exporting and innovating among emerging market firms: the … · exporting and innovating among...

Exporting and innovating among emerging

market firms: The moderating role

of institutional development

Zhenzhen Xie1 and Jiatao Li2

1School of Economics and Management, Tsinghua

University, Beijing, China; 2Department of

Management, School of Business andManagement, Hong Kong University of Science &

Technology, Clear Water Bay, Kowloon,

Hong Kong, SAR, Hong Kong

Correspondence:J Li, Department of Management, School ofBusiness and Management, Hong KongUniversity of Science & Technology, ClearWater Bay, Kowloon, Hong Kong, SAR,Hong Kong.Tel: (852) 2358 7757;e-mail: [email protected]

AbstractBuilding on the recombinatory view of innovation and an institutional

perspective, this study investigated how the level of institutional

development in a firm’s home region and the institutional distance betweenthe home and export markets might influence the effectiveness of learning

through exporting. For exporters in emerging economies, more support for

R&D and better-developed market intermediaries at home were found toenhance the positive effect of exporting on firms’ innovation, while market

openness in the home region tends to dampen it. Exporters exporting more to

other emerging economies tend to be more innovative than those exportingmore to advanced markets. These findings arise from an empirical study of

exporting and innovation among Chinese manufacturers.

Journal of International Business Studies (2017).doi:10.1057/s41267-017-0118-4

Keywords: exporting; institutional development; innovation; emerging economies;China

INTRODUCTIONExporting provides firms an opportunity to access knowledge fromelsewhere, which can serve as an effective means of organizationallearning (Bratti & Felice, 2012; Lileeva & Trefler, 2010; Salomon &Jin, 2010). Governments in emerging markets (EMs) activelyencourage exporting and consider export performance as one ofthe most important indicators of firms’ technological sophistica-tion (Thun, 2006). However, EM exporters find it hard to translatetheir information advantage into an innovation advantage (Navas-Aleman, 2011; Yao, 2012). This is not surprising, as prior researchhas found that such translation usually requires strong technolog-ical competence, superior absorptive capacity, and close and directrelationships with technologically sophisticated customers (Li,Chen & Shapiro, 2010; Salomon, 2006; Smith, 2014), all of whichEM exporters are typically not endowed with. While it is difficultfor EM firms to overcome such limitations, macro-level institu-tional development in EMs might help such firms to benefit morefrom what they learn through exporting.

Received: 21 October 2014Revised: 24 July 2017Accepted: 15 August 2017

Journal of International Business Studies (2017)ª 2017 Academy of International Business All rights reserved 0047-2506/17

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Prior research’s focus on firm-level, internalfactors specific to exporters might have obscuredhow macro-level, external institutions can help orhinder EM exporters’ innovation (Corredoira &McDermott, 2014). Building on the recombinatoryview of innovation and the institutional perspec-tive, this study was designed to explore to whatextent aspects of institutional development in afirm’s home region and institutional distancesbetween the home and destination countries mightinfluence EM firms’ ability to benefit from export-ing. Although institutional development has beenproceeding in most EMs in recent years, not all ofthe developments are equally beneficial for theirfirms (Cuervo-Cazurra & Dau, 2009; Kim, Kim &Hoskisson, 2010).

An important reason why exporters tend to bemore innovative is that they have access to overseasknowledge via value chain activities integrated intothe global market (Alcacer & Oxley, 2014; Bratti &Felice, 2012; Lileeva & Trefler, 2010). But such aninformation advantage can be offset if firms can getsimilar access without exporting. A home marketthat is open to the cross-border transfer of capital,goods, and knowledge allows for such offsetting.Firms may benefit from knowledge spillovers takingplace within the home region via demonstrationeffects and personnel mobility, or seek foreignknowledge by importing technology and capitalgoods, collaborating with multinational investorsor even reaching out through outward foreigndirect investment (FDI) (Bertschek, 1995; Braga &Willmore, 1991; Chang & Xu, 2008; Khanna &Palepu, 2010; Luo & Tung, 2007). Therefore, whilemarket openness may improve the innovationabilities of local firms in general, it weakens theinformation and innovation advantages gainedthrough exporting.

Getting access to overseas knowledge is only thefirst step. Prior research has shown that innovationsoften involve the novel recombination of knowl-edge which was previously considered unrelated(Fleming, Chen & Mingo, 2007; Hargadon & Sut-ton, 1997). EM exporters need to process theknowledge they acquire through exporting, whichcan involve extensive adaptation, combining itwith relevant local knowledge (Corredoira &McDermott, 2014; Herrigel, Wittke & Voskamp,2013). Home institutions that support local R&Dactivity tend to create a pool of technologicallycapable local entities, serving as an importantsource of local knowledge (Corredoira & McDer-mott, 2014). Effective market intermediaries in the

home region facilitate the combination of overseasand local knowledge by facilitating knowledgeflows between exporters and local entities (Khanna& Palepu, 2010; Kostinets, 2014; Nicholas &Shimizu, 2013; Zhang, Li, Hitt & Cui, 2007).In addition to the level of institutional develop-

ment at home, the institutional distance betweenhome and overseas markets may also influence therelationship between exporting and innovationperformance. Exporting to countries with a similarlevel of institutional development may make iteasier to combine local knowledge with the knowl-edge acquired through exporting and thus toinnovate (Lane & Lubatkin, 1998). This is especiallylikely to be true for EM firms, who suffer from poortechnological capabilities and limited internationalexperience.The results of this empirical study of Chinese

manufacturers largely support these arguments.Export intensity was found to be positively relatedwith the output of new products. The relationshipwas stronger in provinces where governmentsinvest more in R&D and where market-supportingintermediaries are better developed. In provinceswhere market is more open to the rest of the world,the relationship was dampened. Those Chineseexporters exporting a larger proportion of theirproducts to other emerging economies tend to bemore innovative than those exporting more toadvanced markets. Further analysis showed thatChinese exporters’ innovation benefited more fromexporting to countries with a similar culture,similar levels of knowledge, and similar globalconnectedness.This research broadens the application of the

institutional perspective in international businessresearch. It extends prior work on learning byexporting by showing that the effectiveness oflearning by exporting can be influenced not onlyby internal, firm-level factors but also by external,macro-level institutions. Market-opening policies,systems supporting innovation, and market inter-mediary development influence different stages ofthe journey from exporting to innovating. The firsttwo aspects of institutional development shape theforeign and local resources available for EM firms’innovation, while the third influences the cost ofresource recombination. Limited market openness,weak local support for innovation, and weak mar-ket-supporting intermediaries can all be consideredas aspects of weak institutions which impededevelopment, but efforts to ameliorate them seemnot to benefit all firms equally. EM exporters can

Exporting and innovating among emerging market firms Zhenzhen Xie and Jiatao Li

Journal of International Business Studies

take advantage of the isolation of their homeregion to create arbitrage opportunities and todevelop an edge in innovation over their competi-tors. Market openness constrains such opportuni-ties. Some other aspects of institutionaldevelopment benefit exporters more than non-exporters, such as government support for localR&D and the development of marketintermediaries.

The findings of this study also highlight theimportance of considering the dissimilarities orgaps in institutions between the economiesinvolved. The data show that such gaps dampenthe effectiveness of cross-border combination ofknowledge. Further analysis shows that betterabsorptive capacity for technology does not reducethe negative effect of institutional distance, indi-cating that absorptive capacity for technology maynot be the only limiting factor. EM firms mightneed to develop a different sort of absorptivecapacity based on the institutional practices theymust deal with if they are to make the best use ofthe knowledge they can gain by exporting to moredeveloped countries.

THEORETICAL BACKGROUND

Exporting and Learning by Emerging MarketFirmsThe exchange of tangible goods often leads to thetransfer of intangible knowledge (Grossman &Helpman, 1993). Exporting firms are usuallyexposed to new technology and market knowledgeoverseas that is more diverse than that available athome, if it is available at all. This allows forinformation arbitrage (Kogut, 1989), and the accu-mulation of diverse knowledge about markets andtechnology tends to promote performance amongexporters. Early firm-level empirical studies onlearning-by-exporting mainly focused on discover-ing any causal relationship between exporting andfirms’ productivity (for a review, see Wagner, 2007).The inconclusive findings drove this stream ofresearch to shift its focus in recent years from theeffect on productivity to that on innovation (e.g.,Chittoor, Aulakh & Ray, 2015; Li et al., 2010). It isbelieved that innovation ‘‘…can more directlymeasure the outcome of interest – learning byexporting’’ (Salomon & Shaver, 2005). Today, firm-level empirical studies relating exporting withinnovation report reasonably consistent positiveresults. While firms from both developed countries

and EMs have been shown to benefit from export-ing in terms of innovation performance (Bratti &Felice, 2012; Damijan, Kostevc & Polanec, 2010;Fafchamps, Hamineb & Zeufackc, 2008; Li et al.,2010; Salomon & Jin, 2008, 2010), the benefits tendto be especially important for the latter (Amsden,1989, 2001; Hobday, 1995).Compared with FDI and other routes to interna-

