mne in turkey 2010
TRANSCRIPT
Institutional and transaction costdeterminants of Turkish MNEs’
location choiceMehmet Demirbag
Management School, University of Sheffield, Sheffield, UK
Ekrem TatogluFaculty of Economics and Administrative Sciences, Bahcesehir University,
Istanbul, Turkey, and
Keith W. GlaisterManagement School, University of Sheffield, Sheffield, UK
Abstract
Purpose – Drawing on institutional and transaction cost theories, the purpose of this paper is toexamine the location choice for a sample of 522 foreign affiliates of Turkish multinational enterprises(MNEs).
Design/methodology/approach – Binary logistic regressions are conducted to test a number ofhypotheses on the functional relationships between the hypothesized effect of variables and locationchoice of Turkish MNEs based on a secondary data drawn from official sources.
Findings – In general, the findings provide support for the majority of the study’s hypotheses andtend to confirm the theoretical perspectives adopted. The level of political constraints, the level ofknowledge infrastructure in the host country market, subsidiary density, industry R&D intensity andsubsidiary size are found to have the expected impact on the Turkish MNE’s location choice amonggeographic alternatives. No support is found for the impact of ownership mode of subsidiary and thegroup affiliation on Turkish MNEs’ location choice for their subsidiaries.
Research limitations/implications – The paper focuses on Turkish MNEs and the findings maynot be generalizable to other emerging country (EC) MNEs. Also, the classification of geographiclocation into developed versus emerging countries may be too crude.
Practical implications – In general, the paper posits that Turkish MNEs have a motive of strategicasset seeking to enhance their global competitiveness when they enter developed countries, whereasthey simply attempt to exploit their firm-specific advantages or competencies when they accessemerging countries.
Originality/value – Given the increasing number of EC MNEs entering other emerging anddeveloped markets, this paper adds to the understanding of the determinants of location strategies ofTurkish MNEs by identifying key regional characteristics that lead Turkish MNEs to select particularlocations, among the several geographic alternatives.
Keywords Turkey, International investments, Multinational companies, Transaction costs
Paper type Research paper
1. IntroductionWhile the landscape of multinational enterprises (MNEs) has been dominated bydeveloped country (DC) firms, the picture is changing commensurate with theincreasing participation of firms from emerging countries (ECs). According to WorldInvestment Report 2008, the amount of outward foreign direct investment (FDI)
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Received June 2008Revised February 2009,October 2009Accepted October 2009
International Marketing ReviewVol. 27 No. 3, 2010pp. 272-294q Emerald Group Publishing Limited0265-1335DOI 10.1108/02651331011047989
flows from emerging and transition economies reached a record level of $253 billionin 2007, representing about 13 percent of worldwide outward flows (UNCTAD, 2008).This surge has come mainly from the rapid pace of economic development and theliberal market policies governments have implemented in these countries. The leadingEC sources of FDI are China, Russia, and Taiwan, but other ECs, including Brazil,Malaysia, Mexico, South Korea, India, and Turkey, are also becoming importantplayers in outward FDI.
The geographic distribution of FDI activity by DC MNEs has been well researchedwith most studies focusing on FDI that originated from North America or Japan.Although some recent empirical studies have examined EC MNEs’ strategies (Buckleyet al., 2007; Bonaglia et al., 2007; Yiu et al., 2007; Filatotchev et al., 2007), the locationchoice of these companies related to DCs and other ECs has yet to be studied. Given theincreasing number of EC MNEs entering other EC and DC markets, it becomesimperative to investigate their location choice strategies. The location choice decision,to a large extent, is intertwined with the MNE’s international market entry decisions.Findings in this paper, therefore, provide important implications for both theinternational marketing and international business literature.
This paper investigates factors affecting location choice of Turkish MNEs and offersa number of contributions. First, most prior research on EC MNE activity has beenconfined to a single host country or region with relatively few studies examining howEC MNEs make location choices among different regions or countries. The majority ofthese studies have focused on delineating trends, patterns and determinants of outwardEC MNE activity based on econometric analyses of secondary data (Buckley et al., 2007;Kumar, 2007; Yiu et al., 2007), while some have examined the internationalizationstrategies and motives of EC MNEs drawing on primary data from selected casestudies or based on anecdotal evidence (Sim and Pandian, 2003; Wee, 2007; Deng, 2007;Erdilek, 2008).
Second, we derive a conceptual framework of location choice that draws on bothinstitution specific and transaction cost specific variables as determinants of FDIlocation choice by EC MNEs. This framework synthesizes the institution specific andtransaction cost specific variables in a new manner. We test hypotheses derived fromthis framework by concentrating on FDI emanating from one EC, i.e. Turkey. There is apaucity of empirical research on global strategic location strategies of Turkish MNEs.The only notable exception is a recent survey by Demirbag et al. (2009) that provided anempirical analysis of the determinants of equity-based entry mode strategies of TurkishMNEs in several host country markets. Compared with other ECs such as China, Brazil,Taiwan, and Malaysia, Turkey is not yet a significant outward investor. However, itsoutward FDI is growing and it is certainly becoming an economy with a remarkableFDI potential. As shown in Table I, the outward FDI stock held by Turkish MNEsreached $12.21 billion in 2007, rising from $1.15 billion in 1990, making Turkey the 21stmost active EC investor (UNCTAD, 2008). Therefore, examining the location strategiesof Turkish MNEs in global markets has the potential of making a useful contributionto existing research on EC MNE activity.
Third, given the emerging nature of the market and the transitional characteristicsof the institutional environment (Cavusgil et al., 2002), Turkish outward FDI activityprovides a good case to test a number of new dimensions alongside previouslytested variables on the determinants of location choice strategies of EC MNEs.
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Among the leading ECs, Turkey possesses important features, such as its geographicallocation, and associated cultural and linguistic proximities to Central Asian, European,and Middle Eastern markets (Tatoglu et al., 2003). In this regard, the Turkish context alsoprovides an interesting research setting, characterized by Turkey’s attempts to becomea more Western style market economy and ongoing membership negotiations with theEuropean Union (EU). Turkey is the first Muslim country to bid for EU membership.
