rapid expansion of international new ventures across ... · rapid expansion of international new...

Rapid expansion of international new

ventures across institutional distance

Ziliang Deng1 ,Ruey-Jer ‘‘Bryan’’ Jean2

and Rudolf R Sinkovics3,4,5

1Renmin Business School, Renmin University of

China, Beijing 100872, China; 2Department of

International Business, National ChengchiUniversity, Taipei, Taiwan, China; 3Alliance

Manchester Business School, The University of

Manchester, Manchester, UK; 4Lappeenranta

University of Technology, Lappeenranta, Finland;5Fox School of Business, Temple University,

Philadelphia, USA

Correspondence:Z Deng, Renmin Business School, RenminUniversity of China, Beijing 100872, China.Tel: (86)-10-82500498;e-mail: [email protected]

AbstractRapid export expansion into institutionally distant locations has become more

possible in the era of digital economy. Will such rapid expansion bring desirableoutcome to firms? In a context of international new ventures (INVs) from

emerging markets, we reconceptualize export expansion speed as the pace of

exporting across institutional distance over a certain period of time. We thenexamine the relationship between rapid export expansion across institutional

distance and overall firm performance. We incorporate directionality into

export expansion and hypothesize the relationship to be positive when INVsexport upwardly to more open countries, yet the relationship to be negative

when INVs export downwardly to less open countries. We also hypothesize that

the degree of market liberalization in subnational regions of origin of the INVs

moderates the above speed–performance relationships. Instrumental variablemodels based on data of Chinese indigenous INVs during 2000–2009 support

these hypotheses. This study both zooms in and zooms out the analytical lens

along the location-related institutional axis, examines the joint effect ofinstitutions involved in supranational directions and subnational origins on firm

performance, and advances institutional theory.

Journal of International Business Studies (2018) 49, 1010–1032.https://doi.org/10.1057/s41267-017-0108-6

Keywords: international new ventures (INVs); institutional distance; learning advantagesof newness; diseconomies of time compression; emerging markets; instrumental variablemodels

INTRODUCTIONRapidly expanding exports to institutionally distant locations hasbecome more possible for international new ventures (INVs) giventhe unprecedented convenience in gathering information fromoverseas markets with modern information and communicationtechnologies (Oviatt & McDougall, 1994; Yamin & Sinkovics,2006). Despite the increasing possibility of exploring new markets,two intriguing and important location-centric institutional issuesemerge. The first issue is the destination location and its associatedinstitutional environment of the rapid export expansion. Thedestination country is crucial for the performance of internationalbusiness (Makino, Isobe, & Chan, 2004). On one hand, exportersfrom emerging markets may prefer to rapidly expand into moreopen countries because these markets host more transparent and

The online version of this article is available Open Access

Received: 1 November 2015Revised: 31 July 2017Accepted: 15 August 2017Online publication date: 20 October 2017

Journal of International Business Studies (2018) 49, 1010–1032ª 2017 The Author(s) All rights reserved 0047-2506/18

www.jibs.net

http://orcid.org/0000-0002-4767-2803

http://orcid.org/0000-0003-2461-7454

http://orcid.org/0000-0002-4471-5054

mature institutions. However, the countries offerequal market access to all exporters worldwide.Thus, the rapidly mounting pressure caused bypost-entry competition in these destination mar-kets is challenging. On the other hand, exportersmay choose to venture into destination countriesthat are less open. Despite benefits such as low localcompetition, rapid entry into such marketsinvolves daunting entry costs and post-entry uncer-tainties. Both directions of rapid market expansionpose challenges to nascent firms that usually lackstrong organizational resources, e.g., diseconomiesof time compression (Vermeulen & Barkema, 2002),conflicts between organizational learning andunlearning associated with exposures to differentinstitutions (Autio, Sapienza, & Almeida, 2000;Zhou & Guillen, 2015). Which direction of rapidexport expansion is better, upward one into moreopen countries or downward one into less opencountries?

The second issue concerns the location and asso-ciated institutions of subnational origin of the INVsconducting rapid export expansion. Most countriesin the world exhibit a certain degree of regionalvariations in institutions (Chan, Makino, & Isobe,2010). For example, in Mexico, it took only twodays to register a property transfer in Colima, but ittook 74 days in Mexico City in 2013 (World Bank,2016, p. 23). In particular, a terraced pattern inliberalization is commonly observed across differ-ent subnational regions and industries, particularlywithin a country transitioning from a centrallyplanned status to a deregulated one (Zhou, Tse, &Li, 2006). Thus, another question arises: will INVsoriginating from more liberalized regions acquirebetter financial returns in rapid expansion acrossinstitutional distance?

Motivated by the two location-centric questions,we draw on the notions of learning advantages ofnewness (Autio et al., 2000) and diseconomies oftime compression (Vermeulen & Barkema, 2002) tobuild a theoretical model on why and how rapidexport expansion across national institutions mat-ters. We reconceptualize internationalization speedby examining how fast an INV expands acrossnational difference in trade openness. Trade open-ness refers to an economy’s overall level of open-ness for products and services imported fromforeign countries (Edwards, 1993). Trade opennessaffects exports most directly among various insti-tutional dimensions. We focus on INVs as they are

featured with an innovative, proactive, and risk-taking organizational culture (Oviatt & McDougall,2005), and are rather possible to explore institu-tionally distant markets.We posit that the rapid upward export expansion

of INVs from less open home markets to more opendestination markets positively affects firm perfor-mance. The underlying factor is the strong learningadvantages of newness caused by rapid efficiencyimprovement, preview of pro-market institutions,and lower cost structure (Barnett & McKendrick,2004; Dau, 2013). By contrast, we argue that rapiddownward export expansion of INVs from emerg-ing markets to a less open destination marketgenerates net negative effects on firm performanceas a result of significant diseconomies of timecompression associated with rapid cost surge andconflicting organizational routines (Hashai, 2011;Vermeulen & Barkema, 2002). Finally, we encapsu-late subnational institutional heterogeneity intothe theoretical framework. We argue that locationin liberalized subnational home country regionsstrengthens the positive relationship between rapidupward expansion and firm performance whileweakening the negative relationship between rapiddownward expansion and firm performance. Totest these hypotheses, we employed panel data of8681 Chinese manufacturing INVs from 31 pro-vinces from 2000 to 2009. After controlling forpotential endogeneity in export expansion speed,the empirical findings consistently support thehypotheses.This study makes several contributions to the

literature. First, the study incorporates an institu-tional perspective into the literature on interna-tionalization process and INVs, and enriches theconstruct of internationalization speed that hasexamined timing, country scope, and foreign com-mitment dimensions (Oviatt & McDougall, 2005).Second, it challenges the widely accepted norm foranalyzing institutional distance and derived con-structs. The study incorporates direction of distance(Dau, 2013; Tsang & Yip, 2007; Zaheer, Schomaker,& Nachum, 2012) and conceptualizes upward anddownward export expansion speeds. Third, thestudy assesses if and how rapid upward and down-ward export expansions generate opposite effectson firm performance and complements the litera-ture that has overly underscored the negativeeffects of institutional distance and associatedconcepts (Stahl, Tung, Kostova, & Zellmer-Bruhn,

Rapid expansion across institutions Ziliang Deng et al

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Journal of International Business Studies

2016). Fourth, this study advances institutionaltheory from a locational perspective by examiningthe joint effect of different aspects of institutions(Holmes, Miller, Hitt, & Salmador, 2013), namelyheterogeneity in supranational institutional dis-tance and heterogeneity in subnational institu-tional quality.

LITERATURE

Internationalization Speed, INVs,and PerformanceThe literature examines internationalization speedmainly in three dimensions (Kuivalainen, Sundq-vist, & Servais, 2007), namely earliness of initialforeign entry or post-entry expansion pace (Li,Qian, & Qian, 2015; Reuber, Dimitratos, & Kuiv-alainen, 2017; Sapienza, Autio, George, & Zahra,2006), scope of country (Hashai, 2011; Vermeulen& Barkema, 2002), and intensity of foreign com-mitment (Li, Qian, & Qian, 2012; Oviatt &McDougall, 2005). The first dimension (timing) isessential (Rahaman, 2016), as it has become acommon practice to define INVs if they startinternational business within 6 years of inception(Coviello, 2015; Deng, Jean, & Sinkovics, 2017).Nonetheless, institutional dimension is overlookedin the literature. Considering the heterogeneity ininstitutional environment, examining the effect ofrapid supranational institutional exposure on orga-nizational routines and performance bears greatstrategic importance.

Rapid internationalization is challenging to orga-nizations because of diseconomies of time com-pression, which arise when organizations areunprepared to accommodate rapid expansion andnew experiences (Vermeulen & Barkema, 2002).Rapid internationalization exposes firms to differ-ent customers, competitors, suppliers, institutions,and knowledge in a rather short period. Potentialconflicts of business practices cause managers todevote suboptimal attention to each overseasproject.

In contrast, for INVs, unfavorable shocks causedby rapid internationalization could be counterbal-anced by the firms’ intrinsic learning advantages ofnewness (Autio et al., 2000; Sapienza et al., 2006).Unlike older firms, INVs bear minimal burden ofold organizational routines established for theirhome market environment. Therefore, INVs aremore capable of unlearning old routines and con-structing new routines for identifying, evaluating,

and exploring global emerging opportunities(McDougall & Oviatt, 2000).