tionalization, exporting involves less commitmentor risk, and requires less sophisticated managementskills (Cassiman & Golovko, 2011). Exporting istherefore the usual first step for EM firms seeking tointernationalize their sales (Luo & Tung, 2007). Thelearning benefits of exporting are one of thereasons why many EM governments encourageexporting with policies such as setting up export-processing zones, export tax incentives, exportcartels, and export quality inspection (Amsden,1989; Wade, 1990). In EMs, learning by exportingtakes place in many different ways. For example, inorder to ensure the quality and performance of thegoods they import, foreign importers may transferextensive knowledge about production techniques,quality and cost control measures, their customers’needs, and even information about competingproducts (Evenson & Westphal, 1995; Hobday,1995; Wu, Sinkovics, Cavusgil & Roath, 2007).But EM firms may not benefit from exporting to theextent that AE firms do (Navas-Aleman, 2011; Yao,2012).The effectiveness of inter-organizational learning

is jointly determined by the characteristics of theknowledge provider, the knowledge receiver, andthe relationship between them (Ingram, 2002).Scholars have found that the effectiveness oflearning by exporting is constrained by many firm-and dyad-level factors such as the exporter’s tech-nological capabilities and absorptive capacity (Al-cacer & Oxley, 2014; Li et al., 2010; Salomon & Jin,2010; Smith, 2014), the technological sophistica-tion of the foreign customer (Alcacer & Oxley,2014; Salomon, 2006), and the duration, closeness,and quality of the relationship between the expor-ter and the foreign customer (Alcacer & Oxley,2014; Salomon, 2006). Unfortunately, EM firmstend to be disadvantaged in all of these areas. Theirtechnology is usually weak, they are inexperiencedin international business, and they are unfamiliarpartners for foreign customers involved in low-endtransactions. Salomon and Jin (2010) found thattechnology laggards apply for fewer patents thantechnology leaders subsequent to exporting. Li andhis colleagues have shown that Chinese firms need



to invest in R&D and advertising in order toenhance the positive relationship between export-ing and innovation (Li et al., 2010). Smith foundthat the net effect of exporting on a firm can evenbe negative if it does not ‘‘…possess the necessarytechnological capabilities, absorptive capacity, andresources at home to utilize spillover benefits fromabroad fully, or to meet demand for more advancedproducts abroad’’ (Smith, 2014: 254). While export-ing creates a channel for learning from overseas,how that knowledge is applied to innovation is acomplex matter. Looking into the process may helpexplain how some EM firms benefit more fromexporting than others.

A Recombinatory View of InnovationStarting from Schumpeter (1934), many influentialscholars have viewed innovation as a new recom-bination of existing knowledge (Cohen & Malerba,2001; Fleming, 2001, Henderson & Clark, 1990;Kogut & Zander, 1992, Nelson & Winter, 1982).That conceptualization considers innovation as notonly a search for new knowledge, but also an effortto combine new and old components in novel ways(Fleming, 2001). Firms with multiple knowledgesources are likely to have access to more diverseinputs, and recombining it is believed to give themmore potent opportunities for high-quality andmore valuable innovation (Faems, de Visser,Andries & Van Looy, 2010; Wang, Chen & Chang,2011). In addition, diverse knowledge sourcesencourage management to make an explicit cogni-tive investment in retrospective sense-making. Thisminimizes the risk of inappropriate generalizationof knowledge acquired in each specific context, andit also stimulates more-deliberate learning in anorganization (Zollo & Winter, 2002).

Access to diverse knowledge sources leads to adiverse knowledge base internally. Scholars havefound that firms with more diversified knowledgeportfolios tend to invest more in R&D, file morepatent applications, and announce more break-through innovations (Garcia-Vega, 2006; Quin-tana-Garcia & Benavides-Velasco, 2008; Srivastava& Gnyawali, 2011). This may help to explain whyfirms have become increasingly active in organizingcomplex knowledge-seeking activities in recentyears (Chesbrough, 2003; Faems et al., 2010).Suppliers, customers, competitors, universities,and others can all serve as external knowledgesources for firms (Savino, Petruzzelli & Albino,2017). Some firms internationalize their valuechain explicitly to tap into knowledge sources in

different countries (Hitt, Hoskisson & Kim, 1997;Singh, 2008). Some others manage a portfolio ofdiverse alliance partners and/or diverse businessactivities in order to maintain access to diverseknowledge sources (Powell, Koput & Smith-Doerr,1996).Diverse external knowledge sources and a diverse

internal knowledge base enlarge the pool of knowl-edge components that a firm may potentiallyapply, but they could be useless if a firm fails tointegrate new knowledge with its own (Enkel &Gassman, 2010; Savino et al., 2017; Zhao & Islam,2017). That suggests that exporters observed to bemore innovative than non-exporters may achievethat superior innovation performance not only bytapping into overseas knowledge, but also bysuccessfully combining what they learn with theirlocal, experiential knowledge. As Corredoira andMcDermott (2014) have shown, combining domes-tic and foreign knowledge is a key mechanism bywhich EM firms innovate. They found that inter-actions with multinational customers were mosteffective in improving the technological compe-tence of Argentine auto parts suppliers when therewere complementary interactions with local enti-ties such as industry associations and the govern-ment’s supporting institutions.

Institutional Development as a ResourceInstitutions can be regarded as constraints devisedto structure interaction among social entities(North, 1990). They are supported by normative,cognitive, and coercive pillars (Scott, 1995). Theystrongly influence people’s behavior, and organiza-tions’ strategies and performance (Meyer & Rowan,1977; DiMaggio & Powell, 1983). Internationalbusiness scholars usually consider a society’s insti-tutions as a resource, a major determinant oftransaction costs, and a common basis of differenceamong economies (Jackson & Deeg, 2008). Thesethree roles of institutions tend to influence differ-ent stages of learning by exporting and innovationthrough recombination.Institutions to some extent shape an economy’s

resource environment and thus the resources andcapabilities of firms embedded in that environment(Jackson & Deeg, 2008; Spencer, 2008). Wheninstitutional arrangements isolate a market fromcapital, goods, or technology transfer from over-seas, it tends to be very deficient in overseasknowledge. Such knowledge is rare and difficult toreplace, giving it greater potential to generatesustainable competitive advantage for a firm which



can acquire it (Barney, 1991). When exporting ispermitted, exporters transacting with overseas cus-tomers are more likely to be the owners of such rareknowledge, giving them an information advantage.That competitive edge gives exporters morechances than non-exporters to make novel recom-binations constituting innovation.

In a market more open to trade and foreigncapital, the information advantage of exportersmay be offset by alternative access to overseasknowledge. Multinational enterprises (MNEs) mayarrive with their products and investment. Theybring overseas knowledge and may share it withtheir local partners or transaction counterparties.Even local firms with no direct relationship withan MNE may benefit through unintentionalknowledge spillovers generated via demonstrationeffects and personnel mobility. In addition, allfirms in a more open institutional environmenthave better chances to seek overseas knowledge byimporting technology and capital goods, or evenreaching out through investing overseas(Bertschek, 1995; Braga & Willmore, 1991; Chang& Xu, 2008; Khanna & Palepu, 2010; Luo & Tung,2007). Conner and Prahalad have shown thatsuch organizational solutions are often moreeffective than arm’s length transactions in trans-ferring tacit or complex knowledge (Conner &Prahalad, 1996).

After the market is open, exporters tend to losetheir information advantage. They may no longerbe able to sustain outstanding innovation perfor-mance, as a marginal increase in exporting may notbring in as much innovation advantage as before.Even if they are still very innovative, exporters mayalso have other access to overseas knowledge andno longer rely heavily on exporting as a majorknowledge source. In an open market, exporting isjust an elementary internationalization techniquewhich may not be able to bring in rare resources forwhich there is no ready substitute. Its contributionto firms’ innovation is thus reduced.

Compared with more advanced economies, EMsare generally less open to overseas capital andproducts. Within an EM, the level of marketopenness often varies among regions (Chin, 2010;Kafouros, Wang, Piperopoulos & Zhang, 2015;Kozhikode & Li, 2012; Pack & Saggi, 1997).Exporters’ information advantage is more likely tobe offset in a home region that is relatively open toglobal trade and investment (Chang & Xu, 2008;Zhang, Li, Li & Zhou, 2010).

Hypothesis 1: Greater openness to the globalmarket in the home region weakens the positiverelationship between exporting and innovationfor emerging market firms.

Even with superior access to knowledge fromabroad, exporters may not be able to properlyexploit that advantage and innovate if their otherknowledge sources are inadequate. The homeregion is, however, an alternative source of knowl-edge that is conveniently at hand for EM firms.Emerging economies are frequently considered aslacking advanced technology, but recent scholar-ship on comparative capitalism has shown thatlocal organizations in emerging economies can beimportant sources of applied, experiential knowl-edge which is particularly relevant locally (Corre-doira & McDermott, 2014). Studies have revealedthat in both emerging markets and developedcountries, new production capabilities are mainlydeveloped by adapting knowledge to new contexts,combining routines and techniques from differentsources (Herrigel et al., 2013; MacDuffie, 2013).Among all exporters, those with better access tohome knowledge tend to have a better chance tocombine it with overseas knowledge and innovate.Countries and regions develop institutions such

as innovation systems to promote local sourcing ofknowledge. These institutions provide resources tolocal knowledge contributors, while the majorknowledge contributors – typically enterprises,public research institutes, universities, etc. – mayvary from country to country (Kumaraswamy,Mudambi, Saranga & Tripathy, 2012; Motohashi& Yun, 2007). For example, financial support fromthe government, typically through subsidy pro-grams and tax preferences, provides basic resourcesfor innovation and at the same time reduces thepotential costs of failure (Hall & Lerner, 2010). Thisenables local entities to engage in innovativeactivities and to generate more local knowledge.Such institutional resources are especially beneficialfor exporters as they help to develop a pool oftechnologically capable and motivated local playerswhich can serve as alternative sources of knowl-edge. Exporters operating in such an environmenthave better access to local knowledge which theycan recombine in novel ways with the knowledgethey acquire through exporting. It has been shownthat where local competitiveness is appropriate,foreign buyers can contribute to upgrading tech-nology in emerging markets (Giuliani, Pietrobelli &Rabellotti, 2005).