Fourth, drawing on official data from the Turkish Treasury (the key agency forTurkish outward FDI operations), this paper seeks to further our understanding of thedeterminants of location strategies of Turkish MNEs by identifying key regionalcharacteristics that lead Turkish MNEs to select particular locations, among the severalgeographic alternatives. We compare two specific country groups and two broadregions in terms of their investment characteristics: the EU and the Former Soviet Union(FSU), and the DCs and ECs.
The paper is organized as follows. Section 2 reviews the extant theoreticalperspectives on foreign investors’ location strategies and develops the hypotheses of
Rank Countries 2007
1 China (including Hong Kong) 1,122,3862 Russian Federation 255,2113 Taiwan Province of China 158,3614 British Virgin Islands 154,8625 Singapore 149,5266 Brazil 129,8407 South Korea 66,2208 Malaysia 58,1759 South Africa 54,562
10 Cayman Islands 47,78711 Mexico 44,70312 Chile 32,46913 India 29,41214 United Arab Emirates 27,03015 Argentina 26,87316 Panama 24,75117 Saudi Arabia 22,05018 Indonesia 21,42519 Kuwait 16,88420 Venezuela 13,81421 Turkey 12,21022 Colombia 10,38323 Bahrain 7,72024 Thailand 7,02525 Qatar 6,33926 Ukraine 6,07727 Philippines 5,57328 Nigeria 5,51429 Azerbaijan 4,67630 Croatia 3,495
Note: Billions of US$Source: UNCTAD (2008)
Table I.Top 30 emergingcountries in terms ofoutward FDI stock (2007)
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the study. Section 3 sets out the research methods. Results and discussion are inSection 4. Conclusions are in the final section.
2. Theoretical background on location choiceThere is a paucity of empirical research on the growing participation of EC firms inglobal business and their impact on global competition. While there is a plethora ofresearch on internationalization and international market entry mode choice of DCMNEs (see Brouthers and Hennart, 2007 for a detailed review), it is argued that existingtheoretical perspectives may not adequately explain the behavior of EC firms in theglobal market place (Child and Rodrigues, 2005; Khanna and Palepu, 2006; Mathews,2006; Luo and Tung, 2007; Yiu et al., 2007).
It may be noted that the development and internationalization paths of EC MNEs aredistinct from those of DC MNEs (Khanna and Palepu, 2006; Mathews, 2006; Luo andTung, 2007; Bonaglia et al., 2007; Chittoor and Ray, 2007; Cuervo-Cazurra, 2007). In thepast EC MNEs, so-called third world MNEs, targeted other developing countries andwere more involved in resource oriented activities (Heenan and Keegan, 1979;Kumar, 1982; Lall, 1983; Lecraw, 1977; Wells, 1983). More recent trends indicate that ECMNEs target their operations toward both developed and developing markets, in bothresource intensive and higher value added activities (OECD, 2007; Luo and Tung, 2007).Previous research on EC MNEs has mainly concentrated on governance (Filatotchevet al., 2007), determinants of Chinese outward FDI (Wong and Chan, 2003; Buckley et al.,2007; Yiu et al., 2007) and FDI strategies of EC MNEs in other ECs (Lecraw, 1977;Raju and Prahalad, 1980; Kumar, 1982; Lee and Beamish, 1995; Yeung, 1998; 1999;Chen, 1998).
Factors affecting the location choice decision of MNEs have been well documentedby economists (Porter, 1990; Krugman, 1991), geographers (Florida and Kenney, 1994),and international business scholars (Dunning, 1998, 1993). Traditional comparativeadvantage theory focused on factor endowments of a country as a source of competitiveadvantage whereas dynamic comparative advantage theory emphasizes that countrylevel advantages evolve over time (Doh et al., 2008). In analyzing the location choicedecision of MNEs, in general, recent studies have also focused on identifying types ofactivities of MNEs and locations for these operations (Alcacer, 2006; Alcacer andChung, 2007; Doh et al., 2008).
Dunning’s investment development cycle model of FDI is the most widely used inthe international business/marketing literature for explaining location choice decisionsof EC MNEs. The model is based on the systematic relationship between thedeterminants of outward/inward FDI flows ownership, location, and internalization(OLI) and the stage and structure of economic development of a country (Dunning,2006). As a country develops, initially its FDI position changes from importer then to anexporter, and in the first phase FDI tends to be directed at accessing natural resources orbenefiting from exploitation of resource endowments in neighboring countries. At anadvanced stage of development, it is argued that there will be a re-convergence ofoutward and inward investment flows, not because of differences in factor endowments,but because of the advantages of internalizing international markets (Yeung, 1999).Dunning also argues that apart from location related specific advantages, motives foran investment decision are also important determinants of location choice decisions
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(Dunning, 1998). Similarly, Makino et al. (2002) argue that motives have a significantimpact on the choice of location (DCs versus ECs).
This array of literature identifies asset exploiting and asset seeking (particularlystrategic asset seeking) motives and efficiency seeking motives. From the strategicasset seeking perspective, FDI is viewed as a vehicle to acquire strategic assets,whereas from an asset exploitation perspective, FDI is viewed as the transfer of afirm’s proprietary assets across borders (Makino et al., 2002). According to Wells(1983), firms from ECs tend to develop small scale, labor intensive operations based onflexible processes which are more suitable to other ECs, usually to countries much lessdeveloped than their home countries (Makino et al., 2002). Recent trends in FDI byEC MNEs, however, indicate that firms from these countries invest across borders notonly with asset exploitation motives, but also with strategic asset seeking motives(Dunning, 1998; Makino et al., 2002; Pak and Park, 2005; Luo and Tung, 2007; Alcacerand Chung, 2007). It is argued that EC MNEs tend to build competitive advantagesthrough FDI in locations where their needed strategic assets are available (Makinoet al., 2002). Recent studies and reviews indicate that there is a growing number ofMNEs from ECs such as China, India, Mexico, Russia, and Turkey, investing in bothDC and other EC markets (Erdilek, 2003; Kumar, 2007; Aulakh, 2007; Daniels et al.,2007; Liuhto and Vahtra, 2007; Luo and Tung, 2007; Kalotay, 2008). Regardless ofthe motives for investment, the institutional facilities of an investment location(a country or region) are equally important for MNEs as they increasingly seeklocations which enhance their competitive advantage. Therefore, Dunning (2006)argues that institutional factors should be integrated with the investment developmentpath or cycle.
