Separable but not equal: The location determinants of discrete ...

Journal of International Business Studies (2009) 40, 926–943

& 2009 Academy of International Business All rights reserved 0047-2506 www.jibs.net

Separable but not equal: The location determinants of discrete services offshoring activities Jonathan P Doh1, Kraiwinee Bunyaratavej2 and Eugene D Hahn3 1 Department of Management, Villanova School of Business, Villanova University, Villanova, USA; 2 Department of Business Administration and Accounting, Wesley College, Dover, USA; 3 Department of Information and Decision Sciences, Salisbury University, Salisbury, USA

Correspondence: JP Doh, Department of Management, Villanova School of Business, Villanova University, 800 Lancaster Avenue, Villanova, PA 19085, USA. Tel: þ 1 610 519 7798; Fax: þ 1 610 519 6566

Abstract In this paper we explore the question of why firms offshore particular services to specific geographic locations. We draw on research related to the unique characteristics of services in trade and commerce, and more recent analyses of the transnational unbundling and spatial dispersion of business processes. We move beyond a simple assessment of the cost sensitivity or relative sophistication of offshoring services and develop a typology emphasizing the degree to which offshoring services activities are interactive, repetitive, or innovative. We suggest that the location of offshoring projects will depend on the particular mix of these attributes, and test this assertion using a data set of 595 export-oriented offshore services projects initiated from 2002 to 2005 by US and UK company parents in 45 developed and developing countries. We find that offshore location choices greatly depend on these services characteristics, and in sometimes surprising ways, and draw implications from our findings for international business theory, policy, and practice. Journal of International Business Studies (2009) 40, 926–943. doi:10.1057/jibs.2008.89 Keywords: offshoring; FDI; location; empirical; services; business processes

Received: 17 July 2006 Revised: 19 June 2008 Accepted: 16 July 2008 Online publication date: 13 November 2008

INTRODUCTION Offshoring constitutes an important economic and social phenomenon that has generated considerable attention in practitioner outlets (Corbett, 2004), in the popular press (Baker & Kripalani, 2004), and in political circles (Drezner, 2004). Most of these accounts, including those in the nascent academic literature, tend to emphasize cost minimization or a simple calculation of the relative sophistication of service processes in their analysis of offshore location selection. In this paper we draw from research on the unique attributes of services and more recent analyses of the transnational unbundling of business processes to offer a more comprehensive account. We develop a three-dimensional theoretical framework that emphasizes the degree to which offshore processes are interactive, repetitive, or innovative, suggesting a more fine-grained and multidimensional interpretation of offshore location choice. We argue that the decision of where to locate a specific offshore facility should be viewed as one in which firms trade off competing factors, seeking the best combination of cost and other productive inputs to maximize overall utility for that particular offshore activity and its defining attributes. Our

Location determinants of services offshoring

Jonathan P Doh et al

927

perspective contradicts the popularly held belief that low wages in developing countries such as India constitute the exclusive – or even the principal – driver of offshoring location. Exploiting a unique database of offshoring projects initiated by US and UK parents in 45 developed and developing countries, we extend prior research by showing that wages, education, and English language capability – but not information and communication technology (ICT) infrastructure – all play a role in predicting where firms offshore in aggregate. Of greater interest, however, we uncover several surprising findings that contradict or call into question conventional wisdom. Call centers do not gravitate to the lowest-wage locations, but rather to those that share a common language with the countries of their parent companies, and which feature strong ICT infrastructure. Shared services are especially sensitive to wage cost, but also tend to locate in regions low in risk, because – we argue – of their sensitivity to the potential disruptions of political instability. These findings suggest that offshoring represents a more finely variegated phenomenon than the popular or academic literature has documented. We present the remainder of the paper as follows. In the next section, we review recent research on the radical changes under way in services trade and investment, and especially the spatial disintegration of work processes and the associated geographic unbundling of services production and consumption. We develop our three-dimensional typology of services, and propose hypotheses to predict which country factors will be more important to particular offshoring activities based on that typology. We then describe our data and methods, and present our results both for the overall data set and for specific administrative and technical sectors. We conclude by discussing the implications of our study for IB theory, limitations associated with our analysis, and suggestions for future research.