Heterogeneity in Subnational Institutions1

Institutions within a country exhibit both homo-geneity and heterogeneity. With national bordersas stabilizers (Daniels, Radebaugh, & Sullivan,2014), institutions are homogenous within a coun-try in some dimensions, e.g., import tariffs, andnon-tariff import barriers. Subnational govern-ments in most countries, particularly the memberstates of the World Trade Organization (WTO),possess no authority to design their own tradepolicies. For example, in Brazil, all nine majormeasures directly affecting its imports are formu-lated by country-level government agencies (WTO,2013).Nonetheless, the other dimensions of subna-

tional institutions may diversify (Gao et al.,2017). Many formal regulative institutions formu-lated by central or federal governments are even-tually interpreted and implemented by subnationalgovernment agencies. The subnational differencesin the endowments of resources, culture, andhistorical legacy also lead to variations in institu-tions (Chan et al., 2010; Kirkman et al., 2017; Maet al., 2013a). In particular, subnational hetero-geneity in government intervention and legislativeenvironment widely exists in emerging markets(Chan et al., 2010; Ma, Tong, & Fitza, 2013b; WorldBank, 2016). Managers need to calculate the ben-efits and costs associated with subnational loca-tions (Ma et al., 2013a).

HYPOTHESES

Supranational Expansion Speed, Direction,and Performance of INVs in a Trade OpennessContextWe confine hypothesis development to the rela-tionship between export expansion speed andperformance of INV manufacturers from emergingmarkets in a trade openness context, since export-ing is the most typical form of internationalizationadopted by INVs or born global firms (Cavusgil &Knight, 2015). Exporters are surrounded by variousinstitutional environments in destination markets,e.g., legal, cultural, and regulative ones, but not allinstitutional dimensions bear the same importancefor international business (Cuervo-Cazurra & Genc,2008, p. 963). One of the most pertinent dimen-sions for exporters is trade openness or


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liberalization (Baggs & Brander, 2006; Khandelwal,Schott, & Wei, 2013). Trade openness, as a form offormal institution, refers to the efforts of a govern-ment to lower both import tariffs and non-tariffbarriers against foreign products (Edwards, 1993).By deconstructing institutions and focusing on thissingle institutional dimension (Zaheer et al., 2012,p. 22), we streamline hypothesis development byarticulating the detailed mechanisms of how thespeed of export expansion across institutionaldistance in trade openness affects firm perfor-mance. We discuss the independent effects of expan-sion speed and direction on the firm performanceof INVs, and then we examine their joint effects.

Speed and PerformanceIn the trade openness context, we extend theconstruct of internationalization speed in theinstitutional dimension. We conceptualize exportexpansion speed across institutional distance ashow fast an exporter expands to a market with adifferent degree of trade openness within a certainperiod. This new construct of speed may explicitlyunderscore shocks to organizational routines andreconfiguration (Autio et al., 2000). A firm rapidlyexporting to countries with different degrees oftrade openness will encounter different countryinstitutional profiles (Kostova, 1999). Therefore, itneeds to unlearn some old organizational routinesand simultaneously construct new routines for thenew institutional environment.

The difference between old and new routinesexists saliently in two forms. The first differencearises between (old) home-oriented routines and(new) export-oriented ones. INVs should ensure thattheir organizational routines in their productdesign, manufacturing, marketing, and sales teamsare oriented toward foreign market institutions. Forexample, when a toy manufacturer rapidly exportsits toys to the European Union, the products mustsatisfy a series of EN71 standards on toy safety,which act as a form of technical barrier to trade(WTO, 2012). This series of standards requires thefirm to rapidly implement internationalized, high-quality management routines during raw materialprocurement and assembly. The health, safety, andcompliance issues of INVs are monitored by thehome country government, however. Therefore,organizational routines in human resources andgovernment relations management are focused onlocal government requirements. As institutions areembedded in specific contexts, local government

requirements differ from foreign ones (Kostova,1999). Such a difference potentially causes conflictsbetween organizational routines.The second difference exists between routines

tailored for (old) existing destination markets andthose for new markets. For example, a food-process-ing firm faces the strictest safety and sanitarystandards in the US market, but it faces looselyregulated markets in other countries. When thefirm simultaneously engages in rapid export expan-sion into both types of markets, it encounters adilemma in effectively managing double standardsin material procurement, quality control, and san-itary specifications. Adhering to the US standardundermines the cost advantages of products inloosely regulated markets, while adjusting themanufacturing processes to the lower standards inloosely regulated markets jeopardizes the image ofthe product in the US market. Installation offlexible manufacturing facilities can mitigate thisdilemma, but it costs time and financial resources.Rapid reconciliation of such routine differences

exposes INVs with an opportunity to promptlyadopt efficiency-enhancing routines (Autio et al.,2000), along with a great pressure upon absorption(Vermeulen & Barkema, 2002). INVs international-ize earlier and faster than their counterparts tocapture opportunities globally (Oviatt & McDou-gall, 1994). In that process, INVs encounter ratherdifferent market entry barriers, governance quality,antitrust regulations, and policy uncertainties indifferent destination countries. The faster INVsexpose themselves to institutional diversity, themore effectively they build long-lasting organiza-tional routines in multi-institutional awarenessalong with their global footprint (Zhou & Guillen,2015).Rapid expansion across institutions can also

cause diseconomies of time compression (Vermeu-len & Barkema, 2002; Yamin & Sinkovics, 2006),which substantially constrain INVs’ resources tohandle external shocks over a short period. Asstartup firms, INVs typically lack manufacturingexperience, abundant cash flow, talent resources,proven technology, and market legitimacy (Mu-dambi & Zahra, 2007). INV managers shouldmaintain a delicate balance of resource allocationto different functions. The uncertainties duringrapid expansion across institutional distance neces-sitate extra attention and resources, and thereforeINV managers inevitably overstretch resources andencounter routine conflicts. For example, Xiaomi, a


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headline smartphone brand founded in China in2010, internationalized quickly to the Indian mar-ket in 2014. However, the Delhi High Court bannedits sales in India for several months upon Ericsson’splea of an alleged patent violation (Chatterjee &Shih, 2014). Consequently, Xiaomi needed to sig-nificantly shift its strategic focus to building astronger patent portfolio.

Direction and PerformanceWe examine the effects of export expansion direc-tion on performance by focusing on two types ofinstitutional environment from a sequential per-spective, namely (a) entry barriers at nationalborders and (b) post-entry competition environ-ment in destination markets. As trade liberalizationlowers entry barriers, it substantially enhancesmarket competition (Baggs & Brander, 2006; Khan-delwal et al., 2013; Melitz, 2003; Pavcnik, 2002).When manufacturers export their products from aless open country to a more open one (hereafterreferred to as ‘‘upward export expansion’’ or ‘‘up-ward expansion,’’ see Figure 1), they encounterlower import tariffs, fewer non-tariff barriers,shorter custom queuing, and consequently betterprofitability prospects (Baggs & Brander, 2006). Inan open destination market, buyers and locallegislators hold a relatively unbiased attitudetoward foreign products. Such a fair institutionalenvironment helps accommodate foreign entrepre-neurial manufacturing firms with relatively limitedresources and capabilities. Influx of imported prod-ucts from other origins creates intense competition

(Khandelwal et al., 2013). The competition propelsthe least efficient firms to quit the market (Melitz,2003), and increases the overall productivity of thesurviving firms (Pavcnik, 2002). The competitionalso expedites early organizational learning ofINVs, rapidly develops their organizational routinesfor international markets, and helps achieve an‘‘immunization’’ effect (Burke & Hussel, 2013).When entrepreneurial manufacturers export

products from a more open country to a less opencountry (hereafter referred to as ‘‘downward exportexpansion’’ or ‘‘downward expansion,’’ see Fig-ure 1), they encounter higher tariffs that substan-tially increase entry costs and weaken pricecompetitiveness of their products in the destina-tion markets. Non-tariff entry barriers (e.g., importquotas, anti-dumping duties, countervailing duties)considerably aggravate operational risks and uncer-tainties too (Miller, Holmes, Feulner, Kim, Riley, &Roberts, 2012). Because INVs have limited firmresources (Cavusgil & Knight, 2015; Oviatt &McDougall, 1994), they are usually not in anadvantageous position to effectively overcomeinstitutional constraints or fully accommodatenegative shocks caused by high entry barriers andpost-entry uncertainties.

Upward Expansion Speed and PerformanceWe now examine the joint effects of expansionspeed and direction on INV performance (seeFigure 2). The new construct of internationaliza-tion speed differs from the previous ones in theliterature in two aspects. First, it incorporatesinstitution as a new dimension of speed (apartfrom timing, scope, and intensity dimensions) andexamines how fast a firm expands its export intoinstitutionally different countries. Second, it addsdirection to expansion speed and differentiates anupward expansion from a downward one. We firstargue how learning advantages of newness outbal-ance diseconomies of time compression in therapid upward expansion.Rapidly expanding INVs inevitably encounter

diseconomies of time compression in the compet-itive markets of more open destination countries.Countries with a high degree of trade freedom offera relatively equal market access to exporters fromany country of origin, resulting in intense localcompetition. Entrepreneurial manufacturers fromemerging markets usually face insufficient compe-tition from foreign rivals and enjoy relatively highlevels of government protection (Edwards, 1993).Therefore, they are generally not as efficient as

Countries with low trade openness

Upward export expansion

Downward export expansion

Countries with high trade openness

Firm in a country with medium trade openness

Figure 1 Upward and downward export expansions.