Although some studies have examined location choice of MNEs from an investmentdevelopment path perspective (Galan et al., 2007) this paper is the first attempt tointegrate both perspectives from an EC MNE’s perspective. Investment developmentpath and institutional perspectives should not be considered mutually exclusive; ratherthey should be viewed as complementary. Recently, Dunning (2006) has attempted toextend the (OLI advantages) framework by considering arguments from institutionaltheory. Furthermore, “asset exploitation” and asset “augmentation motives” mayco-exist as internationalization drivers of EC MNEs as they compete both in matureand other ECs and DCs. As Yiu et al. (2007, p. 522) argue, the driving force behind ECMNEs’ internationalization is not an “either-or” situation of asset exploitation or assetaugmentation, but rather a matter of integrating the concepts in order to providegreater insight.
The following subsections detail the rationale for the hypothesized effects ofthe institution and transaction specific influences and firm level variables on ECMNEs’ location choice for their foreign affiliates. The proposed relationships betweenpredictor variables of transaction costs and institution specific influences, and thelocation decision of EC MNEs are delineated in the conceptual framework shown inFigure 1.
2.1 Institution specific influences2.1.1 Political constraints. In terms of FDI motivation, EC MNEs may be largelyseeking strategic assets when entering developed host country markets, while theymay be pursuing market opportunities when serving emerging host country markets.
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It is commonly accepted that institutional constraints in host country markets exist inboth DCs and ECs, although the degree or threat of such constraints may differbetween the two groups. It is therefore reasonable to argue that in the institutionalcontext the distinction between host country markets stemming from the nature of thehost country (DC versus EC) is likely to influence the determinants of location choice ofEC MNEs. Political hazards (e.g. political instability, unpredictable regulatory changes,government interference, ambiguous laws, and regulations, etc.) constitute animportant dimension of the institutional environment in a host country (Witt andLewin, 2007). An institutionally more efficient business environment provides betteropportunities for EC MNEs to exploit and upgrade their competitive advantages (Luoand Tung, 2007). As asset-augmenting investments are expected to target DC markets,it is plausible to expect lower political risk and an efficient institutional environment insuch markets. Both the OLI paradigm and institutional theory posit that in countriescharacterized by low political risk, firms with asset exploitation motives will tend tochoose higher control/higher equity modes (Henisz, 2000; Buckley et al., 2007). Miller(1992, 1993) and Kobrin (1976) also emphasized the importance of the dimension ofpolitical risk and uncertainties on the MNE’s operations and performance. Brouthersand Brouthers (2000) found a relationship between political uncertainties and theMNE’s entry mode decision and performance. EC MNEs are therefore more likely toprefer developed host countries characterized by lower political risk and a moreefficient institutional environment as compared to ECs that generally suffer frommarket failures and a weak financial and legal infrastructure. Thus, we hypothesizethat:
H1. As the level of political constraints increase in a host country environment,Turkish MNEs will prefer DCs over ECs.
2.1.2 Knowledge infrastructure. The physical infrastructure of a host country has beenperceived as a significant factor in the location choice literature (Dunning, 1998;
Figure 1.A conceptual framework
of location choice ofemerging country MNEs
Institution-specific influences
• Political constraints• Knowledge infrastructure• Subsidiary density
Transaction cost-specific influences
• R&D intensity• Ownership mode• Subsidiary size• Group affiliation
Location choicestrategy
Control variables• Timing of entry• Industry of subsidiary
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Alcacer, 2006; Alcacer and Chung, 2007). Similar to physical infrastructure, Smith(1997) argues that there exists a complex of knowledge infrastructure created bypublic and private organizations or institutions. These organizations or institutionsundertake activities such as maintenance, distribution, management, and protection ofknowledge. This web of institutions is also defined as a national innovation system(NIS). The NIS of a host country may influence the location choice strategies of foreigninvestors from ECs. Different actors, such as foreign firms, local firms, local andnational governments, research institutes, and universities are all components of anNIS (Frederiksson and Liang, 2006). From a location choice perspective, knowledgeinfrastructure is of great importance as it is directly related to industrial technologycreation and industrial production. Smith (1997) argues that industrial technologyconsists essentially of knowledge related material transformation. Tassey (1991) definesthe combination of knowledge and organizational base as technology infrastructurewhich “consist of science, engineering and technological knowledge available to privateindustry.” As Tassey (1991, p. 347) argues, such knowledge can be embodied in human,institutional or facility forms. Therefore, knowledge infrastructure at the regional andcountry level is an important location choice factor, particularly for knowledgeintensive industries. According to Smith (1997, p. 104), “the knowledge infrastructureactivities absorb somewhere between 5 and 10 percent of national income in mostOECD countries.” NISs in DCs are better endowed with innovative capacity. Forinstance, research institutes and design experts might be more available in thesecountries. Further, competitive pressure may force firms to invest in locations thatcan offer complementary or better conditions for certain activities (Frederiksson andLiang, 2006).
Demirbag et al. (2009) argue that MNEs from ECs are driven by fast learning, whichfacilitates leapfrogging some intermediate stages of internationalization. Similarly,Luo and Tung (2007) argue that these latecomers have some weaknesses in terms ofcompetitiveness (i.e. brand image, country image, knowledge creation, and R&Dcapability) and these firms may overcome these disadvantages by acquiring criticalassets from mature MNEs to compensate for their competitive weaknesses (Luo andTung, 2007; Cuervo-Cazurra, 2007; Bonaglia et al., 2007). There are opportunities tocompensate for such weaknesses by acquiring existing operations in developed hostcountries. DCs also provide a strong knowledge infrastructure (Tsang and Yip, 2007;Demirbag et al., 2009). The availability of skilled engineers or scientists in acquiredoperations can be treated as elements of an asset augmenting strategy (access tospecific incorporated knowledge), which is also associated with the OLI paradigm.
This discussion leads to the second hypothesis:
H2. The higher the knowledge infrastructure of a host country, the more likelythat Turkish MNEs will prefer DCs over ECs.
