lowastlowast minus0160lowastlowastlowast 0045lowastlowastlowast 0071lowastlowastlowast 0024lowast(0015) (0036) (0011) (0010) (0014)

LCflow minus0003lowastlowast minus0005lowastlowast minus0000(0002) (0002) (0002)

Constant 9033lowastlowastlowast 17980lowastlowastlowast minus7436lowastlowastlowast minus8479lowastlowastlowast minus7302lowastlowastlowast(1203) (2798) (0922) (1058) (1203)

Time effect Controlled ControlledIndustryeffect Controlled Controlled

Sample size 757 757 628 625 608Notes lowastlowastlowast lowastlowast and lowast mean significant at the significance level of 1 5 and 10 respectively +e figures in parentheses are standard errors the same asbelow Lvariables represent variables with a lag of one period

8 Complexity

Columns 2 to 8 of Table 2 report the effects of financingconstraints in different subindustries in strategic emergingindustries on technological innovation It is found that thereare pronounced industry differences in the impact of fi-nancing constraints on technological innovation Except forthe biological industry and new energy industry the impactof financing constraints on technological innovation in-vestment in the other five industries is uniformly negative Inthe new generation information technology industry ad-vanced equipment manufacturing and new energy vehicleindustry with high RampD intensity the negative impact isstatistically significant but not in the new material industryand energy conservation and environmental protectionindustry with relatively low RampD intensity +is is becausethe higher the RampD intensity is the more the money forRampD investment will be and the stronger the influence offinancing constraints will be In the regression analysis of thebiological industry and the new energy industry the fi-nancing constraintsrsquo coefficient is not negative +e mainreason is that these two industries are industries with rel-atively small financing constraints and are less affected byfinancial factors +erefore financing constraints have noobvious inhibitory effect on technological innovation andthus Hypothesis 2 has been verified

522 Whether It Is a Manufacturing Enterprise We dividethe listed companies in strategic emerging industries intomanufacturing and nonmanufacturing industries accordingto the classification standards of Chinarsquos high-tech indus-tries +e regression results of Table 3 show that the in-hibitory effect of the financing constraints on thetechnological innovation input and output of themanufacturing industry is lower than that of the non-manufacturing industry +e possible reason is that Chinarsquoslabor costs have risen sharply which has seriously affected

the operating profits of enterprises in the labor-intensive andcapital-intensive manufacturing industries +erefore themanufacturing industry is more willing to improve laborproductivity through technological innovation and tech-nological innovation is of greater significance tomanufacturing enterprises Manufacturing companies investmore in RampD and have a longer cycle for capital recoveryOnce the technical innovation project is launched the fundsneed to be continually invested Otherwise the invested costwill become a sunk cost and cannot be recovered +echaracteristic of high adjustment cost of RampD input is moreobvious in the manufacturing industry and manufacturingenterprises will pay more attention to the sustainability ofRampD +erefore technological innovation will not react sosensitively to the capital situation Even if the financingconstraints are intensified the technological innovation inthe manufacturing industry will not be reduced rapidly butneed a long time to adjust However nonmanufacturingindustries are mostly asset-light enterprises which involverelatively less investment in technological innovation withlow RampD intensity and less dependence on technologicalinnovation +erefore in these enterprises technologicalinnovation is more sensitive to the financial situation andthus Hypothesis 3 has been verified

523 Level of the Dependence of Industry External Financing+e companies are divided into two groups according to theexternal financing dependence in the industry to testwhether the inhibitory effect of financing constraints ontechnological innovation is related to the external financingdependence of the industry One group are the companieswith low external financing dependence and the other arethe companies with medium- and high-level external fi-nancing dependence +e grouping results of the sample inTable 4 show that only a few strategic emerging enterprises

Table 2 Regression analysis of different subindustries in strategic emerging industries

VariableNext-generationinformationtechnology

Advancedequipment

manufacturing

Newmaterialindustry

Biologicalindustry

New energyautomobileindustry

Newenergyindustry

Environmentalprotection industry

LFC minus0238lowastlowastlowast minus0084lowast minus0011 0211 minus0126lowast 0178 minus0005(0066) minus0049 (0039) (0170) (0073) (0143) (0041)

Lsize minus0098 minus0586lowastlowastlowast minus0642lowastlowastlowast minus0579 minus0065 minus0077 minus1002lowastlowastlowast(0273) (0161) (0136) (0723) (0211) (0212) (0204)

LTobinQ 0025 0002 0082lowast 0004 0037 1011lowastlowast minus0263lowastlowastlowast(0054) (0060) (0042) (0149) (0134) (0496) (0058)

LConcentr minus0021 minus0025lowastlowast 0008 minus0075 minus0008 0006 0009(0020) (0012) (0009) (0055) (0014) (0013) (0008)

LSgrowth 0009lowastlowastlowast minus0000 0006lowastlowastlowast minus0002 minus0001 0008 minus0001(0002) (0001) (0002) (0007) (0003) (0006) (0001)

Age minus0276lowastlowastlowast minus0096lowast 0020 minus0249 minus0036 0041 minus0071lowast(0073) (0050) (0039) (0179) (0056) (0031) (0040)

Sample size 290 257 95 43 19 14 24Notes according to the Guiding Catalogue of Key Products and Services for Strategic Emerging Industries (2016 Edition) issued by the National Developmentand Reform Commission in 2017 the strategic emerging industries are divided into nine sectors (including related service industries) Considering the smallnumber of samples of digital cultural and creative industries and related service industries they are not separately analyzed by industry

Complexity 9

are in the industries with low-level external financing de-pendence with only 14 samples accounting for 19 of thetotal samples It indicates that most enterprises in thestrategic emerging enterprises are in the industries with highexternal financing dependence +e regression results inTable 4 show that the negative effect of financing constraintson the technical innovation input and output is not sig-nificant in the samples of industries with low external fi-nancing dependence However in the industry withmedium- and high-level external financing dependence theimpact of financing constraints on the technical innovationrsquosinput and output is significant at the 10 significance leveland thus Hypothesis 4 has been verified

53 8e Moderating Effect of the External Environment

531 8e Economic Uncertainty as the Moderator As anexternal factor affecting enterprisesrsquo technological innova-tion economic policy has an impact on the technologicalinnovation activities of enterprises from amacroperspectiveOnce a countryrsquos economic policy changes enterprisesrsquoinnovation activities will inevitably be affected by the policy

Columns 2 and 3 in Table 5 are the regression resultsafter adding the cross terms of economic policy uncertaintyand financing constraints to the regression model It showsthat the negative effect of financing constraints on tech-nological innovation is still significant and the coefficient ofthe cross term is significantly positive indicating thateconomic policy uncertainty alleviates the inhibitory effectof financing constraints on technological innovation Spe-cifically when the uncertainty of the economic policy en-vironment increases the inhibitory effect of financingconstraints on the technological innovation input may bereduced+ere are two main explanations On the one handthe increasing uncertainty of economic policy means thatenterprisesrsquo business environment will change significantly

+is is both an opportunity and a challenge and the survivalof the fittest among enterprises will be presented Only thoseenterprises with high production efficiency and strongcompetitiveness can finally survive in the industry Fur-thermore innovation ability is the key factor for enterprisesto enhance their core competitiveness Enterprises can makeuse of the opportunities brought by the economic policyuncertainty and improve their core competitiveness by in-creasing the input in technological innovation activities so asto boost their chances of winning in the future marketcompetition On the other hand the uncertainty of eco-nomic policies will then spread to the external financingmarket leading to the fluctuation of the capital market andthen affect the external financing of technological innova-tion According to the research of Li and Yang [47] it can beseen that the economic policy uncertainty inhibits generalinvestment activities +erefore when economic policyuncertainty increases ordinary investment activities will notbe active and there will be abundant capital in the capitalmarket However technological innovation was fraught withuncertainty and the future is full of opportunities When theeconomic policy uncertainty increases financing for tech-nological innovation is more likely compared with general

Table 4 +e classification test result of the level of the external financing dependence in the industry

Variable

RampD concentration RampD intensity Number of patent applicationsLow externalfinancing

dependence

Medium- and high-level external

financing dependence

Low externalfinancing

dependence

Medium- and high-level external

financing dependence

Low externalfinancing

dependence

Medium- and high-level external

financing dependence

LFC 0107 minus0034lowastlowast minus0060 minus0116lowastlowastlowast minus0003 minus0021lowast(0113) (0014) (0117) (0033) (0088) (0013)

Allcontrols Controlled Controlled Controlled Controlled Controlled Controlled

Samplesize 14 743 14 743 14 614

Table 5 +e moderating effect of economic policy uncertainty

Variable RampD concentration RampD intensityLFC minus0069lowastlowastlowast (0026) minus0286lowastlowastlowast (0060)Lsize minus0273lowastlowastlowast (0050) minus0592lowastlowastlowast (0120)LTobinQ minus0022 (0029)LConcertr minus0009lowastlowast (0004) minus0025lowastlowastlowast (0009)LSgrowth minus0000 (0000) minus0002lowastlowastlowast (0001)Age minus0037lowastlowast (0015) minus0160lowastlowastlowast (0036)EPUlowastLFC 0000lowast (0000) 0001lowastlowastlowast (0000)EPU 0003lowastlowastlowast (0001) 0013lowastlowastlowast (0002)Constant 8409lowastlowastlowast (1162) 1580lowastlowastlowast (2775)Sample size 757 757

Table 3 +e classification test results of manufacturing enterprises and nonmanufacturing ones

VariableRampD concentration RampD intensity Number of patent applications

Manufacturing Nonmanufacturing Manufacturing Nonmanufacturing Manufacturing Nonmanufacturing

LFC minus0015 minus0094lowastlowast minus0100lowastlowastlowast minus0151lowastlowast minus0021lowast minus0061lowast(0012) (0043) (0035) (0075) (0012) (0034)

All controls Controlled Controlled Controlled Controlled Controlled ControlledSample size 543 214 543 214 484 144

10 Complexity

investment activities +erefore economic policy uncer-tainty can ease the inhibitory effect of financing constraintson technological innovation and thus Hypothesis 5 hasbeen verified

532 8eMarketization as the Moderator Columns 2 and 3in Table 6 are the regression results after adding the crossterms of marketization and financing constraints to theregression model It shows that the coefficient of financingconstraints is significantly negative the coefficient of mar-ketization is significantly positive and the coefficient of across term is significantly positive indicating that themarketization can help to alleviate the inhibitory effect offinancing constraints on technological innovation Specifi-cally when the market environment improves the inhibi-tory effect of financing constraints on technologicalinnovation will be reduced +erefore the more the market-oriented the lower the inhibitory effect of financing con-straints on technological innovation and vice versa In theend the Matthew effect occurs when the strong get strongerand the weak get weaker Under such effect the differencesin technological innovation between regions are constantlywidened +e possible explanation is as followsIn highlymarket-oriented regions there are a sound legal system highinformation transparency a more robust financial systemsound and abundant financial resources Such a perfectinstitutional environment can ease the enterprisersquos financingconstraints provide a proper external financing environ-ment for technological innovation and better escort theenterprisersquos technological innovation which is conducive toencourage enterprises to carry out technological innovationIn regions with less market-oriented it is impossible toprovide a strong institutional guarantee for technologicalinnovation For an enterprise facing financing constraintsthis adverse institutional environment intensifies the in-hibitory effect of financing constraints on technologicalinnovation and is not conducive to the enterprisersquos tech-nological innovation and thus Hypothesis 6 has beenverified

54 Robustness Test

541 Solutions to Endogenous Problems Financial con-straints inhibit enterprisesrsquo technological innovation activ-ities and technological innovation may make financialconstraints of enterprises get more serious thus leading to abidirectional causal relationship between financial con-straints and technical innovation In order to avoid possiblereverse causality all control variables and the explanatoryvariables are delayed by one period Simultaneously in thestudy to avoid the endogenous problems caused by theomitted variables the industry and time fixed effects arecontrolled in the empirical analysis

542 Recalculation of Financing Constraint Index In theempirical analysis we use the FC index to measure the fi-nancing constraints However there are various methods tomeasure the constraints with different emphases In order to

ensure robust conclusions the SA index proposed byHadlock and Pierce [48] is used to remeasure the financingconstraints +at is SA minus0737 Size + 0043 Size2minus004Age +e above empirical process is then repeated tocheck whether the financing constraints have an inhibitoryeffect on technological innovation

+e SA index is generally negative +e greater the ab-solute value of SA is the more serious the financing con-straints faced by the enterprise will be+erefore the SA indexis negatively correlated with the level of financing constraints+e regression results in Table 7 show that the SA index ispositively correlated with technological innovation at thesignificance level of 5 indicating the more serious the fi-nancing constraints is the stronger the inhibitory effect ontechnological innovation activities will be +is is consistentwith the conclusion of the test based on the FC index whichonce again verifies that financing constraints have an inhib-itory effect on technological innovation

543 Recalculation of the Technological Innovation Output+e increment of intangible assets is adopted to remeasurethe technological innovation output following Ju et al [49]Moreover the ratio of the increment of intangible assets tothe total assets at the end of the period is used as the proxyvariable of the technological innovation output to remeasurethe technological innovation output Finally the empiricalresults of the influence of financing constraints on thetechnological innovation output are shown in Table 8

+e results in Table 8 show that financing constraintshave a significant inhibitory effect on the technologicalinnovation output regardless of if the FC index or SA indexis used It is consistent with the conclusion where thenumber of patent applications is adopted as the techno-logical innovation output

544 8e Ordinary Panel Model as the Counting Model+e panel counting model is used to study the influence offinancing constraints on the technical innovation outputwhich shows that financing constraints have a significantinhibitory effect on the technological innovation output Inorder to test the robustness of the results the number ofpatent applications as the explained variable is taken as acontinuous common variable and then the ordinary panelmodel is used for empirical analysis +e empirical resultsare shown in Table 9

As shown in Table 9 the conclusion obtained using theordinary panel model is that the financing constraints sig-nificantly inhibit the technological innovation output After

Table 6 +e moderating effect of the marketization

Variable RampD concentration RampD intensityLFC minus0036lowastlowastlowast (0012) minus0168lowastlowastlowast (0030)Marketization 0838lowastlowastlowast (0266) 1319lowastlowast (0658)MarketizationlowastLFC 0017lowastlowastlowast (0005) 0024lowast (0013)All controls Controlled ControlledSample size 757 757

Complexity 11

dividing the number of patent applications into inventionpatents and noninvention patents we find that the inhibi-tory effect of the financing constraints on the inventionpatents is significantly higher than that of the noninventionpatents which is consistent with the conclusion obtainedusing the negative binomial regression model of panel data

6 Conclusions and Implications

As the world economy enters a stage of innovation compe-tition the decision-making problem of technological innova-tion in emerging industries is a practical problem worthy ofresearch and attention Based on the panel data of the listedcompanies in Chinarsquos strategic emerging industries from 2009to 2019 we adopt the negative binomial regression model ofpanel data and empirically study the technological innovationdecision-making issues of emerging industries from the per-spective of financing constraints Our results show that thefinancial constraints in strategic emerging industries in Chinasignificantly inhibit technological innovation in terms oftechnological innovation inputs and outputsWhat ismore the

inhibitory effect of substantive innovation like the inventionpatent is more pronounced than that of strategic innovationlike the noninvention patent +e influence of financingconstraints on technological innovation varies significantlybetween industries within strategic emerging industriesmanufacturing industries and nonmanufacturing industriesand among industries with different external financing de-pendence However the economic policy uncertainty and themarketization can help to alleviate the inhibitory effect of fi-nancing constraints on technological innovation

Our research conclusions lead to the following policyimplications First of all the government should focus onsolving the financial constraints by speeding up the reformof the financial system Promoting the reform of the financialsupply side improving the efficiency of financial resourceallocation and reducing the financing cost should also beemphasized In addition it is necessary to expand thecoverage of financial services to develop financing platformsand tools exclusive to RampD intensive industries such asstrategic emerging industries and to encourage venturecapital and long-term capital to invest in technological

Table 9 Robustness test results of the ordinary panel model

Variable Number of patent applications Number of invention patents Number of noninvention patentsLFC minus0045lowastlowastlowast (0012) minus0059lowastlowastlowast (0012) minus0028lowastlowast (0013)LTobinQ minus0003 (0008) 0002 (0008) minus0018lowastlowast (0009)LConcertr 0005lowastlowast (0002) 0004 (0003) 0006lowastlowast (0003)LSgrowth minus0000 (0000) minus0000 (0000) minus0000 (0000)Llev 0017lowastlowastlowast (0002) 0017lowastlowastlowast (0002) 0016lowastlowastlowast (0002)Age 0003 (0009) 0012 (0010) minus0004 (0010)LCflow minus0004lowastlowast (0002) minus0005lowastlowastlowast (0002) minus0003 (0004)Sample size 691 691 691

Table 8 Robustness test results of intangible assets increment

Variable FC index SA indexLFC minus0042lowastlowast (0019) 0249lowastlowast (0108)Lsize 0000 (0041)LTobinQ 0039lowastlowast (0019) 0037lowast (0019)LConcentr 0000 (0003) 0002 (0003)LCflow minus0014lowastlowastlowast (0004) minus0012lowastlowastlowast (0001)LSgrowth minus0009 (0042) 0004 (0042)Age minus0039lowastlowastlowast (0008)Ltangibility 0000 (0003)Constant 0508 (1039) 0316 (0593)Sample size 757 757