SERVICES, OFFSHORING, AND THE LOCATION OF BUSINESS PROCESSES We define offshoring of services as the transnational relocation or dispersion of services activities. Offshoring can include captive (internal) or externalized (outsourced) activities. The United Nations Conference on Trade and Development (UNCTAD, 2004) reported that offshoring of services reached about $32 billion in 2001, and expected IT-enabled services to reach $24 billion in 2007, up from just $1 billion in 2002. Offshoring’s impact on broader

political debates underscores real concerns about the role of global trade and integration in an era of economic insecurity and uncertainty. Yet the business news, the broader media, and even the nascent academic literature often treat offshoring as a monolithic phenomenon. For example, a 2007 Wall Street Journal front page story suggested that ‘‘as many as 40 million American jobs [were] at risk of being shipped out of the country in the next decade or two’’ (Wessel & Davis, 2007: A1). We believe such a coarse view does little to advance knowledge, practice, or policy. International business (IB) scholars must bring a more sophisticated and nuanced eye to this important phenomenon. As part of this analysis, IB researchers should examine whether offshoring of services (and knowledge work generally) suggests a reassessment of established precepts of industrial location theories, especially theories related to the location of foreign direct investment (FDI). Offshoring therefore provokes important questions for scholars, practitioners, and policy-makers (Bunyaratavej, Hahn, & Doh, 2008; Doh, 2005; Dossani & Kenney, 2007; Kotabe & Murray, 2004; Manning, Massini, & Lewin, 2008).

The Services Revolution and IB Scholarship Worldwide trade in commercial services grew rapidly over the past two decades, recording a more than fivefold increase from under $400 billion in the early 1980s to over $2.1 trillion in 2004. Over the same period the contribution of exports of commercial services to total world exports (goods and commercial services) rose significantly, from 16% in 1980 to 19% in 2004 (UNCTAD, 2004). An electronic search of the Journal of International Business Studies from 1970 to 2006 underscores the relatively limited IB research on services, yielding just 11 articles with the term ‘‘service’’ or ‘‘services’’ in the title, many of which simply identified some service (advertising, law) as the subject of the study. Although Hill (1977) and others distinguish services characteristics from those of goods, Boddewyn, Halbrich, and Perry (1986: 54) argue that no special FDI theories for international services firms are necessary. The existing ones can be readily accommodated through relatively simple qualifications and elaborations while we wait for the initiation and results of more international services studies.

This assertion presents an appropriate starting point for examining the applicability of FDI location theory to offshoring.

Journal of International Business Studies



928

Scholars suggest that the nascent research in international services investment generally and offshoring in particular requires much further development and elaboration (Doh, 2005; Graf & Mudambi, 2005; Kotabe & Murray, 2004). In this regard, Doh and Pearce (2003) developed (but did not test) a framework of international trade in services based on a product life cycle (PLC) model and internalization theory, arguing that the (im)mobility of services challenges the traditional staged production model of the PLC, and limits the range of services functions that firms can exploit through internalization. Erramilli and Rao (1993) used a transaction-cost approach to explain service firms’ entry mode choice, and Bunyaratavej et al. (2008) examined the impact of efficiency and core competencies of host countries for services using data envelopment analysis. Despite these contributions, past research focused largely on offshoring in the aggregate, overlooking the diversity and complexity of offshore services activities and the related location decisions geared toward specific offshoring functions.

Services, Offshoring, and the Location of Economic Activity Economists and geographers have long explored the factors that influence the location of economic activity (see Isard (1956) for a review of these contributions). Alfred Weber’s (1909/1928) early research explored the question of why certain types of production concentrate in particular regions. He focused on the degree to which location decisions minimize transportation costs, and proposed strategies for optimizing combinations of inputs and production at alternative locations. Weber’s analyses included consideration of scale economies and diseconomies (the possibility of costs/unit going up even as total units increased) and the substitutability of location at any one level of production. In many ways, theories of industrial location have changed little since Weber’s contributions nearly a century ago. For more than 50 years, IB research on the country-level variables and specific firm-level rationales for FDI centered on locationbound factors that make a given geography suitable to a given activity (Buckley & Casson, 1976; Dunning, 1981, 1988, 1993, 1998; Porter, 1990). Traditional comparative advantage emphasized factor endowments (availability of basic factors of production, such as cheap labor or energy, or natural resources) as essentially inherited.