Hypothesis 2: Institutions that support localinnovation in the home region strengthen thepositive relationship between exporting andinnovation for emerging market firms.

Market Intermediaries and the Costsof Knowledge RecombinationThe existence of multiple sources of knowledgedoes not guarantee that they are readily availablefor firms at reasonable cost. The cost of knowledgesearching, contracting, and monitoring can behuge if professional market intermediaries are notwell developed in a market. Transaction costs tendto discourage knowledge transactions and recom-bination (Williamson, 1975). In addition to pro-viding resources, a second role of institutions in theprocess of innovation is to reduce the cost ofknowledge transactions (Jackson & Deeg, 2008),facilitating knowledge transfer and recombination.

Compared with advanced economies, EMs typi-cally suffer from a lack of specialized intermediariessuch as brokerages, law firms, accounting firms,consulting firms, and industry associations. Suchinstitutional voids may pervade the capital, factor,and product markets in an emerging economy(Khanna & Palepu, 2010). They make it expensiveand sometimes impossible to search for potentialtransaction counterparties, to make contractssmoothly and efficiently, and perhaps to enforcecontracts which have been signed. That situation isespecially difficult when there are intangible assetssuch as knowledge involved in a transaction.Intermediaries are usually required to play the roleof pricing, trust-building, and endorsement in suchtransactions. While informal institutions such asguanxi may sometimes replace the role of marketintermediaries in EMs, they are typically less effec-tive in facilitating transactions between unfamiliarparties (Peng, 2003). The absence of market inter-mediaries can be an important antecedent ofmarket failure (Khanna & Palepu, 2000). ManyEMs seek to fill such voids, but firms benefitdifferently from such efforts (Chittoor et al., 2015;Cuervo-Cazurra & Dau, 2009; Kim et al., 2010). Forexample, Dau (2013) found that in Latin America,pro-market reforms enhanced the competitivenessof internationalized firms more than that of purelydomestic ones.

Effective market intermediaries might beexpected to improve the innovativeness of expor-ters more than that of non-exporters. By facilitatingthe flow of knowledge, brokering between knowl-edge sellers and buyers, and providing

complementary expertise and resources to reduceinteraction costs (Kostinets, 2014; Nicholas &Shimizu, 2013), market intermediaries may signif-icantly reduce the transaction costs involved insourcing from multiple knowledge sources andhelp exporters with their adaptation and recombi-nation efforts, such as hiring quality R&D staff,studying dynamic market conditions, adapting orfurther improving their products, and looking forlocal suppliers or financial support. That shouldhelp exporters to build on the knowledge they haveacquired through exporting and that they haveacquired locally to innovate.

Hypothesis 3: Better-developed market inter-mediaries in the home region strengthen thepositive relationship between exporting andinnovation for emerging market firms.

Institutional Distance and InnovationIn addition to the existence of multiple knowledgesources and efficient access to them, the dissimi-larity of knowledge acquired from different sourcesshould be another important determinant of suc-cessful recombination. This is where institutionaldistance between nations enters the picture, as toolarge a distance can make knowledge acquired indifferent markets too different to combine.Knowledge tends to be localized geographically

(Almeida & Kogut, 1999; Owen-Smith & Powell,2004) and imperfectly distributed (Dicken, 2003;Hayek, 1945). Those pieces relevant to organiza-tional practices vary from country to country sincethey have evolved and are being used in differentinstitutions (Kostova, 1999). Firms exporting todifferent countries dealing with such diversity mayhave access to and absorb different types of knowl-edge with different levels of sophistication anddiffering relevance at home. Institutional distancemakes the knowledge acquired in different marketsless similar, less consistent, and less related witheach other (Eden & Miller, 2004; Ghemawat, 2001;Kostova, 1999).Knowledge components that are more similar to

each other have a better chance of being integrated.Research on technological innovation has shownthat technology coherence – the extent to which atechnology portfolio consists of technologies ‘‘thatpartly share a common knowledge base, that relyon common scientific principles, or that havesimilar heuristics of search’’ (Leten, Belderbos &Van Looy, 2007: 570), tends to make the effect oftechnology diversification on innovation more



positive. Research on diversity has shown that taskgroups working in very different task environmentstend to find little similarity in their knowledgebases, to have less to share, and to feel they haveless to gain from sharing (De Dreu, 2006; Jehn,1995). This may lead to dysfunctional conflict,hinder communication and coordination, anddecrease social integration, all of which would beexpected to inhibit knowledge transfer, sharing andintegration within a firm (De Dreu, 2006; Harrison& Klein, 2007; Jehn, 1995; Li & Hambrick, 2005).

Firms usually find it difficult to recombine knowl-edge acquired from very different environments(Breschi, Lissoni & Malerba, 2003; Lane & Lubatkin,1998; Li & Shenkar, 1996; Li & Yue, 2008; Phene,Fladmoe-Lindquist & Marsh, 2006), specificallyoverseas markets with very large institutional dis-tance from their home country (Kostova, 1999).Institutional distance leads to different technicaland organizational problems, calling for dissimilar-ity in incentive schemes and organization structuresfor innovation. This tends to substantially decreasethe coherence between the knowledge exportersmay source from destination countries and whatthey can source locally (Lane&Lubatkin, 1998). Thisis especially true for EM firms, which tend to sufferfrom limited international experience and weakabsorptive capacity. EM firms may find it easier toidentify, understand, and combine the knowledgetheyhave acquired through exporting todestinationcountries with institutions more similar to those athome. It has been found, for example, thatmanagersof EM firms are able to operate more easily in othercountries with similar institutions because they‘‘…can more easily understand and adapt to [the]poor conditions than can their developed countrycounterparts’’ (Cuervo-Cazurra & Genc, 2008).Exporting to destination countries institutionallydistant from the home country may make it moredifficult for exporting firms to innovate.

Hypothesis 4: Exporting to overseas marketswith similar institutions promotes innovation byemerging market firms more effectively thanexporting to overseas markets with very differentinstitutions.

METHODS

DataThe substantial volume of its exports and its signif-icant institutional fragmentation make China asuitable context for studying these issues

empirically. The hypotheses were tested usingarchival data collected in annual censuses of indus-trial enterprises conducted by China’s NationalBureau of Statistics (NBS) between 2005 and 2007.Each year the NBS collects financial information onindustrial firms in China with annual sales of at leastUS $600,000 (roughly, at the 2006 exchange rate)and publishes aggregated information in the officialChina Statistics Yearbooks. That database has beenused in prior studies, including those of Buckley(Buckley, Clegg & Wang, 2002), Chang and Xu(2008), Pan (Pan, Li & Tse, 1999), and Park’s group(Park, Li & Tse, 2006). The 2005–2007 datawere usedhere mainly because some of the most importantmeasures for this study, such as export intensity,new product output, and R&D investment, haveonly been available during this period.Hypotheses 1–3 focus on the effects of exporting

and home region institutional development onexporting firms’ innovation performance. Testingthem involved comparing the innovation perfor-mance of exporters and non-exporters. The sampleused to test the first two hypotheses consisted of allmanufacturing firms with two-digit China StandardIndustry Classification codes from 13 to 43 coveredby the census (482 four-digit SIC sectors in total),with or without export sales. Those firms whoseinformation was incomplete were excluded. Firmswhich changed their primary sector during theperiod studied were also dropped because a changein primary sector usually means a change in thefirm’s primary business, or at least substantialproduct diversification. The complex interactionsbetween product diversification, exporting, andinnovation were beyond the scope of this study.The yearly data were matched using unique

company identifiers to assemble an unbalancedpanel database. All the explanatory variables werelagged by one year in the analyses to deal withcausality. This reduced the sample to thoseobserved in 2006 and 2007. The final sampleinvolved 253,559 firms and 431,673 firm-yearobservations (203,198 observations in 2006, and228,475 in 2007). Among the observations in 2006,69.7% of the firms said they did not export, 8.3%claimed to export part of their production, and22.0% exported all their products in the previousyear, i.e., 2005. In 2007, the three ratios were 71.2,7.8, and 21.0%, respectively. 10.6% of the manu-facturers said they introduced new products in2006, and 9.4% claimed to have done so in 2007.Hypothesis 4 focuses on institutional distance. The

non-exporters (who of course face no institutional



distance) were excluded in that analysis. The infor-mation about export destinations is published byChina’s General Administration of Customs. Theirdatabase contains information about each cross-border transaction since 2001. For each transaction,the database reports whether it was importing orexporting, the first origin or final destination coun-try of the goods, the type, quantity, unit price, andtotal value of the goods, and the name, location, andcontact information of the Chinese firms involved.

In 2005, there were 4.8 million transactionsrecorded in the customs database involving43,304 firms recorded in the NBS database, ofwhich 38,934 firms were involved in exporting. In2006, the corresponding figures were 6.4 milliontransactions involving 51,160 firms of which45,527 firms were involved in exporting. Combin-ing the customs database and NBS databasesyielded a two-year panel of data where the explana-tory variables were measured in 2005 and 2006 andthe dependent variables were measured in 2006 and2007. After dropping the observations without anyexporting to EMs or AEs and those with incompleteinformation, the final sample had 78,790 firm-yearobservations, among which 36,286 exported in2005 and 42,504 exported in 2006.