Table 7 Regression results of the impact of financing constraints on technological innovation through the SA index

Variable RampD concentration RampD intensity Number of patent applicationsLSA 0351lowastlowastlowast (0135) 0995lowastlowastlowast (0316) 0300lowast (0182)LLEV minus0444lowast (0240) minus4102lowastlowastlowast (0549) minus0003 (0002)LTobinQ 0003 (0013) minus0039 (0030) minus0005 (0012)LConcentr minus0010lowastlowast (0004) minus0020lowastlowast (00098) minus0000 (00037)LCflow 0004lowast (0003) minus0004 (0006)Ltangibility 0011lowastlowastlowast (0003) 0002 (0006)LSgrowth minus0011 (0023) minus0118lowastlowast (0053)Constant 2159lowastlowastlowast (0592) 5283lowastlowastlowast (1360) minus9301lowastlowastlowast (1361)Sample size 757 757 628

12 Complexity

innovation projects A technological innovation financingsystem based on the market mechanism guided by gov-ernment investment dominated by enterprises and widelyparticipated by social funds should be established Morefinancial resources can flow to technological innovationprojects and to enterprises with technological innovationactivities

Second at present Chinarsquos economy is in a ldquonew nor-malrdquo that requires innovation In view of the selective effectof financing constraints on innovation activities relevantdepartments should make effort to build a good externalfinancing environment and use effective financial policiesand administrative methods to help enterprises release theirinnovative vitality more fully For example relevant de-partments should pay attention to building a good market-oriented system and increase institutional guarantees forinnovative activities so as to improve the operating con-ditions of enterprises and promote high-quality innovativeactivities When companies face financing constraints thesemeasures that are conducive to improving operating con-ditions will help to stimulate innovation

Finally since the financing constraints have a moreobvious restraining effect on enterprisesrsquo substantive inno-vation of high quality the government should encourageenterprises to increase RampD investment in substantive in-novation [50ndash53] while guiding them to pursue high-qualityinnovation so as to avoid such innovations that emphasizeonly the quantity and speed In addition the governmentalso should continue to increase financial investment inbasic research for making up for the lack of substantiveinnovation of enterprises and aiming to truly improve in-novation ability and quality of the enterprise

Data Availability

+e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

+e authors declare that there are no conflicts of interestregarding the publication of this study

Acknowledgments

+is research was partly supported by the Natural ScienceFoundation of Zhejiang Province (Grant no LQ19G030007)and Zhejiang Gongshang University Online and OfflineHybrid Teaching Reform Project (Grant no1010XJ2919103)

References

[1] H Lei and W Wang ldquo+e impact of network structure oninnovation efficiency an agent-based study in the context ofinnovation networksrdquo Complexity vol 21 no 2 pp 111ndash1222015

[2] W B Arthur and W Polak ldquo+e evolution of technologywithin a simple computer modelrdquo Complexity vol 11 no 5pp 23ndash31 2010

[3] J J Pang G Lin R L Yang et al ldquoPromote continuouseconomic development economic trendsrdquo Economic Dy-namics vol 7 pp 3ndash19 2019

[4] M Cai and S J Liu ldquo+e impact of government subsidies onthe leverage ratio of strategic emerging industriesrdquo Con-temporary Economic Research vol 8 pp 90ndash100 2020

[5] J R Brown S M Fazzari and B C Petersen ldquoFinancinginnovation and growth cash flow external equity and the1990s RampD boomrdquo Journal of Finance vol 64 pp 151ndash1852010

[6] J S Ang Y Cheng and C Wu ldquoDoes enforcement of in-tellectual property rights matter in China evidence fromfinancing and investment choices in the high-tech industryrdquoReview of Economics and Statistics vol 96 no 2 pp 332ndash3482014

[7] S M Fazzari R G Hubbard B C Petersen A S Blinder andJ M Poterba ldquoFinancing constraints and corporate invest-mentrdquo Brookings Papers on Economic Activity vol 1988 no 1206 pages 1988

[8] T Beck and R Levine ldquoIndustry growth and capital alloca-tionrdquo Journal of Financial Economics vol 64 no 2pp 147ndash180 2002

[9] M Gertler and S Gilchrist ldquo+e role of credit market im-perfections in the monetary transmission mechanism argu-ments and evidencerdquo8e Scandinavian Journal of Economicsvol 95 no 1 pp 43ndash64 1993

[10] R G Rajan and L Zingales ldquoFinancial dependence andgrowthrdquo Social Science Electronic Publishing vol 88 no 3pp 559ndash586 1998

[11] G Borisova and J R Brown ldquoRampD sensitivity to asset saleproceeds new evidence on financing constraints and intan-gible investmentrdquo Journal of Banking amp Finance vol 37 no 1pp 159ndash173 2013

[12] Y S Hwang H G Min and S H Han ldquo+e influence offinancial development on RampD activity cross-country evi-dencerdquo Review of Pacific Basin Financial Markets amp Policiesvol 13 no 3 pp 381ndash401 2010

[13] A Hall G Bockett S Taylor M V K Sivamohan andN Clark ldquoWhy research partnerships really matter inno-vation theory institutional arrangements and implications fordeveloping new technology for the poorrdquo World Develop-ment vol 29 no 5 pp 783ndash797 2001

[14] B H Hall ldquoInvestment and research and development at thefirm level does the source of financing matterrdquo NBERWorking Papers 1992

[15] C P Himmelberg and B C Petersen ldquoRampD and internalfinance a panel study of small firms in high-tech industriesrdquo8e Review of Economics and Statistics vol 76 pp 38ndash511994

[16] Z He and M B Wintoki ldquo+e cost of innovation RampD andhigh cash holdings in US firmsrdquo Journal of Corporate Financevol 41 pp 280ndash303 2016

[17] B Mulkay B H Hall and J Mairesse Firm Level Investmentand RampD in France and the United States A comparisonSocial Science Electronic Publishing Berlin Germany 2001

[18] D Harhoff Are there financing constraints for RampD and in-vestment in German manufacturing firms 8e Economics andEconometrics of Innovation Springer Berlin Germany 2000

[19] S Bond and D H V Harhoff ldquoInvestment RampD and fi-nancial constraints in Britain and Germanyrdquo AnnalesdrsquoEconomie et de Statistique vol 7980 pp 433ndash460 2005

[20] C Bloch ldquoRampD investment and internal finance the cash floweffectrdquo Economics of Innovation and New Technology vol 14no 3 pp 213ndash223 2005

Complexity 13

[21] G Scellato ldquoPatents firm size and financial constraints anempirical analysis for a panel of Italian manufacturing firmsrdquoCambridge Journal of Economics vol 31 pp 55ndash76 2007

[22] E Ughetto ldquoDoes internal finance matter for RampD newevidence from a panel of Italian firmsrdquo Cambridge Journal ofEconomics vol 32 no 6 pp 907ndash925 2008

[23] D Czarnitzki and H Hottenrott ldquoRampD investment and fi-nancing constraints of small and medium-sized firmsrdquo SmallBusiness Economics vol 36 no 1 pp 65ndash83 2011

[24] B H Hall ldquo+e financing of research and developmentrdquoOxford Review of Economic Policy vol 18 no 1 pp 35ndash512002

[25] J R Brown and B C Petersen ldquoCash holdings and RampDsmoothingrdquo Journal of Corporate Finance vol 17 no 3pp 694ndash709 2011

[26] D Li ldquoFinancial constraints RampD investment and stockreturnsrdquo Review of Financial Studies vol 24 no 9pp 2974ndash3007 2011

[27] A Mina H Lahr and A Hughes ldquo+e demand and supply ofexternal finance for innovative firmsrdquo Industrial and Cor-porate Change vol 22 no 4 pp 869ndash901 2013

[28] X Gu Y Chen and S Pan ldquoEconomic policy uncertainty andinnovation evidence from listed companies in Chinardquo Reviewof Financial Studies vol 2 pp 109ndash123 2018

[29] F Knight Risk Uncertainty and Profit Houghton MifflinCompany Boston MA USA 1921

[30] W Hao W Wei and J Wen ldquoHow does economic policyuncertainty affect corporate innovationmdashperspective ofmechanism of action of real option theoryrdquo EconomicManagement vol 38 pp 40ndash54 2016

[31] G Fan X Wang and G Ma ldquo+e contribution of Chinarsquosmarketization process to economic growthrdquo Economic Re-search vol 46 no 9 pp 4ndash16 2011

[32] K Z Dai and Y J Liu ldquo+e impact of marketization reformon RampD investment in Chinarsquos high-tech industryrdquo ScienceStudies vol 31 pp 50ndash57 2013

[33] P Li and X Liu ldquo+e impact of market-oriented institutionalchange on Chinarsquos technological progressmdashbased on theperspective of independent R amp D and technology intro-ductionrdquo Economic Perspectives vol 4 pp 42ndash50 2015

[34] K J Arrow ldquoEconomic welfare and the allocation of resourcesfor inventionrdquo 8e Rate and Direction of Inventive Activityvol 12 pp 609ndash626 1962

[35] P Kijkasiwat and P Phuensane ldquoInnovation and firm per-formance the moderating and mediating roles of firm sizeand small and medium enterprise financerdquo Journal of Riskand Financial Management vol 13 no 5 p 97 2020

[36] X N Yuan ldquoManufacturing innovation and service inno-vation comparison and convergencerdquo Finance and TradeResearch vol 20 pp 14ndash19 2009

[37] Y M Zhu and M E Jia ldquoMarketization process financialconstraints and corporate technology innovationrdquo Com-mercial ResearchInnovation Technology vol 1 pp 49ndash562017

[38] C P Wu and D Tang ldquoIntellectual property rights en-forcement corporate innovation and operating performanceevidence from Chinarsquos listed companiesrdquo Economic ResearchJournal vol 51 no 11 pp 125ndash139 2016

[39] B H Hall and D Harhoff ldquoRecent research on the economicsof patentsrdquo Annual Review of Economics vol 4 pp 541ndash5652012

[40] L Owen P Christopher and J Saaa-Requejo ldquoFinancialconstraints and stock returnsrdquo Review of Financial Studiesvol 14 pp 529ndash554 2001

[41] S R Baker N Bloom and S J Davis ldquoMeasuring economicpolicy uncertaintyrdquo 8e Quarterly Journal of Economicsvol 131 no 4 pp 1593ndash1636 2016

[42] J F He and X J Zhang ldquo+e influence of rising labor cost onenterprise innovationrdquo 8e Journal of Quantitative amp Tech-nical Economics vol 45 no 8 pp 56ndash73 2018

[43] W J Li and M N Zheng ldquoIs it substantive innovation orstrategic innovationmdashimpact of macroeconomic policies onmicro-enterprisesrsquo innovationrdquo Economic Research Journalvol 4 pp 60ndash73 2016

[44] C Andrea ldquoFinancing constraints radical versus incrementalinnovation and aggregate productivityrdquo American EconomicJournal Macroeconomics vol 11 no 2 pp 75ndash309 2019

[45] J M Hilbe Modeling Count Data Cambridge UniversityPress Cambridge UK 2014

[46] S U Khan A Shah and M F Rizwan ldquoDo financingconstraints matter for technological and non-technologicalinnovation a (re) examination of developing marketsrdquoEmerging Markets Finance and Trade vol 57 pp 1ndash28 2019

[47] F Y Li and M Z Yang ldquoCan economic policy uncertaintyinfluence corporate investment the empirical research byusing China economic policy uncertainty indexrdquo Journal ofFinancial Research vol 57 no 9 pp 2739ndash2766 2015

[48] C J Hadlock and J R Pierce ldquoNew evidence on measuringfinancial constraints moving beyond the KZ indexrdquo Review ofFinancial Studies vol 23 no 5 pp 1909ndash1940 2010

[49] X S Ju D Lu and Y H Yu ldquoFinancial constraints workingcapital management and the sustainability of firm innova-tionrdquo Economic Research Journal vol 48 pp 4ndash16 2013

[50] J Li J Liang V Shi and Q Wang ldquo+e benefit of manu-facturer encroachment considering consumerrsquos environ-mental awareness and product competitionrdquo Annals ofOperation Research vol 3 pp 1ndash19 2021

[51] J Li F Wang and Y He ldquoElectric vehicle routing problemwith battery swapping considering energy consumption andcarbon emissionsrdquo Sustainability vol 12 no 24 Article ID10537 2020

[52] J Li L Yi V Shi and X Chen ldquoSupplier encroachmentstrategy in the presence of retail strategic inventory cen-tralization or decentralizationrdquo Omega vol 98 Article ID102213 2021

[53] J Li Z Hu V Shi and Q Wang ldquoManufacturerrsquos en-croachment strategy with substitutable green productsrdquo In-ternational Journal of Production Economics vol 235 ArticleID 108102 2021

14 Complexity

Columns 2 to 8 of Table 2 report the effects of financingconstraints in different subindustries in strategic emergingindustries on technological innovation It is found that thereare pronounced industry differences in the impact of fi-nancing constraints on technological innovation Except forthe biological industry and new energy industry the impactof financing constraints on technological innovation in-vestment in the other five industries is uniformly negative Inthe new generation information technology industry ad-vanced equipment manufacturing and new energy vehicleindustry with high RampD intensity the negative impact isstatistically significant but not in the new material industryand energy conservation and environmental protectionindustry with relatively low RampD intensity +is is becausethe higher the RampD intensity is the more the money forRampD investment will be and the stronger the influence offinancing constraints will be In the regression analysis of thebiological industry and the new energy industry the fi-nancing constraintsrsquo coefficient is not negative +e mainreason is that these two industries are industries with rel-atively small financing constraints and are less affected byfinancial factors +erefore financing constraints have noobvious inhibitory effect on technological innovation andthus Hypothesis 2 has been verified

522 Whether It Is a Manufacturing Enterprise We dividethe listed companies in strategic emerging industries intomanufacturing and nonmanufacturing industries accordingto the classification standards of Chinarsquos high-tech indus-tries +e regression results of Table 3 show that the in-hibitory effect of the financing constraints on thetechnological innovation input and output of themanufacturing industry is lower than that of the non-manufacturing industry +e possible reason is that Chinarsquoslabor costs have risen sharply which has seriously affected

the operating profits of enterprises in the labor-intensive andcapital-intensive manufacturing industries +erefore themanufacturing industry is more willing to improve laborproductivity through technological innovation and tech-nological innovation is of greater significance tomanufacturing enterprises Manufacturing companies investmore in RampD and have a longer cycle for capital recoveryOnce the technical innovation project is launched the fundsneed to be continually invested Otherwise the invested costwill become a sunk cost and cannot be recovered +echaracteristic of high adjustment cost of RampD input is moreobvious in the manufacturing industry and manufacturingenterprises will pay more attention to the sustainability ofRampD +erefore technological innovation will not react sosensitively to the capital situation Even if the financingconstraints are intensified the technological innovation inthe manufacturing industry will not be reduced rapidly butneed a long time to adjust However nonmanufacturingindustries are mostly asset-light enterprises which involverelatively less investment in technological innovation withlow RampD intensity and less dependence on technologicalinnovation +erefore in these enterprises technologicalinnovation is more sensitive to the financial situation andthus Hypothesis 3 has been verified

523 Level of the Dependence of Industry External Financing+e companies are divided into two groups according to theexternal financing dependence in the industry to testwhether the inhibitory effect of financing constraints ontechnological innovation is related to the external financingdependence of the industry One group are the companieswith low external financing dependence and the other arethe companies with medium- and high-level external fi-nancing dependence +e grouping results of the sample inTable 4 show that only a few strategic emerging enterprises

Table 2 Regression analysis of different subindustries in strategic emerging industries

VariableNext-generationinformationtechnology

Advancedequipment

manufacturing

Newmaterialindustry

Biologicalindustry

New energyautomobileindustry

Newenergyindustry

Environmentalprotection industry

LFC minus0238lowastlowastlowast minus0084lowast minus0011 0211 minus0126lowast 0178 minus0005(0066) minus0049 (0039) (0170) (0073) (0143) (0041)

Lsize minus0098 minus0586lowastlowastlowast minus0642lowastlowastlowast minus0579 minus0065 minus0077 minus1002lowastlowastlowast(0273) (0161) (0136) (0723) (0211) (0212) (0204)

LTobinQ 0025 0002 0082lowast 0004 0037 1011lowastlowast minus0263lowastlowastlowast(0054) (0060) (0042) (0149) (0134) (0496) (0058)

LConcentr minus0021 minus0025lowastlowast 0008 minus0075 minus0008 0006 0009(0020) (0012) (0009) (0055) (0014) (0013) (0008)

LSgrowth 0009lowastlowastlowast minus0000 0006lowastlowastlowast minus0002 minus0001 0008 minus0001(0002) (0001) (0002) (0007) (0003) (0006) (0001)

Age minus0276lowastlowastlowast minus0096lowast 0020 minus0249 minus0036 0041 minus0071lowast(0073) (0050) (0039) (0179) (0056) (0031) (0040)

Sample size 290 257 95 43 19 14 24Notes according to the Guiding Catalogue of Key Products and Services for Strategic Emerging Industries (2016 Edition) issued by the National Developmentand Reform Commission in 2017 the strategic emerging industries are divided into nine sectors (including related service industries) Considering the smallnumber of samples of digital cultural and creative industries and related service industries they are not separately analyzed by industry