‘‘Dynamic’’ comparative advantage models increasingly acknowledged that country-level advantage evolves endogenously over time (Grossman & Helpman, 1990, 1991; Krugman, 1981). A subset of this research focused specifically on the East Asian development experience as an important exemplar of this process (Amsden, 1989). Competitive advantage, as articulated by Porter (1990), extended this work, asserting that states can influence their competitive position by manipulating factor conditions to make them more specialized, promoting domestic competition that increases demand conditions, investing in the development of related and supporting industries, and encouraging local rivalry among firms in a given industry. Recent extensions of the comparative advantage framework explore the dynamic changes in capabilities among nations. Manning et al. (2008: 45), for example, argue that the ‘‘offshore space y needs to be seen as a dynamic competitive environment in which locations arise and evolve specialized clusters of talent with particular skills for certain business functions.’’ Blinder (2006: 114) notes in a recent essay on offshoring that ‘‘patterns of man-made comparative advantage can and do change over time,’’ and that these changes challenge traditional comparative advantage. Research on the spatial dispersion of business processes responds to the limitations of traditional location theory. Initially, most policy and practitioner analyses of offshoring focused on the role and attractiveness of lower-cost locations. Building on the earlier work of economic geographers, more recent research explores the emergence of concentrations of economic activity in clusters (Porter, 2000), and the agglomeration economies that result from shared institutions, supporting services, and positive spillovers that come from locating in a vibrant business cluster such as that of Silicon Valley or Bangalore, India (Florida, 2002; McEvily & Zaheer, 1999). These analyses focus especially on the clusters of ICT activity in various locations, and have speculated on the role of educated human capital, infrastructure – especially ICT – and cultural proximity in the emergence of those clusters (Lewin & Peeters, 2006). According to Manning and Lewin (2007: 9): In the offshoring space, a new type of cluster seems to have developed. Its main characteristic is a large pool of highskilled talent for particular functions such as IT services y Rather than just concentrating in particular fields, clusters



929

in the offshoring space seem to emerge around growing pools and communities of S&E talent sought for particular business functions across industries.

Basic Host-Country Conditions and Offshoring Location Choice Cost undoubtedly remains an important consideration for services offshoring, especially for basic, routine activities. For example, Farrell (2005) asserts that US companies save $0.58 for every dollar spent on jobs they move to India. Likewise, German companies save h0.52 for every euro spent similarly (Farrell, 2005). In a recent study, 93% of the respondents cited cost reductions as the number one reason why they offshore (Duke University CIBER/Archstone Consulting, 2005). The largest portion of these cost savings typically comes from the difference in wages between developed and developing countries. For firms that engage in captive (internal) offshoring, these savings come not only from the availability of cheaper labor but also from the consolidation of activities in fewer locations, and from economies of scale (Doh, 2005; Farrell, 2005). More recently, researchers have shifted attention to location advantages that emphasize a broader portfolio of assets beyond lower input costs, availability of resources, or savings from tariff avoidance. For instance, according to Doh (2005), the abundance and quality of human capital are increasingly important drivers of offshore location decisions. When examining offshoring in aggregate, many analysts have noted the presence of high educational achievement as a powerful magnet for offshore investment. Dossani and Kenney (2003) find that while investors were initially attracted to India for cost reasons, they retained and expanded facilities because of the high quality of the workforce, which, itself, was partially a function of the agglomeration of earlier investors and the positive externalities they generated. Bunyaratavej, Hahn, and Doh (2007) conclude that firms seek relative parity with their home country workforce quality and associated costs, although with some wage discount in relation to domestic costs. Infrastructure serves as a strong draw for FDI generally and, presumably, offshore investment in particular. Although the OLI model considers a broad range of host-country factors including infrastructure, those factors that influence manufacturing location decisions are not necessarily