2.1.3 Subsidiary density. Both institutional and transaction cost theories use subsidiarydensity as an indication of attractiveness of a host country (Demirbag et al., 2007a;Miller and Eden, 2006). Subsidiary density is a proxy measure indicating isomorphictendencies of firms (Yiu and Makino, 2002; Li et al., 2007). From a transaction costperspective, a high presence of subsidiaries from the same home country is an indicationof perceived importance of a location, whereas from an institutional perspective it can beinterpreted as a mimetic behavior of firms (frequency-based imitation) from the same
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home country (Henisz and Delios, 2001). From an institutional theory perspective, thelocation choice decision of EC MNEs can be influenced by two types of imitativebehavior, namely frequency- and trait-based imitations (Henisz and Delios, 2001).Foreign investors who are eager to fit in with the host country environment tend toadopt the organizational forms chosen by early entrants from the same home country (Liet al., 2007). Frequency-based imitation occurs when decisions are based on the numberof other firms that have decided to locate their operation to a country or region. That is,behavior of other firms from the same home country may trigger new entrants to locatetheir operations to the same location. The trait-based imitation is a more selectiveprocess in which the focal organization imitates firms in the same industry (Guillen,2000). While the institutional perspective predicts location choice decision on the basisof frequency and imitation of industry leaders, Dunning’s investment developmentcycle model emphasizes that EC MNEs will enter other ECs first and then move in stagestowards a more global pattern of investment as knowledge of operating in thesecountries is internalized (Dunning, 2006). Therefore, relying on the arguments ofinstitutional and transaction cost perspectives, we hypothesize that:
H3. The greater the subsidiary density of Turkish MNEs in a given host country,the more likely that Turkish MNEs will prefer ECs than DCs.
2.2 Transaction cost specific influences2.2.1 R&D intensity of industry. Transaction cost theorists generally use industry R&Dintensity as a proxy measure for firm level capability and industry technology intensity(Gomes-Casseres, 1989; Delios and Henisz, 2000; Dhanaraj and Beamish, 2004; Chungand Beamish, 2005; Xu and Lu, 2007) to indicate the need for protection of firm levelcapabilities from opportunistic behavior (Delios and Henisz, 2000, p. 313). Countries orregions may be endowed with differing levels of embedded knowledge, competence andinfrastructure to facilitate innovative activities. It is argued that R&D intensiveoperations of EC MNEs, or FDI in R&D intensive industries will be located in DCs for anumber of reasons: knowledge acquisition or augmentation (Kumar, 2001; Alcacer andChung, 2007), using operations in certain clusters as listening posts (Florida andKenney, 1994). Makino et al. (2002) argue that when EC MNEs operate in R&D intensiveindustries or at least to source advanced technology, they are more likely to prefer DCsover ECs. There are a number of factors motivating EC MNEs to locate such operationsin DCs (i.e. sophisticated NISs, a larger pool of advanced strategic assets, spatialconcentration of innovation clusters, and regulations governing intellectual propertyprotection) (Pak and Park, 2005). Based on these arguments, we hypothesize that:
H4. The more R&D intensive an industry is the more likely that Turkish MNEs inthat industry will prefer DCs over ECs.
2.2.2 Ownership mode. While most ECs have liberalized FDI legislation over the pasttwo decades, there are still institutionally embedded regulations or lack thereofincreasing transaction costs of market internalization through higher equity ownership.Apart from country specific advantages or disadvantages, region-specific advantagesor disadvantages may play a role in equity ownership strategies of EC MNEs. In DCswhere markets and institutions are more predictable, EC MNEs tend to prefer higherequity ownership. In other ECs, they prefer lower equity ownership due to the high levelof market and institutional uncertainties. It is argued that in DCs higher equity
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ownership through acquisitions are a common entry mode by EC MNEs (Luo andTung, 2007) driven mostly by asset augmentation or asset acquisition motives thatenable the investing firms to catch up with DC MNEs. In entering EC markets, however,EC MNEs aim to exploit standardized technologies (asset exploitation motive), whichusually requires a lower level of commitment (Pak and Park, 2005; Makino et al., 2002).Based on these arguments, we hypothesize that:
H5. Turkish MNEs are more likely to choose a wholly owned subsidiary (WOS)over a joint venture ( JV) when locating in DCs than in ECs.
2.2.3 Size of subsidiary. Buckley and Casson (1976) subsume the scale of internalizedmarkets in internalization theory. From an internalization perspective, the size ofoperation is important as there is a possibility of reduction of “economic efficiency belowwhat it would be under perfectly competitive external markets” (Buckley and Casson,1976, p. 41). Transaction cost theorists, however, treat size of subsidiary as a source oftransaction cost. It is argued that a large-scale operation would require substantialinternal organization and bureaucratic costs, including investment in administrative,legal, and operating infrastructures (Davidson and McFetridge, 1985). According totransaction cost theorists, switching costs stemming from the high overhead of suchlarge-scale investments may also limit the parent firm’s ability to invest in anothersubsidiary (Erramilli and Rao, 1993). The Uppsala model of internationalization,however, assumes low resource commitments and lower control modes at the initialstage of international market entry ( Johanson and Vahlne, 1977).
While the industry of operations may have impact on size of operations, the scaleof operations is also expected to interact with the selection of host country locationsdepending on investment and political risks of host country markets (Root, 1994). ECmarkets are known to be relatively more risky because of their unstable legal andpolitical environment. Further, subsidiary size is argued to have a negative correlationwith host country risk (Pak and Park, 2005). Before engaging in a large-scale resourcecommitment, foreign investors must be cognizant of the potential costs and returns ofsuch investment, as the level of risk exposure is directly associated with the totalinvestment in non-redeployable assets. The scale of investment is therefore related tothe choice of a particular host country location. Foreign investors are not motivated toembark on large-scale investments in host country markets characterized by anunstable political and economic environment. As the DC markets are usually morestable politically and economically, we expect that:
H6. As the investment scale of the subsidiary increases, Turkish MNEs will bemore likely to establish subsidiaries in DCs than in ECs.