Complexity 9

are in the industries with low-level external financing de-pendence with only 14 samples accounting for 19 of thetotal samples It indicates that most enterprises in thestrategic emerging enterprises are in the industries with highexternal financing dependence +e regression results inTable 4 show that the negative effect of financing constraintson the technical innovation input and output is not sig-nificant in the samples of industries with low external fi-nancing dependence However in the industry withmedium- and high-level external financing dependence theimpact of financing constraints on the technical innovationrsquosinput and output is significant at the 10 significance leveland thus Hypothesis 4 has been verified

53 8e Moderating Effect of the External Environment

531 8e Economic Uncertainty as the Moderator As anexternal factor affecting enterprisesrsquo technological innova-tion economic policy has an impact on the technologicalinnovation activities of enterprises from amacroperspectiveOnce a countryrsquos economic policy changes enterprisesrsquoinnovation activities will inevitably be affected by the policy

Columns 2 and 3 in Table 5 are the regression resultsafter adding the cross terms of economic policy uncertaintyand financing constraints to the regression model It showsthat the negative effect of financing constraints on tech-nological innovation is still significant and the coefficient ofthe cross term is significantly positive indicating thateconomic policy uncertainty alleviates the inhibitory effectof financing constraints on technological innovation Spe-cifically when the uncertainty of the economic policy en-vironment increases the inhibitory effect of financingconstraints on the technological innovation input may bereduced+ere are two main explanations On the one handthe increasing uncertainty of economic policy means thatenterprisesrsquo business environment will change significantly

+is is both an opportunity and a challenge and the survivalof the fittest among enterprises will be presented Only thoseenterprises with high production efficiency and strongcompetitiveness can finally survive in the industry Fur-thermore innovation ability is the key factor for enterprisesto enhance their core competitiveness Enterprises can makeuse of the opportunities brought by the economic policyuncertainty and improve their core competitiveness by in-creasing the input in technological innovation activities so asto boost their chances of winning in the future marketcompetition On the other hand the uncertainty of eco-nomic policies will then spread to the external financingmarket leading to the fluctuation of the capital market andthen affect the external financing of technological innova-tion According to the research of Li and Yang [47] it can beseen that the economic policy uncertainty inhibits generalinvestment activities +erefore when economic policyuncertainty increases ordinary investment activities will notbe active and there will be abundant capital in the capitalmarket However technological innovation was fraught withuncertainty and the future is full of opportunities When theeconomic policy uncertainty increases financing for tech-nological innovation is more likely compared with general

Table 4 +e classification test result of the level of the external financing dependence in the industry

Variable

RampD concentration RampD intensity Number of patent applicationsLow externalfinancing

dependence

Medium- and high-level external

financing dependence

Low externalfinancing

dependence

Medium- and high-level external

financing dependence

Low externalfinancing

dependence

Medium- and high-level external

financing dependence

LFC 0107 minus0034lowastlowast minus0060 minus0116lowastlowastlowast minus0003 minus0021lowast(0113) (0014) (0117) (0033) (0088) (0013)

Allcontrols Controlled Controlled Controlled Controlled Controlled Controlled

Samplesize 14 743 14 743 14 614

Table 5 +e moderating effect of economic policy uncertainty

Variable RampD concentration RampD intensityLFC minus0069lowastlowastlowast (0026) minus0286lowastlowastlowast (0060)Lsize minus0273lowastlowastlowast (0050) minus0592lowastlowastlowast (0120)LTobinQ minus0022 (0029)LConcertr minus0009lowastlowast (0004) minus0025lowastlowastlowast (0009)LSgrowth minus0000 (0000) minus0002lowastlowastlowast (0001)Age minus0037lowastlowast (0015) minus0160lowastlowastlowast (0036)EPUlowastLFC 0000lowast (0000) 0001lowastlowastlowast (0000)EPU 0003lowastlowastlowast (0001) 0013lowastlowastlowast (0002)Constant 8409lowastlowastlowast (1162) 1580lowastlowastlowast (2775)Sample size 757 757

Table 3 +e classification test results of manufacturing enterprises and nonmanufacturing ones

VariableRampD concentration RampD intensity Number of patent applications

Manufacturing Nonmanufacturing Manufacturing Nonmanufacturing Manufacturing Nonmanufacturing

LFC minus0015 minus0094lowastlowast minus0100lowastlowastlowast minus0151lowastlowast minus0021lowast minus0061lowast(0012) (0043) (0035) (0075) (0012) (0034)

All controls Controlled Controlled Controlled Controlled Controlled ControlledSample size 543 214 543 214 484 144

10 Complexity

investment activities +erefore economic policy uncer-tainty can ease the inhibitory effect of financing constraintson technological innovation and thus Hypothesis 5 hasbeen verified

532 8eMarketization as the Moderator Columns 2 and 3in Table 6 are the regression results after adding the crossterms of marketization and financing constraints to theregression model It shows that the coefficient of financingconstraints is significantly negative the coefficient of mar-ketization is significantly positive and the coefficient of across term is significantly positive indicating that themarketization can help to alleviate the inhibitory effect offinancing constraints on technological innovation Specifi-cally when the market environment improves the inhibi-tory effect of financing constraints on technologicalinnovation will be reduced +erefore the more the market-oriented the lower the inhibitory effect of financing con-straints on technological innovation and vice versa In theend the Matthew effect occurs when the strong get strongerand the weak get weaker Under such effect the differencesin technological innovation between regions are constantlywidened +e possible explanation is as followsIn highlymarket-oriented regions there are a sound legal system highinformation transparency a more robust financial systemsound and abundant financial resources Such a perfectinstitutional environment can ease the enterprisersquos financingconstraints provide a proper external financing environ-ment for technological innovation and better escort theenterprisersquos technological innovation which is conducive toencourage enterprises to carry out technological innovationIn regions with less market-oriented it is impossible toprovide a strong institutional guarantee for technologicalinnovation For an enterprise facing financing constraintsthis adverse institutional environment intensifies the in-hibitory effect of financing constraints on technologicalinnovation and is not conducive to the enterprisersquos tech-nological innovation and thus Hypothesis 6 has beenverified

54 Robustness Test

541 Solutions to Endogenous Problems Financial con-straints inhibit enterprisesrsquo technological innovation activ-ities and technological innovation may make financialconstraints of enterprises get more serious thus leading to abidirectional causal relationship between financial con-straints and technical innovation In order to avoid possiblereverse causality all control variables and the explanatoryvariables are delayed by one period Simultaneously in thestudy to avoid the endogenous problems caused by theomitted variables the industry and time fixed effects arecontrolled in the empirical analysis

542 Recalculation of Financing Constraint Index In theempirical analysis we use the FC index to measure the fi-nancing constraints However there are various methods tomeasure the constraints with different emphases In order to

ensure robust conclusions the SA index proposed byHadlock and Pierce [48] is used to remeasure the financingconstraints +at is SA minus0737 Size + 0043 Size2minus004Age +e above empirical process is then repeated tocheck whether the financing constraints have an inhibitoryeffect on technological innovation

+e SA index is generally negative +e greater the ab-solute value of SA is the more serious the financing con-straints faced by the enterprise will be+erefore the SA indexis negatively correlated with the level of financing constraints+e regression results in Table 7 show that the SA index ispositively correlated with technological innovation at thesignificance level of 5 indicating the more serious the fi-nancing constraints is the stronger the inhibitory effect ontechnological innovation activities will be +is is consistentwith the conclusion of the test based on the FC index whichonce again verifies that financing constraints have an inhib-itory effect on technological innovation

543 Recalculation of the Technological Innovation Output+e increment of intangible assets is adopted to remeasurethe technological innovation output following Ju et al [49]Moreover the ratio of the increment of intangible assets tothe total assets at the end of the period is used as the proxyvariable of the technological innovation output to remeasurethe technological innovation output Finally the empiricalresults of the influence of financing constraints on thetechnological innovation output are shown in Table 8

+e results in Table 8 show that financing constraintshave a significant inhibitory effect on the technologicalinnovation output regardless of if the FC index or SA indexis used It is consistent with the conclusion where thenumber of patent applications is adopted as the techno-logical innovation output

544 8e Ordinary Panel Model as the Counting Model+e panel counting model is used to study the influence offinancing constraints on the technical innovation outputwhich shows that financing constraints have a significantinhibitory effect on the technological innovation output Inorder to test the robustness of the results the number ofpatent applications as the explained variable is taken as acontinuous common variable and then the ordinary panelmodel is used for empirical analysis +e empirical resultsare shown in Table 9

As shown in Table 9 the conclusion obtained using theordinary panel model is that the financing constraints sig-nificantly inhibit the technological innovation output After

Table 6 +e moderating effect of the marketization

Variable RampD concentration RampD intensityLFC minus0036lowastlowastlowast (0012) minus0168lowastlowastlowast (0030)Marketization 0838lowastlowastlowast (0266) 1319lowastlowast (0658)MarketizationlowastLFC 0017lowastlowastlowast (0005) 0024lowast (0013)All controls Controlled ControlledSample size 757 757

Complexity 11

dividing the number of patent applications into inventionpatents and noninvention patents we find that the inhibi-tory effect of the financing constraints on the inventionpatents is significantly higher than that of the noninventionpatents which is consistent with the conclusion obtainedusing the negative binomial regression model of panel data

6 Conclusions and Implications

As the world economy enters a stage of innovation compe-tition the decision-making problem of technological innova-tion in emerging industries is a practical problem worthy ofresearch and attention Based on the panel data of the listedcompanies in Chinarsquos strategic emerging industries from 2009to 2019 we adopt the negative binomial regression model ofpanel data and empirically study the technological innovationdecision-making issues of emerging industries from the per-spective of financing constraints Our results show that thefinancial constraints in strategic emerging industries in Chinasignificantly inhibit technological innovation in terms oftechnological innovation inputs and outputsWhat ismore the

inhibitory effect of substantive innovation like the inventionpatent is more pronounced than that of strategic innovationlike the noninvention patent +e influence of financingconstraints on technological innovation varies significantlybetween industries within strategic emerging industriesmanufacturing industries and nonmanufacturing industriesand among industries with different external financing de-pendence However the economic policy uncertainty and themarketization can help to alleviate the inhibitory effect of fi-nancing constraints on technological innovation

Our research conclusions lead to the following policyimplications First of all the government should focus onsolving the financial constraints by speeding up the reformof the financial system Promoting the reform of the financialsupply side improving the efficiency of financial resourceallocation and reducing the financing cost should also beemphasized In addition it is necessary to expand thecoverage of financial services to develop financing platformsand tools exclusive to RampD intensive industries such asstrategic emerging industries and to encourage venturecapital and long-term capital to invest in technological

Table 9 Robustness test results of the ordinary panel model

Variable Number of patent applications Number of invention patents Number of noninvention patentsLFC minus0045lowastlowastlowast (0012) minus0059lowastlowastlowast (0012) minus0028lowastlowast (0013)LTobinQ minus0003 (0008) 0002 (0008) minus0018lowastlowast (0009)LConcertr 0005lowastlowast (0002) 0004 (0003) 0006lowastlowast (0003)LSgrowth minus0000 (0000) minus0000 (0000) minus0000 (0000)Llev 0017lowastlowastlowast (0002) 0017lowastlowastlowast (0002) 0016lowastlowastlowast (0002)Age 0003 (0009) 0012 (0010) minus0004 (0010)LCflow minus0004lowastlowast (0002) minus0005lowastlowastlowast (0002) minus0003 (0004)Sample size 691 691 691

Table 8 Robustness test results of intangible assets increment

Variable FC index SA indexLFC minus0042lowastlowast (0019) 0249lowastlowast (0108)Lsize 0000 (0041)LTobinQ 0039lowastlowast (0019) 0037lowast (0019)LConcentr 0000 (0003) 0002 (0003)LCflow minus0014lowastlowastlowast (0004) minus0012lowastlowastlowast (0001)LSgrowth minus0009 (0042) 0004 (0042)Age minus0039lowastlowastlowast (0008)Ltangibility 0000 (0003)Constant 0508 (1039) 0316 (0593)Sample size 757 757

Table 7 Regression results of the impact of financing constraints on technological innovation through the SA index

Variable RampD concentration RampD intensity Number of patent applicationsLSA 0351lowastlowastlowast (0135) 0995lowastlowastlowast (0316) 0300lowast (0182)LLEV minus0444lowast (0240) minus4102lowastlowastlowast (0549) minus0003 (0002)LTobinQ 0003 (0013) minus0039 (0030) minus0005 (0012)LConcentr minus0010lowastlowast (0004) minus0020lowastlowast (00098) minus0000 (00037)LCflow 0004lowast (0003) minus0004 (0006)Ltangibility 0011lowastlowastlowast (0003) 0002 (0006)LSgrowth minus0011 (0023) minus0118lowastlowast (0053)Constant 2159lowastlowastlowast (0592) 5283lowastlowastlowast (1360) minus9301lowastlowastlowast (1361)Sample size 757 757 628

12 Complexity

innovation projects A technological innovation financingsystem based on the market mechanism guided by gov-ernment investment dominated by enterprises and widelyparticipated by social funds should be established Morefinancial resources can flow to technological innovationprojects and to enterprises with technological innovationactivities

Second at present Chinarsquos economy is in a ldquonew nor-malrdquo that requires innovation In view of the selective effectof financing constraints on innovation activities relevantdepartments should make effort to build a good externalfinancing environment and use effective financial policiesand administrative methods to help enterprises release theirinnovative vitality more fully For example relevant de-partments should pay attention to building a good market-oriented system and increase institutional guarantees forinnovative activities so as to improve the operating con-ditions of enterprises and promote high-quality innovativeactivities When companies face financing constraints thesemeasures that are conducive to improving operating con-ditions will help to stimulate innovation

Finally since the financing constraints have a moreobvious restraining effect on enterprisesrsquo substantive inno-vation of high quality the government should encourageenterprises to increase RampD investment in substantive in-novation [50ndash53] while guiding them to pursue high-qualityinnovation so as to avoid such innovations that emphasizeonly the quantity and speed In addition the governmentalso should continue to increase financial investment inbasic research for making up for the lack of substantiveinnovation of enterprises and aiming to truly improve in-novation ability and quality of the enterprise

Data Availability

+e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

+e authors declare that there are no conflicts of interestregarding the publication of this study

Acknowledgments

+is research was partly supported by the Natural ScienceFoundation of Zhejiang Province (Grant no LQ19G030007)and Zhejiang Gongshang University Online and OfflineHybrid Teaching Reform Project (Grant no1010XJ2919103)

References

[1] H Lei and W Wang ldquo+e impact of network structure oninnovation efficiency an agent-based study in the context ofinnovation networksrdquo Complexity vol 21 no 2 pp 111ndash1222015

[2] W B Arthur and W Polak ldquo+e evolution of technologywithin a simple computer modelrdquo Complexity vol 11 no 5pp 23ndash31 2010

[3] J J Pang G Lin R L Yang et al ldquoPromote continuouseconomic development economic trendsrdquo Economic Dy-namics vol 7 pp 3ndash19 2019

[4] M Cai and S J Liu ldquo+e impact of government subsidies onthe leverage ratio of strategic emerging industriesrdquo Con-temporary Economic Research vol 8 pp 90ndash100 2020

[5] J R Brown S M Fazzari and B C Petersen ldquoFinancinginnovation and growth cash flow external equity and the1990s RampD boomrdquo Journal of Finance vol 64 pp 151ndash1852010

[6] J S Ang Y Cheng and C Wu ldquoDoes enforcement of in-tellectual property rights matter in China evidence fromfinancing and investment choices in the high-tech industryrdquoReview of Economics and Statistics vol 96 no 2 pp 332ndash3482014

[7] S M Fazzari R G Hubbard B C Petersen A S Blinder andJ M Poterba ldquoFinancing constraints and corporate invest-mentrdquo Brookings Papers on Economic Activity vol 1988 no 1206 pages 1988

[8] T Beck and R Levine ldquoIndustry growth and capital alloca-tionrdquo Journal of Financial Economics vol 64 no 2pp 147ndash180 2002

[9] M Gertler and S Gilchrist ldquo+e role of credit market im-perfections in the monetary transmission mechanism argu-ments and evidencerdquo8e Scandinavian Journal of Economicsvol 95 no 1 pp 43ndash64 1993

[10] R G Rajan and L Zingales ldquoFinancial dependence andgrowthrdquo Social Science Electronic Publishing vol 88 no 3pp 559ndash586 1998

[11] G Borisova and J R Brown ldquoRampD sensitivity to asset saleproceeds new evidence on financing constraints and intan-gible investmentrdquo Journal of Banking amp Finance vol 37 no 1pp 159ndash173 2013

[12] Y S Hwang H G Min and S H Han ldquo+e influence offinancial development on RampD activity cross-country evi-dencerdquo Review of Pacific Basin Financial Markets amp Policiesvol 13 no 3 pp 381ndash401 2010

[13] A Hall G Bockett S Taylor M V K Sivamohan andN Clark ldquoWhy research partnerships really matter inno-vation theory institutional arrangements and implications fordeveloping new technology for the poorrdquo World Develop-ment vol 29 no 5 pp 783ndash797 2001