Measures

Dependent variableBased on the NBS’ definition, new products are thosethat are new to the market with either a completelynew design, new technology or based on a newscientific principle, or which offer a significantimprovement over existing products. Patented newproducts considered internationally competitive aregiven priority for certification as being new, whichbrings various tax and regulatory advantages (Li et al.,2010; Zhou& Li, 2008). So firms have no incentive tohide their new product output. Previous research hasused similar definitions (e.g., Laursen & Salter, 2006).

Product innovation was quantified as the value ofa firm’s new product output each year. To reducethe skew in this variable, a logarithmic transforma-tion was applied. So new product outputt = ln(1 + the value of new product output in year t).

Explanatory variablesExport intensityt-1 was calculated by dividing exportsales in year t - 1 by total sales in the same year. Alogarithmic transformation was again applied toallow for the possibility of non-linearity and toallow the coefficient to be interpreted as elasticity.

As in other EMs, institutional development inChina varies widely from place to place (Kwon,2012; Lu & Ma, 2008). Some regions have muchbetter-developed institutions than others (Peng,2003; Shi, Sun, Pinkham & Peng, 2014). Marketopenness, support for innovation, and developmentof market intermediaries were thus measured on theprovincial level. Market openness is the extent towhich goods and capital can flow freely across amarket’s boundaries (Finger, 2011). Since the late1970s, China has started to open its markets to therest of the world step by step. The open-door policywas first introduced in the four special economiczones in Shenzhen, Zhuhai, Shantou, andXiamen in1980. The policy loosened restrictions on interna-tional business, providing incentives for interna-tional trade and the cross-border flow of capital,technology, and equipment. In 1983, the policy wasextended to Hainan province and fourteen coastalcities. Latermore andmore areas came to be covered,such as the Yangtze River delta, the Pearl River delta,and theLiaodongandShandongpeninsulas.Numer-ous economic development zones were set up (Liou,2012). The number of special economic zones,coastal cities, and national economic developmentzones in each province in 2005 and 2006was used toquantify the level ofmarketopennessof thedifferentprovinces. The counts were expressed as the variableregional market opennesst-1.Previous research on innovation systems has usu-

ally used the volume and the composition of R&Dinvestments to represent the support for innovationprovided by local institutions (e.g., Motohashi &Yun, 2007). In this research, the R&D investmentsmade by the government in each province in yeart - 1were used to represent institutional support forinnovation as well as the local knowledge supply.The information was collected from the ChinaStatistical Yearbooks on Science and Technologyfor 2006 and 2007 (National Bureau of Statis-tics & Ministry of Science and Technology, 2006,2007). To account for the inter-provincial differ-ences in industry composition, the number ofemployees hired by high-tech industries in thedevelopment zones of each province in year t - 1was used to represent the scale of high-tech industry.The data came from the NBS. Government’s invest-ment in R&D was divided by the number hired toevaluate the variable regional R&D fundingt-1. Theunit of this variable is RMB ten thousand.The development of market intermediaries in

each province was quantified with one of the sub-indices of the marketization index developed by



Fan, Wang, and Zhu (2010). They developed aprovince-based index evaluated annually since2003 designed to capture regional differences inthe development of market-supporting institu-tions. Their composite index is constituted frominformation in five categories: (1) government andmarket forces; (2) development of enterpriseswhich are not state-owned; (3) development ofcommodity markets; (4) development of factormarkets; and (5) development of market interme-diaries and the legal environment. One sub-indexof the fifth category describes the development ofmarket intermediaries such as consulting firms,accounting firms, law firms, and industry associa-tions in each province. The variable was namedregional intermediary developmentt-1. A larger valueindicates better development of marketintermediaries.

China was considered an EM in light of itsprevalent institutional voids (Khanna & Palepu,2010). China’s level of institutional developmentwas treated as similar to that of economiesconsidered EMs by the International MonetaryFund. The variable EM vs AE export intensityt-1 wasdefined to represent a Chinese firm’s exporting toEM versus AE economies. It was defined as thevalue of products exported to other EMs in eachyear as a proportion of the firm’s total exports toall AEs and other EMs. The list of EMs and AEs in2007 was that of the International Monetary Fundin 2005 and 2006. In fact, only 1.48% of China’sexports by value during the period studied wentto the least developed countries as identified bythe United Nations. Chinese firms that onlyexported to such countries were excluded fromthe analysis.

Control variablesMarket openness introduces not only foreignknowledge but also foreign competition into thehome region. Market competition has been shownto influence firms’ product innovation strategies(Aghion, Bloom, Blundell, Griffith & Howitt, 2005).To partial out such effects, a Hirschman–Herfindhalindex (HHI) was used to represent sector marketconcentrationt-1. For each firm i in industry k,

HHIikt�1 ¼Xnkt�1

i¼1

salesikt�1

.Xnkt�1

j¼1salesjkt�1

� �2:

It is generally believed that highly concentratedindustries are limited in competition (e.g., Chen,2008; Li, 2008; Li et al., 2010). Knowledge diversity

has been found to be more beneficial when themarket is more competitive (Pitcher & Smith,2001). The potential moderating effects of marketcompetition were therefore considered by includ-ing the interaction between export intensity andmarket competition in the models as export inten-sityt-1 9 sector market concentrationt-1.Intellectual property (IP) protection tends to

encourage innovation by protecting the hoped-forreturns (Khanna & Palepu, 2010; Zhang et al.,2007). To take this effect into account, regional IPprotectiont-1 was measured using the number of IPlawsuits closed divided by the number of IPlawsuits filed in each province in year t - 1. Theinformation came from the China Yearbooks onIntellectual Property for 2006 and 2007.Most empirical studies have measured capacity

for absorbing technology in terms of prior relatedknowledge, and a majority have operationalized itusing R&D expenditures (for a review, see Volberda,Foss & Lyles, 2010). Following that lead, techabsorptive capacityt-1 was a firm’s R&D expendituresin year t - 1 as a proportion of its sales in that yearand multiplied by 100. A logarithmic transforma-tion was then applied to allow for the possibility ofnon-linearity. To control for any potential influ-ence of absorptive capacity on the effectiveness oflearning by exporting, a term representing theinteraction between export intensityt-1 and techabsorptive capacityt-1 was also included in theregressions.There has been some debate over the impact of

firm size on innovation. Schumpeterian economistssuggest a positive relationship, as larger firms canhedge the risks of R&D by pursuing diverse projects(Braga & Willmore, 1991; Sasidharan & Kathuria,2011). Organization scholars have, however, con-tended that larger firms tend to be less innovativeas they are less affected by market competition(Kathuria, 2008). Previous research has foundmixed empirical evidence, most of which suggestsa non-linear relationship (Kumar & Aggarwal, 2005;Kumar & Saqib, 1996; Siddharthan, 1988). We usedRMB 10 million as the unit for the variable firmsizet-1. Following the lead of previous research (Liet al., 2010), the natural logarithm of a firm’s salesin year t - 1 was used as a measure of firm size totake any potential non-linearity into account.The impact of a firm’s age on its innovation

performance is also not clear. With rich comple-mentary assets accumulated over years, older firmsare more likely to make money from any R&Dinvestment and so may be more motivated to



innovate (Iansiti, 2000; Sasidharan & Kathuria,2011). However, older firms are also more likelyto suffer from organizational inertia, which makesit difficult for them to adapt their resources,processes, and strategies to new technologies orproducts (Dougherty & Hardy, 1996; Hannan &Freeman, 1984). In any case, firm aget-1, calculatedby subtracting a firm’s setup year from year t - 1,was one of the variables included in the analyses.

Organizational slack is also likely to influence afirm’s innovation in different ways (Cyert & March,1963). It on the one hand enables a firm to invest inlong-term R&D projects, and on the other handmay either diminish innovation incentives orencourage high-risk projects (Nohria & Gulati,1996). Previous research has found mixed evidenceabout the impact of organizational slack on firms’innovation (Greve, 2003; Rosner, 1968). In thisstudy, the sector averages were deducted from eachfirm’s gross profit ratio and asset–liability ratio tocreate two operational measures of slack for eachfirm: adjusted asset–liability ratiot-1 and adjustedgross profit ratiot-1. Higher ratios indicate moreorganizational slack.

Foreign-invested firms may prefer concentratingtheir R&D activities at home or in advancedeconomies (Braga & Willmore, 1991; Kathuria,2008). State ownership may burden a firm withcomplex non-economic objectives. Such ownershipeffects were taken into account by including foreignownershipt-1 and state ownershipt-1 percentages foreach firm in the analyses. To control for any fixedyear or industry effects, 31 two-digit industrydummies and a year dummy representing 2007were also included in the models.

Two additional control variables were included inthe models testing hypothesis 4. The first was OEMexport intensityt-1. It was calculated as the value ofproducts manufactured under contract for otherbrands and exported (OEM products) as a propor-tion of the firm’s total export value in each year.Many scholars have found that overseas buyersdominate in OEM relationships and that they keepChinese manufacturers away from innovative tech-nology to avoid creating potential competitors(Kang, 2011; Zhang, Liu & Zheng, 2008). Thesecond control variable that was included in themodels testing hypothesis 4 was market varietyt-1,measured as the number of overseas countries towhich an exporter sold products in year t - 1.Exporting to many different countries may

encourage a firm to work on product adaptationand to introduce new products. The data source forboth of these additional control variables was thecustoms database.