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those firms from advanced economies (Khandelwalet al., 2013). Rapid expansion into liberalizedmarkets exposes these entrepreneurial exporters toa competitive market environment, diverse cus-tomer needs, and reduced government protectionwithin a short period (Baggs & Brander, 2006). INVsface different types of rivals in various destinationmarkets, and each type can adopt a differentcompetition strategy, e.g., cost leadership or pro-duct differentiation. INVs are obliged to quicklyexamine their rivals’ market strategies and refor-mulate corresponding reaction tactics. The diffi-culty lies in that each of these strategies necessitatesdifferent resource commitments, organizationaldesigns, and market focuses. Attempts to rapidlyaccommodate these strategies may overstretch firmresources and management attention.

Nonetheless, INVs possess intrinsic learningadvantages of newness that may effectively coun-terbalance the diseconomies of time compression.The first source of such advantages is the rapidreconstruction of pro-efficiency organizational rou-tines for destination markets. International busi-ness stimulates firms to own new knowledge,routines, and practices suitable for an overseaslocation and its associated institutional environ-ment (Qian, Khoury, Peng, & Qian, 2010). Suchinstitutional knowledge and routines may have animprinting effect in subsequent internationalexpansion to other destination locations (Cantwell,Dunning, & Lundan, 2010). Entrepreneurially ori-ented, INVs are featured with ownership of inno-vative, proactive, and risk-taking organizationalculture (Li et al., 2015; McDougall & Oviatt,2000). They may rapidly adapt their routines for

their external environment in a new destinationlocation through effective organizational learning(Autio et al., 2000). During rapid upward expan-sion, exposure to openness-enabled competitionmakes organizations more competitive and strate-gically alert (Barnett & McKendrick, 2004; Burke &Hussel, 2013). INVs are driven to design efficiency-enhancing culture that stimulates managers andemployees to rapidly innovate or improve coststructure to attract and retain clients. The rapidformation of such culture constitutes a criticalsource of competitive edge, with which INVs maybe better prepared for the competition in futureexport expansion.The second source is the rapid ‘‘preview’’ of pro-

market institutions in an upward expansion to anopen market (Dau, 2013) and resultant ownershipof institutional skills suitable for a liberalized homemarket. INVs originating from emerging marketsexperience a shift from a centrally planned para-digm to a market-oriented one (Shinkle & Kriauci-unas, 2010; Zhou et al., 2006). During marketliberalization, INVs need to rapidly downplay non-market strategies, and develop more market-basedstrategies such as product differentiation. Learningduring rapid upward expansion enables firms toproactively acquire necessary skills and mindset (DeClercq, Sapienza, Yavuz, & Zhou, 2012) that war-rant improved home market performance vis-a-vislocal rivals (Dau, 2013).Moreover, a rapidly improved cost structure

associated with lowered institutional barriers in amore open destination market considerably miti-gates the diseconomies of time compression andstrengthens the learning advantages of newness.

Firm performance

Downward expansion speed

H1a

H1b

H2a

Subnational home market liberalization

H2b

Upward expansion speed

Figure 2 Impact of export expansion speeds on performance.


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Compared with less open target markets, rapidupward expansion into open target markets faceslower tariffs, as well as lower uncertainties involvedin various forms of non-tariff barriers (WTO,2012).2 Therefore, INVs may have a quick reductionin their marketing and maintenance costs, which isespecially helpful if they rely on cost leadership.Equipped with such an ownership advantage ofcost structure, INVs may relieve resource over-stretches and routine conflicts caused by rapidorganizational unlearning and learning. To sum-marize, the learning advantages of newness,through synthesis of ownership of entrepreneurialculture, ownership of institutional knowledge, andlocational advantages, outweigh the diseconomiesof time compression in rapid upward expansion.

Hypothesis 1a: Upward expansion speed posi-tively affects the firm performance of INVs fromemerging markets.

Downward expansion speed and performanceRapid downward expansion causes strong disec-onomies of time compression. Admittedly, knowl-edge from recent experience with the less openhome market is easy to access, retrieve, and applyto the new destination market, and this knowledgemay help exporters adapt to the less open destina-tion market (Boehe, Qian, & Peng, 2016). However,as INVs internationalize early and rapidly (Oviatt &McDougall, 1994), such young firms originatingfrom an emerging market do not usually possess asstrong imprinting experience with their homemarket as more established firms. A moderate levelof institutional distance between home (an emerg-ing economy) and destination markets (also anemerging economy) does not guarantee a betterfinancial performance, also because the institu-tional similarity could breed ‘‘carelessness’’ indeploying marketing resources (O’Grady & Lane,1996, pp. 324–325).

Rapidly expanding to a less open market leads torapid restructuring of organizations to accommo-date non-market routines and strategies. Conse-quently, it causes potential conflicts between oldand new routines. With intrinsic ownership ofentrepreneurial culture, INVs may rapidly divert asignificant portion of management attention andfinancial resources to maintain strong foreignpolitical connections (Rahaman, 2016), e.g., withchambers of commerce, business consulates, and

custom officers in their target countries (Danielset al., 2014). The routines built on such connec-tions may suit a less open market, but they aresometimes counterproductive in more open homemarkets. INVs from emerging markets haveacquired some market-based capabilities and haveformed some organizational routines toward amarket economy in their home countries (Cuervo-Cazurra & Dau, 2009) when they purchase inputs,recruit workers, and manufacture products at theirhome markets. Therefore, exporters with rapiddownward expansion encounter challenges andoverstretch resources in maintaining a balancebetween routines for more open home marketsand those for less open destination markets.Exporters rapidly expanding to less open desti-

nation markets have to quickly accommodateshocks from tariffs, non-tariff barriers, and bureau-cracy in customs and border control agencies(Daniels et al., 2014). This change quickly exhaustsfinancial resources and substantially constrains theadaptability of firms to new business practicesoriented toward less open foreign markets. Differ-ent countries employ different measures to protecttheir indigenous industries, e.g., sanitary standardsand import quotas. Exporters must adjust theirroutines in different directions to address differentimport barriers. For example, an exporter facinganti-dumping hazard in a foreign market needs tomake its accounting system highly transparent forpotential investigations. If that exporter happens toface high sanitary standards in another foreignmarket, it must also improve its procurement andmanufacturing processes. Simultaneously andrapidly overcoming various tariff and non-tariffbarriers is demanding for the financial capabilitiesand organizational flexibility of INVs, because thesebarriers reduce their cost competitiveness in desti-nation markets, increase their operational uncer-tainty, and consequently jeopardize their financialperformance (WTO, 2012). Rapid expansion tothese countries leaves exporters with limited timeand resources to compensate for losses caused byhigh operational costs. Rapidly exporting startupsalso face high post-entry hurdles over a short periodof time, e.g., relatively inadequate market dyna-mism, high agency costs in distribution, and buyerhostility caused by a protectionist atmosphere(Edwards, 1993). In sum, the diseconomies of timecompression outweigh the learning advantages ofnewness in rapid downward expansion.


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Hypothesis 1b: Downward expansion speednegatively affects the firm performance of INVsfrom emerging markets.

Moderating Effects of Subnational Home MarketLiberalizationThe market liberalization in the subnational originof INVs strengthens the positive relationshipbetween upward expansion speed and firm perfor-mance. First, home market liberalization leads tostronger market competition and enhances thelearning advantages of newness during upwardexpansion. Home market liberalization minimizesstate presence in competitive sectors in final prod-ucts, such as electronics and textile (Park, Li, & Tse,2006). The strengthened competition forces theleast efficient firms to exit (Khandelwal et al.,2013). It also stimulates the surviving firms to bemore efficient (Barnett & McKendrick, 2004) andcapable of effectively leveraging the learningadvantages of newness. New startups need todevelop competition-related business routines earlyon to outperform their incumbent counterparts(Chang & Wu, 2014). The familiarity with marketcompetition, as well as ownership of entrepreneur-ial organizational culture, helps INVs to be able toquickly adapt themselves to the competitive envi-ronment in more open destination countries dur-ing rapid upward expansion. Moreover, INVs froma more liberalized home region are more likely topossess a stronger absorptive capacity in rapidly‘‘previewing’’ and decoding the market institutionsin more open destination countries (Dau, 2013) sothat INVs may be better prepared for their businessoperations in the home market.

Second, home market liberalization saves opera-tional costs and alleviates the diseconomies of timecompression in upward expansion. Home marketliberalization deregulates production factors, suchas labor employment, bank loans, and landresources, so that they can be freely allocated andreallocated (World Bank, 2015). In particular, pri-vate entrepreneurial firms experience reduced ide-ological discrimination in obtaining bank loans inthe subnational regions with abated state presence(Park et al., 2006). Reduced manufacturing andoperational cost structure enables INVs to rapidlyexpand to more open countries. The export man-agers have a higher profit margin, more humanresources to understand the business environmentin those markets and formulate export routines formore open markets.3

Hypothesis 2a: The degree of subnationalhome market liberalization strengthens the pos-itive relationship between upward export expan-sion speed and the firm performance of INVsfrom emerging markets.