[14] B H Hall ldquoInvestment and research and development at thefirm level does the source of financing matterrdquo NBERWorking Papers 1992

[15] C P Himmelberg and B C Petersen ldquoRampD and internalfinance a panel study of small firms in high-tech industriesrdquo8e Review of Economics and Statistics vol 76 pp 38ndash511994

[16] Z He and M B Wintoki ldquo+e cost of innovation RampD andhigh cash holdings in US firmsrdquo Journal of Corporate Financevol 41 pp 280ndash303 2016

[17] B Mulkay B H Hall and J Mairesse Firm Level Investmentand RampD in France and the United States A comparisonSocial Science Electronic Publishing Berlin Germany 2001

[18] D Harhoff Are there financing constraints for RampD and in-vestment in German manufacturing firms 8e Economics andEconometrics of Innovation Springer Berlin Germany 2000

[19] S Bond and D H V Harhoff ldquoInvestment RampD and fi-nancial constraints in Britain and Germanyrdquo AnnalesdrsquoEconomie et de Statistique vol 7980 pp 433ndash460 2005

[20] C Bloch ldquoRampD investment and internal finance the cash floweffectrdquo Economics of Innovation and New Technology vol 14no 3 pp 213ndash223 2005

Complexity 13

[21] G Scellato ldquoPatents firm size and financial constraints anempirical analysis for a panel of Italian manufacturing firmsrdquoCambridge Journal of Economics vol 31 pp 55ndash76 2007

[22] E Ughetto ldquoDoes internal finance matter for RampD newevidence from a panel of Italian firmsrdquo Cambridge Journal ofEconomics vol 32 no 6 pp 907ndash925 2008

[23] D Czarnitzki and H Hottenrott ldquoRampD investment and fi-nancing constraints of small and medium-sized firmsrdquo SmallBusiness Economics vol 36 no 1 pp 65ndash83 2011

[24] B H Hall ldquo+e financing of research and developmentrdquoOxford Review of Economic Policy vol 18 no 1 pp 35ndash512002

[25] J R Brown and B C Petersen ldquoCash holdings and RampDsmoothingrdquo Journal of Corporate Finance vol 17 no 3pp 694ndash709 2011

[26] D Li ldquoFinancial constraints RampD investment and stockreturnsrdquo Review of Financial Studies vol 24 no 9pp 2974ndash3007 2011

[27] A Mina H Lahr and A Hughes ldquo+e demand and supply ofexternal finance for innovative firmsrdquo Industrial and Cor-porate Change vol 22 no 4 pp 869ndash901 2013

[28] X Gu Y Chen and S Pan ldquoEconomic policy uncertainty andinnovation evidence from listed companies in Chinardquo Reviewof Financial Studies vol 2 pp 109ndash123 2018

[29] F Knight Risk Uncertainty and Profit Houghton MifflinCompany Boston MA USA 1921

[30] W Hao W Wei and J Wen ldquoHow does economic policyuncertainty affect corporate innovationmdashperspective ofmechanism of action of real option theoryrdquo EconomicManagement vol 38 pp 40ndash54 2016

[31] G Fan X Wang and G Ma ldquo+e contribution of Chinarsquosmarketization process to economic growthrdquo Economic Re-search vol 46 no 9 pp 4ndash16 2011

[32] K Z Dai and Y J Liu ldquo+e impact of marketization reformon RampD investment in Chinarsquos high-tech industryrdquo ScienceStudies vol 31 pp 50ndash57 2013

[33] P Li and X Liu ldquo+e impact of market-oriented institutionalchange on Chinarsquos technological progressmdashbased on theperspective of independent R amp D and technology intro-ductionrdquo Economic Perspectives vol 4 pp 42ndash50 2015

[34] K J Arrow ldquoEconomic welfare and the allocation of resourcesfor inventionrdquo 8e Rate and Direction of Inventive Activityvol 12 pp 609ndash626 1962

[35] P Kijkasiwat and P Phuensane ldquoInnovation and firm per-formance the moderating and mediating roles of firm sizeand small and medium enterprise financerdquo Journal of Riskand Financial Management vol 13 no 5 p 97 2020

[36] X N Yuan ldquoManufacturing innovation and service inno-vation comparison and convergencerdquo Finance and TradeResearch vol 20 pp 14ndash19 2009

[37] Y M Zhu and M E Jia ldquoMarketization process financialconstraints and corporate technology innovationrdquo Com-mercial ResearchInnovation Technology vol 1 pp 49ndash562017

[38] C P Wu and D Tang ldquoIntellectual property rights en-forcement corporate innovation and operating performanceevidence from Chinarsquos listed companiesrdquo Economic ResearchJournal vol 51 no 11 pp 125ndash139 2016

[39] B H Hall and D Harhoff ldquoRecent research on the economicsof patentsrdquo Annual Review of Economics vol 4 pp 541ndash5652012

[40] L Owen P Christopher and J Saaa-Requejo ldquoFinancialconstraints and stock returnsrdquo Review of Financial Studiesvol 14 pp 529ndash554 2001

[41] S R Baker N Bloom and S J Davis ldquoMeasuring economicpolicy uncertaintyrdquo 8e Quarterly Journal of Economicsvol 131 no 4 pp 1593ndash1636 2016

[42] J F He and X J Zhang ldquo+e influence of rising labor cost onenterprise innovationrdquo 8e Journal of Quantitative amp Tech-nical Economics vol 45 no 8 pp 56ndash73 2018

[43] W J Li and M N Zheng ldquoIs it substantive innovation orstrategic innovationmdashimpact of macroeconomic policies onmicro-enterprisesrsquo innovationrdquo Economic Research Journalvol 4 pp 60ndash73 2016

[44] C Andrea ldquoFinancing constraints radical versus incrementalinnovation and aggregate productivityrdquo American EconomicJournal Macroeconomics vol 11 no 2 pp 75ndash309 2019

[45] J M Hilbe Modeling Count Data Cambridge UniversityPress Cambridge UK 2014

[46] S U Khan A Shah and M F Rizwan ldquoDo financingconstraints matter for technological and non-technologicalinnovation a (re) examination of developing marketsrdquoEmerging Markets Finance and Trade vol 57 pp 1ndash28 2019

[47] F Y Li and M Z Yang ldquoCan economic policy uncertaintyinfluence corporate investment the empirical research byusing China economic policy uncertainty indexrdquo Journal ofFinancial Research vol 57 no 9 pp 2739ndash2766 2015

[48] C J Hadlock and J R Pierce ldquoNew evidence on measuringfinancial constraints moving beyond the KZ indexrdquo Review ofFinancial Studies vol 23 no 5 pp 1909ndash1940 2010

[49] X S Ju D Lu and Y H Yu ldquoFinancial constraints workingcapital management and the sustainability of firm innova-tionrdquo Economic Research Journal vol 48 pp 4ndash16 2013

[50] J Li J Liang V Shi and Q Wang ldquo+e benefit of manu-facturer encroachment considering consumerrsquos environ-mental awareness and product competitionrdquo Annals ofOperation Research vol 3 pp 1ndash19 2021

[51] J Li F Wang and Y He ldquoElectric vehicle routing problemwith battery swapping considering energy consumption andcarbon emissionsrdquo Sustainability vol 12 no 24 Article ID10537 2020

[52] J Li L Yi V Shi and X Chen ldquoSupplier encroachmentstrategy in the presence of retail strategic inventory cen-tralization or decentralizationrdquo Omega vol 98 Article ID102213 2021

[53] J Li Z Hu V Shi and Q Wang ldquoManufacturerrsquos en-croachment strategy with substitutable green productsrdquo In-ternational Journal of Production Economics vol 235 ArticleID 108102 2021

14 Complexity

are in the industries with low-level external financing de-pendence with only 14 samples accounting for 19 of thetotal samples It indicates that most enterprises in thestrategic emerging enterprises are in the industries with highexternal financing dependence +e regression results inTable 4 show that the negative effect of financing constraintson the technical innovation input and output is not sig-nificant in the samples of industries with low external fi-nancing dependence However in the industry withmedium- and high-level external financing dependence theimpact of financing constraints on the technical innovationrsquosinput and output is significant at the 10 significance leveland thus Hypothesis 4 has been verified

53 8e Moderating Effect of the External Environment

531 8e Economic Uncertainty as the Moderator As anexternal factor affecting enterprisesrsquo technological innova-tion economic policy has an impact on the technologicalinnovation activities of enterprises from amacroperspectiveOnce a countryrsquos economic policy changes enterprisesrsquoinnovation activities will inevitably be affected by the policy

Columns 2 and 3 in Table 5 are the regression resultsafter adding the cross terms of economic policy uncertaintyand financing constraints to the regression model It showsthat the negative effect of financing constraints on tech-nological innovation is still significant and the coefficient ofthe cross term is significantly positive indicating thateconomic policy uncertainty alleviates the inhibitory effectof financing constraints on technological innovation Spe-cifically when the uncertainty of the economic policy en-vironment increases the inhibitory effect of financingconstraints on the technological innovation input may bereduced+ere are two main explanations On the one handthe increasing uncertainty of economic policy means thatenterprisesrsquo business environment will change significantly

+is is both an opportunity and a challenge and the survivalof the fittest among enterprises will be presented Only thoseenterprises with high production efficiency and strongcompetitiveness can finally survive in the industry Fur-thermore innovation ability is the key factor for enterprisesto enhance their core competitiveness Enterprises can makeuse of the opportunities brought by the economic policyuncertainty and improve their core competitiveness by in-creasing the input in technological innovation activities so asto boost their chances of winning in the future marketcompetition On the other hand the uncertainty of eco-nomic policies will then spread to the external financingmarket leading to the fluctuation of the capital market andthen affect the external financing of technological innova-tion According to the research of Li and Yang [47] it can beseen that the economic policy uncertainty inhibits generalinvestment activities +erefore when economic policyuncertainty increases ordinary investment activities will notbe active and there will be abundant capital in the capitalmarket However technological innovation was fraught withuncertainty and the future is full of opportunities When theeconomic policy uncertainty increases financing for tech-nological innovation is more likely compared with general

Table 4 +e classification test result of the level of the external financing dependence in the industry

Variable

RampD concentration RampD intensity Number of patent applicationsLow externalfinancing

dependence

Medium- and high-level external

financing dependence

Low externalfinancing

dependence

Medium- and high-level external

financing dependence

Low externalfinancing

dependence

Medium- and high-level external

financing dependence

LFC 0107 minus0034lowastlowast minus0060 minus0116lowastlowastlowast minus0003 minus0021lowast(0113) (0014) (0117) (0033) (0088) (0013)

Allcontrols Controlled Controlled Controlled Controlled Controlled Controlled

Samplesize 14 743 14 743 14 614

Table 5 +e moderating effect of economic policy uncertainty

Variable RampD concentration RampD intensityLFC minus0069lowastlowastlowast (0026) minus0286lowastlowastlowast (0060)Lsize minus0273lowastlowastlowast (0050) minus0592lowastlowastlowast (0120)LTobinQ minus0022 (0029)LConcertr minus0009lowastlowast (0004) minus0025lowastlowastlowast (0009)LSgrowth minus0000 (0000) minus0002lowastlowastlowast (0001)Age minus0037lowastlowast (0015) minus0160lowastlowastlowast (0036)EPUlowastLFC 0000lowast (0000) 0001lowastlowastlowast (0000)EPU 0003lowastlowastlowast (0001) 0013lowastlowastlowast (0002)Constant 8409lowastlowastlowast (1162) 1580lowastlowastlowast (2775)Sample size 757 757

Table 3 +e classification test results of manufacturing enterprises and nonmanufacturing ones

VariableRampD concentration RampD intensity Number of patent applications

Manufacturing Nonmanufacturing Manufacturing Nonmanufacturing Manufacturing Nonmanufacturing

LFC minus0015 minus0094lowastlowast minus0100lowastlowastlowast minus0151lowastlowast minus0021lowast minus0061lowast(0012) (0043) (0035) (0075) (0012) (0034)

All controls Controlled Controlled Controlled Controlled Controlled ControlledSample size 543 214 543 214 484 144

10 Complexity

investment activities +erefore economic policy uncer-tainty can ease the inhibitory effect of financing constraintson technological innovation and thus Hypothesis 5 hasbeen verified

532 8eMarketization as the Moderator Columns 2 and 3in Table 6 are the regression results after adding the crossterms of marketization and financing constraints to theregression model It shows that the coefficient of financingconstraints is significantly negative the coefficient of mar-ketization is significantly positive and the coefficient of across term is significantly positive indicating that themarketization can help to alleviate the inhibitory effect offinancing constraints on technological innovation Specifi-cally when the market environment improves the inhibi-tory effect of financing constraints on technologicalinnovation will be reduced +erefore the more the market-oriented the lower the inhibitory effect of financing con-straints on technological innovation and vice versa In theend the Matthew effect occurs when the strong get strongerand the weak get weaker Under such effect the differencesin technological innovation between regions are constantlywidened +e possible explanation is as followsIn highlymarket-oriented regions there are a sound legal system highinformation transparency a more robust financial systemsound and abundant financial resources Such a perfectinstitutional environment can ease the enterprisersquos financingconstraints provide a proper external financing environ-ment for technological innovation and better escort theenterprisersquos technological innovation which is conducive toencourage enterprises to carry out technological innovationIn regions with less market-oriented it is impossible toprovide a strong institutional guarantee for technologicalinnovation For an enterprise facing financing constraintsthis adverse institutional environment intensifies the in-hibitory effect of financing constraints on technologicalinnovation and is not conducive to the enterprisersquos tech-nological innovation and thus Hypothesis 6 has beenverified

54 Robustness Test

541 Solutions to Endogenous Problems Financial con-straints inhibit enterprisesrsquo technological innovation activ-ities and technological innovation may make financialconstraints of enterprises get more serious thus leading to abidirectional causal relationship between financial con-straints and technical innovation In order to avoid possiblereverse causality all control variables and the explanatoryvariables are delayed by one period Simultaneously in thestudy to avoid the endogenous problems caused by theomitted variables the industry and time fixed effects arecontrolled in the empirical analysis

542 Recalculation of Financing Constraint Index In theempirical analysis we use the FC index to measure the fi-nancing constraints However there are various methods tomeasure the constraints with different emphases In order to

ensure robust conclusions the SA index proposed byHadlock and Pierce [48] is used to remeasure the financingconstraints +at is SA minus0737 Size + 0043 Size2minus004Age +e above empirical process is then repeated tocheck whether the financing constraints have an inhibitoryeffect on technological innovation

+e SA index is generally negative +e greater the ab-solute value of SA is the more serious the financing con-straints faced by the enterprise will be+erefore the SA indexis negatively correlated with the level of financing constraints+e regression results in Table 7 show that the SA index ispositively correlated with technological innovation at thesignificance level of 5 indicating the more serious the fi-nancing constraints is the stronger the inhibitory effect ontechnological innovation activities will be +is is consistentwith the conclusion of the test based on the FC index whichonce again verifies that financing constraints have an inhib-itory effect on technological innovation

543 Recalculation of the Technological Innovation Output+e increment of intangible assets is adopted to remeasurethe technological innovation output following Ju et al [49]Moreover the ratio of the increment of intangible assets tothe total assets at the end of the period is used as the proxyvariable of the technological innovation output to remeasurethe technological innovation output Finally the empiricalresults of the influence of financing constraints on thetechnological innovation output are shown in Table 8

+e results in Table 8 show that financing constraintshave a significant inhibitory effect on the technologicalinnovation output regardless of if the FC index or SA indexis used It is consistent with the conclusion where thenumber of patent applications is adopted as the techno-logical innovation output

544 8e Ordinary Panel Model as the Counting Model+e panel counting model is used to study the influence offinancing constraints on the technical innovation outputwhich shows that financing constraints have a significantinhibitory effect on the technological innovation output Inorder to test the robustness of the results the number ofpatent applications as the explained variable is taken as acontinuous common variable and then the ordinary panelmodel is used for empirical analysis +e empirical resultsare shown in Table 9

As shown in Table 9 the conclusion obtained using theordinary panel model is that the financing constraints sig-nificantly inhibit the technological innovation output After

Table 6 +e moderating effect of the marketization

Variable RampD concentration RampD intensityLFC minus0036lowastlowastlowast (0012) minus0168lowastlowastlowast (0030)Marketization 0838lowastlowastlowast (0266) 1319lowastlowast (0658)MarketizationlowastLFC 0017lowastlowastlowast (0005) 0024lowast (0013)All controls Controlled ControlledSample size 757 757

Complexity 11

dividing the number of patent applications into inventionpatents and noninvention patents we find that the inhibi-tory effect of the financing constraints on the inventionpatents is significantly higher than that of the noninventionpatents which is consistent with the conclusion obtainedusing the negative binomial regression model of panel data

6 Conclusions and Implications

As the world economy enters a stage of innovation compe-tition the decision-making problem of technological innova-tion in emerging industries is a practical problem worthy ofresearch and attention Based on the panel data of the listedcompanies in Chinarsquos strategic emerging industries from 2009to 2019 we adopt the negative binomial regression model ofpanel data and empirically study the technological innovationdecision-making issues of emerging industries from the per-spective of financing constraints Our results show that thefinancial constraints in strategic emerging industries in Chinasignificantly inhibit technological innovation in terms oftechnological innovation inputs and outputsWhat ismore the

inhibitory effect of substantive innovation like the inventionpatent is more pronounced than that of strategic innovationlike the noninvention patent +e influence of financingconstraints on technological innovation varies significantlybetween industries within strategic emerging industriesmanufacturing industries and nonmanufacturing industriesand among industries with different external financing de-pendence However the economic policy uncertainty and themarketization can help to alleviate the inhibitory effect of fi-nancing constraints on technological innovation