Analytical MethodsFollowing the lead of previous empirical studieswith panel data (Bertschek, 1995; Kumar & Aggar-wal, 2005), generalized linear regression (GLS)models were evaluated in the analyses. Random-effects regression was used so that the industrydummies, which did not vary from year to year,would not be dropped from the regressions. Toreduce any multicollinearity among the explana-tory variables, all of the variables involved ininteraction terms were mean-centered.Research on firm-specific advantages suggests

that a firm’s intangible capabilities such as innova-tiveness may be the driver for rather than a result ofthe firm’s internationalization (Cassiman &Golovko, 2011; Delios & Beamish, 1999; Rodri-guez-Duarte, Sandulli, Minguela-Rata & Lopz-San-chez, 2007; Salomon & Shaver, 2005). If so, modelslike these may suffer from reverse causality prob-lems. In addition, there may be some unobservedfirm characteristics determining both exportingand innovation simultaneously, leading to endo-geneity problems (Melitz & Costantini, 2007). Todeal with this possibility, the export intensityt-1

variable was instrumented with a first-stage ran-dom-effects linear model and the predicted valueswere used in the second-stage innovation regres-sions (Golovko & Valentini, 2014). Following Chit-toor’s lead (Chittoor et al., 2015), sector-levelexport intensity was used as the instrument forfirm-level export intensity. Similarly, EM vs AEexport intensityt-1 and market varietyt-1 were instru-mented with the respective industry-level variables.In addition, all of the explanatory variables werelagged by one year, and the lagged dependentvariable, new product outputt-1, was also included inthe models. This allowed for a dynamic componentin any firm-specific effects, reducing the potentialfor serial correlation of the errors while controllingfor any endogeneity of exporting (Greene, 2003).Wooldridge (2005) has described such a procedureas a way to address endogeneity concerns in paneldata analysis. Chittoor and his coauthors (Chittooret al., 2015), Cassiman and Golovko (2011), andSalomon and Jin (2008) used similar methods intheir research.



RESULTSTable 1 presents descriptive statistics and a correla-tion matrix describing the key variables and theirinter-relationships. The unit of new product outputtand new product outputt-1 was RMB 10 million inTable 1. Since there were some high correlationsbetween explanatory variables, variance inflationfactors (VIFs) were calculated to test for multi-collinearity. The mean VIF obtained in the fullmodel was around 1.43, and the maximum VIF was2.37. Both are substantially below 10, which issuggested as the rule-of-thumb cutoff (Ryan, 1997),minimizing multicollinearity concerns. To savespace, the first-stage regression results are notreported.

Table 2 reports the results of the second-stagerandom-effects GLS regressions. All of the modelsincluded the control variables. The interactionterms were added sequentially. Model 1 is thebaseline model with the control variables only.Model 2 tests hypothesis 1, and model 3 testshypothesis 2. Model 4 tests hypothesis 3. Model 5 isthe complete model testing hypotheses 1–3. Thesignificance level of the Wald Chi-squared statisticsin all of the models indicates that the explanatoryvariables explained a significant portion of thevariation in the dependent variable. The signifi-cance of the coefficients of the explanatory vari-ables supports the hypotheses.

Hypothesis 1 predicts that firms operating inmore open environments tend to enjoy less infor-mation advantage from exporting. The coefficientof the term representing the interaction of exportintensityt-1 with regional market opennesst-1 wasnegative and significant (p B 0.001 in models 2and 5). Hypothesis 1 was thus strongly supported.

Hypothesis 2 expects that institutions in thehome region which support innovation canstrengthen the positive relationship betweenexporting and innovation. The coefficient of theterm representing the interaction of export inten-sityt-1 with regional R&D fundingt-1 was significantand positive (p B 0.001 in models 3 and 5). Thisdelivers support for hypothesis 2.

Hypothesis 3 predicts a positive moderating effectof regional intermediary developmentt-1 on the rela-tionship between export intensityt-1 and new productoutputt. The coefficient of the term representing theinteraction of export intensityt-1 with regional inter-mediary developmentt-1 was significant and positive(p B 0.001 in model 4, p B 0.01 in model 5).Hypothesis 3 is therefore supported as well.

The main effect relating export intensityt-1 withproduct innovation was significant and positive(p B 0.001 in all models). In addition to its negativemoderating effect, regional market opennesst-1

showed a significant and positive direct relation-ship with innovation (p B 0.001 in models 2 and5). This is consistent with the previous findings ofresearch on cross-border knowledge spillovers,which has found that trade and inward FDIpromote better performance within an industrybut also promote competition in the neighborhood(Bansant & Fikkert, 1996; Chang & Xu, 2008;Edwards, 1993). The direct effects of regional R&Dfundingt-1 were significant and positive (p B 0.001in models 3 and 5). The direct effect of regionalintermediary developmentt-1 was significant and pos-itive in model 4 (p B 0.01). The direct effect ofsector market concentrationt-1 was significant andpositive (p B 0.05 in models 1, 3 and 4), consistentwith Turner’s arguments (Turner, Mitchell & Bettis,2010). The interaction between export intensityt-1

and sector market concentrationt-1 was not signifi-cant in any of the models. As expected, regional IPprotectiont-1 showed a significant and positive rela-tionship with product innovation (p B 0.001 in allmodels). Tech absorptive capacityt-1 and its interac-tion with export intensityt-1 both showed significantand positive relationships (p B 0.001 in all models).For absorptive capacity, that is not surprising. Afirm with good absorptive capacity should be goodat learning in general, including learning fromexporting. The positive moderating effect ofabsorptive capacity delivers support for previousfindings about learning through exporting whichhas shown that absorptive capacity increases itseffectiveness (e.g., Li et al., 2010).The effects of firm sizet-1 and firm aget-1 were

both positive (p B 0.001 in all models). This indi-cates that larger and older manufacturers are moreinnovative in the empirical context of this study.The two measures of organizational slack showedno consistent effect. Foreign ownershipt-1 has asignificant negative effect (p B 0.001 in models1–4, p B 0.01 in model 5), which indicates thatexporting foreign investors were not as interestedin introducing new products as their local com-petitors. This finding is consistent with those ofprevious studies (Fan & Hu, 2007; Kathuria, 2008).The effect of state ownershipt-1 turned significantand positive after the institutional variables wereincluded (p B 0.01 in model 2, p B 0.001 in models3 and 5). This is surprising and deserves more



Table

1Descriptive

statisticsandco

rrelationmatrix

Variables

Mean

SD

12

34

56

78

910

11

12

13

14

15

1.New

product

output t

1.50

44.21

2.Export

intensity

t-1

0.18

0.34

0.01*

3.Sectorexport

intensity

t-1

0.18

0.17

0.00

0.50*

4.Regional

market

openness

t-1

5.23

2.69

-0.01*

0.26*

0.28*

5.RegionalR&D

funding

t-1

2.64

2.07

0.00

0.04*

0.04*

-0.07*

6.Regional

interm

ediary

development t-1

4.70

2.52

0.01*

0.06*

0.06*

0.14*

0.29*

7.Sectormarket

concentration

t-1

0.02

0.06

0.01*

-0.04*

-0.07*

-0.02*

-0.01*

0.03*

8.RegionalIP

protection

t-1

0.86

0.16

0.00*

-0.01*

-0.03*

-0.08*

0.18*

-0.02*

0.00

9.Tech

absorptive

capacity

t-1

0.39

121.95

0.00

0.00

0.00

0.00*

0.00

0.00

0.00

0.00

10.Firm

size

t-1

9.64

83.08

0.61*

0.02*

-0.02*

0.00*

-0.01*

0.01*

0.02*

0.00

0.00

11.Firm

age

t-1

8.24

9.34

0.03*

-0.03*

-0.08*

-0.08*

0.00

0.00

0.02*

0.01*

0.00

0.06*

12.Adjusted

asset–liability

ratio

t-1

0.00

0.30

0.00

0.00

0.00

0.03*

0.05*

0.03*

0.00

0.01*

0.00

0.00

0.10*

13.Adjusted

gross

profit

ratio

t-1

0.00

11.84

0.00

0.00

0.00

0.00

0.00

0.00

0.00

-0.01*

0.10*

0.00

-0.01*

-0.01*

14.Fo

reign

ownership

t-1

0.17

0.35

0.01*

0.41*

0.25*

0.22*

-0.09*

0.11*

0.00*

-0.04*

0.00

0.04*

-0.07*

-0.09*

0.00

15.State

ownership

t-1

0.04

0.18

0.03*

-0.07*

-0.09*

-0.15*

-0.03*

-0.05*

0.02*

0.01*

0.01*

0.05*

0.33*

0.09*

-0.01*

-0.08*

16.New

product

output t-1

1.18

37.11

0.74*

0.01*

0.00

-0.01*

0.00

0.01*

0.01*

0.00*

0.00

0.64*

0.03*

0.00

0.00

0.01*

0.02*

Note

s:N

=431,673.

*Indicatesaco

rrelationsignificantatthe

pB

0.05levelofco

nfidence.