The market liberalization in the subnationalorigin of INVs mitigates the negative relationshipbetween downward expansion speed and firmperformance. First, home market liberalizationstrengthens the strategic flexibility of INVs andconsequently alleviates diseconomies of time com-pression during rapid downward expansion. TheINVs may benefit from liberalization-stimulatedcompetition (Burke & Hussel, 2013) and becomemore productive (Chang & Wu, 2014) and moreagile to accommodate market uncertainty. Suchagility may be embodied in the managers’ mindsetand transferred to overseas markets. They help INVsassimilate the rapidly incoming new market infor-mation and adjust their market routines for theprotectionist measures in the destination marketsduring rapid downward expansion. For example,when a destination country’s government imposesan import quota, firms from a liberalized homemarket more swiftly contact the government orlocal import agency to apply for quota.Second, INVs from a liberalized home origin may

enjoy a lower cost structure (Park et al., 2006),which is critical in facilitating the INVs to cushionthe cost pressure and diseconomies of time com-pression in rapid downward expansion. Due to thecost-saving effect of home market liberalization,the export managers of INVs may offer their pricequote in a relatively advantageous position com-pared with their counterparts from a less liberalizedsubnational home origin. Furthermore, the exportmanagers may also have a higher degree of finan-cial freedom to adopt costly but necessary non-market strategies, e.g., developing connectionswith local customs agencies in the destinationmarkets. Consequently, the relatively strong mar-ket return in rapid export expansion may grantexport managers stronger power and confidence intransferring their best practices (Kostova, 1999) tofuture expansion. Therefore, during rapid down-ward expansion, home market liberalization helpsINVs accommodate negative impact of trade barri-ers and adapt business routines.

Hypothesis 2b: The degree of subnationalhome market liberalization weakens the negativerelationship between downward export


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expansion speed and the firm performance ofINVs from emerging markets.

METHOD

SampleWe chose Chinese INVs in manufacturing sectorsfrom 2000 to 2009 as the research setting. Thesample period witnessed dynamic changes in bothtrade openness and home market liberalizationsince China’s WTO accession in 2001. Nonetheless,the economy is featured with a startling regionaldisparity in market liberalization. The easternregion is traditionally more commercializedbecause of its proximity to the coast and logisticconvenience for trade. Driven by the influx offoreign and private capital and economic develop-ment, the local governments in that region tend tohold a favorable attitude toward market liberaliza-tion (Zhou et al., 2006).

We consolidated two firm-level datasets. The firstone is from the National Bureau of Statistics ofChina, which covers 90% of all Chinese manufac-turing output. This dataset contains demographicand financial information on firms with indepen-dent legal and accounting status. The second one isa census dataset from the General Administrationof Customs of China covering all Chinese exports.This dataset reports detailed yearly information onfirm name, country of destination, and exportvalue for every destination. Both datasets areannually updated, thus ensuring the absence ofsurvival bias. As these two datasets employ differentfirm coding systems, we conducted firm matchingusing firm names instead of numerical identifiers(Khandelwal et al., 2013).

We confined the research sample to indigenousChinese INVs and excluded all firms foundedbefore the start year of the sample period, whichis 2000. Thus, we ensured each firm began export-ing during 2000–2009, and calculated the numberof years the firms have conducted their exportactivities. We deleted state-owned enterprises andforeign-invested enterprises. In accordance withthe international entrepreneurship literature, weonly included firms that start exports within6 years of inception (Coviello, 2015; Deng et al.,2017), and used 500 employees as the maximumsampling point (Lu & Beamish, 2001; Sui & Baum,2014). The datasets do not provide information on

whether a firm is affiliated to a large corporation orwhether it owns subsidiaries. To purge the potentialdisturbance caused by such a group affiliation, weincluded firm-level control variables, such as pro-ductivity and firm size (in total assets), which areintroduced in the variable section. Furthermore, weexcluded firms with more than 250 employees inthe first year of their business operation. We alsoexcluded firms that never exported, but retained allobservations (even without export in certain years)of firms that exported for at least 1 year during theentire sample period. Finally, to remove potentialround-tripping exports and re-imports, we deletedfour special destination markets, namely HongKong, Macau, Taiwan, and Chinese Mainland. Allfirms left in the sample exported their manufac-tured products to at least one overseas countryduring the sample period.

Potential Endogeneity and Model DesignWe constructed an instrumental variable two-stageleast squares (2SLS) model (Hashai, 2011) to controlfor the potential endogeneity in upward anddownward export expansion speeds, since endo-geneity is commonly observed in the relationshipbetween international expansion and firm perfor-mance (Jean, Deng, Kim, & Yuan, 2016; Mudambi& Zahra, 2007; Sui & Baum, 2014).4 Instrumentalvariables affect the dependent variable throughtheir effects on the endogenous variable only(Hashai, 2011; Reeb, Sakakibara, & Mahmood,2012). The main regression of the 2SLS model onthe effect of expansion speed on firm profitability isspecified as follows:

profitability ¼ a1 þ c1 � speed þ c2 � speed�moderator þ c3 �moderator þ b

� exogenousþ e1; ð1Þ

where speed is the speed of export expansion. Speedis potentially an endogenous variable with twoforms, namely upward expansion speed anddownward expansion speed. They are specified inthe variable section. To avoid the potential inter-twining effects of multicollinearity with endo-geneity, these two variables are entered separately.Exogenous refers to a vector of exogenous vari-ables, and moderator represents home market liber-alization. If an endogeneity test showed that speedcorrelated with the error term e1, then we ran thefirst stage of the 2SLS on speed.


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speed ¼ a2 þ d1 � exogenousþ l1 � instrument1 þ e2;

ð2Þ

where instrument1 denotes a vector of addedinstrumental variables. A critical identificationcondition is l1 = 0. That is, after separating theeffects of exogenous variables, speed and instru-ment1 remain correlated (Wooldridge, 2002b, pp.473–74). If the endogeneity test showed that speedcorrelated with the error term e1, then its interac-tion term speed 9 moderator is correlated with e1,i.e., the interaction term is endogenous as well(Wooldridge, 2002a, pp. 121–22). Therefore, weneed to estimate an additional first-stage 2SLSmodel on speed 9 moderator.

speed �moderator ¼ a3 þ d2 � exogenousþ l2� instrument2 þ e3 : ð3Þ

The fitted values of speed and speed 9 moderatorobtained from Eqs. (2) and (3) were employed toreplace the original values in Eq. (1) (the secondstage of 2SLS).

Variables

Dependent variable: profitabilityWe measured profitability with firm-level ratherthan export project-level return on sales (ROS),which is calculated by net profit divided by totalsales (Boehe et al., 2016; Chan et al., 2010), for thefollowing reasons. First, export expansion speedfundamentally reshapes the organizational routinesof INVs that usually internationalize early andrapidly. Exports represent an important portion offirm sales in the sample, averaging 41.7% for allobservations and 58.2% in exporting years. Thischaracteristic suggests that exports significantlyaffect overall corporate performance. Second, theliterature on the relationship between exportexpansion speed and performance mainly focuseson corporate-level rather than project-level perfor-mance (Chang & Rhee, 2011; Li et al., 2012;Mudambi & Zahra, 2007; Vermeulen & Barkema,2002). Such a corporate-level construct and mea-sure may fully capture the fundamental effect ofrapid export expansion on firm routines, culture,survival, and performance. Third, a firm maydeploy strategically low pricing in a specific coun-try market in certain years. Thus, short-termvolatility occurs in country-specific export andmakes it less precise in reflecting the effect of rapidexport expansion on firms’ overall organizational

routines and performance. Firm-level profitabilityuses global sales as the denominator and thussmoothens the effect of sales volatility in certaincountries (Chan & Makino, 2007).

Independent variables: speeds of upward vsdownward export expansion‘‘Speed’’ refers to ‘‘the rate at which somebody orsomething moves or travels’’ (Turnbull et al., 2010).Vermeulen and Barkema (2002) measure interna-tionalization speed with the number of overseassubsidiaries divided by the number of years spenton foreign expansion. However, we needed toincorporate country institutional profiles (Kostova,1999) into this measure to cater for our study onspeed across the distance of trade openness. Inaddition, a firm’s international footprint coversmultiple countries, and the repercussions fromforeign markets vary depending on the countryinstitutional profile and share of sales in eachforeign market (Zhou & Guillen, 2015).Similar to Dau (2013), we dichotomized export

expansion into upward and downward export expan-sions to refer to expansion tomore openmarkets andless open markets, respectively. Upward expansionspeed is the total sales share in amore open country ofa firm (indexed by i) divided by the number of yearsspanning the period of the first export activity of thefirm (t0) up to the current year (t).We add one to (t –t0) to avoid zero denominators.

upward expansion speedi;t ¼P

j si;j;t

t � t0 þ 1;

if opennessi;j;t [ opennessi;China;t ;

ð4Þ

downward expansion speedi;t ¼P

j si;j;t

t � t0 þ 1;

if opennessi;j;t\opennessi;China;t ;

ð5Þ

where si,j,t is the share of export from the homecountry (China) to the jth country in the total salesof the ith firm to all destinations (including homeand export market sales) in year t, that is,

si;j;t ¼ exporti;j;t=total salesi;t : ð6Þ

To measure trade openness in the home countryand in the export destination countries, we adoptedan index from the Indices of Economic Freedom(Miller et al., 2012), which have been extensivelyemployed in international business studies (e.g.,Shinkle & Kriauciunas, 2010). These indices com-prise 10 dimensions, one of which is ‘‘trade


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freedom.’’ The trade freedom index measures aneconomy’s overall level of openness (both tariffs andnon-tariff measures) for products imported fromforeign countries. Therefore, adopting the tradefreedom index is more appropriate than simplyusing tariffs to measure overall trade freedom.Country-level trade freedom also determines thedegree of competition in destinationmarkets (Baggs& Brander, 2006), thus potentially influencing post-entry operational costs and firm performance withrapid export expansion. We did not employ aposteriori indicators of trade openness, e.g., ratio oftotal trade to gross domestic product (GDP), becausesuch indicators are heavily dependent on economysize and structure andmaynot ‘‘capture the extent towhich commercial policy impedes trade’’ (Edwards,1993, p. 1390). The trade freedom index is a dynamicmetric that reports a value on the trade openness ofeach country in each year.