Our research conclusions lead to the following policyimplications First of all the government should focus onsolving the financial constraints by speeding up the reformof the financial system Promoting the reform of the financialsupply side improving the efficiency of financial resourceallocation and reducing the financing cost should also beemphasized In addition it is necessary to expand thecoverage of financial services to develop financing platformsand tools exclusive to RampD intensive industries such asstrategic emerging industries and to encourage venturecapital and long-term capital to invest in technological

Table 9 Robustness test results of the ordinary panel model

Variable Number of patent applications Number of invention patents Number of noninvention patentsLFC minus0045lowastlowastlowast (0012) minus0059lowastlowastlowast (0012) minus0028lowastlowast (0013)LTobinQ minus0003 (0008) 0002 (0008) minus0018lowastlowast (0009)LConcertr 0005lowastlowast (0002) 0004 (0003) 0006lowastlowast (0003)LSgrowth minus0000 (0000) minus0000 (0000) minus0000 (0000)Llev 0017lowastlowastlowast (0002) 0017lowastlowastlowast (0002) 0016lowastlowastlowast (0002)Age 0003 (0009) 0012 (0010) minus0004 (0010)LCflow minus0004lowastlowast (0002) minus0005lowastlowastlowast (0002) minus0003 (0004)Sample size 691 691 691

Table 8 Robustness test results of intangible assets increment

Variable FC index SA indexLFC minus0042lowastlowast (0019) 0249lowastlowast (0108)Lsize 0000 (0041)LTobinQ 0039lowastlowast (0019) 0037lowast (0019)LConcentr 0000 (0003) 0002 (0003)LCflow minus0014lowastlowastlowast (0004) minus0012lowastlowastlowast (0001)LSgrowth minus0009 (0042) 0004 (0042)Age minus0039lowastlowastlowast (0008)Ltangibility 0000 (0003)Constant 0508 (1039) 0316 (0593)Sample size 757 757

Table 7 Regression results of the impact of financing constraints on technological innovation through the SA index

Variable RampD concentration RampD intensity Number of patent applicationsLSA 0351lowastlowastlowast (0135) 0995lowastlowastlowast (0316) 0300lowast (0182)LLEV minus0444lowast (0240) minus4102lowastlowastlowast (0549) minus0003 (0002)LTobinQ 0003 (0013) minus0039 (0030) minus0005 (0012)LConcentr minus0010lowastlowast (0004) minus0020lowastlowast (00098) minus0000 (00037)LCflow 0004lowast (0003) minus0004 (0006)Ltangibility 0011lowastlowastlowast (0003) 0002 (0006)LSgrowth minus0011 (0023) minus0118lowastlowast (0053)Constant 2159lowastlowastlowast (0592) 5283lowastlowastlowast (1360) minus9301lowastlowastlowast (1361)Sample size 757 757 628

12 Complexity

innovation projects A technological innovation financingsystem based on the market mechanism guided by gov-ernment investment dominated by enterprises and widelyparticipated by social funds should be established Morefinancial resources can flow to technological innovationprojects and to enterprises with technological innovationactivities

Second at present Chinarsquos economy is in a ldquonew nor-malrdquo that requires innovation In view of the selective effectof financing constraints on innovation activities relevantdepartments should make effort to build a good externalfinancing environment and use effective financial policiesand administrative methods to help enterprises release theirinnovative vitality more fully For example relevant de-partments should pay attention to building a good market-oriented system and increase institutional guarantees forinnovative activities so as to improve the operating con-ditions of enterprises and promote high-quality innovativeactivities When companies face financing constraints thesemeasures that are conducive to improving operating con-ditions will help to stimulate innovation

Finally since the financing constraints have a moreobvious restraining effect on enterprisesrsquo substantive inno-vation of high quality the government should encourageenterprises to increase RampD investment in substantive in-novation [50ndash53] while guiding them to pursue high-qualityinnovation so as to avoid such innovations that emphasizeonly the quantity and speed In addition the governmentalso should continue to increase financial investment inbasic research for making up for the lack of substantiveinnovation of enterprises and aiming to truly improve in-novation ability and quality of the enterprise

Data Availability

+e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

+e authors declare that there are no conflicts of interestregarding the publication of this study

Acknowledgments

+is research was partly supported by the Natural ScienceFoundation of Zhejiang Province (Grant no LQ19G030007)and Zhejiang Gongshang University Online and OfflineHybrid Teaching Reform Project (Grant no1010XJ2919103)

References

[1] H Lei and W Wang ldquo+e impact of network structure oninnovation efficiency an agent-based study in the context ofinnovation networksrdquo Complexity vol 21 no 2 pp 111ndash1222015

[2] W B Arthur and W Polak ldquo+e evolution of technologywithin a simple computer modelrdquo Complexity vol 11 no 5pp 23ndash31 2010

[3] J J Pang G Lin R L Yang et al ldquoPromote continuouseconomic development economic trendsrdquo Economic Dy-namics vol 7 pp 3ndash19 2019

[4] M Cai and S J Liu ldquo+e impact of government subsidies onthe leverage ratio of strategic emerging industriesrdquo Con-temporary Economic Research vol 8 pp 90ndash100 2020

[5] J R Brown S M Fazzari and B C Petersen ldquoFinancinginnovation and growth cash flow external equity and the1990s RampD boomrdquo Journal of Finance vol 64 pp 151ndash1852010

[6] J S Ang Y Cheng and C Wu ldquoDoes enforcement of in-tellectual property rights matter in China evidence fromfinancing and investment choices in the high-tech industryrdquoReview of Economics and Statistics vol 96 no 2 pp 332ndash3482014

[7] S M Fazzari R G Hubbard B C Petersen A S Blinder andJ M Poterba ldquoFinancing constraints and corporate invest-mentrdquo Brookings Papers on Economic Activity vol 1988 no 1206 pages 1988

[8] T Beck and R Levine ldquoIndustry growth and capital alloca-tionrdquo Journal of Financial Economics vol 64 no 2pp 147ndash180 2002

[9] M Gertler and S Gilchrist ldquo+e role of credit market im-perfections in the monetary transmission mechanism argu-ments and evidencerdquo8e Scandinavian Journal of Economicsvol 95 no 1 pp 43ndash64 1993

[10] R G Rajan and L Zingales ldquoFinancial dependence andgrowthrdquo Social Science Electronic Publishing vol 88 no 3pp 559ndash586 1998

[11] G Borisova and J R Brown ldquoRampD sensitivity to asset saleproceeds new evidence on financing constraints and intan-gible investmentrdquo Journal of Banking amp Finance vol 37 no 1pp 159ndash173 2013

[12] Y S Hwang H G Min and S H Han ldquo+e influence offinancial development on RampD activity cross-country evi-dencerdquo Review of Pacific Basin Financial Markets amp Policiesvol 13 no 3 pp 381ndash401 2010

[13] A Hall G Bockett S Taylor M V K Sivamohan andN Clark ldquoWhy research partnerships really matter inno-vation theory institutional arrangements and implications fordeveloping new technology for the poorrdquo World Develop-ment vol 29 no 5 pp 783ndash797 2001

[14] B H Hall ldquoInvestment and research and development at thefirm level does the source of financing matterrdquo NBERWorking Papers 1992

[15] C P Himmelberg and B C Petersen ldquoRampD and internalfinance a panel study of small firms in high-tech industriesrdquo8e Review of Economics and Statistics vol 76 pp 38ndash511994

[16] Z He and M B Wintoki ldquo+e cost of innovation RampD andhigh cash holdings in US firmsrdquo Journal of Corporate Financevol 41 pp 280ndash303 2016

[17] B Mulkay B H Hall and J Mairesse Firm Level Investmentand RampD in France and the United States A comparisonSocial Science Electronic Publishing Berlin Germany 2001

[18] D Harhoff Are there financing constraints for RampD and in-vestment in German manufacturing firms 8e Economics andEconometrics of Innovation Springer Berlin Germany 2000

[19] S Bond and D H V Harhoff ldquoInvestment RampD and fi-nancial constraints in Britain and Germanyrdquo AnnalesdrsquoEconomie et de Statistique vol 7980 pp 433ndash460 2005

[20] C Bloch ldquoRampD investment and internal finance the cash floweffectrdquo Economics of Innovation and New Technology vol 14no 3 pp 213ndash223 2005

Complexity 13

[21] G Scellato ldquoPatents firm size and financial constraints anempirical analysis for a panel of Italian manufacturing firmsrdquoCambridge Journal of Economics vol 31 pp 55ndash76 2007

[22] E Ughetto ldquoDoes internal finance matter for RampD newevidence from a panel of Italian firmsrdquo Cambridge Journal ofEconomics vol 32 no 6 pp 907ndash925 2008

[23] D Czarnitzki and H Hottenrott ldquoRampD investment and fi-nancing constraints of small and medium-sized firmsrdquo SmallBusiness Economics vol 36 no 1 pp 65ndash83 2011

[24] B H Hall ldquo+e financing of research and developmentrdquoOxford Review of Economic Policy vol 18 no 1 pp 35ndash512002

[25] J R Brown and B C Petersen ldquoCash holdings and RampDsmoothingrdquo Journal of Corporate Finance vol 17 no 3pp 694ndash709 2011

[26] D Li ldquoFinancial constraints RampD investment and stockreturnsrdquo Review of Financial Studies vol 24 no 9pp 2974ndash3007 2011

[27] A Mina H Lahr and A Hughes ldquo+e demand and supply ofexternal finance for innovative firmsrdquo Industrial and Cor-porate Change vol 22 no 4 pp 869ndash901 2013

[28] X Gu Y Chen and S Pan ldquoEconomic policy uncertainty andinnovation evidence from listed companies in Chinardquo Reviewof Financial Studies vol 2 pp 109ndash123 2018

[29] F Knight Risk Uncertainty and Profit Houghton MifflinCompany Boston MA USA 1921

[30] W Hao W Wei and J Wen ldquoHow does economic policyuncertainty affect corporate innovationmdashperspective ofmechanism of action of real option theoryrdquo EconomicManagement vol 38 pp 40ndash54 2016

[31] G Fan X Wang and G Ma ldquo+e contribution of Chinarsquosmarketization process to economic growthrdquo Economic Re-search vol 46 no 9 pp 4ndash16 2011

[32] K Z Dai and Y J Liu ldquo+e impact of marketization reformon RampD investment in Chinarsquos high-tech industryrdquo ScienceStudies vol 31 pp 50ndash57 2013

[33] P Li and X Liu ldquo+e impact of market-oriented institutionalchange on Chinarsquos technological progressmdashbased on theperspective of independent R amp D and technology intro-ductionrdquo Economic Perspectives vol 4 pp 42ndash50 2015

[34] K J Arrow ldquoEconomic welfare and the allocation of resourcesfor inventionrdquo 8e Rate and Direction of Inventive Activityvol 12 pp 609ndash626 1962

[35] P Kijkasiwat and P Phuensane ldquoInnovation and firm per-formance the moderating and mediating roles of firm sizeand small and medium enterprise financerdquo Journal of Riskand Financial Management vol 13 no 5 p 97 2020

[36] X N Yuan ldquoManufacturing innovation and service inno-vation comparison and convergencerdquo Finance and TradeResearch vol 20 pp 14ndash19 2009

[37] Y M Zhu and M E Jia ldquoMarketization process financialconstraints and corporate technology innovationrdquo Com-mercial ResearchInnovation Technology vol 1 pp 49ndash562017

[38] C P Wu and D Tang ldquoIntellectual property rights en-forcement corporate innovation and operating performanceevidence from Chinarsquos listed companiesrdquo Economic ResearchJournal vol 51 no 11 pp 125ndash139 2016

[39] B H Hall and D Harhoff ldquoRecent research on the economicsof patentsrdquo Annual Review of Economics vol 4 pp 541ndash5652012

[40] L Owen P Christopher and J Saaa-Requejo ldquoFinancialconstraints and stock returnsrdquo Review of Financial Studiesvol 14 pp 529ndash554 2001

[41] S R Baker N Bloom and S J Davis ldquoMeasuring economicpolicy uncertaintyrdquo 8e Quarterly Journal of Economicsvol 131 no 4 pp 1593ndash1636 2016

[42] J F He and X J Zhang ldquo+e influence of rising labor cost onenterprise innovationrdquo 8e Journal of Quantitative amp Tech-nical Economics vol 45 no 8 pp 56ndash73 2018

[43] W J Li and M N Zheng ldquoIs it substantive innovation orstrategic innovationmdashimpact of macroeconomic policies onmicro-enterprisesrsquo innovationrdquo Economic Research Journalvol 4 pp 60ndash73 2016

[44] C Andrea ldquoFinancing constraints radical versus incrementalinnovation and aggregate productivityrdquo American EconomicJournal Macroeconomics vol 11 no 2 pp 75ndash309 2019

[45] J M Hilbe Modeling Count Data Cambridge UniversityPress Cambridge UK 2014

[46] S U Khan A Shah and M F Rizwan ldquoDo financingconstraints matter for technological and non-technologicalinnovation a (re) examination of developing marketsrdquoEmerging Markets Finance and Trade vol 57 pp 1ndash28 2019

[47] F Y Li and M Z Yang ldquoCan economic policy uncertaintyinfluence corporate investment the empirical research byusing China economic policy uncertainty indexrdquo Journal ofFinancial Research vol 57 no 9 pp 2739ndash2766 2015

[48] C J Hadlock and J R Pierce ldquoNew evidence on measuringfinancial constraints moving beyond the KZ indexrdquo Review ofFinancial Studies vol 23 no 5 pp 1909ndash1940 2010

[49] X S Ju D Lu and Y H Yu ldquoFinancial constraints workingcapital management and the sustainability of firm innova-tionrdquo Economic Research Journal vol 48 pp 4ndash16 2013

[50] J Li J Liang V Shi and Q Wang ldquo+e benefit of manu-facturer encroachment considering consumerrsquos environ-mental awareness and product competitionrdquo Annals ofOperation Research vol 3 pp 1ndash19 2021

[51] J Li F Wang and Y He ldquoElectric vehicle routing problemwith battery swapping considering energy consumption andcarbon emissionsrdquo Sustainability vol 12 no 24 Article ID10537 2020

[52] J Li L Yi V Shi and X Chen ldquoSupplier encroachmentstrategy in the presence of retail strategic inventory cen-tralization or decentralizationrdquo Omega vol 98 Article ID102213 2021

[53] J Li Z Hu V Shi and Q Wang ldquoManufacturerrsquos en-croachment strategy with substitutable green productsrdquo In-ternational Journal of Production Economics vol 235 ArticleID 108102 2021

14 Complexity

investment activities +erefore economic policy uncer-tainty can ease the inhibitory effect of financing constraintson technological innovation and thus Hypothesis 5 hasbeen verified

532 8eMarketization as the Moderator Columns 2 and 3in Table 6 are the regression results after adding the crossterms of marketization and financing constraints to theregression model It shows that the coefficient of financingconstraints is significantly negative the coefficient of mar-ketization is significantly positive and the coefficient of across term is significantly positive indicating that themarketization can help to alleviate the inhibitory effect offinancing constraints on technological innovation Specifi-cally when the market environment improves the inhibi-tory effect of financing constraints on technologicalinnovation will be reduced +erefore the more the market-oriented the lower the inhibitory effect of financing con-straints on technological innovation and vice versa In theend the Matthew effect occurs when the strong get strongerand the weak get weaker Under such effect the differencesin technological innovation between regions are constantlywidened +e possible explanation is as followsIn highlymarket-oriented regions there are a sound legal system highinformation transparency a more robust financial systemsound and abundant financial resources Such a perfectinstitutional environment can ease the enterprisersquos financingconstraints provide a proper external financing environ-ment for technological innovation and better escort theenterprisersquos technological innovation which is conducive toencourage enterprises to carry out technological innovationIn regions with less market-oriented it is impossible toprovide a strong institutional guarantee for technologicalinnovation For an enterprise facing financing constraintsthis adverse institutional environment intensifies the in-hibitory effect of financing constraints on technologicalinnovation and is not conducive to the enterprisersquos tech-nological innovation and thus Hypothesis 6 has beenverified

54 Robustness Test

541 Solutions to Endogenous Problems Financial con-straints inhibit enterprisesrsquo technological innovation activ-ities and technological innovation may make financialconstraints of enterprises get more serious thus leading to abidirectional causal relationship between financial con-straints and technical innovation In order to avoid possiblereverse causality all control variables and the explanatoryvariables are delayed by one period Simultaneously in thestudy to avoid the endogenous problems caused by theomitted variables the industry and time fixed effects arecontrolled in the empirical analysis

542 Recalculation of Financing Constraint Index In theempirical analysis we use the FC index to measure the fi-nancing constraints However there are various methods tomeasure the constraints with different emphases In order to

ensure robust conclusions the SA index proposed byHadlock and Pierce [48] is used to remeasure the financingconstraints +at is SA minus0737 Size + 0043 Size2minus004Age +e above empirical process is then repeated tocheck whether the financing constraints have an inhibitoryeffect on technological innovation