Table

2Coefficients

ofseco

nd-stagerandom-effectsGLS

regressionspredictingnew

product

output(H

1–H3)

Model1

Model2

Model3

Model4

Model5

H1:export

intensity

t-19

regionalmarketopenness

t-1

-0.07***(0.01)

-0.08***(0.01)

H2:export

intensity

t-19

regionalR&D

fundingt-1

0.06***(0.01)

0.05***(0.01)

H3:export

intensity

t-19

regionalinterm

ediary

development t-1

0.04***(0.01)

0.03**

(0.01)

Export

intensity

t-1

0.22***(0.02)

0.20***(0.02)

0.19***(0.02)

0.22***(0.02)

0.16***(0.02)

Regionalmarketopenness

t-1

0.03***(0.00)

0.06***(0.00)

RegionalR&D

funding

t-1

0.20***(0.00)

0.24***(0.00)

Regionalinterm

ediary

development t-1

0.01**

(0.00)

-0.13***(0.01)

Sectormarketco

ncentration

t-1

0.02*(0.01)

0.01�(0.01)

0.01*(0.01)

0.02*(0.01)

0.01�(0.01)

Export

intensity

t-19

sectormarketco

ncentration

t-1

0.03(0.03)

0.02(0.03)

0.02(0.03)

0.03(0.03)

0.02(0.03)

RegionalIP

protection

t-1

0.18***(0.00)

0.19***(0.00)

0.15***(0.00)

0.18***(0.00)

0.15***(0.00)

Tech

absorptive

capacity

t-1

0.34***(0.00)

0.33***(0.00)

0.34***(0.00)

0.34***(0.00)

0.33***(0.00)

Export

intensity

t-19

tech

absorptive

capacity

t-1

0.05***(0.01)

0.05***(0.01)

0.04***(0.01)

0.05***(0.01)

0.05***(0.01)

Firm

size

t-1

0.11***(0.00)

0.10***(0.00)

0.12***(0.00)

0.12***(0.00)

0.11***(0.00)

Firm

age

t-1

0.05***(0.00)

0.04***(0.00)

0.05***(0.00)

0.05***(0.00)

0.05***(0.00)

Adjustedasset–liability

ratio

t-1

-0.00(0.00)

-0.00(0.00)

-0.01***(0.00)

-0.00(0.00)

-0.01*(0.00)

Adjustedgross

profitratio

t-1

0.00(0.00)

0.00(0.00)

0.00(0.00)

0.00(0.00)

0.00(0.00)

Foreignownership

t-1

-0.08***(0.01)

-0.07***(0.01)

-0.05***(0.01)

-0.09***(0.01)

-0.02**

(0.01)

State

ownership

t-1

0.01�(0.00)

0.01**

(0.00)

0.01***(0.00)

0.01�(0.00)

0.02***(0.00)

New

product

output t-1

1.65***(0.01)

1.77***(0.01)

1.66***(0.00)

1.64***(0.01)

1.76***(0.00)

Constant

0.82***(0.01)

0.85***(0.01)

0.87***(0.01)

0.83***(0.02)

0.80***(0.01)

Wald’s

Chisquared

323,782***

396,354***

343,716***

316,579***

406,766***

Note

s:Standardizedco

efficients

are

reported.

MeanVIF

ofModel5=

1.43.

Industry

dummiesandyeardummywere

includedin

allmodels.

Allinteractionterm

swere

standardizedin

allmodels.Standard

errors

are

inparentheses.

�Indicatessignificance

atthe

pB

0.10(*

pB

0.05;**

pB

0.01;***

pB

0.001)levelofco

nfidence

(two-tailedtest).



Table

3Descriptive

statisticsandco

rrelationmatrix

oftheexportingsample

Variables

Mean

SD

12

34

56

78

910

11

12

13

14

15

16

1.New

product

output t

3.51

66.34

2.EM

vsAEexport

intensity

t-1

0.24

0.34

0.01*

3.SectorEM

vsAEexport

intensity

t-1

0.24

0.12

0.04*

0.35*

4.Tech

absorptive

capacity

t-1

0.26

2.01

0.03*

0.03*

0.04*

5.OEM

export

intensity

t-1

0.28

0.41

0.01*

-0.25*

-0.15*

-0.04*

6.Marketvariety

t-1

8.61

10.42

0.07*

0.16*

0.09*

0.02*

0.01

7.Export

intensity

t-1

51.17

41.06

-0.03*

-0.18*

-0.24*

-0.07*

0.29*

0.18*

8.Regionalmarket

openness

t-1

6.15

2.33

-0.02*

-0.06*

-0.06*

-0.06*

0.26*

0.07*

0.22*

9.RegionalR&D

funding

t-1

2.72

2.18

0.00

0.14*

0.09*

0.03*

-0.28*

0.10*

0.02*

-0.14*

10.Regionalinterm

ediary

development t-1

5.12

2.57

0.01*

-0.02*

0.00

0.03*

0.01*

0.02*

-0.01

0.00

0.32*

11.Sectormarket

concentration

t-1

0.02

0.05

0.02*

0.03*

0.08*

0.03*

-0.01*

0.03*

-0.07*

-0.04*

-0.02*

0.03*

12.RegionalIP

protection

t-1

0.86

0.15

0.01*

0.01*

0.01*

0.01*

-0.02*

0.00

0.00

-0.12*

0.16*

0.02*

0.01

13.Firm

size

t-1

17.35

130.92

0.61*

0.01*

0.04*

0.02*

0.04*

0.12*

-0.04*

-0.02*

-0.02*

0.01

0.04*

0.00

14.Firm

age

t-1

8.50

13.07

0.03*

0.03*

0.06*

0.03*

0.00

0.03*

-0.05*

-0.05*

-0.02*

0.00

0.01*

0.00

0.05*

15.Fo

reignownership

t-1

0.46

0.45

-0.01*

-0.26*

-0.17*

-0.06*

0.45*

-0.04*

0.25*

0.20*

-0.19*

0.09*

-0.02*

-0.05*

0.00

-0.08*

16.State

ownership

t-1

0.02

0.13

0.04*

0.04*

0.07*

0.05*

-0.05*

0.00

-0.10*

-0.16*

-0.03*

-0.05*

0.03*

0.03*

0.08*

0.16*

-0.13*

17.New

product

output t-1

2.87

62.82

0.51*

0.01

0.03*

0.02*

0.02*

0.06*

-0.02*

-0.02*

0.00

0.02*

0.02*

0.01*

0.68*

0.02*

0.00

0.03*

Note

s:N

=78,790.

*Indicatesco

rrelationsignificantatthe

pB

0.05levelofco

nfidence.

Thesummary

statisticsfortheadjustedasset–liability

ratio

t-1andadjustedgross

profitratio

t-1are

notreporteddueto

limitedspace

andtheirlack

ofsignificance

inregressionmodels.



Table

4Coefficients

ofseco

nd-stagerandom-effectsGLS

regressionspredictingnew

product

output(H

4)

Model6

Model7

ModelS1

ModelS2

H4:EM

vsAEexport

intensity

t-1

0.11*(0.05)

0.11*(0.05)

EM

vsAEexport

intensity

t-19

Tech

absorptive

capacity

t-1

0.09**

(0.03)

Institutionaldistance

t-1

-0.04*(0.02)

Export

intensity

t-19

regionalmarketopenness

t-1

0.00(0.01)

0.01(0.01)

0.01(0.01)

0.00(0.01)

Export

intensity

t-19

regionalR&D

funding

t-1

0.05*(0.03)

0.06*(0.03)

0.06*(0.03)

0.05*(0.03)

Export

intensity

t-19

regionalinterm

ediary

development t-1

-0.07(0.05)

-0.08(0.05)

-0.08(0.05)

-0.06(0.05)

Export

intensity

t-1

0.03**

(0.01)

0.03*(0.01)

0.03*(0.01)

0.04**

(0.01)

Regionalmarketopenness

t-1

-0.01(0.01)

0.02*(0.01)

0.02*(0.01)

-0.01(0.01)

RegionalR&D

funding

t-1

0.36***(0.01)

0.31***(0.01)

0.31***(0.01)

0.36***(0.02)

Regionalinterm

ediary

development t-1

-0.18***(0.02)

-0.18***(0.01)

-0.18***(0.01)

-0.17***(0.02)

Sectormarketco

ncentration

t-1

0.01(0.01)

0.01(0.01)

0.01(0.01)

0.01(0.01)

Export

intensity

t-19

sectormarketco

ncentration

t-1

-0.01(0.01)

-0.00(0.01)

-0.00(0.01)

-0.01(0.01)

RegionalIP

protection

t-1

0.10***(0.01)

0.12***(0.01)

0.12***(0.01)

0.10***(0.01)

Tech

absorptive

capacity

t-1

0.39***(0.01)

0.34***(0.01)

0.31***(0.01)

0.39***(0.01)

Export

intensity

t-19

tech

absorptive

capacity

t-1

0.02(0.01)

0.02(0.01)

0.02(0.01)

0.02(0.01)

OEM

export

intensity

t-1

-0.00(0.01)

0.03�(0.01)

0.02(0.01)

0.01(0.01)

Marketvariety

t-1

0.22***(0.05)

0.11*(0.04)

0.11*(0.04)

0.20***(0.05)

Firm

size

t-1

0.44***(0.02)

0.37***(0.02)

0.37***(0.02)

0.44***(0.02)

Firm

age

t-1

0.06***(0.01)

0.05***(0.01)

0.05***(0.01)

0.06***(0.01)

Foreignownership

t-1

-0.15***(0.01)

-0.10***(0.01)

-0.10***(0.01)

-0.14***(0.01)

State

ownership

t-1

0.09***(0.01)

0.06***(0.01)

0.06***(0.01)

0.09***(0.01)

Adjustedasset–liability

ratio

t-1

-0.02(0.01)

-0.01(0.01)

-0.01(0.01)

-0.01(0.01)

Adjustedgross

profitratio

t-1

0.00(0.01)

0.01(0.01)

0.01(0.01)

0.01(0.01)

New

product

output t-1

1.86***(0.01)

2.35***(0.01)

2.35***(0.01)

1.86***(0.01)

Constant

1.14***(0.06)

1.23***(0.05)

1.22***(0.05)

1.16***(0.06)

Wald’s

Chisquared

55,239***

94,803***

94,788***

55,184***

Note

s:N

=78,790.