Panel (a) in Figure 3 displays the significantvariation of the average scores of trade freedomacross different countries from 2000 to 2009. Theminimum and maximum scores are 0 for NorthKorea and 85.9 for Singapore, respectively. Inaddition, trade liberalization reform after China’saccession into the WTO caused the country’sranking to rise from the 135th place among 155economies in 2000 to the 108th place among 173economies covered by the Index in 2009. Themedium position of China in the dimension oftrade freedom justifies the appropriateness of Chinaas a research setting, since Chinese manufacturershave abundant destination country options in bothupward and downward expansions.

Moderator: degree of home market liberalizationWe adopted a longitudinal provincial marketiza-tion index to measure the annual degree of liber-alization of the market economies of 31 Chineseprovinces from 2000 to 2009 (Fan, Wang, & Zhu,2011). The index indicates the liberalization of aprovince (not annual change), and its valueexhibits a dynamic pattern of market liberalizationover time. This institutional index consists of fivepillars: (1) minimization of government interven-tion in the economy, (2) development of privatesectors, (3) reduction of the protectionism for localfirms, (4) development of the markets of produc-tion factors, and (5) protection of the interests ofproducers, intellectual properties, and consumerrights. Thus, the index concerns liberalization inthe domestic market that forms the institutionalenvironment of purchasing, manufacturing,

recruiting, and innovation of INVs. The index iscomputed and updated annually using data fromyearbooks, government reports, and survey data,among others. As market liberalization is an endur-ing process (Park et al., 2006), its effect on firmperformance is mainly due to its degree and not tothe marginal changes from year to year (Cuervo-Cazurra & Dau, 2009). The index has been exten-sively applied in the recent literature (e.g., Chang &Wu, 2014). Panel (b) in Figure 3 illustrates thevariation in the degrees of market liberalization (asaveraged over the sample period) across provinces.The lowest value is 0 for Tibet in 2000, whereas thehighest is 11.8 for Zhejiang in 2009, and the scoresexhibit a dynamic pattern in the sample periodwith a national average of 4.278 in 2000 and 7.335in 2009.

Instrumental variablesWe considered three instrumental variable candi-dates that are potentially the determinants of rapidexport expansion. Instrumental variables shouldsatisfy two requirements. The first is they signifi-cantly affect the two potentially endogenous speedvariables in the first-stage regression. We adoptedF-statistics in testing the coefficients of instrumen-tal variables. The second requirement is instrumen-tal variables should be uncorrelated with the errorterm in the second-stage regression of 2SLS estima-tion (Jean et al., 2016; Wooldridge, 2002a,pp. 83–84).5 For that requirement, we needed touse at least one instrumental variable more thanendogenous variables (Wooldridge, 2002b,pp. 484–485), and then used Sargan over-identifi-cation test (Sargan, 1958).The first instrumental variable candidate is firm

age. This variable captures the learning experienceof entrepreneurial firms (Chandra, 2017; Li et al.,2015). As new ventures mature, they build aparticular set of capabilities to evaluate opportuni-ties (Rahaman, 2016). In emerging markets, theexport growth rate of nascent private firms declineswhen they age, become embedded in a homemarket institutional environment, and gain stronginstitutional legitimacy (Shinkle & Kriauciunas,2010). Firm age is calculated by deducting a firm’sfoundation year from its current observation year.The second candidate is competition that captures

the industrial competitive effects in the homecountry. While destination market competitiondirectly affects export and corporate performance,home market competition only exerts a partialeffect on firm performance directly, particularly


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because of the high export intensity of the sampleemployed. Thus, home market competition is anappropriate candidate for instrumental variables.

We expected it to positively affect downwardexpansion speed in less open markets with weakmarket competition, that is, an escape from high

Figure 3 Trade openness and subnational market liberalization, 2000–2009. Notes Average scores for the 10 years are employed.

Data source of panel (a) is the index of Miller et al. (2012) that covers 177 economies. The economies not covered in the index are

shown in white in panel (a). Data source of panel (b) is the index of Fan et al. (2011) that covers 31 regions. Hong Kong, Macau, and

Taiwan regions not covered in the index are shown in white in panel (b).


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home market competition (Xia, Ma, Lu, & Yiu,2014). Conversely, we expected a negative effect ofcompetition on upward expansion speed in moreopen markets with strong market competition.Competition in a four-digit industry is measured byone minus the score of market concentration(Palmer & Wiseman, 1999):

competitiont ¼ 1�X

ihms2i;t ; ð7Þ

where hmsi,t denotes the ith firm’s home marketshare in its industry in year t. A large variableindicates a low degree of industry concentrationand high degree of competition among firms inthat particular sector.

The third candidate is a firm’s manufacturingproductivity, which reflects the overall manufacturingefficiency in transforming all inputs (e.g., labor,machine, and intermediate materials) into finalproducts (Chang & Wu, 2014). Productivity can beexplained by the previous experience of the execu-tives, especially by their international experience(Cassiman & Golovko, 2011; Le & Kroll, 2017).Productivity plays a decisive role in determining theexport propensity (Melitz, 2003). Productive firms arecost effective and can afford the entry costs incurredin initiating new export projects and expanding tooverseas countries. Firm capabilities reflected in pro-duction efficiency also moderate the location deci-sion during firm export expansion. Nonetheless,institutional barriers buffer the supporting effect ofproductivity and make highly productive new firmsless able tomaximize the benefit of high productivity(Chang & Wu, 2014). When INV manufacturersengage in rapid downward expansion into less opencountries with high entry costs and post-entry oper-ational costs, the linkage between manufacturingproductivity and ultimate financial performance willbecome substantially looser. Therefore, it is reason-able to include productivity as an instrumentalvariable for downward expansion speed. Productivityis calculated using an approach developed by Levin-sohn and Petrin (2003).

Multi-level control variablesAt the firm level, we used the share of new productsales in total sales tomeasure innovation (Cassiman&Golovko, 2011). Innovativeness is one of the defin-ing characteristics of international entrepreneurship(McDougall & Oviatt, 2000). We included financialliability which is measured as the INV’s long-termliabilities divided by total assets. It reflects thefinancial health and risk-taking attitude, as such an

attitude has been one of the core features of inter-national entrepreneurship too (McDougall &Oviatt,2000). The variable also reflects the firm’s capabilityto acquire financing from banks and foster itsoverseas expansion. We measured firm size withthe natural logarithm of total assets, since bothtangible and intangible assets are important for theperformance of INVs (Cavusgil & Knight, 2015). Weemployed export intensity to capture the strategicimportance of exports in the overall business port-folio of exporters (Lu & Beamish, 2001), the experi-ence and knowledge acquired from foreign markets(Chandra, 2017), as well as risk exposure duringexport expansion. Export intensity is measured byexport value divided by total sales. Since the homecountry currency (Renminbi) experienced an appre-ciation in the sample period, we expected a exportintensity negatively affects firm performance (Lu &Beamish, 2001, p. 578).We also constructed several variables to depict

the characteristics of target countries. Number ofcountries measures the total number of foreigncountries where a firm operates. It reflects theinternational experience (Chandra, 2017), as wellas the challenges and opportunities associated withthe extended span of control (Vermeulen &Barkema, 2002). Geographic distance is measured bythe weighted geographical distance, in 1000 km,between an INV’s home country (i.e., China) andits destination markets (Berry, Guillen, & Zhou,2010). The share of export to each destinationmarket in total export is the weighing factor.We included market size to control for the impact

of the purchasing power and economic magnitudein destination markets (Chan & Makino, 2007).Countries with a large market size usually attractsignificant international marketing efforts and con-tribute numerous sales to foreign exporters. Desti-nation market size is measured by the population ofits destination markets, weighted by the shares ofexport to destination markets in total export. Weobtained country-level population data from theWorld Bank World Development Indicators.The variable tariff is measured by the import tariff

rates between an exporter’s home country and itsdestination markets, weighted by the shares ofexport to destination markets. We obtained tariffdata from the WTO (for its member countries) andthe World Development Indicators (for other coun-tries). This variable gauges the product-specificbarriers to exports (WTO, 2013) and helps tocontrol for the industry-level institutional environ-ment in target countries.


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We included total export and total export growth tocontrol for the potential disturbance caused by themagnitude and growth of total export from hometo destination markets (Chan & Makino, 2007).Exporters tend to export more to countries with alarge import demand, and total export is measuredby the weighted export value between an exporter’shome country and its destination markets (in USDbillions). The shares of export in destination mar-kets are taken as the weighing factor. Similarly, totalexport growth is measured by the weighted exportgrowth rates. Data on the country-level export areobtained from the United Nations CommodityTrade Database.

We also included several home country industry-level control variables to capture industry idiosyn-cratic factors. First, we included export tax rebaterates at the six-digit product level. Data areobtained from the General Administration of Cus-toms of China. As a firm may export multipleproducts in the same year, we calculated theweighted tax rebate rates for each firm in everyyear with the export share of each product as theweight. It reflects both the degree of governmentsupport and policy dependence of the focal firm.Second, we included the dummy variable high-techindustry. Consistent with the widely adopted Orga-nization for Economic Co-operation and Develop-ment (OECD) approach using R&D intensity as acriterion to define high-technology sectors (OECD,2011), National Bureau of Statistics in China cate-gorizes 59 out of 480 sectors into high-technologyones, such as optical fiber manufacturing. Third, weincluded variables industry dynamism and industrygrowth. We first regressed the logarithm of industry-level sales of each four-digit industry in China inthe preceding 5 years against the year variable.Then, industry dynamism is obtained with theantilogs of the standard errors (Keats & Hitt,1988). Industry growth is measured by the growthrate of all domestic sales in each of the sectors inChina in a year (Keats & Hitt, 1988). Fourth, weincluded INV density. Density of similar firmsstrengthens the legitimacy of similar entities butalso intensifies the potential competition amongthem for similar resources in a local environment(Barnett & McKendrick, 2004). We measured INVdensity with the number of INVs in each province–industry–year group to differentiate from theinstrumental variable industry competition.