2.2.4 Group affiliation. EC MNEs are mostly business groups and some of them arewidely diversified (Nachum, 1999; Khanna and Palepu, 2006; Cuervo-Cazurra, 2007).Despite the apparent growth in the number of academic studies concerning businessgroups in ECs (Gunduz and Tatoglu, 2003), there is a paucity of information andresearch relating to the role of group affiliation on location choice of EC MNEs.A growing body of empirical work, set in a range of ECs, shows that group affiliationhas a robustly large and often beneficial effect on the performance of member firms(Khanna and Palepu, 2000a, b; Khanna and Rivkin, 2001) owing to the large financialresources and corporate synergies it provides. Peng et al. (2005) examine group
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affiliation from an institutional perspective and hypothesize a positive relationshipbetween institutional relatedness and the scope of the firm in the short run. It is alsoargued that group structure (conglomerate) of a firm provides benefits in someECs where a dense network of ties exists with dominant institutions (Rugman and Oh,2008). In their study of understanding the characteristics of Japanese FDI in the Eastand the West, Pak and Park (2005) noted that Keiretsu membership marginallyinfluenced the location selection of Japanese MNEs between East and West. On thebasis of existing empirical evidence, we hypothesize that:
H7. Turkish MNEs in the form of a business group are more likely than non-groupTurkish MNEs to choose ECs over DCs to invest in affiliates.
3. Research methods3.1 DataThis paper relies on the database of a government agency, the General Directorate ofBanking and Exchange (GDBE) within the Undersecretariat of Treasury. The databaseof GDBE consists of all Turkish outward FDI firms established since 1990 and asof July 2006 includes 2,397 outward FDI firms. The GDBE database also providesinformation about the name of the host country, the sector of operation, the proportionof Turkish equity shareholding, the amount of investment, and the entry date.
Figure 2 shows the distribution of Turkish FDI by host country and regions. Fromthe total value of $8.6 billion outward FDI, EU countries including both DCs andemerging EU countries accounted for nearly 57 percent of all outward FDI. Of theEU countries, five countries including Germany, the UK, France, The Netherlands, andItaly were the main destinations for FDI. These countries are among the leadingtrading partners of Turkey and are also the main sources of inward FDI to Turkey(Tatoglu and Glaister, 1996; Demirbag et al., 2007b). They have well-established
Figure 2.Breakdown of Turkish
FDI by hostcountry/region
Emerging EU4%
Emergingnon-EU
1%
Developed non-EU1%
Other developed3%
Other emerging1%
MiddleEast and Africa
3%
FormerSoviet Union
34%
Developed EU53%
Source: GDBENote: As of 2007
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political and economic links particularly through the EU. The FSU countries constitutethe second most attractive host market, accounting for nearly 34 percent of all TurkishFDI outflows. With the fall of communism in the FSU, a huge market characterized bya relatively large and consumption-prone population has been opened to Turkishinvestors. The newly independent Turkish speaking Central Asian countries havebecome the major recipients of Turkish FDI due to their geographic and culturalproximity to Turkey (Demirbag et al., 1998).
From the original list of 2,397 outward FDI firms in the database, a new dataset wascompiled based on the capital value of the subsidiary and the proportion of Turkishequity shareholding. Those ventures with capital value of less than $500,000 wereexcluded. Most of these firms are owned by a single person or established by means ofordinary partnerships. For the purposes of this survey, it was not considered feasible toinclude these firms in the sampling frame. This paper also uses the 10 and 90 percentcut-off points to capture the alternative ownership structures. An affiliate is defined asa JV when foreign equity ownership ranges from 10 to 90 percent, while an affiliatewith foreign equity shareholding of over 90 percent is considered to be a WOS. Theinvestments with foreign ownership of less than 10 percent are considered to beportfolio investments and are excluded from the database. Based on the above criteria,the new dataset included a list of 522 Turkish FDI firms that constitutes the sample ofthis paper.
The average foreign equity ownership is 69.3 percent. The distribution of ownershipis as follows: minority foreign-owned, 28.2 percent; co-ownership, 10.2 percent;majority foreign-owned, 14.6 percent; and full ownership 47.1 percent. The average ageof affiliate is 8.33 years. In terms of the host country/region origin, the data set includes202 subsidiaries in EU countries, 48 in the USA, 119 in FSU countries, 67 in MiddleEastern and African countries, and the remainder in other DCs or ECs. The sampleincludes 231 (44.3 percent) group affiliated subsidiaries and 291 non-group affiliatedsubsidiaries (55.7 percent). The distribution of the data set in terms of the sectorof operation is as follows: auto, transport and related equipment, 4.0 percent; electrical,electronics and durables, 6.5 percent; food and beverages, 9.6 percent; chemicals,4.4 percent; textiles, apparel and leather, 8.2 percent; metal, iron and steel, 2.9 percent;mining, petroleum and gas, 4.2 percent; other manufacturing, 5.2 percent;export-import trading, 14.8 percent; tourism, 3.1 percent; banking and financialservices, 21.6 percent, construction and logistics, 12.3 percent and other services,5.0 percent.
3.2 Operationalization of variables3.2.1 Dependent variable. The previous literature on international business andmarketing suggests that there exist remarkable differences between DCs and ECs interms of economic, political, institutional, and cultural environments (Szymanski et al.,1993; Makino et al., 2004; Pak and Park, 2005). As compared to EC markets, developedhost country markets are more mature and competitive and are usually characterizedby rigorous institutional, financial, and legal infrastructure. In contrast, EC marketsare generally characterized by huge potential for business growth but suffer fromlack of economic and financial stability along with weak institutional support. In linewith this logic and prior research, we have broadly classified FDI locations intotwo main categories, DCs and ECs. The former group of host countries involved EU,
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the USA, and other DCs, while the latter group included FSU countries, Middle Easternand African countries, and other ECs. Thus, the location choice of Turkish MNEs wastested using a dummy variable. A value of one was assigned if the location chosen wasa developed host country market, while a zero value was assigned if the locationchosen was an emerging host country market.
3.2.2 Independent variables. The political constraints index (POLCON) measures thefeasibility of a change in policy given the structure of a nation’s political institutionsand the preference of actors that inhabit them Henisz (2000) and Henisz and Delios(2001). The POLCON database covers almost all major nations and is computed forvirtually all countries for the post-war period (1960-2004), which fits well with thedatabase used for this paper. Scores range from 0, which indicates the executivehas political discretion and could change existing policies at any point in time, to 1,which indicates that a change of existing policies is totally infeasible (Neumayer andSpees, 2005). As the value of the index approaches 0, then an increase is expected in thelevel of political constraints for a given host country market.