+e SA index is generally negative +e greater the ab-solute value of SA is the more serious the financing con-straints faced by the enterprise will be+erefore the SA indexis negatively correlated with the level of financing constraints+e regression results in Table 7 show that the SA index ispositively correlated with technological innovation at thesignificance level of 5 indicating the more serious the fi-nancing constraints is the stronger the inhibitory effect ontechnological innovation activities will be +is is consistentwith the conclusion of the test based on the FC index whichonce again verifies that financing constraints have an inhib-itory effect on technological innovation

543 Recalculation of the Technological Innovation Output+e increment of intangible assets is adopted to remeasurethe technological innovation output following Ju et al [49]Moreover the ratio of the increment of intangible assets tothe total assets at the end of the period is used as the proxyvariable of the technological innovation output to remeasurethe technological innovation output Finally the empiricalresults of the influence of financing constraints on thetechnological innovation output are shown in Table 8

+e results in Table 8 show that financing constraintshave a significant inhibitory effect on the technologicalinnovation output regardless of if the FC index or SA indexis used It is consistent with the conclusion where thenumber of patent applications is adopted as the techno-logical innovation output

544 8e Ordinary Panel Model as the Counting Model+e panel counting model is used to study the influence offinancing constraints on the technical innovation outputwhich shows that financing constraints have a significantinhibitory effect on the technological innovation output Inorder to test the robustness of the results the number ofpatent applications as the explained variable is taken as acontinuous common variable and then the ordinary panelmodel is used for empirical analysis +e empirical resultsare shown in Table 9

As shown in Table 9 the conclusion obtained using theordinary panel model is that the financing constraints sig-nificantly inhibit the technological innovation output After

Table 6 +e moderating effect of the marketization

Variable RampD concentration RampD intensityLFC minus0036lowastlowastlowast (0012) minus0168lowastlowastlowast (0030)Marketization 0838lowastlowastlowast (0266) 1319lowastlowast (0658)MarketizationlowastLFC 0017lowastlowastlowast (0005) 0024lowast (0013)All controls Controlled ControlledSample size 757 757

Complexity 11

dividing the number of patent applications into inventionpatents and noninvention patents we find that the inhibi-tory effect of the financing constraints on the inventionpatents is significantly higher than that of the noninventionpatents which is consistent with the conclusion obtainedusing the negative binomial regression model of panel data

6 Conclusions and Implications

As the world economy enters a stage of innovation compe-tition the decision-making problem of technological innova-tion in emerging industries is a practical problem worthy ofresearch and attention Based on the panel data of the listedcompanies in Chinarsquos strategic emerging industries from 2009to 2019 we adopt the negative binomial regression model ofpanel data and empirically study the technological innovationdecision-making issues of emerging industries from the per-spective of financing constraints Our results show that thefinancial constraints in strategic emerging industries in Chinasignificantly inhibit technological innovation in terms oftechnological innovation inputs and outputsWhat ismore the

inhibitory effect of substantive innovation like the inventionpatent is more pronounced than that of strategic innovationlike the noninvention patent +e influence of financingconstraints on technological innovation varies significantlybetween industries within strategic emerging industriesmanufacturing industries and nonmanufacturing industriesand among industries with different external financing de-pendence However the economic policy uncertainty and themarketization can help to alleviate the inhibitory effect of fi-nancing constraints on technological innovation

Our research conclusions lead to the following policyimplications First of all the government should focus onsolving the financial constraints by speeding up the reformof the financial system Promoting the reform of the financialsupply side improving the efficiency of financial resourceallocation and reducing the financing cost should also beemphasized In addition it is necessary to expand thecoverage of financial services to develop financing platformsand tools exclusive to RampD intensive industries such asstrategic emerging industries and to encourage venturecapital and long-term capital to invest in technological

Table 9 Robustness test results of the ordinary panel model

Variable Number of patent applications Number of invention patents Number of noninvention patentsLFC minus0045lowastlowastlowast (0012) minus0059lowastlowastlowast (0012) minus0028lowastlowast (0013)LTobinQ minus0003 (0008) 0002 (0008) minus0018lowastlowast (0009)LConcertr 0005lowastlowast (0002) 0004 (0003) 0006lowastlowast (0003)LSgrowth minus0000 (0000) minus0000 (0000) minus0000 (0000)Llev 0017lowastlowastlowast (0002) 0017lowastlowastlowast (0002) 0016lowastlowastlowast (0002)Age 0003 (0009) 0012 (0010) minus0004 (0010)LCflow minus0004lowastlowast (0002) minus0005lowastlowastlowast (0002) minus0003 (0004)Sample size 691 691 691

Table 8 Robustness test results of intangible assets increment

Variable FC index SA indexLFC minus0042lowastlowast (0019) 0249lowastlowast (0108)Lsize 0000 (0041)LTobinQ 0039lowastlowast (0019) 0037lowast (0019)LConcentr 0000 (0003) 0002 (0003)LCflow minus0014lowastlowastlowast (0004) minus0012lowastlowastlowast (0001)LSgrowth minus0009 (0042) 0004 (0042)Age minus0039lowastlowastlowast (0008)Ltangibility 0000 (0003)Constant 0508 (1039) 0316 (0593)Sample size 757 757

Table 7 Regression results of the impact of financing constraints on technological innovation through the SA index

Variable RampD concentration RampD intensity Number of patent applicationsLSA 0351lowastlowastlowast (0135) 0995lowastlowastlowast (0316) 0300lowast (0182)LLEV minus0444lowast (0240) minus4102lowastlowastlowast (0549) minus0003 (0002)LTobinQ 0003 (0013) minus0039 (0030) minus0005 (0012)LConcentr minus0010lowastlowast (0004) minus0020lowastlowast (00098) minus0000 (00037)LCflow 0004lowast (0003) minus0004 (0006)Ltangibility 0011lowastlowastlowast (0003) 0002 (0006)LSgrowth minus0011 (0023) minus0118lowastlowast (0053)Constant 2159lowastlowastlowast (0592) 5283lowastlowastlowast (1360) minus9301lowastlowastlowast (1361)Sample size 757 757 628

12 Complexity

innovation projects A technological innovation financingsystem based on the market mechanism guided by gov-ernment investment dominated by enterprises and widelyparticipated by social funds should be established Morefinancial resources can flow to technological innovationprojects and to enterprises with technological innovationactivities

Second at present Chinarsquos economy is in a ldquonew nor-malrdquo that requires innovation In view of the selective effectof financing constraints on innovation activities relevantdepartments should make effort to build a good externalfinancing environment and use effective financial policiesand administrative methods to help enterprises release theirinnovative vitality more fully For example relevant de-partments should pay attention to building a good market-oriented system and increase institutional guarantees forinnovative activities so as to improve the operating con-ditions of enterprises and promote high-quality innovativeactivities When companies face financing constraints thesemeasures that are conducive to improving operating con-ditions will help to stimulate innovation

Finally since the financing constraints have a moreobvious restraining effect on enterprisesrsquo substantive inno-vation of high quality the government should encourageenterprises to increase RampD investment in substantive in-novation [50ndash53] while guiding them to pursue high-qualityinnovation so as to avoid such innovations that emphasizeonly the quantity and speed In addition the governmentalso should continue to increase financial investment inbasic research for making up for the lack of substantiveinnovation of enterprises and aiming to truly improve in-novation ability and quality of the enterprise

Data Availability

+e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

+e authors declare that there are no conflicts of interestregarding the publication of this study

Acknowledgments

+is research was partly supported by the Natural ScienceFoundation of Zhejiang Province (Grant no LQ19G030007)and Zhejiang Gongshang University Online and OfflineHybrid Teaching Reform Project (Grant no1010XJ2919103)

References

[1] H Lei and W Wang ldquo+e impact of network structure oninnovation efficiency an agent-based study in the context ofinnovation networksrdquo Complexity vol 21 no 2 pp 111ndash1222015

[2] W B Arthur and W Polak ldquo+e evolution of technologywithin a simple computer modelrdquo Complexity vol 11 no 5pp 23ndash31 2010

[3] J J Pang G Lin R L Yang et al ldquoPromote continuouseconomic development economic trendsrdquo Economic Dy-namics vol 7 pp 3ndash19 2019

[4] M Cai and S J Liu ldquo+e impact of government subsidies onthe leverage ratio of strategic emerging industriesrdquo Con-temporary Economic Research vol 8 pp 90ndash100 2020

[5] J R Brown S M Fazzari and B C Petersen ldquoFinancinginnovation and growth cash flow external equity and the1990s RampD boomrdquo Journal of Finance vol 64 pp 151ndash1852010

[6] J S Ang Y Cheng and C Wu ldquoDoes enforcement of in-tellectual property rights matter in China evidence fromfinancing and investment choices in the high-tech industryrdquoReview of Economics and Statistics vol 96 no 2 pp 332ndash3482014

[7] S M Fazzari R G Hubbard B C Petersen A S Blinder andJ M Poterba ldquoFinancing constraints and corporate invest-mentrdquo Brookings Papers on Economic Activity vol 1988 no 1206 pages 1988

[8] T Beck and R Levine ldquoIndustry growth and capital alloca-tionrdquo Journal of Financial Economics vol 64 no 2pp 147ndash180 2002

[9] M Gertler and S Gilchrist ldquo+e role of credit market im-perfections in the monetary transmission mechanism argu-ments and evidencerdquo8e Scandinavian Journal of Economicsvol 95 no 1 pp 43ndash64 1993

[10] R G Rajan and L Zingales ldquoFinancial dependence andgrowthrdquo Social Science Electronic Publishing vol 88 no 3pp 559ndash586 1998

[11] G Borisova and J R Brown ldquoRampD sensitivity to asset saleproceeds new evidence on financing constraints and intan-gible investmentrdquo Journal of Banking amp Finance vol 37 no 1pp 159ndash173 2013

[12] Y S Hwang H G Min and S H Han ldquo+e influence offinancial development on RampD activity cross-country evi-dencerdquo Review of Pacific Basin Financial Markets amp Policiesvol 13 no 3 pp 381ndash401 2010

[13] A Hall G Bockett S Taylor M V K Sivamohan andN Clark ldquoWhy research partnerships really matter inno-vation theory institutional arrangements and implications fordeveloping new technology for the poorrdquo World Develop-ment vol 29 no 5 pp 783ndash797 2001

[14] B H Hall ldquoInvestment and research and development at thefirm level does the source of financing matterrdquo NBERWorking Papers 1992

[15] C P Himmelberg and B C Petersen ldquoRampD and internalfinance a panel study of small firms in high-tech industriesrdquo8e Review of Economics and Statistics vol 76 pp 38ndash511994

[16] Z He and M B Wintoki ldquo+e cost of innovation RampD andhigh cash holdings in US firmsrdquo Journal of Corporate Financevol 41 pp 280ndash303 2016

[17] B Mulkay B H Hall and J Mairesse Firm Level Investmentand RampD in France and the United States A comparisonSocial Science Electronic Publishing Berlin Germany 2001

[18] D Harhoff Are there financing constraints for RampD and in-vestment in German manufacturing firms 8e Economics andEconometrics of Innovation Springer Berlin Germany 2000

[19] S Bond and D H V Harhoff ldquoInvestment RampD and fi-nancial constraints in Britain and Germanyrdquo AnnalesdrsquoEconomie et de Statistique vol 7980 pp 433ndash460 2005

[20] C Bloch ldquoRampD investment and internal finance the cash floweffectrdquo Economics of Innovation and New Technology vol 14no 3 pp 213ndash223 2005

Complexity 13

[21] G Scellato ldquoPatents firm size and financial constraints anempirical analysis for a panel of Italian manufacturing firmsrdquoCambridge Journal of Economics vol 31 pp 55ndash76 2007

[22] E Ughetto ldquoDoes internal finance matter for RampD newevidence from a panel of Italian firmsrdquo Cambridge Journal ofEconomics vol 32 no 6 pp 907ndash925 2008

[23] D Czarnitzki and H Hottenrott ldquoRampD investment and fi-nancing constraints of small and medium-sized firmsrdquo SmallBusiness Economics vol 36 no 1 pp 65ndash83 2011

[24] B H Hall ldquo+e financing of research and developmentrdquoOxford Review of Economic Policy vol 18 no 1 pp 35ndash512002

[25] J R Brown and B C Petersen ldquoCash holdings and RampDsmoothingrdquo Journal of Corporate Finance vol 17 no 3pp 694ndash709 2011

[26] D Li ldquoFinancial constraints RampD investment and stockreturnsrdquo Review of Financial Studies vol 24 no 9pp 2974ndash3007 2011

[27] A Mina H Lahr and A Hughes ldquo+e demand and supply ofexternal finance for innovative firmsrdquo Industrial and Cor-porate Change vol 22 no 4 pp 869ndash901 2013

[28] X Gu Y Chen and S Pan ldquoEconomic policy uncertainty andinnovation evidence from listed companies in Chinardquo Reviewof Financial Studies vol 2 pp 109ndash123 2018

[29] F Knight Risk Uncertainty and Profit Houghton MifflinCompany Boston MA USA 1921

[30] W Hao W Wei and J Wen ldquoHow does economic policyuncertainty affect corporate innovationmdashperspective ofmechanism of action of real option theoryrdquo EconomicManagement vol 38 pp 40ndash54 2016

[31] G Fan X Wang and G Ma ldquo+e contribution of Chinarsquosmarketization process to economic growthrdquo Economic Re-search vol 46 no 9 pp 4ndash16 2011

[32] K Z Dai and Y J Liu ldquo+e impact of marketization reformon RampD investment in Chinarsquos high-tech industryrdquo ScienceStudies vol 31 pp 50ndash57 2013

[33] P Li and X Liu ldquo+e impact of market-oriented institutionalchange on Chinarsquos technological progressmdashbased on theperspective of independent R amp D and technology intro-ductionrdquo Economic Perspectives vol 4 pp 42ndash50 2015

[34] K J Arrow ldquoEconomic welfare and the allocation of resourcesfor inventionrdquo 8e Rate and Direction of Inventive Activityvol 12 pp 609ndash626 1962

[35] P Kijkasiwat and P Phuensane ldquoInnovation and firm per-formance the moderating and mediating roles of firm sizeand small and medium enterprise financerdquo Journal of Riskand Financial Management vol 13 no 5 p 97 2020

[36] X N Yuan ldquoManufacturing innovation and service inno-vation comparison and convergencerdquo Finance and TradeResearch vol 20 pp 14ndash19 2009

[37] Y M Zhu and M E Jia ldquoMarketization process financialconstraints and corporate technology innovationrdquo Com-mercial ResearchInnovation Technology vol 1 pp 49ndash562017

[38] C P Wu and D Tang ldquoIntellectual property rights en-forcement corporate innovation and operating performanceevidence from Chinarsquos listed companiesrdquo Economic ResearchJournal vol 51 no 11 pp 125ndash139 2016

[39] B H Hall and D Harhoff ldquoRecent research on the economicsof patentsrdquo Annual Review of Economics vol 4 pp 541ndash5652012

[40] L Owen P Christopher and J Saaa-Requejo ldquoFinancialconstraints and stock returnsrdquo Review of Financial Studiesvol 14 pp 529ndash554 2001

[41] S R Baker N Bloom and S J Davis ldquoMeasuring economicpolicy uncertaintyrdquo 8e Quarterly Journal of Economicsvol 131 no 4 pp 1593ndash1636 2016

[42] J F He and X J Zhang ldquo+e influence of rising labor cost onenterprise innovationrdquo 8e Journal of Quantitative amp Tech-nical Economics vol 45 no 8 pp 56ndash73 2018

[43] W J Li and M N Zheng ldquoIs it substantive innovation orstrategic innovationmdashimpact of macroeconomic policies onmicro-enterprisesrsquo innovationrdquo Economic Research Journalvol 4 pp 60ndash73 2016

[44] C Andrea ldquoFinancing constraints radical versus incrementalinnovation and aggregate productivityrdquo American EconomicJournal Macroeconomics vol 11 no 2 pp 75ndash309 2019

[45] J M Hilbe Modeling Count Data Cambridge UniversityPress Cambridge UK 2014

[46] S U Khan A Shah and M F Rizwan ldquoDo financingconstraints matter for technological and non-technologicalinnovation a (re) examination of developing marketsrdquoEmerging Markets Finance and Trade vol 57 pp 1ndash28 2019

[47] F Y Li and M Z Yang ldquoCan economic policy uncertaintyinfluence corporate investment the empirical research byusing China economic policy uncertainty indexrdquo Journal ofFinancial Research vol 57 no 9 pp 2739ndash2766 2015

[48] C J Hadlock and J R Pierce ldquoNew evidence on measuringfinancial constraints moving beyond the KZ indexrdquo Review ofFinancial Studies vol 23 no 5 pp 1909ndash1940 2010

[49] X S Ju D Lu and Y H Yu ldquoFinancial constraints workingcapital management and the sustainability of firm innova-tionrdquo Economic Research Journal vol 48 pp 4ndash16 2013

[50] J Li J Liang V Shi and Q Wang ldquo+e benefit of manu-facturer encroachment considering consumerrsquos environ-mental awareness and product competitionrdquo Annals ofOperation Research vol 3 pp 1ndash19 2021