Finally, we included a single entry of the mod-erator as a control variable, namely home marketliberalization (Chang & Wu, 2014; Park et al., 2006).

It has both enabling and disabling effects on INVs.On one hand, home market liberalization lowersinstitutional barriers, offers entrepreneurial firmsrelatively equal market opportunities, enhances thecost advantages of firms, and stimulates firms to bemore agile and productive. On the other hand,home market liberalization invites stronger marketcompetition that may squeeze the profit margins ofhome market sales and crowd out the least efficientfirms (Chang &Wu, 2014, p. 1115) and firms overlydependent on the home sales. Therefore, the netdirect effect of home market liberalization on firmperformance is mixed and is subject to an empiricaltest. We included 9 year dummy variables, andlagged all independent variables by a year.

RESULT

Baseline ResultTable 1 reports the descriptive analysis and thecorrelation coefficients of the major variables. Thecoefficients are lower than 0.33. The average vari-ance inflation factor (VIF) value in models is below1.5, and all VIF values of the variables involved inthe interaction effects are below 2.6. The low VIFvalues remove the concern of multicollinearity.Table 2 presents the main empirical findings

obtained from the second-stage model of 2SLSestimation.6 Model 1 includes upward expansionspeed as a potential endogenous variable. Durbin v2

test confirms the endogeneity of upward expansionspeed, and the F-statistics reports the joint signifi-cance of the coefficients on the instrumentalvariables in the first-stage regression after separat-ing the effect of exogenous variables. F-test suggeststhat the included instrumental variables (age andcompetition) are highly correlated with the endoge-nous variable. The Sargan test (0.842) on over-identification restriction justifies the validity ofinstrumental variables (Sargan, 1958). Therefore,H1a is supported. Model 2 includes downwardexpansion speed as the main explanatory variable,and both the F-test and Sargan test support compe-tition and productivity as valid instrumental vari-ables. Downward expansion speed has a significantlynegative coefficient, thus supporting H1b.To test the moderating effect of home market

liberalization, we included its interaction term withupward expansion speed in Model 3. Following Aikenand West (1991), we mean-centered the two speedvariables and the moderator to avoid the potentialmulticollinearity caused by the interaction terms.


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Table 1 Descriptive statistics and correlation coefficients

Variable 1 2 3 4 5 6 7 8 9 10 11

1. Profitability 1.00

2. Innovation 0.05 1.00

3. Financial liability -0.02 0.01 1.00

4. Size 0.05 0.03 0.10 1.00

5. Export intensity -0.10 0.07 -0.05 -0.12 1.00

6. Age -0.03 -0.10 0.00 0.20 0.04 1.00

7. Productivity 0.11 -0.04 0.03 0.15 -0.11 0.04 1.00

8. Competition -0.01 -0.04 0.01 -0.02 0.01 0.02 0.06 1.00

9. No. of countries -0.02 0.03 -0.03 0.11 0.25 0.08 0.00 -0.08 1.00

10. Geographic distance -0.02 0.03 -0.01 -0.03 0.04 -0.03 -0.03 -0.02 0.14 1.00

11. Tariff -0.02 0.00 0.00 -0.01 0.00 -0.04 0.00 0.03 0.02 0.01 1.00

12. Market size 0.00 0.02 0.00 0.02 -0.02 -0.03 0.00 0.00 -0.04 0.11 0.06

13. Total export 0.02 -0.02 0.01 -0.02 -0.11 -0.03 0.02 0.01 -0.25 0.12 -0.08

14. Total export growth -0.01 0.00 0.03 -0.04 -0.16 -0.09 -0.02 0.02 -0.32 0.03 0.10

15. Tax rebate 0.00 0.06 -0.02 0.00 0.05 0.01 -0.13 -0.09 0.08 0.06 0.00

16. High-tech industry 0.05 0.08 0.00 0.06 -0.05 0.04 -0.10 -0.13 0.01 0.01 -0.06

17. Industry dynamism 0.00 0.05 0.01 0.03 -0.09 -0.10 0.03 0.07 0.01 0.02 0.04

18. Industry growth 0.02 0.00 0.01 -0.07 0.00 -0.05 0.03 -0.03 0.01 0.00 -0.03

19. INV density -0.03 -0.05 -0.03 -0.02 0.03 0.05 0.00 0.22 -0.02 -0.01 0.05

20. Home market liberalization -0.07 -0.01 -0.07 -0.03 0.18 0.12 -0.18 -0.01 0.15 0.05 -0.04

21. Upward expansion speed -0.05 0.06 -0.03 -0.21 0.29 -0.22 -0.14 -0.01 0.22 0.09 -0.06

22. Downward expansion speed -0.02 0.01 -0.02 -0.09 0.10 -0.11 -0.06 -0.01 0.11 -0.12 0.17

Mean 0.03 0.05 0.03 9.68 0.42 4.32 177.79 0.99 7.25 0.67 0.09

SD 0.07 0.19 0.09 1.02 0.40 1.98 223.84 0.03 7.24 0.29 0.12

Variable 12 13 14 15 16 17 18 19 20 21 22

1. Profitability

2. Innovation

3. Financial liability

4. Size

5. Export intensity

6. Age

7. Productivity

8. Competition

9. No. of countries

10. Geographic distance

11. Tariff

12. Market size 1.00

13. Total export 0.22 1.00

14. Total export growth 0.03 0.25 1.00

15. Tax rebate 0.04 -0.06 -0.04 1.00

16. High-tech industry 0.04 0.01 0.01 0.16 1.00

17. Industry dynamism 0.05 -0.01 0.06 0.05 0.02 1.00

18. Industry growth -0.01 0.03 0.02 -0.16 -0.05 0.00 1.00

19. INV density -0.02 -0.01 -0.01 0.10 -0.12 0.21 -0.10 1.00

20. Home market liberalization -0.01 -0.05 -0.07 0.13 0.01 -0.01 0.01 0.24 1.00

21. Upward expansion speed -0.04 -0.12 -0.19 -0.01 -0.04 -0.01 0.06 0.00 0.05 1.00

22. Downward expansion speed 0.08 -0.09 -0.06 0.04 -0.02 0.03 -0.04 0.00 -0.01 0.05 1.00

Mean 0.01 0.01 0.05 0.11 0.06 1.00 0.35 0.20 9.72 0.13 0.02

SD 0.01 0.04 0.11 0.04 0.24 0.00 0.17 0.32 1.63 0.17 0.07

Note 8681 firms with 18,539 observations. Absolute value of correlation coefficient C 0.02, significant at p\0.01.


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Upward expansion speed is potentially correlatedwith the error term, as is the interaction termupward expansion speed 9 home market liberalization.Therefore, the interaction term is an additional

endogenous variable. To implement the Sargan teston two endogenous variables, we must include atleast three instrumental variables. Following Wool-dridge (2002a, pp. 121–22), we added the

Table 2 Regression results

Variables Variable level Model 1 Model 2 Model 3 Model 4

Intercept 1.528 6.411 2.549 -3.262

(2.605) (5.803) (2.493) (7.504)

Innovation Firm 0.016*** 0.016** 0.015*** -0.007

(0.003) (0.007) (0.003) (0.010)

Financial liability Firm -0.016*** -0.020 -0.019*** -0.040**

(0.006) (0.014) (0.005) (0.018)

Size Firm 0.004*** -0.010*** 0.003*** 0.002

(0.001) (0.003) (0.001) (0.002)

Export intensity Firm -0.017*** -0.004 -0.018*** -0.012**

(0.001) (0.004) (0.001) (0.005)

No. of countries Target country 0.000 0.002*** 0.000 0.000

(0.000) (0.000) (0.000) (0.000)

Geographic distance Target country -0.003 -0.064*** -0.003 -0.004

(0.002) (0.013) (0.002) (0.006)

Market size Target country -0.025 0.811*** 0.002 0.077

(0.045) (0.196) (0.044) (0.141)

Tariff Target country -0.000 0.101*** -0.000 0.002

(0.004) (0.022) (0.004) (0.013)

Total export Target country 0.023 -0.005 0.013 0.084*

(0.015) (0.037) (0.014) (0.049)

Total export growth Target country 0.037** -0.080* 0.032** 0.052

(0.015) (0.043) (0.014) (0.049)

Tax rebate Home industry -0.006 -0.015 -0.008 -0.032**

(0.005) (0.012) (0.005) (0.016)

High-tech industry Home industry 0.012*** 0.007 0.011*** 0.006

(0.002) (0.005) (0.002) (0.007)

Industry dynamism Home industry -1.517 -6.205 -2.509 3.547

(2.603) (5.800) (2.492) (7.515)

Industry growth Home industry 0.004*** 0.002 0.003** 0.000

(0.001) (0.003) (0.001) (0.004)

INV density Home industry & region -0.001 0.004 -0.001 -0.013**

(0.002) (0.004) (0.002) (0.006)

Home market liberalization Home region -0.002*** -0.005*** -0.005*** -0.029***

(0.000) (0.001) (0.001) (0.006)

Year dummy variables Included Included Included Included

H1a: Upward expansion speed 0.022*** 0.011**

(0.006) (0.006)

H1b: Downward expansion speed -1.391*** -4.154***

(0.268) (0.997)

H2a: Upward expansion speed 0.016***

9 Home market liberalization (0.005)

H2b: Downward expansion speed 3.139***

9 Home market liberalization (0.726)

Durbin v2 (endogeneity test) 14.954*** 172.619*** 21.498*** 196.693***

F-statistics (first stage) 36.166*** 15.792*** 27.000*** 49.874***

17.412*** 21.708***

Sargan p value 0.842 0.600 0.732 0.577

Note Standard errors in parentheses. * if p\0.1, ** if p\0.05, *** if p\0.01. In Models 3 and 4, we report two F-test values as two sets of first-stageregressions correspond to two endogenous variables, respectively, i.e. speed and its interaction with home market liberalization.