important for services. McCann and Mudambi (2004) examined a range of host-country infrastructure factors that are important to manufacturing, including the existing manufacturing base, per capita energy consumption, population, and proximity to major markets. Because of the emergence of a telecommunications technology platform that has facilitated the ‘‘decoupling’’ of services production and consumption (Cole, 1994; Gadfrey & Gallouj, 1998; Richardson, Belt, & Marshall, 2000), ICT would appear to be the most vital aspect of the physical infrastructure from the perspective of offshoring. Cultural affinity also drives FDI location decisions. In the classic conceptualization of internationalization, when firms enter new markets they tend to invest in countries that have less psychic distance from their own, which includes considerations such as culture, business practices and language (Davidson, 1980; Johanson & Vahlne, 1977), because, in part, of the transaction costs (Williamson, 1985) associated with operating in unfamiliar markets. Common language constitutes one important element that bridges cultural and psychic distance, and facilitates business exchanges and lowers transaction costs. Countries that share language may also share other aspects of cultural, institutional, and historical experience (Lazear, 1999). Language also has a more practical contribution to the operation of offshoring. Many offshoring businesses, especially call centers and to a lesser degree software services centers, require high levels of home-country language facility. In reflecting on some of the preliminary results of the Duke/Archstone consulting study, Lewin (2005: 490) asks, ‘‘Do companies that conduct their business in English have an advantage over nonEnglish speaking companies?’’ We argue that, from a transaction cost perspective, language commonality greatly facilitates interactive services exchanges. Offshoring firms must also consider political risk. Kobrin (1979, 1982) defines political risk as the likelihood of unanticipated government actions having an impact on business operations. Political risk can emanate from the real or threatened expropriation by national governments of foreignowned assets (Murtha & Lenway, 1994; Sethi & Luther, 1986) or the explicit or implicit repudiation of contractual obligations by host governments (Lenway & Murtha, 1994; Levy & Spiller, 1996). This first risk has declined precipitously since the 1960s and 1970s (Ramamurti, 2001), while the




930

second continues to affect many investment projects in emerging markets (Doh & Ramamurti, 2003). We expect that the potential disruptions to business that emanate from political protests, terrorism, and insurrection would be critical to offshore investments (Suder, 2004). Hence the basic factors that appear to encourage offshoring include cost savings, an abundant educated workforce, fluency in the language of the investing countries, quality ICT infrastructure, and a stable and supportive host government (Bunyaratavej et al., 2008; Eden, 2005; Lewin, Massini, & Peeters, forthcoming). As a baseline we propose that: Hypothesis 1: Ceteris paribus, (a) the lower the wages, (b) the greater the abundance of educated workers, (c) the higher the ICT infrastructure investment, (d) the greater the use of the source home country language, and (e) the lower the political risk in a foreign country, the greater the number of offshore services facilities that will gravitate to that country.

Offshoring and the Defining Characteristics of Services Moving beyond these aggregate analyses, we now explore how the defining characteristics of specific offshoring services influence offshoring location choice. Drawing from and integrating some of the research work in services and that of industrial location, we develop a specific theoretical typology to explain why some offshoring activities locate in geographic spaces with particular mixes of the attributes included in Hypothesis 1 above. Economists have generally distinguished between ‘‘tradable’’ and ‘‘nontradable’’ products: services, in general, have fallen into the latter category. More specifically, researchers characterize services by arguing that they possess four attributes: heterogeneity, intangibility, perishability, and simultaneity (Bessom & Jackson, 1975). According to this view, the heterogeneity of services emanates from their customization and specialization, with intrasectoral heterogeneity resulting from the diverse nature of each services offering. Since services are intangible, that is, not physical products or artifacts, their form and composition are difficult to describe, and their transfer and exchange are difficult to measure. Perishability and simultaneity reflect that services must be ‘‘consumed’’ at or near the place and time of their production.