Standardizedco

efficients

are

reported.

Standard

errors

are

inparentheses.

MeanVIF

ofModelS1=

1.54.

Allinteractionterm

swere

centeredin

allmodels.

�Indicatessignificance

atthe

pB

0.10(*

pB

0.05;**

pB

0.01;***

pB

0.001)levelofco

nfidence

(two-tailedtest).



research in the future. The significance of thepositive effect of the dependent variable newproduct outputt-1 lagged by one year indicates strongpersistence in innovative activities (p B 0.001 in allmodels).

Table 3 presents descriptive statistics and a cor-relation matrix for the key variables used to testhypothesis 4. Again, the unit of new product outputtand new product outputt-1 was RMB 10 million inTable 3. The maximum VIF was 3.09, and the meanVIF of the full model was approximately 1.54.Multicollinearity again should not have been amajor issue in the analyses.

Table 4 reports the results of the second-stagerandom-effects GLS regressions. The control vari-ables were included in all of the models, and theindependent variables and interaction terms wereadded sequentially. Model 6 is the baseline formu-lation. Model 7 tests hypothesis 4. The Wald Chi-squared statistics were again highly significant.

Hypothesis 4 predicts a positive relationshipbetween EM vs AE export intensityt-1 and new productoutputt. The coefficient of the EM vs AE exportintensityt-1 term was significant and positive(p B 0.05 in model 7), as predicted. Most of theeffects of the control variables remained the sameas in models 1–4, with two exceptions. OEM exportintensityt-1 was not included in models 1–4. Sur-prisingly, OEM business does not seem to impedeChinese exporters’ innovation, as no significanteffect was detected. Market varietyt-1 showed asignificant and positive relationship with innova-tion (p B 0.001 in model 6, p B 0.05 in model 7),consistent with expectations.

Supplementary AnalysesSome additional analyses were conducted to inves-tigate which aspects of distance actually make EMexporters exporting more to other EMs more inno-vative than those exporting more to AEs. Institu-tional distance is a multidimensional concept. Theinstitutional dimensions that are especially rele-vant to this research context are major candidates.Since superior technological capabilities and mar-ket knowledge have been found to constitutecompetitive advantages in product innovation(Atuhene-Gima & Ko, 2001), the effects of distancesin technology or market demand are examined.

If it is the distance in technological sophistica-tion between AEs and EMs that matters, strongabsorptive capacity for technology might help EM

exporters to overcome the difficulties created bysuch mismatches. If EM exporters with absorptivecapacity for technology which is better than that oftheir peers manage to innovate more by exportingto AEs, the recombinatory advantage of exportingto other EMs proposed in hypothesis 4 might beweakened. This predicts a negative moderatingeffect of tech absorptive capacityt-1 on the relation-ship between EM vs AE export intensityt-1 and newproduct outputt. However, the coefficient of the termrepresenting the interaction of EM vs AE exportintensityt-1 with tech absorptive capacityt-1 was sig-nificant but positive (p B 0.01 in model S1,Table 4). This indicates that absorptive capacityfor technology strengthens Chinese exporters’learning from exporting to EMs more than export-ing to AEs. That is to say, absorptive capacity fortechnology alone does not make EM firms learnmore effectively from AEs than from other EMs. Inaddition to technology gaps, is there perhaps a rolefor other dimensions of distance in explaining whyEM firms learn more effectively by exporting toother EMs?In addition to distance in technological knowl-

edge, distance in market demand could be anothermajor antecedent that makes knowledge acquiredoverseas less relevant at home. Market demand ismainly shaped by the economic development andculture of a nation. The level of global connected-ness is also likely to play a role as isolated nationsare more likely to be idiosyncratic. We adopted themeasure of economic distance, cultural distance,and distance in global connectedness together withknowledge distance, all developed by Berry, Guil-len, and Zhou (2010). For each firm i exporting tonit-1 different countries in year t - 1,

Economic distanceit�1 ¼Xnit�1

k¼1

Economic distancekt�1

� Exporting salesikt�1=

Xnit�1

k¼1

Exporting salesikt�1

Cultural distanceit�1 ¼Xnit�1

k¼1

Cultural distancekt�1


Xnit�1

k¼1




Knowledge distanceit�1 ¼Xnit�1

k¼1

Knowledge distancekt�1


Xnit�1

k¼1


Distance in global connectednessit�1 ¼Xnit�1

k¼1

Distance in global connectednesskt�1

� Exporting salesikt�1=Xnit�1

k¼1


:

Economic/Cultural/Knowledge distancekt-1 repre-sents the economic/cultural/knowledge distancebetween country k and China in year t - 1. Distancein global connectednesskt-1 represents the distance inglobal connectedness between country k and Chinain year t - 1. These dimensions of institutionaldistances were standardized and summed up toconstitute a composite variable, i.e., institutionaldistancet-1. The correlation between institutionaldistancet-1 and EM vs AE export intensityt-1 is-0.62. Model S2 was used to examine the effectof institutional distancet-1. To deal with any endo-geneity concerns, the sector average distance ineach dimension was used as an instrument. Con-sistent with the earlier arguments, the coefficient ofinstitutional distancet-1 has a significant and nega-tive relationship with Chinese exporters’ innova-tion (p B 0.05 in model S2, Table 4). A moredetailed analysis with four individual dimensionsseparately in the models showed that among thefour dimensions, the negative effect of culturaldistance (p B 0.05) and that of distance in globalconnectedness (p B 0.01) were especially signifi-cant. The negative effect of knowledge was onlymarginal, while the effect of economic distance wasnot significant. The results indicate that Chineseexporters may find it especially difficult to becomemore innovative by exporting to overseas marketswith a very different culture or degree of globalconnectedness.

Robustness CheckSeveral robustness checks examined whether theregression results were sensitive to the choice ofspecific operational measures. Four alternativeoperational measures for market openness wereconstructed following the lead of previous research-ers (Fan et al., 2010; Kafouros et al., 2015). Annual

output was used to weight the foreign equityinvested in each province in year t - 1, and theresult was interpreted as quantifying regional FDIintensity t-1. Sector FDI intensityt-1 was similarlycalculated for each four-digit sector based on eachfirm’s Chinese Standard Industry Classification inyear t - 1. The trading data are not available byfour-digit SIC in the China Customs StatisticsYearbook, so sector trade intensityt-1 was quantifiedusing the total value of exports and imports dividedby the total output of each two-digit SCI sector inyear t - 1. Regional trade intensityt-1 was also calcu-lated similarly for each province. The interaction ofexport intensityt-1 with these alternative measuresshowed significant and negative relationships(p B 0.001) with new product sales similar to thatof regional market opennesst-1. The inclusion of thesealternative explanatory variables did not changethe support for hypothesis 4.Comparing the results reported in Tables 2 and 4

shows that the moderating effects of market open-ness, support for innovation, and intermediarydevelopment are not very significant when thesample is limited to exporters. This may indicate alarge difference between exporters and non-ex-porters. A dummy variable export statust-1 wastherefore created to distinguish the two groups offirms. Export statust-1 equaled 1 if a firm sold tooverseas markets, and 0 otherwise. The regressionswere then re-evaluated with export statust-1 replac-ing export intensityt-1. Hypotheses 1–3 were stillsupported.New product intensityt was tested as an alternative

dependent variable to replace new product outputt. Itwas calculated as new product intensityt = ln(1 + value of annual new product outputt/value ofannual outputt). Although the correlation betweennew product intensityt and new product outputt wasonly 0.14 in the full sample and 0.17 in exportingsample, all of the hypotheses were still stronglysupported. The detailed results of the robustnesschecks are available on request.

DISCUSSION AND CONCLUSIONNations deemed EMs are dreaming of ‘‘catchingup.’’ Previous experience (especially that of Ger-many, Japan, and the Asian tigers) shows thatsuccessful catch-up involves both firms which canserve as important drivers of economic develop-ment (Teece, 2000) and indigenous innovation asan important step on the development ladder(Hobday, 1995). Firm-level innovation is thus



crucial, and it deserves academic attention.Although there is still some debate, two mainrequirements for catching up are generally recog-nized: (1) transferring advanced technology fromoverseas in order to skip costly and risky R&D; and(2) undertaking ‘‘…necessary changes [in institu-tional arrangements] at the new and radicallyenlarged scale that modern technology require[s]’’(Fagerberg & Godinho, 2005: 4).

Compared with non-exporting EM firms, EMexporters have an advantage in information arbi-trage. Unfortunately, though some EM exportersare very competitive in international markets,many others still remain behind the leaders, withpoor innovation performance (Hobday, 1995; Li &Kozhikode, 2009; Navas-Aleman, 2011; Yao, 2012).What limits the innovation of EM exporters? Theexamination of hypotheses 1–4 has partiallyanswered this question by suggesting that appro-priate institutional arrangements are important ifEM firms are to enhance their innovation capabil-ities through exporting.