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interaction term between the instrumental variableage and the moderating variable home market liber-alization as an additional instrumental variable(age 9 home market liberalization). Again, the Dur-bin endogeneity test, F-test, and Sargan test alljustify our model design. The interaction betweenhas a significantly positive sign, thus supportingH2a.

Finally, we added an interaction term betweendownward expansion speed and home market liberal-ization in Model 4. Consequently, there are nowtwo endogenous variables (downward expansionspeed and downward expansion speed 9 home marketliberalization). We added the interaction termbetween an instrumental variable competitionand the moderating variable home market liberaliza-tion as an additional instrumental variable (compe-tition 9 home market liberalization). The coefficientof downward expansion speed in Model 4 is negative,consistently supporting H1b. The coefficient of theinteraction term in Model 4 is positive, suggestingthat home market liberalization mitigates the neg-ative relationship between downward expansionspeed and performance, which supports H2b.7

Robustness TestsWe conducted various sets of tests using differentmodels and measures to ensure the robustness ofour findings.8 First, we employed four alternativeindicators of institution that affect exports, becauseit potentially overlooks the complexity and ambi-guity in institutional forms to uniquely positioncountries on a linear scale of trade openness.Following the international business literature thatemphasizes the influence of economic develop-ment level on institutions (Boehe et al., 2016;Cuervo-Cazurra & Genc, 2008; Tsang & Yip, 2007),we first used the worldwide median level of GDPper capita as the cutoff point to dichotomize alldestination economies into more open versus lessopen economies. Following Shinkle and Kriauciu-nas (2010), we also measured the export-pertinentinstitutional profiles of China and destinationcountries using the average of three sub-indices ofthe Indices of Economic Freedom, that is, tradefreedom, financial freedom, and freedom fromcorruption. We also underscored the effects ofgovernance environment on exporting using theWorld Bank’s Worldwide Governance Indicators(Kaufmann & Kraay, 2016). With China’s score asthe cutoff point, we grouped the destination coun-tries of export expansion into upward versusdownward ones. Finally, with the International

Monetary Fund’s standard, which includes 35 ‘‘ad-vanced economies’’ and 24 ‘‘emerging and devel-oping economies’’ (International Monetary Fund,2015), we labeled export expansion to the formercohort as ‘‘upward’’ and the latter as ‘‘downward.’’The empirical results of the four alternatives ofinstitutional dichotomy all support the hypotheses.Second, we modified the speed measure by

incorporating the exact institution distance inEqs. (4) and (5) and using the speed to preciselygauge the net effect of potential learning advan-tages of newness regarding diseconomies of timecompression.9 We found the average institutionaldistance in the dimension of trade opennessbetween China and more open markets decreasesand even becomes smaller than that between Chinaand less open markets after 2005. This fact suggeststhat the institutional distance between China anddownward destinations is non-trivial. We con-structed a firm-level weighted distance measuresimilar to that of Zhou and Guillen (2015). Speed ismeasured as weighted distance divided by time.Specifically, speed of firm export expansion in thetth year is measured by the weighted distance intrade openness between China and all exportdestinations (collectively denoted by j) of the ithINV divided by the export tenure since the firm’sfirst export project in year t0

upward expansion speedi;t

¼P

j ððopennessi;j;t � opennessi;China;tÞ � si;j;tÞt � t0 þ 1

;

if opennessi;j;t [ opennessi;China;t ;

ð8Þ

downward expansion speedi;t

¼P

j ððopennessi;China;t � opennessi;j;tÞ � si;j;tÞt � t0 þ 1

;

if opennessi;j;t\opennessi;China;t :

ð9Þ

The variables opennessi,j,t and opennessi,China,t arethe same as those use in Eqs. (4) and (5).10 Empir-ical results consistently support all the hypotheses.We used Worldwide Governance Indicators toreplace the Index of Economic Freedom in measur-ing country-level openness and obtain similarresults. Finally, we modified the speed variables toaccommodate the recency effect in institutionalexposure (Perkins, 2014). Prior to the INV’s expan-sion into foreign markets, its organizational routine


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is affected by the firm’s exposure to the homemarket institutions. Given the firm’s recent insti-tutional experience at home (emerging markets),the less open countries are more familiar to that athome. Such an institutional similarity triggers theentry into similar markets (Zhou & Guillen, 2015)and brings desirable performance (Perkins, 2014).Moreover, knowledge from recent experience iseasy to access, retrieve, and apply. We changed thevalue of opennessi,China,t in Eqs. (4) and (5) to theaverage value of the past 3 years to capture theimprinting effects of the recent institutional expo-sure, i.e., opennessi,China,t = (opennessi,China,t + open-nessi,China,t-1 + opennessi,China,t-2)/3. We alsoconsidered the ‘‘depreciation’’ effect of a previousexperience, i.e., opennessi,China,t = (0.5 9 open-nessi,China,t + 0.3 9 opennessi,China,t-1 + 0.2 9 open-nessi,China,t-2). Both measures support all thehypotheses.

Third, we employed new speed variables con-structed based on entropy. Entropy measureaccounts for both the scope of target markets fromwhich a firm obtains sales (i.e., geographic spread)and the relative importance of each country to totalsales (i.e., geographic concentration) (Qian et al.,2010). Specifically, the speed of firm export expan-sion is the international market entropy of the ithexporter, divided by one plus the tenure in exportmarket (t - t0):

upward expansion speedi;t ¼P

j si;j;t lnð1=si;j;tÞt � t0 þ 1

;

if opennessi;j;t [ opennessi;China;t ; ð10Þ

downward expansion speedi;t ¼P

j si;j;t lnð1=si;j;tÞt � t0 þ 1

;

if opennessi;j;t\opennessi;China;t : ð11Þ

The empirical results consistently support all thehypotheses.

Fourth, we performed three independent robust-ness tests on the firm size in sampling. We first used250 employees as the sampling threshold by fol-lowing the OECD standard (OECD, 2016, p. 21).Then, we lowered the maximum number to 100employees in the entire sample period (Li et al.,2015). Finally, we lowered the ceiling to a maxi-mum of 100 employees in the first year but set noupper limit for subsequent employment growth toavoid ‘‘growth penalty.’’ All three tests obtainedconsistent results.

Fifth, we used different firm ages in sampling. Asthe main arguments of the study are based on thenotion of learning advantages of newness, usingolder firms is logical in contrast with INVs toprovide strong evidence supporting the theory. Weconstructed various new samples of older (moreestablished) firms. When we used firms that werefounded during 2000–2009 but confined the cohortto firms older than 6 years old, the effects of bothupward and downward expansion speeds becomeinsignificant. This finding suggests weaker learningadvantages of newness in upward expansion andweaker diseconomies of time compression (possiblydue to the recency effect) in the downward expan-sion of older firms. When we constructed a differ-ent sample of even older firms that were foundedbefore 2000, the upward expansion speed has aninsignificant or even negative effect on firm prof-itability. The effects of downward speed becomeless negative, and the effects even become positivein rare cases (e.g., when number of destinationcountries equals one and firm age is larger than 10),which is consistent with the findings in the ratherlimited literature suggesting the positive effects ofemerging economy firms expanding into similar(emerging) economies (Boehe et al., 2016; Cuervo-Cazurra & Genc, 2008). To summarize, the newresults based on older firms exhibit a clear andcommon pattern; that is, the learning advantagesof newness and diseconomies of time compressionfade out when firms become older. The contrastbetween INVs and old firms corroborates the valid-ity of our hypotheses on INVs.Sixth, we employed a generalized method of

moment approach in the instrumental variableregression. Robust estimates have also beenobtained after controlling for clustering on eachfirm, i.e., within-firm correlation among observa-tions. The results support all the hypotheses.Finally, considering the manufacturing nature ofthe sample firms and the constraining effects oftotal assets in firm expansion, we employed ROA toreplace ROS. We calculated ROA with net profitdivided by total assets (Lu & Beamish, 2001).Although the correlation coefficient between ROAand manufacturing productivity is 0.29, the Sargantest shows that the correlation between productivityand the error terms of ROA regressions is trivial,justifying productivity as a valid instrumental vari-able. The results consistently support all thehypotheses.


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DISCUSSION

ContributionsFrom a locational perspective, this study providesfour theoretical contributions to the literature oninstitutional theory and INVs. First, the studyincorporates an institutional dimension into thespeed of INV internationalization. Cultural orinstitutional difference has been an indispensableand fundamental factor to consider the processmodel of internationalization (Johanson & Vahlne,1977). However, the literature on the speed ofinternationalization has in general only examinedthe dimensions in timing, country scope, andforeign commitment (Kuivalainen et al., 2007;Oviatt & McDougall, 2005). Institutional dimen-sion helps to broaden the lens to examine the speedof internationalization, and to highlight the richinstitutional contexts during the internationaliza-tion process.