Hill (1977, 1999) argues that services imply relationships between producers and consumers: Providers offer their services to another economic unit. Hill advances a ‘‘multi-purpose definition’’ of a service as a change in the condition of one economic unit produced by the activity of another unit. Hill (1977) argues that, in economic terms, services cannot be accumulated in the form of ‘‘stocks,’’ making it impossible to establish ownership rights over a service or to transfer ownership from one economic unit to another. Unlike goods, ‘‘services cannot be traded independently of their production and consumption y Services can be, and are, exported, but only by resident producers providing the services directly to non-resident consumers’’ (Hill, 1999: 442). Hill (1999: 442, 443) therefore emphasizes that the constraints on the timing and location of service production imposed by the relationships which must exist between individual producers and consumers bring into question the relevance to services of those parts of economic theory that implicitly or explicitly assume that production and trading are two separate economic activities.

Because of rapid advances in technology and telecommunications, an increasing proportion of services trade across borders, including architectural, engineering, accounting, financial, and other services (Blinder, 2006). Moreover, services offshoring – which involves FDI of a service facility and the ‘‘export’’ of the output of that facility – challenges many of the fundamental assumptions about services trade, notably the expectation that services production and delivery must occur in the same geographic space. According to Eden (2005: 2), ‘‘Information technology has enabled the disassembly of service processes into a number of relatively separable activities; codifiable interfaces between these activities enable them to be allocated to legally independent organizations and placed in physically distant locations.’’ Therefore firms may now disaggregate, codify, and digitize services. ‘‘Business processes’’ (BP) are those services categories most closely associated with offshoring. According to Dossani and Kenney (2003: 9), BP is the catch-all term for the myriad white-collar processes that any bureaucratic entity undertakes in servicing its employees, vendors, and customers. BPs include human resources, accounting, auditing, customer care, telemarketing, tax preparation, claims processing, document management, and many other chores necessary for firm functioning.

International offshoring and international services trade are related to the extent that the output of



931

offshoring activity provides a service to an internal or external customer. Offshoring may permit electronic transmission of services and storage for consumption at a later date, challenging the classic assertion that services must be consumed at or near their point of origin. Hill (1977, 1999) described a continuum of services characteristics in which one extreme feature generic and undifferentiated activities, and the other, bespoke or customized ones. We extend Hill’s perspective to develop a more fine-grained, three-dimensional typology of offshoring services attributes, and use this model to construct hypotheses regarding which location factors are likely to draw particular types of offshoring activities.

Interactivity. Our first dimension of offshoring services captures the concept of interactivity. Hill (1999) notes that services, by definition, must be provided from one to another economic unit. Different offshore services activities may imply more or less interactivity between the producer and consumer of the service. Here we define interactivity as real-time person-to-person information exchanges. Interaction captures the very nature of some offshore services activities but is more peripheral to others. Different host-country factors that provide facilitating support mechanisms for interaction would be of varying importance to these various services activities. Activities that have a relatively high level of interaction include, for example, call centers. Speaking in the same language as the home country would substantially help improve service quality, reliability, and efficiency. In addition, reliable ICT infrastructure that minimizes interruptions, can withstand major disruptions, and provides highquality service facilitates client and supplier communication and enhances services quality. Voice, data, and video communication depend on ICT infrastructure, and improvements in the overall quality of ICT have facilitated the overall offshoring phenomenon. We therefore obtain the following hypothesis: Hypothesis 2: Ceteris paribus, offshoring services that have a strong interactive component will gravitate to country locations with relatively higher levels of ICT infrastructure investment and relatively high use of the home-country language of the foreign investing firm.

Repetition. Repetition – the degree to which providers replicate, produce, and perform services in quantity – represents the second dimension of interest. Hill (1999) notes that providers typically offer services to a particular customer for a specific use, and this close producer–consumer connection severely limits opportunities for scale economies. However, some firms transmit certain offshore activities on a recurrent or repeated basis, and therefore would appear to realize scale economies. In the contexts of services, activities such as processing monthly payroll checks, performing routine benefits reports, and preparing and distributing invoices would all represent relatively repetitive service transactions. The degree to which firms replicate offshore services activity of a similar fashion from one customer to another constitutes a core characteristic that will influence the choice of country locations for that services facility. Firms offering services that have a strong repetitive quality will gravitate toward locations with relatively low wages, given the undifferentiated, standardized nature of the services. Further, because these services depend on steady production without interruption, we believe that environments characterized by instability and potential interference in the form of political risk could pose challenges to this steady production and therefore should be important to the location of services that reflect this quality. Hypothesis 3: Ceteris paribus, offshoring services that have a strong repetitive component will gravitate to country locations with relatively low wages and relatively stable political environments.