This study has identified three aspects of institu-tional development as particularly important: mar-ket openness, support for innovation, and thedevelopment of market intermediaries. When localinnovation is encouraged, EM firms’ exportingactivities more readily promote product innova-tion. This finding highlights the importance ofemerging home countries as a valuable source ofknowledge. In addition to the positive direct effect,the positive moderating effect shows that localsupport for innovation is especially beneficial forexporters, who tend to have foreign knowledgethey can combine with local knowhow. Similarly,the development of market intermediaries alsobenefits exporters more than non-exporters,though it tends to increase EM firms’ productinnovation in general.

A more open market reduces exporters’ scope forinformation arbitrage, and their product innova-tion increase less with export value. While previousresearch has found that market openness and othermarket-supporting institutions have similar effectson the commercial activities in EMs (Doh, Teegen& Mudambi, 2004), this research has shown thatsometimes they can have opposite effects. EMexporters in a less open market can take advantageof the limited access to overseas knowledge athome and innovate more actively than their non-exporting counterparts. There may be two reflec-tions on this negative interaction effect. First,market openness may make non-exporters/less

intensive exporters more innovative, so that anincrease in exporting intensity does not increasefirms’ innovation performance as much as in lessopen economies. Market openness could also bringalternative sources of overseas knowledge to expor-ters, making them rely less on exporting forknowledge access.The examination of hypothesis 4 has shown that

EM exporters become more innovative by export-ing to relatively similar overseas markets. This maybe because firms can more effectively combine theknowledge acquired from destination countrieswith a similar level of institutional development.The additional analyses in this study were intendedto try and figure out what made EM exporters learnless effectively from AEs. Better absorptive capacityfor technology failed to predict more effectivelearning from more advanced countries, indicatingthat absorptive capacity for technology may not bethe limiting factor. Even the Chinese exporterswith greater absorptive capacity for technology didnot demonstrate more innovation as a result ofexporting more to advanced countries. The role ofinstitutional distance in other aspects is thereforeimportant. In this research, absorptive capacity wasoperationalized in terms of R&D intensity. ActiveR&D should help a firm better absorb advancedtechnology, but that may not be enough to over-come the learning difficulties created by a mis-match in the level of institutional developmentbetween the home and destination countries. EMfirms may need to develop absorptive capacity notonly for technology but also for coping withdifferences in different dimensions of institutions.In addition to technical knowledge per se, tech-

niques for managing innovation vary among coun-tries with different institutions. For example, theconstituent components of innovation systems andtheir interaction can be quite different. The majorsources of knowledge can be quite different crosscountries (Corredoira & McDermott, 2014). Whereinstitutional voids prevail, firms need to developtechniques to manage guanxi rather than rely onintermediaries to search for or source knowledge.Where law enforcement is uncertain, firms learn toprotect their knowledge through careful modular-ization, secrecy, and perhaps even co-opting theauthorities (Cai, Huang & Li, 2014; Li & Xie, 2016;Zhao, 2006). For all these reasons, managing inno-vation in an environment with weak institutionscan call for some special techniques. Institutionalvoids may distort the value of certain resources andcapabilities acquired in better-developed markets



(Cuervo-Cazurra & Genc, 2008). Salomon (2006)has reported that exporting to advanced countriesimproved the innovation performance of Spanishfirms. That seems to be consistent with the findingshere, as Spain, a member of the OECD, would havea level of institutional development similar to thatof other advanced economies.

Absorptive capacity hinges on multiple factorssuch as prior experience, accumulated knowledge,and the diversity of a firm’s knowledge sources(Phene et al., 2006). It has been shown that firm-specific experience is not very helpful in dealingwith institutional uncertainties (Henisz & Delios,2001). This is consistent with the finding here thatfirms’ investment in R&D does not help mitigatethe learning difficulties caused by distances ininstitutional development. Experience in diverseinstitutional contexts abroad may help develop acapacity for dealing with institutional distance, buthow to develop such a capacity still remains aninteresting area for future research.

The findings are rather consistent across differentmeasures of the variables. This may give them richpolicy as well as managerial implications. EMgovernments usually encourage exporting in anearly stage of development for two reasons. It ofcourse earns foreign currency (Harwit, 1995), butbeyond that, export performance is usually consid-ered very persuasive evidence of technologicalupgrading (Thun, 2006). As EM economies developover time, concern about exporting shifts to how itmay enable EM firms to learn, with expectationsthat they would eventually climb up the value-added chain and start to innovate themselves. Theresults of this study suggest that institutions thatpromote local R&D and the development of marketintermediaries at home complement export pro-motion policies and facilitate transforming expor-ters from knowledge recipients into knowledgecreators. Open market policies, in contrast, mayreduce the effectiveness of export promotion, asthey weaken the information advantage of expor-ters. While the direct effects of market openness ispositive, its indirect effects on innovation areusually negative for exporters.

The findings of this research call managers’attention to the information advantage EM firmsgain by exporting when the market in their homeregion is not well integrated into the world market.They have a better chance to build up innovationcapability before the market at home region is fullyopened. Operating in regions with more R&Dinvestment and better-developed market

intermediaries helps EM exporters become moreinnovative by learning through exporting. Further,managers in EMs may be encouraged to pay moreattention to destination countries which have asimilar level of institutional development.Although the exposure to frontier technologymay be limited in such markets, exporting to otherEMs is nevertheless an effective way of improving afirm’s innovation performance. Other EMs similarto an EM exporter’s home can serve as an effectiveand inexpensive source of valuable knowledge,especially applied, experiential knowledge (Pietro-belli & Rabellotti, 2011). Access to this type ofknowledge is very important to EM firms seeking toinnovate (Amsden, 1989; Corredoira & McDermott,2014).Investment in R&D alone may not help a firm

learn from countries with which there is a large gapin institutional development. Investment in whatmight be termed ‘‘institutional absorptive capacity’’may be more important, but what that involvesremains unclear. It should be noted that thisstudy’s focus on China should not be taken ascircumscribing the applicability of the theoreticalframework. The arguments about interactionbetween national-level and firm-level internation-alization should apply to economies in all stages ofdevelopment.Taking advantage of the high variance in insti-

tutional development within China, this researchteased out some potential unobservable, sub-na-tional-level heterogeneities. The cost is that somereadily available measures of national institutionaldevelopment, such as tariff barriers and restrictionson foreign capital, could not be used. Futureresearch might fruitfully extend this study bydeveloping better operational measures of institu-tional variables at the national as well as the sub-national level. Another limitation of this researchlies in the short panel data used to test thehypotheses, which did not allow analysis oflonger-term effects. While it may be difficult tobenefit from exporting to AEs in the short term, it isvery likely that EM exporters benefit in the long runby gradually digesting and absorbing the advancedknowledge they can acquire by doing so. Econo-mists have proposed another mechanism that linksexporting and innovation – economies of scale.Exporting expands potential markets and enlargesthe potential return to successful innovation. Thismay encourage firms to innovate (Hitt et al., 1997).While this may help to explain the central rela-tionship between exporting and innovation, it fails



to explain the moderating effects found in thisstudy. Future research which tests these competingexplanations would clearly be helpful.

ACKNOWLEDGEMENTSWe are grateful to Editor Jonathan Doh, Alain Verbeke,and three anonymous reviewers for their insightfulfeedback during the review process. The article hasbenefited from helpful comments and discussions with

many individuals, particularly Prithviraj Chattopad-hyay, Jinyu He, Christopher Marquis, Naubahar Sharif,Bilian Ni Sullivan, Danyang Xie, and Haibin Yang. Wewould like to thank these individuals while retainingfull responsibility for any errors or omissions. Theresearch is supported in part by the National NaturalScience Foundation of China (Grant No. 71502091)and the Research Grants Council of Hong Kong(HKUST#16501814).

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ABOUT THE AUTHORSZhenzhen Xie is an Assistant Professor in theDepartment of Innovation, Entrepreneurship andStrategy in the School of Economics and Manage-ment, Tsinghua University. She received her PhD inManagement of Organization from the Hong KongUniversity of Science & Technology. Her researchinterests include the cross-border mergers andacquisitions conducted by multinational corpora-tions from emerging economies, the research anddevelopment strategies of multinational corpora-tions in emerging economies, and the impact ofinternationalization on the innovativeness ofindigenous firms and corporate governance. Herresearch has appeared in European ManagementReview, Asia Pacific Journal of Management,



http://www.reuters.com/article/2012/04/12/us-china-economy-idUSBRE83B07K20120412

http://www.reuters.com/article/2012/04/12/us-china-economy-idUSBRE83B07K20120412

Management International Review, and Journal ofInternational Management.

Jiatao Li is Chair Professor of Management, LeeQuo Wei Professor of Business, and Director ofCenter for Business Strategy and Innovation, HongKong University of Science and Technology. Hereceived his PhD in strategy and internationalmanagement from the University of Texas at

Dallas, and was previously with McKinsey & Com-pany in Hong Kong. His current research interestsare in the areas of strategic alliances, corporategovernance, innovation, and entrepreneurship,with a focus on issues related to global firms andthose from emerging economies. He is an electedFellow of the Academy of International Business(AIB).

Accepted by Jonathan Doh, Guest Editor, 15 August 2017. This article has been with the authors for three revisions.



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