A measure for host country knowledge infrastructure (KNOWLEDGE) wascomputed from data published in multiple editions of World Economic Forum’sGlobal Competitiveness Report (WEF, 2003, 2004, 2005). Global CompetitivenessReport is an annual multidimensional analysis of how far national environments areconducive or detrimental to the global competitiveness of companies operating in thecountries involved. Based on three year average figures, we have created a compositeindex of five knowledge indicators for each host country market in our data set.Knowledge indicators used for the composite index are “availability of scientists andengineers,” “technological readiness,” “quality of scientific research institutions,” “firmlevel technology absorption,” and “quality of mathematics and science education.” Thismeasure has also been recently used by Demirbag et al. (2009).
Subsidiary density (SUBS_DENS) in a country was operationalized as the numberof Turkish subsidiaries in the host country for the year before the entry, i.e. the numberof Turkish subsidiaries at the end of the calendar year before the entry date.Similar measures have also been used in previous studies (Miller and Eden, 2006;Miller et al., 2008).
In this paper, the measure of industry R&D intensity (R&D_INT) was adapted fromthe UK Department of Trade and Industry (DTI, 2006) index of R&D intensity ofindustries. The DTI scoreboard is based on the ratio of R&D expenditures to sales atthe four-digit Standard Industrial Classification (SIC). This scoreboard was alsocompared with Turkey’s Statistical Yearbook data, which reports R&D expendituresand sales by industries (SIC four digit for the last five years) (SIS, 2004, p. 380). Bothscales indicated a high level of correlation. As the DTI scale was specifically created tomeasure R&D intensity of industries, we decided to employ the DTI scale. A similarmeasure has also been used by Martins and Price (2004) and Demirbag et al. (2007a).
Ownership mode of subsidiary (OWN_MODE) was measured by a dummy variablethat takes the value of 1 if the subsidiary was a WOS or 0 if it was a JV.
Subsidiary size (LNSIZE) is measured using the logarithm of the amount of totalinvestment in US dollars. The logarithmic transformation is generally used tonormalize the size variable, which might otherwise be badly skewed.
Group affiliation (AFFILIATE) is measured by a dummy variable that takes a valueof 1 if the subsidiary belongs to a business group and 0 otherwise. A business group is
Turkish MNEs’location choice
283
defined as having group members operating in more than two industries where eachindustry is assigned a two-digit SIC code.
3.2.3 Control variables.3.2.3.1 Date of entry. A dummy variable for the timing of entry (DATE) was
assigned to control for variation that might result from the characteristics of Turkey’seconomic and business development program first initiated in the early 1980s andaccelerated later in the mid 1990s with the start of the customs union with the EU in1996. A value of 0 was given for outward Turkish FDI entries during the 1980-1995period and 1 for post-1995 entries.
3.2.3.2 Industry of subsidiary. To control for industry variations, three broadindustrial groupings are introduced as dummy variables. Group 1 manufacturingindustries included food, beverages, textile, apparel, leather, metal, iron, steel, mining,petroleum, and gas, Group 2 manufacturing industries comprised of auto, transport andrelated equipment, electrical, electronics, durables, and chemicals. Tertiary industriesincluded export-import trading, tourism, banking and financial services, construction,logistics, and other services. The distinction between Group 1 manufacturing and Group2 manufacturing was made because of the generally more sophisticated technologyassumed to be embodied in the products and processes of Group 2 manufacturingcompared to Group 1 manufacturing.
4. Results and discussionThe correlation matrix of the independent variables in the study is shown Table II.The pairwise correlations do not seem to present serious multicollinearity problemsfor the multivariate analysis, as none of the variables have correlation coefficients above0.50. Wetherill (1986) recommends an analysis of variance inflation factor (VIF) whenthree or more variables are involved. In the near dependency, the correlations betweenrelevant pairs of variables need not be large (Wetherill, 1986). This is where VIF mayplay an important role and should not be larger than ten. Since the highest VIF value forindependent variables was significantly lower than this cut-off point, multicollinearityin the explanatory variables for the data set does not seem to be a problem.
Owing to the nature of the dependent variable, binary logistic regressionswere conducted to test the hypotheses on the functional relationships between thehypothesized effect of variables and location choice of Turkish MNEs. The results ofthe logistic regressions are shown in Table III, where three sets of coefficients arereported. Model 1 shows the test results for the impact of the hypothesized variables onthe Turkish MNEs’ location choice between developed and emerging host countries.The estimated coefficients represent the likelihood of choosing a DC over an EC. Apositive coefficient for an independent variable means that it increases the probabilityof a DC compared to an EC. A negative coefficient means that an EC is more likelythan a DC.
As EU and FSU countries were selected as typical country groups within broaderDC and EC groupings, respectively, we tested the extent to which subsidiariesoperating in EU countries have shared characteristics with other DCs; and similarlyto what extent subsidiaries operating in FSU countries share characteristics with otherECs. The t-test and x 2-test results indicated that in five of our seven hypothesizedvariables, subsidiaries active in EU countries were indeed typical of those in other DCswith the exception of two variables, political constraints and knowledge infrastructure,
IMR27,3
284
Var
iab
len
ame
Defi
nit
ion
12
34
56
78
910
1.P
OL
CO
NP
olit
ical
con
stra
ints
ind
ex1.
002.
KN
OW
LE
DG
EK
now
led
ge
infr
astr
uct
ure
0.22
*1.
003.
SU
BS
_D
EN
SS
ub
sid
iary
den
sity
0.16
*2
0.05
1.00
4.R
&D
_IN
TR
&D
inte
nsi
tyof
ind
ust
ry0.
010.
072
0.01
1.00
5.O
WN
_M
OD
EO
wn
ersh
ipm
ode
0.03
0.01
0.16
*2
0.15
*1.
006.
LN
SIZ
EL
ogar
ith
mof
the
cap
ital
size
0.06
0.06
0.03
20.
100.
101.
007.
DA
TE
Per
iod
ofen
try
0.01
20.