Innovativeness. Manning et al. (2008) report that global hubs of innovation are developing in specific geographies around the world, many of which are still highly dependent on foreign participation and investment. They document the increasing willingness of highly sophisticated companies such as GE, IBM, and Cisco to consider offshoring innovative activities to emerging markets such as India, China, and Eastern and Central Europe. They also note that certain countries – such as Central and Eastern European as well as South Asian nations – specialize in attracting particular business functions from companies based in particular regions of the world: Cisco, for example, recently established ‘‘Cisco Center East’’ in Bangalore India as the new hub of innovation, by creating a parallel corporate structure under the leadership




932

of the first globalization officer of the company. Their former San Jose based US headquarters is now labeled ‘‘Cisco Center West’’, reflecting its new role in the corporation. This decision has major strategic and organizational implications. For example, US- and India-based operations and their employees are going to be competitors for corporate resources and mandates. At the same time, they need to specialize in providing certain functions and serving certain markets. (Manning & Lewin, 2007: 12)

Hence we suggest that innovation – the degree to which firms apply new ideas and approaches to the processes in question (Rogers, 1983) – will influence the location of specific offshoring tasks. Activities such as engineering or software development that have a relatively high level of innovation require a highly educated workforce (Lewin et al., Forthcoming). As a result, firms will look to offshore these activities to countries that have a large pool of well-educated workers. In support of this, Manning et al. (2008) report that various countries are investing in their educational systems in order to increase their attractiveness for innovative offshoring activity. Hypothesis 4: Ceteris paribus, offshoring services that have a strong innovative component will gravitate to country locations with relatively higher levels of a well-educated workforce.

DATA, METHODS, AND RESULTS In this section we explain the data and the variables used in the analysis, including the classification of services according to the typology introduced above. Subsequently, we describe the specifications of the two models used in the analysis. The first model, employing OLS regression, examines the

Table 1

overall macro picture of the factors influencing services offshoring location decisions. The second model, employing multinomial logit, tests the location factors that are most important to specific services sectors according to their defining attributes.

Data Our dependent variables were drawn from a worldwide database of FDI projects, termed the LOCOmonitor database, maintained by OCO Consulting. From a global database of over 36,000 FDI projects initiated since 2002 we obtained information about FDI projects in the following three sectors: call contact centers (e.g., help desks, customer technical support, information services, and customer relationship management); IT services centers (e.g., software development, software design, and applications testing); and shared services centers (e.g., data processing, transaction processing, and claims and payroll processing). These three sectors represent the main categories of services offshoring (UNCTAD, 2004; see Table 1). In the entire database of offshore projects that were available to us, the USA was the largest source country, accounting for more than 48% of the total number of projects. The UK was the second largest source country, with more than 9%, followed by Germany and Japan at 7.7% and 4.8%, respectively. Because the issue of language was of interest to us, and English was the dominant language among our source countries, to operationalize our language hypothesis we chose to limit our sample of source countries to those where English is the principal language. Hence we examined the number of greenfield and expansion offshore services

Definition of export-oriented FDI projects related to offshore services

Call contact center services

Shared services centers

IT services centers

Help desk Technical support/advice after-sales

Claims processing Account processing Transaction processing Query management processing Customer administration processing HR/payroll processing Data processing IT sourcing Logistics processing Quality assurance Supplier invoices

Software development Application testing Content development Engineering and design Product optimization

Employee inquiries Customer support/advice market research Answering services Prospecting Information services Customer relationship management

Source: World Investment Report (UNCTAD, 2004: 159).