[51] J Li F Wang and Y He ldquoElectric vehicle routing problemwith battery swapping considering energy consumption andcarbon emissionsrdquo Sustainability vol 12 no 24 Article ID10537 2020

[52] J Li L Yi V Shi and X Chen ldquoSupplier encroachmentstrategy in the presence of retail strategic inventory cen-tralization or decentralizationrdquo Omega vol 98 Article ID102213 2021

[53] J Li Z Hu V Shi and Q Wang ldquoManufacturerrsquos en-croachment strategy with substitutable green productsrdquo In-ternational Journal of Production Economics vol 235 ArticleID 108102 2021

14 Complexity

dividing the number of patent applications into inventionpatents and noninvention patents we find that the inhibi-tory effect of the financing constraints on the inventionpatents is significantly higher than that of the noninventionpatents which is consistent with the conclusion obtainedusing the negative binomial regression model of panel data

6 Conclusions and Implications

As the world economy enters a stage of innovation compe-tition the decision-making problem of technological innova-tion in emerging industries is a practical problem worthy ofresearch and attention Based on the panel data of the listedcompanies in Chinarsquos strategic emerging industries from 2009to 2019 we adopt the negative binomial regression model ofpanel data and empirically study the technological innovationdecision-making issues of emerging industries from the per-spective of financing constraints Our results show that thefinancial constraints in strategic emerging industries in Chinasignificantly inhibit technological innovation in terms oftechnological innovation inputs and outputsWhat ismore the

inhibitory effect of substantive innovation like the inventionpatent is more pronounced than that of strategic innovationlike the noninvention patent +e influence of financingconstraints on technological innovation varies significantlybetween industries within strategic emerging industriesmanufacturing industries and nonmanufacturing industriesand among industries with different external financing de-pendence However the economic policy uncertainty and themarketization can help to alleviate the inhibitory effect of fi-nancing constraints on technological innovation

Our research conclusions lead to the following policyimplications First of all the government should focus onsolving the financial constraints by speeding up the reformof the financial system Promoting the reform of the financialsupply side improving the efficiency of financial resourceallocation and reducing the financing cost should also beemphasized In addition it is necessary to expand thecoverage of financial services to develop financing platformsand tools exclusive to RampD intensive industries such asstrategic emerging industries and to encourage venturecapital and long-term capital to invest in technological

Table 9 Robustness test results of the ordinary panel model

Variable Number of patent applications Number of invention patents Number of noninvention patentsLFC minus0045lowastlowastlowast (0012) minus0059lowastlowastlowast (0012) minus0028lowastlowast (0013)LTobinQ minus0003 (0008) 0002 (0008) minus0018lowastlowast (0009)LConcertr 0005lowastlowast (0002) 0004 (0003) 0006lowastlowast (0003)LSgrowth minus0000 (0000) minus0000 (0000) minus0000 (0000)Llev 0017lowastlowastlowast (0002) 0017lowastlowastlowast (0002) 0016lowastlowastlowast (0002)Age 0003 (0009) 0012 (0010) minus0004 (0010)LCflow minus0004lowastlowast (0002) minus0005lowastlowastlowast (0002) minus0003 (0004)Sample size 691 691 691

Table 8 Robustness test results of intangible assets increment

Variable FC index SA indexLFC minus0042lowastlowast (0019) 0249lowastlowast (0108)Lsize 0000 (0041)LTobinQ 0039lowastlowast (0019) 0037lowast (0019)LConcentr 0000 (0003) 0002 (0003)LCflow minus0014lowastlowastlowast (0004) minus0012lowastlowastlowast (0001)LSgrowth minus0009 (0042) 0004 (0042)Age minus0039lowastlowastlowast (0008)Ltangibility 0000 (0003)Constant 0508 (1039) 0316 (0593)Sample size 757 757

Table 7 Regression results of the impact of financing constraints on technological innovation through the SA index

Variable RampD concentration RampD intensity Number of patent applicationsLSA 0351lowastlowastlowast (0135) 0995lowastlowastlowast (0316) 0300lowast (0182)LLEV minus0444lowast (0240) minus4102lowastlowastlowast (0549) minus0003 (0002)LTobinQ 0003 (0013) minus0039 (0030) minus0005 (0012)LConcentr minus0010lowastlowast (0004) minus0020lowastlowast (00098) minus0000 (00037)LCflow 0004lowast (0003) minus0004 (0006)Ltangibility 0011lowastlowastlowast (0003) 0002 (0006)LSgrowth minus0011 (0023) minus0118lowastlowast (0053)Constant 2159lowastlowastlowast (0592) 5283lowastlowastlowast (1360) minus9301lowastlowastlowast (1361)Sample size 757 757 628

12 Complexity

innovation projects A technological innovation financingsystem based on the market mechanism guided by gov-ernment investment dominated by enterprises and widelyparticipated by social funds should be established Morefinancial resources can flow to technological innovationprojects and to enterprises with technological innovationactivities

Second at present Chinarsquos economy is in a ldquonew nor-malrdquo that requires innovation In view of the selective effectof financing constraints on innovation activities relevantdepartments should make effort to build a good externalfinancing environment and use effective financial policiesand administrative methods to help enterprises release theirinnovative vitality more fully For example relevant de-partments should pay attention to building a good market-oriented system and increase institutional guarantees forinnovative activities so as to improve the operating con-ditions of enterprises and promote high-quality innovativeactivities When companies face financing constraints thesemeasures that are conducive to improving operating con-ditions will help to stimulate innovation

Finally since the financing constraints have a moreobvious restraining effect on enterprisesrsquo substantive inno-vation of high quality the government should encourageenterprises to increase RampD investment in substantive in-novation [50ndash53] while guiding them to pursue high-qualityinnovation so as to avoid such innovations that emphasizeonly the quantity and speed In addition the governmentalso should continue to increase financial investment inbasic research for making up for the lack of substantiveinnovation of enterprises and aiming to truly improve in-novation ability and quality of the enterprise

Data Availability

+e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

+e authors declare that there are no conflicts of interestregarding the publication of this study

Acknowledgments

+is research was partly supported by the Natural ScienceFoundation of Zhejiang Province (Grant no LQ19G030007)and Zhejiang Gongshang University Online and OfflineHybrid Teaching Reform Project (Grant no1010XJ2919103)

References

[1] H Lei and W Wang ldquo+e impact of network structure oninnovation efficiency an agent-based study in the context ofinnovation networksrdquo Complexity vol 21 no 2 pp 111ndash1222015

[2] W B Arthur and W Polak ldquo+e evolution of technologywithin a simple computer modelrdquo Complexity vol 11 no 5pp 23ndash31 2010

[3] J J Pang G Lin R L Yang et al ldquoPromote continuouseconomic development economic trendsrdquo Economic Dy-namics vol 7 pp 3ndash19 2019

[4] M Cai and S J Liu ldquo+e impact of government subsidies onthe leverage ratio of strategic emerging industriesrdquo Con-temporary Economic Research vol 8 pp 90ndash100 2020

[5] J R Brown S M Fazzari and B C Petersen ldquoFinancinginnovation and growth cash flow external equity and the1990s RampD boomrdquo Journal of Finance vol 64 pp 151ndash1852010

[6] J S Ang Y Cheng and C Wu ldquoDoes enforcement of in-tellectual property rights matter in China evidence fromfinancing and investment choices in the high-tech industryrdquoReview of Economics and Statistics vol 96 no 2 pp 332ndash3482014

[7] S M Fazzari R G Hubbard B C Petersen A S Blinder andJ M Poterba ldquoFinancing constraints and corporate invest-mentrdquo Brookings Papers on Economic Activity vol 1988 no 1206 pages 1988

[8] T Beck and R Levine ldquoIndustry growth and capital alloca-tionrdquo Journal of Financial Economics vol 64 no 2pp 147ndash180 2002

[9] M Gertler and S Gilchrist ldquo+e role of credit market im-perfections in the monetary transmission mechanism argu-ments and evidencerdquo8e Scandinavian Journal of Economicsvol 95 no 1 pp 43ndash64 1993

[10] R G Rajan and L Zingales ldquoFinancial dependence andgrowthrdquo Social Science Electronic Publishing vol 88 no 3pp 559ndash586 1998

[11] G Borisova and J R Brown ldquoRampD sensitivity to asset saleproceeds new evidence on financing constraints and intan-gible investmentrdquo Journal of Banking amp Finance vol 37 no 1pp 159ndash173 2013

[12] Y S Hwang H G Min and S H Han ldquo+e influence offinancial development on RampD activity cross-country evi-dencerdquo Review of Pacific Basin Financial Markets amp Policiesvol 13 no 3 pp 381ndash401 2010

[13] A Hall G Bockett S Taylor M V K Sivamohan andN Clark ldquoWhy research partnerships really matter inno-vation theory institutional arrangements and implications fordeveloping new technology for the poorrdquo World Develop-ment vol 29 no 5 pp 783ndash797 2001

[14] B H Hall ldquoInvestment and research and development at thefirm level does the source of financing matterrdquo NBERWorking Papers 1992

[15] C P Himmelberg and B C Petersen ldquoRampD and internalfinance a panel study of small firms in high-tech industriesrdquo8e Review of Economics and Statistics vol 76 pp 38ndash511994

[16] Z He and M B Wintoki ldquo+e cost of innovation RampD andhigh cash holdings in US firmsrdquo Journal of Corporate Financevol 41 pp 280ndash303 2016

[17] B Mulkay B H Hall and J Mairesse Firm Level Investmentand RampD in France and the United States A comparisonSocial Science Electronic Publishing Berlin Germany 2001

[18] D Harhoff Are there financing constraints for RampD and in-vestment in German manufacturing firms 8e Economics andEconometrics of Innovation Springer Berlin Germany 2000

[19] S Bond and D H V Harhoff ldquoInvestment RampD and fi-nancial constraints in Britain and Germanyrdquo AnnalesdrsquoEconomie et de Statistique vol 7980 pp 433ndash460 2005

[20] C Bloch ldquoRampD investment and internal finance the cash floweffectrdquo Economics of Innovation and New Technology vol 14no 3 pp 213ndash223 2005

Complexity 13

[21] G Scellato ldquoPatents firm size and financial constraints anempirical analysis for a panel of Italian manufacturing firmsrdquoCambridge Journal of Economics vol 31 pp 55ndash76 2007

[22] E Ughetto ldquoDoes internal finance matter for RampD newevidence from a panel of Italian firmsrdquo Cambridge Journal ofEconomics vol 32 no 6 pp 907ndash925 2008

[23] D Czarnitzki and H Hottenrott ldquoRampD investment and fi-nancing constraints of small and medium-sized firmsrdquo SmallBusiness Economics vol 36 no 1 pp 65ndash83 2011

[24] B H Hall ldquo+e financing of research and developmentrdquoOxford Review of Economic Policy vol 18 no 1 pp 35ndash512002

[25] J R Brown and B C Petersen ldquoCash holdings and RampDsmoothingrdquo Journal of Corporate Finance vol 17 no 3pp 694ndash709 2011

[26] D Li ldquoFinancial constraints RampD investment and stockreturnsrdquo Review of Financial Studies vol 24 no 9pp 2974ndash3007 2011

[27] A Mina H Lahr and A Hughes ldquo+e demand and supply ofexternal finance for innovative firmsrdquo Industrial and Cor-porate Change vol 22 no 4 pp 869ndash901 2013

[28] X Gu Y Chen and S Pan ldquoEconomic policy uncertainty andinnovation evidence from listed companies in Chinardquo Reviewof Financial Studies vol 2 pp 109ndash123 2018

[29] F Knight Risk Uncertainty and Profit Houghton MifflinCompany Boston MA USA 1921

[30] W Hao W Wei and J Wen ldquoHow does economic policyuncertainty affect corporate innovationmdashperspective ofmechanism of action of real option theoryrdquo EconomicManagement vol 38 pp 40ndash54 2016

[31] G Fan X Wang and G Ma ldquo+e contribution of Chinarsquosmarketization process to economic growthrdquo Economic Re-search vol 46 no 9 pp 4ndash16 2011

[32] K Z Dai and Y J Liu ldquo+e impact of marketization reformon RampD investment in Chinarsquos high-tech industryrdquo ScienceStudies vol 31 pp 50ndash57 2013

[33] P Li and X Liu ldquo+e impact of market-oriented institutionalchange on Chinarsquos technological progressmdashbased on theperspective of independent R amp D and technology intro-ductionrdquo Economic Perspectives vol 4 pp 42ndash50 2015

[34] K J Arrow ldquoEconomic welfare and the allocation of resourcesfor inventionrdquo 8e Rate and Direction of Inventive Activityvol 12 pp 609ndash626 1962

[35] P Kijkasiwat and P Phuensane ldquoInnovation and firm per-formance the moderating and mediating roles of firm sizeand small and medium enterprise financerdquo Journal of Riskand Financial Management vol 13 no 5 p 97 2020

[36] X N Yuan ldquoManufacturing innovation and service inno-vation comparison and convergencerdquo Finance and TradeResearch vol 20 pp 14ndash19 2009

[37] Y M Zhu and M E Jia ldquoMarketization process financialconstraints and corporate technology innovationrdquo Com-mercial ResearchInnovation Technology vol 1 pp 49ndash562017

[38] C P Wu and D Tang ldquoIntellectual property rights en-forcement corporate innovation and operating performanceevidence from Chinarsquos listed companiesrdquo Economic ResearchJournal vol 51 no 11 pp 125ndash139 2016

[39] B H Hall and D Harhoff ldquoRecent research on the economicsof patentsrdquo Annual Review of Economics vol 4 pp 541ndash5652012

[40] L Owen P Christopher and J Saaa-Requejo ldquoFinancialconstraints and stock returnsrdquo Review of Financial Studiesvol 14 pp 529ndash554 2001

[41] S R Baker N Bloom and S J Davis ldquoMeasuring economicpolicy uncertaintyrdquo 8e Quarterly Journal of Economicsvol 131 no 4 pp 1593ndash1636 2016

[42] J F He and X J Zhang ldquo+e influence of rising labor cost onenterprise innovationrdquo 8e Journal of Quantitative amp Tech-nical Economics vol 45 no 8 pp 56ndash73 2018

[43] W J Li and M N Zheng ldquoIs it substantive innovation orstrategic innovationmdashimpact of macroeconomic policies onmicro-enterprisesrsquo innovationrdquo Economic Research Journalvol 4 pp 60ndash73 2016

[44] C Andrea ldquoFinancing constraints radical versus incrementalinnovation and aggregate productivityrdquo American EconomicJournal Macroeconomics vol 11 no 2 pp 75ndash309 2019

[45] J M Hilbe Modeling Count Data Cambridge UniversityPress Cambridge UK 2014

[46] S U Khan A Shah and M F Rizwan ldquoDo financingconstraints matter for technological and non-technologicalinnovation a (re) examination of developing marketsrdquoEmerging Markets Finance and Trade vol 57 pp 1ndash28 2019

[47] F Y Li and M Z Yang ldquoCan economic policy uncertaintyinfluence corporate investment the empirical research byusing China economic policy uncertainty indexrdquo Journal ofFinancial Research vol 57 no 9 pp 2739ndash2766 2015

[48] C J Hadlock and J R Pierce ldquoNew evidence on measuringfinancial constraints moving beyond the KZ indexrdquo Review ofFinancial Studies vol 23 no 5 pp 1909ndash1940 2010

[49] X S Ju D Lu and Y H Yu ldquoFinancial constraints workingcapital management and the sustainability of firm innova-tionrdquo Economic Research Journal vol 48 pp 4ndash16 2013

[50] J Li J Liang V Shi and Q Wang ldquo+e benefit of manu-facturer encroachment considering consumerrsquos environ-mental awareness and product competitionrdquo Annals ofOperation Research vol 3 pp 1ndash19 2021

[51] J Li F Wang and Y He ldquoElectric vehicle routing problemwith battery swapping considering energy consumption andcarbon emissionsrdquo Sustainability vol 12 no 24 Article ID10537 2020

[52] J Li L Yi V Shi and X Chen ldquoSupplier encroachmentstrategy in the presence of retail strategic inventory cen-tralization or decentralizationrdquo Omega vol 98 Article ID102213 2021

[53] J Li Z Hu V Shi and Q Wang ldquoManufacturerrsquos en-croachment strategy with substitutable green productsrdquo In-ternational Journal of Production Economics vol 235 ArticleID 108102 2021

14 Complexity

innovation projects A technological innovation financingsystem based on the market mechanism guided by gov-ernment investment dominated by enterprises and widelyparticipated by social funds should be established Morefinancial resources can flow to technological innovationprojects and to enterprises with technological innovationactivities

Second at present Chinarsquos economy is in a ldquonew nor-malrdquo that requires innovation In view of the selective effectof financing constraints on innovation activities relevantdepartments should make effort to build a good externalfinancing environment and use effective financial policiesand administrative methods to help enterprises release theirinnovative vitality more fully For example relevant de-partments should pay attention to building a good market-oriented system and increase institutional guarantees forinnovative activities so as to improve the operating con-ditions of enterprises and promote high-quality innovativeactivities When companies face financing constraints thesemeasures that are conducive to improving operating con-ditions will help to stimulate innovation