Second, the research extends the widely acceptednorm in analyzing supranational difference betweenlocations, which usually does not account for direc-tion or asymmetry (Zaheer et al., 2012). Based on therather limited literature addressing the direction ofinternationalization (Dau, 2013; Tsang&Yip, 2007),the current research goes a step further and incor-porates the direction into the speed of internationalexpansion. It explicitly divides rapid export expan-sion into upward and downward ones from aninstitutional perspective and reveals the fundamen-tal differences in their effects.

Third, this research complements the literaturethat has overly stressed the negative effects ofinstitutional distance and associated concepts onorganizations (Stahl et al., 2016), and it disclosessheer contrast among rapid export expansiontoward different destination locations. We suggestsome scenarios under which seemingly disadvan-tageous startup firms from emerging markets mayovercompensate their weakness and enjoy financialgains. The findings show the value of adjusting ouranalytical mindset and identifying the potentialbenefits of some constructs that have been con-ventionally regarded negative.

Fourth, this study both zooms in and zooms outthe analytical lens along the location-related insti-tutional axis, examines the joint effect of institu-tions involved in supranational directions andsubnational origins on firm performance, andadvances institutional theory. Different aspects ofinstitutions may exert different effects on corporate

decisions at different organizational levels in inter-national business (Chan &Makino, 2007). In partic-ular, this study goes beyond the conventionalconstructs of country of origin and scrutinizes theroles of subnational region of origin in internationalbusiness (Kirkman et al., 2017). The findings suggestthe importance of research on integrating differentdimensions of institutions (Holmes et al., 2013).

Limitations and Future ResearchAdmittedly, our research setting of entrepreneurialmanufacturers in a trade openness context does notrepresent all international business scenarios. Thegeneralizability of our findings remains subject toconceptual reexaminations and empirical tests indifferent contexts. First, future studies can investi-gate how broader dimensions (e.g., historical andcultural) of institutional distance (Kirkman et al.,2017; Sojli & Tham, 2017), apart from trade open-ness and the ones used in the robustness tests,affect the performance of rapid export expansion.Second, subnational dimensions of institutions indestination countries should be considered andcombined too when one extends the sample fromexporters to MNEs. Compared with exporters, MNEsubsidiaries in overseas markets are less affected bythe home country institutions (Zhou & Guillen,2015). Geographic variations of institutions intarget markets are crucial for local manufacturing,recruiting, and innovation as well as the isomor-phism strategies of MNE subsidiaries (Chan et al.,2010; Ma et al., 2013b). Third, due to data limita-tion, the study cannot measure individual-levelinnovative, proactive, and risk-taking attributes ofthe executives of INVs (McDougall & Oviatt, 2000;Oviatt & McDougall, 2005). We have includedsome firm-level variables such as innovation, finan-cial leverage, and productivity to control for suchcharacteristics. Nevertheless, future studies mayexplore the roles of the individual-level factors (Liet al., 2015) in the relationship between rapidexport expansion and performance. Finally, thetwo essential concepts in hypotheses, namelylearning advantages of newness and diseconomiesof time compression, still lack precise empiricaloperationalization, and future research maydevelop concrete measurement.

ImplicationsThis study offers important insights for managers inINVs. First, if entrepreneurs plan to export theirproducts to multiple institutionally different


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countries in a relatively short period, they shouldconsider the degree of trade openness in thedestination locations. Rapid upward export expan-sion across institutional distance positively affectsfirm performance, but rapid downward expansionshould be generally avoided. Second, entrepreneursshould strategically consider a liberalized subna-tional region for their main location of operations.This insight is particularly pertinent in emergingmarkets in which entrepreneurs usually observesubstantial differences across regions in terms oftheir progress in institutional improvement.

ACKNOWLEDGEMENTSThe authors are grateful to the comments receivedfrom the editors John Cantwell, Shige Makino, RamMudambi, Gongming Qian, anonymous reviewers,Jean Boddewyn, Michael Carney, Katrin Muehlfeld,Gracy J. Yang, Daphne W. Yiu, and audience of thepresentations at SMS China 2012, CUHK CIBS Sum-mer Research Forum 2013, JIBS Paper DevelopmentWorkshop 2013 (Istanbul), AIB 2013, AIB 2014, AIB2015, JIBS Special Issue Conference 2016 (NewOrleans), Renmin University of China, and Universityof International Business and Economics on earlierversions of this manuscript. Deng appreciates thefinancial support from the National Natural ScienceFoundation of China (71202149; 71772175).

NOTES

1Drawing on recent literature (Chan et al., 2010;Gao, Wang, & Che, 2017; Kirkman, Lowe, & Gibson,2017; Ma, Delios, & Lau, 2013a), we adopt a broadterm ‘‘subnation’’ for intra-national regions, althoughdifferent countries use different terms, e.g., provinces,states, prefectures, counties, boroughs, and towns.

2Most non-tariff measures (NTMs) occur betweendeveloped countries rather than between developedand developing countries (WTO, 2012, pp. 114–115).

3Admittedly, the home market competition causedby liberalization may lead to a lower price and profitmargin for the products sold at the home market.Nonetheless, INVs have significant sales contributedby the export market. Therefore, the cost reduction inthe exported products, caused by home marketliberalization, leads to an improved cost structureand stronger profitability in the overseas markets.

4Endogeneity refers to a scenario in which anindependent variable is correlated with the error term

of the regression model (Wooldridge, 2002b, p. 28). Amain source of endogeneity is a set of omittedvariables that should have been included in theregression but are difficult to measure explicitly(Wooldridge, 2002a, p. 105). For example, thestrategies of firm export expansion are formulated bymanagers with consideration of internal resources andthe external environment. At the same time, theseinternal and external factors directly affect the perfor-mance and survival of business operations (Chang &Rhee, 2011; Mudambi & Zahra, 2007). Simply run-ning an ordinary least squares regression betweenendogenous variables (e.g., export expansion) anddependent variables (e.g., performance) leads toinconsistent coefficients (Wooldridge, 2002a, p. 83).

5Instrumental variables should not be included inthe second-stage regressions of 2SLS estimation. Theindependent variable (i.e., speed) in the second stageis fitted values obtained from the first-stage regressionwhere the instrumental variables are included. Thefitted value of speed is highly correlated with theinstrumental variables. If one erroneously includes theinstrumental variable in the second stage, that leads tosevere multicollinearity problems.

6The results of the first-stage model are availableupon request.

7The coefficient of home market liberalization, as acontrol variable, is negative throughout the fourmodels though. It suggests that the crowding-outeffect in the home market outweighs the enablingeffect on the overall ROS. The result is consistent withthe finding in a recent study employing a highlysimilar sample (Chang & Wu, 2014, p. 1115). Thatnegative direct effect is not contradictory with thepositive moderating effect of home market liberaliza-tion. INVs in our sample have export intensity with42%. While the crowding-out effect mainly applies tothe sales in the home market sales, the enabling effectmainly applies to rapid expansion in the exportmarket.

8Results are available upon request. We thank aneditor (Gongming Qian) and an anonymous reviewerfor suggesting four sets of robustness test.

9For example, a manufacturer in China (institutionalscore: 51) exports 50% upwardly to Australia (institu-tional score: 77) and 50% downwardly to Nigeria(institutional score: 45). In this case, the exporter’sorganizational routines may be strongly affected bythe pro-market institutions in Australia, since theChina–Australia difference is 26 versus the China–Nigeria difference is only 6 (in terms of institutionalscores). Therefore, it accurately measures the effect ofinstitutional distance on firm operations to include


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both institutional distance and export share in calcu-lating a weighted institutional distance (Zhou &Guillen, 2015).

10Suppose a Chinese clothing manufacturer thatbegins export expansion in 2002. It exports to India,Nigeria, Thailand, and Australia with USD 50,000 in2004. Each destination country accounts for 25% ofthe total sales. In that year, the trade openness scores

of these five countries are 24 (India), 45 (Nigeria), 51(China), 66 (Thailand), and 77 (Australia). The firm-level weighted upward expansion speed can becalculated as [(66 - 51) * 25% + (77 - 51) * 25%]/(2004 – 2002 + 1) = 3.42. The weighted downwardexpansion speed is [(51 - 24) * 25% + (51 - 45) *25%]/(2004 – 2002 + 1) = 2.75.

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ABOUT THE AUTHORSZiliang Deng (PhD, Nottingham) is Associate Pro-fessor of International Business at Renmin BusinessSchool, Renmin University of China, Beijing. Hismain research interests are internationalentrepreneurship, international knowledge man-agement, and institution-related global strategies.

Ruey-Jer ‘‘Bryan’’ Jean (PhD, Manchester) is Pro-fessor of International Business at the Departmentof International Business, National ChengchiUniversity, Taipei. His research focuses on inter-organizational relationship management andinternational new ventures in digital and data-richenvironments, with a focus on emerging markets.

Rudolf R Sinkovics (PhD, WU Vienna) is Professorof International Business at The University ofManchester, Manchester, Visiting Professor atLappeenranta University of Technology, Lappeen-ranta, and Visiting Scholar at Fox School of Busi-ness, Temple University, Philadelphia. He haspublished on inter-organizational governance, therole of ICT in firm internationalization, and cur-rently works on rising powers and responsiblebusiness.

Accepted by Shige Makino, Guest Editor, 15 August 2017. This article has been with the authors for four revisions.

Open Access This article is distributed underthe terms of the Creative Commons Attribution 4.0International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricteduse, distribution, and reproduction in any medium,provided you give appropriate credit to the originalauthor(s) and the source, provide a link to theCreative Commons license, and indicate if changeswere made.


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