933

the three types of offshore services in our sample, as reported by UNCTAD.2

60% 50% 40% 30% 20% 10% O th er

U SA

U K

Ca na da Fr an G ce er m an y In di a Ja pa N n et he rla nd s Sw e d Sw e itz n er la nd

0%

35% 30% 25% 20% 15% 10% 5%

A

U K

O th er

us

tra li Ca a na da Ch in a H un ga ry In di a Ire la M nd al a Ph ysi a ili pp in es Sp ai n

0%

Figure 1 Top sources and destination countries for offshore services projects: (a) top source countries for database (N ¼ 1360); (b) final sample of top destination countries for projects with US or UK parents (N ¼ 595).

projects initiated by US and UK firms in the period 2002–2005. Figure 1 shows the distributions of projects by source country for the full data set and the distribution of host countries for which the source country was either the USA or UK. We separated the data into two periods: 2002– 2003 and 2004–2005. The reason for the grouping derives from the fact that our wage data (to be described) were not available annually. With the data falling into two periods, we assembled a (short) panel of data. We therefore additionally estimated Model 1 (to be described) using seemingly unrelated regressions (SUR) (Zellner, 1962) as the panel data methodology. We found the results of the SUR estimation to provide conclusions that were substantively identical to that provided by a pooled regression approach. Given that panel data formulations of the multinomial logit model are both infrequently encountered and not readily available, and also that pooling did not change the results for Model 1, we also pooled the data for Model 2.1 Table 1 presents the components of

Wage. We used 2003 and 2005 country-specific major metropolitan area wage data (UBS, 2003, 2005, 2006) because metropolitan wage data have greater relevance for research involving offshoring activities than data on average national wages, which would include rural and agricultural wages. For a relative comparison of wages in the host country as compared with that in the home country (USA or UK as the case may be), we calculated the ratio of the wage in country i to that of the home country, because the ratio of wages (rather than the difference in wages) is the logical measure for a firm seeking to justify its location decision. Using the logarithm of the ratio transforms the ratio variable to the real line, which assists in having the variable conform to standard regression assumptions. Here, we used the log wage ratio of country i vis-a`-vis the home country such that countries with wages increasingly lower than that of the US will have increasingly negative log wage ratios. In Hypothesis 1a we suggest that the sign of the wage coefficient will be positive. Education. We used the number of students enrolled in secondary education in both public and private schools as indicators for education. This data set was obtained from the World Development Indicator database (World Bank, 2006) and Global Education Digest, 2006 (UIS, 2006). Data from the years 2001 and 2003 were used for the two groupings, as these were the most complete years of relevant data. Nonetheless, 2001 data for Chile were not available, nor were 2003 data for Argentina, Brazil, Canada, or Malaysia. However, for all of these cases two previous years of data were available, permitting us to estimate the relevant values using the growth equation educt1 educt2 ð1Þ educt ¼ educt1 1 þ educt2 with t indexing the year. After doing so, we again used the log ratio formulation with respect to the corresponding number of students in the US as was done previously for wage. Again, we expect the sign to be positive in accordance with Hypothesis 1b.

ICT infrastructure. We used ICT expenditure as a percentage of GDP from the World Development Indicator database (World Bank, 2006) as our variable indicator for ICT infrastructure. We used




934

the 2003 expenditure for the first grouping and the 2004 expenditure for the second. As above, we used the log ratio formulation, with the corresponding home-country ICT infrastructure expenditure forming the denominator. Following the development of Hypothesis 1c, we expect the sign to be positive.