Finally since the financing constraints have a moreobvious restraining effect on enterprisesrsquo substantive inno-vation of high quality the government should encourageenterprises to increase RampD investment in substantive in-novation [50ndash53] while guiding them to pursue high-qualityinnovation so as to avoid such innovations that emphasizeonly the quantity and speed In addition the governmentalso should continue to increase financial investment inbasic research for making up for the lack of substantiveinnovation of enterprises and aiming to truly improve in-novation ability and quality of the enterprise

Data Availability

+e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

+e authors declare that there are no conflicts of interestregarding the publication of this study

Acknowledgments

+is research was partly supported by the Natural ScienceFoundation of Zhejiang Province (Grant no LQ19G030007)and Zhejiang Gongshang University Online and OfflineHybrid Teaching Reform Project (Grant no1010XJ2919103)

References

[1] H Lei and W Wang ldquo+e impact of network structure oninnovation efficiency an agent-based study in the context ofinnovation networksrdquo Complexity vol 21 no 2 pp 111ndash1222015

[2] W B Arthur and W Polak ldquo+e evolution of technologywithin a simple computer modelrdquo Complexity vol 11 no 5pp 23ndash31 2010

[3] J J Pang G Lin R L Yang et al ldquoPromote continuouseconomic development economic trendsrdquo Economic Dy-namics vol 7 pp 3ndash19 2019

[4] M Cai and S J Liu ldquo+e impact of government subsidies onthe leverage ratio of strategic emerging industriesrdquo Con-temporary Economic Research vol 8 pp 90ndash100 2020

[5] J R Brown S M Fazzari and B C Petersen ldquoFinancinginnovation and growth cash flow external equity and the1990s RampD boomrdquo Journal of Finance vol 64 pp 151ndash1852010

[6] J S Ang Y Cheng and C Wu ldquoDoes enforcement of in-tellectual property rights matter in China evidence fromfinancing and investment choices in the high-tech industryrdquoReview of Economics and Statistics vol 96 no 2 pp 332ndash3482014

[7] S M Fazzari R G Hubbard B C Petersen A S Blinder andJ M Poterba ldquoFinancing constraints and corporate invest-mentrdquo Brookings Papers on Economic Activity vol 1988 no 1206 pages 1988

[8] T Beck and R Levine ldquoIndustry growth and capital alloca-tionrdquo Journal of Financial Economics vol 64 no 2pp 147ndash180 2002

[9] M Gertler and S Gilchrist ldquo+e role of credit market im-perfections in the monetary transmission mechanism argu-ments and evidencerdquo8e Scandinavian Journal of Economicsvol 95 no 1 pp 43ndash64 1993

[10] R G Rajan and L Zingales ldquoFinancial dependence andgrowthrdquo Social Science Electronic Publishing vol 88 no 3pp 559ndash586 1998

[11] G Borisova and J R Brown ldquoRampD sensitivity to asset saleproceeds new evidence on financing constraints and intan-gible investmentrdquo Journal of Banking amp Finance vol 37 no 1pp 159ndash173 2013

[12] Y S Hwang H G Min and S H Han ldquo+e influence offinancial development on RampD activity cross-country evi-dencerdquo Review of Pacific Basin Financial Markets amp Policiesvol 13 no 3 pp 381ndash401 2010

[13] A Hall G Bockett S Taylor M V K Sivamohan andN Clark ldquoWhy research partnerships really matter inno-vation theory institutional arrangements and implications fordeveloping new technology for the poorrdquo World Develop-ment vol 29 no 5 pp 783ndash797 2001

[14] B H Hall ldquoInvestment and research and development at thefirm level does the source of financing matterrdquo NBERWorking Papers 1992

[15] C P Himmelberg and B C Petersen ldquoRampD and internalfinance a panel study of small firms in high-tech industriesrdquo8e Review of Economics and Statistics vol 76 pp 38ndash511994

[16] Z He and M B Wintoki ldquo+e cost of innovation RampD andhigh cash holdings in US firmsrdquo Journal of Corporate Financevol 41 pp 280ndash303 2016

[17] B Mulkay B H Hall and J Mairesse Firm Level Investmentand RampD in France and the United States A comparisonSocial Science Electronic Publishing Berlin Germany 2001

[18] D Harhoff Are there financing constraints for RampD and in-vestment in German manufacturing firms 8e Economics andEconometrics of Innovation Springer Berlin Germany 2000

[19] S Bond and D H V Harhoff ldquoInvestment RampD and fi-nancial constraints in Britain and Germanyrdquo AnnalesdrsquoEconomie et de Statistique vol 7980 pp 433ndash460 2005

[20] C Bloch ldquoRampD investment and internal finance the cash floweffectrdquo Economics of Innovation and New Technology vol 14no 3 pp 213ndash223 2005

Complexity 13

[21] G Scellato ldquoPatents firm size and financial constraints anempirical analysis for a panel of Italian manufacturing firmsrdquoCambridge Journal of Economics vol 31 pp 55ndash76 2007

[22] E Ughetto ldquoDoes internal finance matter for RampD newevidence from a panel of Italian firmsrdquo Cambridge Journal ofEconomics vol 32 no 6 pp 907ndash925 2008

[23] D Czarnitzki and H Hottenrott ldquoRampD investment and fi-nancing constraints of small and medium-sized firmsrdquo SmallBusiness Economics vol 36 no 1 pp 65ndash83 2011

[24] B H Hall ldquo+e financing of research and developmentrdquoOxford Review of Economic Policy vol 18 no 1 pp 35ndash512002

[25] J R Brown and B C Petersen ldquoCash holdings and RampDsmoothingrdquo Journal of Corporate Finance vol 17 no 3pp 694ndash709 2011

[26] D Li ldquoFinancial constraints RampD investment and stockreturnsrdquo Review of Financial Studies vol 24 no 9pp 2974ndash3007 2011

[27] A Mina H Lahr and A Hughes ldquo+e demand and supply ofexternal finance for innovative firmsrdquo Industrial and Cor-porate Change vol 22 no 4 pp 869ndash901 2013

[28] X Gu Y Chen and S Pan ldquoEconomic policy uncertainty andinnovation evidence from listed companies in Chinardquo Reviewof Financial Studies vol 2 pp 109ndash123 2018

[29] F Knight Risk Uncertainty and Profit Houghton MifflinCompany Boston MA USA 1921

[30] W Hao W Wei and J Wen ldquoHow does economic policyuncertainty affect corporate innovationmdashperspective ofmechanism of action of real option theoryrdquo EconomicManagement vol 38 pp 40ndash54 2016

[31] G Fan X Wang and G Ma ldquo+e contribution of Chinarsquosmarketization process to economic growthrdquo Economic Re-search vol 46 no 9 pp 4ndash16 2011

[32] K Z Dai and Y J Liu ldquo+e impact of marketization reformon RampD investment in Chinarsquos high-tech industryrdquo ScienceStudies vol 31 pp 50ndash57 2013

[33] P Li and X Liu ldquo+e impact of market-oriented institutionalchange on Chinarsquos technological progressmdashbased on theperspective of independent R amp D and technology intro-ductionrdquo Economic Perspectives vol 4 pp 42ndash50 2015

[34] K J Arrow ldquoEconomic welfare and the allocation of resourcesfor inventionrdquo 8e Rate and Direction of Inventive Activityvol 12 pp 609ndash626 1962

[35] P Kijkasiwat and P Phuensane ldquoInnovation and firm per-formance the moderating and mediating roles of firm sizeand small and medium enterprise financerdquo Journal of Riskand Financial Management vol 13 no 5 p 97 2020

[36] X N Yuan ldquoManufacturing innovation and service inno-vation comparison and convergencerdquo Finance and TradeResearch vol 20 pp 14ndash19 2009

[37] Y M Zhu and M E Jia ldquoMarketization process financialconstraints and corporate technology innovationrdquo Com-mercial ResearchInnovation Technology vol 1 pp 49ndash562017

[38] C P Wu and D Tang ldquoIntellectual property rights en-forcement corporate innovation and operating performanceevidence from Chinarsquos listed companiesrdquo Economic ResearchJournal vol 51 no 11 pp 125ndash139 2016

[39] B H Hall and D Harhoff ldquoRecent research on the economicsof patentsrdquo Annual Review of Economics vol 4 pp 541ndash5652012

[40] L Owen P Christopher and J Saaa-Requejo ldquoFinancialconstraints and stock returnsrdquo Review of Financial Studiesvol 14 pp 529ndash554 2001

[41] S R Baker N Bloom and S J Davis ldquoMeasuring economicpolicy uncertaintyrdquo 8e Quarterly Journal of Economicsvol 131 no 4 pp 1593ndash1636 2016

[42] J F He and X J Zhang ldquo+e influence of rising labor cost onenterprise innovationrdquo 8e Journal of Quantitative amp Tech-nical Economics vol 45 no 8 pp 56ndash73 2018

[43] W J Li and M N Zheng ldquoIs it substantive innovation orstrategic innovationmdashimpact of macroeconomic policies onmicro-enterprisesrsquo innovationrdquo Economic Research Journalvol 4 pp 60ndash73 2016

[44] C Andrea ldquoFinancing constraints radical versus incrementalinnovation and aggregate productivityrdquo American EconomicJournal Macroeconomics vol 11 no 2 pp 75ndash309 2019

[45] J M Hilbe Modeling Count Data Cambridge UniversityPress Cambridge UK 2014

[46] S U Khan A Shah and M F Rizwan ldquoDo financingconstraints matter for technological and non-technologicalinnovation a (re) examination of developing marketsrdquoEmerging Markets Finance and Trade vol 57 pp 1ndash28 2019

[47] F Y Li and M Z Yang ldquoCan economic policy uncertaintyinfluence corporate investment the empirical research byusing China economic policy uncertainty indexrdquo Journal ofFinancial Research vol 57 no 9 pp 2739ndash2766 2015

[48] C J Hadlock and J R Pierce ldquoNew evidence on measuringfinancial constraints moving beyond the KZ indexrdquo Review ofFinancial Studies vol 23 no 5 pp 1909ndash1940 2010

[49] X S Ju D Lu and Y H Yu ldquoFinancial constraints workingcapital management and the sustainability of firm innova-tionrdquo Economic Research Journal vol 48 pp 4ndash16 2013

[50] J Li J Liang V Shi and Q Wang ldquo+e benefit of manu-facturer encroachment considering consumerrsquos environ-mental awareness and product competitionrdquo Annals ofOperation Research vol 3 pp 1ndash19 2021

[51] J Li F Wang and Y He ldquoElectric vehicle routing problemwith battery swapping considering energy consumption andcarbon emissionsrdquo Sustainability vol 12 no 24 Article ID10537 2020

[52] J Li L Yi V Shi and X Chen ldquoSupplier encroachmentstrategy in the presence of retail strategic inventory cen-tralization or decentralizationrdquo Omega vol 98 Article ID102213 2021

[53] J Li Z Hu V Shi and Q Wang ldquoManufacturerrsquos en-croachment strategy with substitutable green productsrdquo In-ternational Journal of Production Economics vol 235 ArticleID 108102 2021

14 Complexity

[21] G Scellato ldquoPatents firm size and financial constraints anempirical analysis for a panel of Italian manufacturing firmsrdquoCambridge Journal of Economics vol 31 pp 55ndash76 2007

[22] E Ughetto ldquoDoes internal finance matter for RampD newevidence from a panel of Italian firmsrdquo Cambridge Journal ofEconomics vol 32 no 6 pp 907ndash925 2008

[23] D Czarnitzki and H Hottenrott ldquoRampD investment and fi-nancing constraints of small and medium-sized firmsrdquo SmallBusiness Economics vol 36 no 1 pp 65ndash83 2011

[24] B H Hall ldquo+e financing of research and developmentrdquoOxford Review of Economic Policy vol 18 no 1 pp 35ndash512002

[25] J R Brown and B C Petersen ldquoCash holdings and RampDsmoothingrdquo Journal of Corporate Finance vol 17 no 3pp 694ndash709 2011

[26] D Li ldquoFinancial constraints RampD investment and stockreturnsrdquo Review of Financial Studies vol 24 no 9pp 2974ndash3007 2011

[27] A Mina H Lahr and A Hughes ldquo+e demand and supply ofexternal finance for innovative firmsrdquo Industrial and Cor-porate Change vol 22 no 4 pp 869ndash901 2013

[28] X Gu Y Chen and S Pan ldquoEconomic policy uncertainty andinnovation evidence from listed companies in Chinardquo Reviewof Financial Studies vol 2 pp 109ndash123 2018

[29] F Knight Risk Uncertainty and Profit Houghton MifflinCompany Boston MA USA 1921

[30] W Hao W Wei and J Wen ldquoHow does economic policyuncertainty affect corporate innovationmdashperspective ofmechanism of action of real option theoryrdquo EconomicManagement vol 38 pp 40ndash54 2016

[31] G Fan X Wang and G Ma ldquo+e contribution of Chinarsquosmarketization process to economic growthrdquo Economic Re-search vol 46 no 9 pp 4ndash16 2011

[32] K Z Dai and Y J Liu ldquo+e impact of marketization reformon RampD investment in Chinarsquos high-tech industryrdquo ScienceStudies vol 31 pp 50ndash57 2013

[33] P Li and X Liu ldquo+e impact of market-oriented institutionalchange on Chinarsquos technological progressmdashbased on theperspective of independent R amp D and technology intro-ductionrdquo Economic Perspectives vol 4 pp 42ndash50 2015

[34] K J Arrow ldquoEconomic welfare and the allocation of resourcesfor inventionrdquo 8e Rate and Direction of Inventive Activityvol 12 pp 609ndash626 1962

[35] P Kijkasiwat and P Phuensane ldquoInnovation and firm per-formance the moderating and mediating roles of firm sizeand small and medium enterprise financerdquo Journal of Riskand Financial Management vol 13 no 5 p 97 2020

[36] X N Yuan ldquoManufacturing innovation and service inno-vation comparison and convergencerdquo Finance and TradeResearch vol 20 pp 14ndash19 2009

[37] Y M Zhu and M E Jia ldquoMarketization process financialconstraints and corporate technology innovationrdquo Com-mercial ResearchInnovation Technology vol 1 pp 49ndash562017

[38] C P Wu and D Tang ldquoIntellectual property rights en-forcement corporate innovation and operating performanceevidence from Chinarsquos listed companiesrdquo Economic ResearchJournal vol 51 no 11 pp 125ndash139 2016

[39] B H Hall and D Harhoff ldquoRecent research on the economicsof patentsrdquo Annual Review of Economics vol 4 pp 541ndash5652012

[40] L Owen P Christopher and J Saaa-Requejo ldquoFinancialconstraints and stock returnsrdquo Review of Financial Studiesvol 14 pp 529ndash554 2001

[41] S R Baker N Bloom and S J Davis ldquoMeasuring economicpolicy uncertaintyrdquo 8e Quarterly Journal of Economicsvol 131 no 4 pp 1593ndash1636 2016

[42] J F He and X J Zhang ldquo+e influence of rising labor cost onenterprise innovationrdquo 8e Journal of Quantitative amp Tech-nical Economics vol 45 no 8 pp 56ndash73 2018

[43] W J Li and M N Zheng ldquoIs it substantive innovation orstrategic innovationmdashimpact of macroeconomic policies onmicro-enterprisesrsquo innovationrdquo Economic Research Journalvol 4 pp 60ndash73 2016

[44] C Andrea ldquoFinancing constraints radical versus incrementalinnovation and aggregate productivityrdquo American EconomicJournal Macroeconomics vol 11 no 2 pp 75ndash309 2019

[45] J M Hilbe Modeling Count Data Cambridge UniversityPress Cambridge UK 2014

[46] S U Khan A Shah and M F Rizwan ldquoDo financingconstraints matter for technological and non-technologicalinnovation a (re) examination of developing marketsrdquoEmerging Markets Finance and Trade vol 57 pp 1ndash28 2019

[47] F Y Li and M Z Yang ldquoCan economic policy uncertaintyinfluence corporate investment the empirical research byusing China economic policy uncertainty indexrdquo Journal ofFinancial Research vol 57 no 9 pp 2739ndash2766 2015

[48] C J Hadlock and J R Pierce ldquoNew evidence on measuringfinancial constraints moving beyond the KZ indexrdquo Review ofFinancial Studies vol 23 no 5 pp 1909ndash1940 2010

[49] X S Ju D Lu and Y H Yu ldquoFinancial constraints workingcapital management and the sustainability of firm innova-tionrdquo Economic Research Journal vol 48 pp 4ndash16 2013

[50] J Li J Liang V Shi and Q Wang ldquo+e benefit of manu-facturer encroachment considering consumerrsquos environ-mental awareness and product competitionrdquo Annals ofOperation Research vol 3 pp 1ndash19 2021

[51] J Li F Wang and Y He ldquoElectric vehicle routing problemwith battery swapping considering energy consumption andcarbon emissionsrdquo Sustainability vol 12 no 24 Article ID10537 2020

[52] J Li L Yi V Shi and X Chen ldquoSupplier encroachmentstrategy in the presence of retail strategic inventory cen-tralization or decentralizationrdquo Omega vol 98 Article ID102213 2021

[53] J Li Z Hu V Shi and Q Wang ldquoManufacturerrsquos en-croachment strategy with substitutable green productsrdquo In-ternational Journal of Production Economics vol 235 ArticleID 108102 2021

14 Complexity