062
0.11
20.
010.
052
0.01
1.00
8.A
FF
ILIA
TE
Gro
up
affi
liat
ion
0.01
0.12
20.
010.
082
0.12
0.19
*2
0.03
1.00
9.G
RO
UP
1_IN
DG
rou
p1
man
ufa
ctu
rin
gin
du
stri
es2
0.12
20.
22*
20.
012
0.30
*0.
020.
010.
072
0.10
1.00
10.
TE
RT
IAR
Y_
IND
Ter
tiar
yin
du
stri
es0.
16*
0.21
*0.
032
0.29
*0.
042
0.02
20.
102
0.01
20.
39*
1.00
Notes:
Sig
nifi
can
tat
:* p
,0.
001
(tw
o-ta
iled
test
);n¼
522
Table II.Correlation matrix
Turkish MNEs’location choice
285
Mod
el1
Mod
el2
Mod
el3
Hy
pot
hes
isV
aria
ble
nam
eD
efin
itio
nE
xp
ecte
dsi
gn
Coe
ffici
ent
Wal
dst
atis
tic
Coe
ffici
ent
Wal
dst
atis
tic
Coe
ffici
ent
Wal
dst
atis
tic
H1
PO
LC
ON
Pol
itic
alco
nst
rain
tsin
dex
(þ)
13.6
2*
**
44.8
415
.99
**
*43
.88
21.6
9*
**
37.3
4H2
KN
OW
LE
DG
EK
now
led
ge
infr
astr
uct
ure
(þ)
3.29
**
*90
.54
3.36
**
*74
.38
4.33
**
*53
.93
H3
SU
BS
_D
EN
SS
ub
sid
iary
den
sity
(2)
20.
02*
**
17.5
52
0.02
**
*22
.57
20.
02*
**
18.2
6H4
R&
D_
INT
R&
Din
ten
sity
ofin
du
stry
(þ)
0.49
**
4.50
0.45
**
3.53
0.43
*3.
08H5
OW
N_
MO
DE
Ow
ner
ship
mod
e(þ
)2
0.15
0.21
20.
310.
840.
030.
01H6
LN
SIZ
EL
ogar
ith
mof
the
cap
ital
size
(þ)
0.33
**
*7.
070.
29*
*5.
120.
40*
**
9.60
H7
AF
FIL
IAT
EG
rou
paf
fili
atio
n(2
)2
0.23
0.10
20.
811.
052
0.32
0.17
Con
trol
var
iab
les
DA
TE
Per
iod
ofen
try
0.42
1.32
0.51
1.82
0.39
1.03
GR
OU
P1_
IND
Gro
up
1m
anu
fact
uri
ng
ind
ust
ries
0.59
0.55
0.07
0.01
0.70
0.70
TE
RT
IAR
Y_
IND
Ter
tiar
yin
du
stri
es0.
650.
780.
090.
010.
801.
07In
terc
ept
226
.98
**
*65
.24
227
.06
**
*58
.14
236
.45
**
*54
.69
Mod
elx
245
3.53
**
*26
5.39
**
*38
7.19
**
*
Sen
siti
vit
y0.
950.
960.
93S
pec
ifici
ty0.
870.
770.
86C
orre
ctra
tio
0.91
0.89
0.89
Pro
p’a
lch
ance
crit
erio
n0.
500.
560.
51C
ox&
Sn
ellR
20.
580.
480.
57N
agel
ker
keR
20.
770.
670.
77N
um
ber
ofca
ses
522
(dev
elop
ed:
270;
emer
gin
g:
252)
403
(dev
elop
ed:
270;
emer
gin
gw
/oF
SU
:13
3)
Notes:
Sig
nifi
can
tat
:* p
,0.
1,*
* p,
0.05
,an
d*
** p
,0.
01(t
wo-
tail
edte
st);
Mod
el1:
dev
elop
edco
un
trie
s(1
)v
ersu
sem
erg
ing
cou
ntr
ies
(0);
Mod
el2:
dev
elop
edco
un
trie
s(1
)v
ersu
sem
erg
ing
cou
ntr
ies
excl
ud
ing
FS
Uco
un
trie
s(0
);M
odel
3:E
Uco
un
trie
s(1
)v
ersu
sem
erg
ing
cou
ntr
ies
(0)
Table III.Logistic regressions forthe location choice ofTurkish MNEs
IMR27,3
286
where significant differences were found between subsidiaries in the EU and those inother DCs ( p , 0.01). As for ECs, however, significant differences were noted betweensubsidiaries in FSU and those in other ECs for four of the hypothesized variableswhich include political constraints ( p , 0.01), knowledge infrastructure ( p , 0.01),subsidiary density ( p , 0.01), and subsidiary size ( p , 0.05). Therefore, we conductedtwo further tests. Model 2 shows the test results related to the Turkish MNEs’ locationchoice between DCs and ECs excluding FSU countries; and Model 3 shows the testresults concerning the Turkish MNEs’ location choice between the EU and ECs.
All three models have high overall explanatory power with significant x 2-values(p , 0.01). All the models have a good fit with classification rates ranging from 89 to91 percent of the observations, rates that are higher than would be expected by chance. Thespecificity (i.e. capacity to correctly predict EC categories) of the three models ranges from77 to 87 percent, while their sensitivity (i.e. capacity to correctly predict DC categories)ranges from 93 to 96 percent. Pseudo R 2 measures confirm that the models havesatisfactory explanatory power.
The coefficient of POLCON is positive and significant in all three models ( p , 0.01)providing strong support for H1 and suggesting that in a business environmentwith fewer political constraints foreign investors tend to choose DCs over ECs. Thisfinding tends to confirm the arguments of the OLI paradigm and institutional theorythat the firms with asset seeking motives will prefer DC markets characterized bylow political risks and more efficient institutional environment as compared to EC ortransition countries that suffer from market failures and weak financial and legalinfrastructure.
There is strong support for H2. The positive and significant coefficient forknowledge infrastructure (KNOWLEDGE) ( p , 0.01) in all three models confirms theview that relative to ECs, Turkish MNEs would prefer DCs that are generallycharacterized by a high level of knowledge infrastructure. This finding is in line withthe widely accepted view that EC MNEs are latecomers into global markets and theyovercome these disadvantages by acquiring critical assets from mature MNEsemanating from DCs characterized by strong knowledge infrastructure.
There is also strong support for H3. The coefficient of (SUBS_DENS) subsidiarydensity is negative and significant in all three models ( p , 0.01). This finding providessupport to Dunning’s investment development cycle model which posits that ECMNEs will enter other ECs first and then move up in the stages toward a more globalpattern of investment as they internalize knowledge of operating in these countries.
There is support for H4 in that the coefficient of R&D_INT (R&D intensity ofindustry) is positive and significant in all three models (Models 1 and 2 at p , 0.05;Model 3 at p , 0.1) suggesting that R&D intensity of industry differentiated TurkishMNEs’ location selection between DCs and ECs. This finding along with knowledgeinfrastructure confirms the underlying strategic motives of Turkish MNEsoperating in the EU and DCs. That is, Turkish MNEs having a keen interest inR&D intensive industries are investing in order to transfer technological know-howand to improve their innovation capabilities by gaining access to these countrymarkets. This finding tends to corroborate that of Pak and Park (2005) for JapaneseFDI in the West and the USA and also that of Makino et al. (2002) who indicated thatTaiwanese asset-seeking FDI was aimed at DCs while asset-exploiting FDI targeted ECs.
Turkish MNEs’location choice
287
The positive and significant coefficients on capital size (LNSIZE) in all three models(Models 1 and 3 at p , 0.01; Model 2 at p , 0.05) show that Turkish MNEs investorsare more likely to establish subsidiaries in DCs than in ECs as the capital size of thesubsidiary increases. These findings support H6 confirming the view that when atarget location is characterized by high investment risk due to an unstable political andlegal environment, EC MNEs will not be motivated to invest heavily in ECs.
No support was found for H5 and H7 in that the coefficients of ownership mode(OWN_MODE) and group affiliation (AFILIATE) are not significant in all three models.That is, ownership mode of subsidiary and the group affiliation are not related toTurkish MNEs’ location choice. These findings are at odds with those of Pak and Park(2005) who relying on arguments from the OLI paradigm and the knowledge-based viewof the firm noted that Japanese MNEs had higher equity ownership in the West than inthe East and also that keiretsu membership had some influence on the location choice ofJapanese MNEs between East and West.
Examination of the control variables shows that neither period of entry (DATE)nor industry of subsidiary (GROUP1_IND and TERTIARY_IND) have significantcoefficients.
5. ConclusionsDrawing on institutional and transaction cost theories, this study has examined thelocation selection for a sample of 522 foreign affiliates of Turkish MNEs.Key determinants of location strategies that lead Turkish MNEs to select particularlocations among several geographic alternatives were investigated. Two specificcountry groups (EU and FSU) and two broad regions (DCs and ECs) were compared toidentify the underlying global strategic motives of Turkish MNEs.
A research framework delineating the proposed relationships between predictorvariables of transaction costs, institution specific variables and the location decision ofTurkish MNEs was tested based on a series of binary logistic regressions. In general,the findings provide support for the majority of the study’s hypotheses and tend toconfirm the theoretical perspectives adopted.
The results show that the main institutional and transaction cost-specificdeterminants of location selection influence the geographic dispersion of TurkishMNEs’ foreign affiliates. The level of political constraints, the level of knowledgeinfrastructure in the host country market, subsidiary density, industry R&D intensity,and subsidiary size were found to have the expected impact on the Turkish MNE’slocation choice among geographic alternatives. No support was found, however, for theimpact of ownership mode of subsidiary and the group affiliation on Turkish MNEs’location choice for their affiliates. In general, our study posits that when TurkishMNEs engage in FDI in DCs or particularly in the EU, the primary strategicmotive appears to be asset-augmentation, while in ECs (including the FSU) the mainstrategic motive becomes asset-exploitation. That is, Turkish MNEs have a motive ofstrategic asset seeking to enhance their global competitiveness when they enter theDCs and EU countries, whereas they simply attempt to exploit their firm-specificadvantages or competencies when they access ECs or neighboring Middle Eastern andFSU countries.
This paper offers a number of implications for international marketing strategiesof EC MNEs. Our findings indicate that the subsidiary density of Turkish MNEs
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increases the likelihood of Turkish MNEs choosing EC markets as a location. AsEnderwick (2009) argues, ECs provide the opportunity for rapid internationalization ofEC MNEs. Subsidiary density, in terms of uncertainties in ECs, serves an importantrole. For instance, subsidiary density may facilitate information exchange and networksupport to subsidiaries from the same country of origin for a rapid market entry.Subsidiary density may also have implications for distribution channel sharing andlogistics for firms in smaller EC markets.
This paper also finds that knowledge infrastructure is a significant determinant oflocation choice of EC MNEs. This implies that subsidiaries located in DCs playsignificant role for reverse knowledge transfer to parent companies of EC MNEs. Whileother ECs such as India and China offer attractive markets (with strong incomeand entrepreneurial activity), DCs still remain the location for R&D intensiveoperations. In some cases, operations located in DCs may also serve as listening postsor R&D support to the rest of the EC MNE’s network.
Limitations of the study should be noted. First, the study focuses on Turkish MNEsand the findings may not be generalizable to other EC MNEs. Second, our classificationof geographic location into DCs versus ECs may be too crude. While we examinesome variation among this broad categorization of country groups, it would be usefulto further investigate a more fine grained distinction between various countries withrespect to EC MNEs’ location choice. Another limitation is the lack of importantfirm-level data such as entry mode (acquisition versus greenfield), partnershipstructure, parent firm size and international experience. Such data is extremely difficultto obtain for a sample of outward Turkish FDI firms based in several different hostcountries. Our analysis is cross-sectional in nature. Future research that analyzeschanges over time in location patterns would further enhance understanding of theimpact of institutional changes on location choice. Future research that investigates theperformance of Turkish affiliates located in different geographic locations would beuseful. Finally, comparative studies of MNEs from several ECs would provide usefulinsights into the understanding of location behavior of EC MNEs among alternativegeographic locations.
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Further reading
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Corresponding authorEkrem Tatoglu can be contacted at: [email protected]
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