Language. Following the transaction cost argument of Hypothesis 1d, we focused our attention on the common language associated with the most prevalent linguistic category of offshoring projects: English. English constitutes a valuable resource for countries seeking to attract investment in the global offshoring market, particularly FDI from US and UK MNEs. Investing in Englishspeaking countries could also reduce the costs associated with training, and ease interactions with internal and external clients. We therefore entered an English language variable in the model for the countries in the sample. Language information on countries worldwide can be found at The World Factbook, Central Intelligence Agency (CIA, 2006). We coded the English dummy variable 1 if the primary language of a country was English, or if English was one of several official languages (e.g., Canada), and 0 otherwise. With respect to Hypothesis 1d, we expected the coefficient of English to be positive.3 Political risk. We used composite political risk data from the International Country Risk Guide (ICRG) (PRS Group, 2002, 2004). ICRG compiles monthly data on a variety of political, financial and economic risk factors to calculate risk indices in each of these categories, as well as a composite risk index. The political risk category incorporates 13 factors, with each assigned a numerical rating within a specified range. The specified allowable range for each factor reflects the weight attributed to that factor.4 A higher score indicates less risk. The ICRG risk measures are widely used by both practitioners and academics (e.g., La Porta, Lopez de Silanes, Shleifer, & Vishny, 1997). In Hypothesis 1e, we expect the sign of the political risk coefficient to be positive. Control variables. As our data contain both developed and developing countries, we included the log of GDP per capita as a control variable. In international economic research gravity models have demonstrated that spatial distances (especially proximity) are associated with closer


(higher level) trading and investment relationships (see Anderson (1979), for a review). Hence we include geographical distance as a control. We operationalize this variable as the log of geographic distance from the capital city of the home country to that of the host country. We also included a home country control (US/UK dummy), as we would expect the USA to be the source of more projects than the UK.5,6

Relative emphasis of offshoring sectors by services characteristics. We reviewed the offshoring sectors described above (call contact centers, IT services centers, shared services centers) in the database according to the three offshore services characteristics discussed above. We drew on the specific description of the services in Table 1 and a broad survey of academic and practitioner literature (e.g., Dossani & Kenney, 2007; Lewin et al., Forthcoming) to determine the extent to which each of these offshoring services tasks demonstrated one or more of the three attributes of repetitiveness, interactivity, and innovativeness. Our evaluation of the three sectors according to the three characteristics appears in Figure 2 and Table 2. Call contact centers that include activities such as help desks or information services incorporate front-line tasks in which an employee interacts

Interactivity

Call contact centers

Repetition

Shared services centers

IT services centers

Innovativeness

Figure 2 Location of three offshoring sectors along the IRI distribution.

Table 2 Location characteristics

of three

Call contact centers Shared services centers IT services centers

offshore

sectors

by services

Repetitive

Interactive

Innovative

Medium High Low

High Low Low

Low Low High



935

with a specific customer. The activities in this category therefore have a relatively high level of interactivity compared with those in shared services centers and IT. Some call center activities also have a certain degree of repetitiveness, but high levels of person-to-person interaction constitute their defining feature. On the other hand, activities in shared services centers reflect back-office activities such as payroll or claims processing that have a relatively high level of repetitiveness compared with those in call centers and IT. Unlike the frontoffice operations, these activities rarely engage with customers, and as a result have a lower level of interactivity. The repetitive activities also require less innovative ability, since the work activities remain relatively constant over time (e.g., payroll processing). Hence the defining feature of shared services centers is repetitiveness. Lastly, owing to the sophisticated work involved in IT services such as software development, innovative capacity reflects an integral aspect of IT services activities, especially in comparison with call centers and shared services centers. Therefore we view innovativeness as the defining feature of IT services described here.7

Methods In Model 1, we incorporate the economic concept of elasticities. Following Eichengreen and Irwin (1995), we add 1 to the value of y and then take the log of the result. Since log(y) approximately equals log(y þ 1) for large y, the constant-elasticity interpretation8 holds for large values of y. Eichengreen and Irwin (1995) compared the results of SUR estimation using the above transformation with the results under Tobit estimation and found the substantive conclusions to be the same under both approaches. In using the Eichengreen and Irwin transformation, we also gain the skewness-correcting feature of the log, which greatly improved the normality of the residuals (see below). Model 1 is therefore a regression model with the following specification: logðNumber of projectsi þ 1Þ ¼ b1 þ b2 logðWagei =WageHome Country Þ

Hence Model 1 serves as an overall model with a macro or country-level focus, in that it relates the predictor variables to the overall number of projects located in a particular country. The ratios used for wage, education, and infrastructure have in their denominators the corresponding values of the variable in the home country. The ratios convert the wage, education, and infrastructure information into a percentage in which the home country serves as the referent. The use of the Eichengreen and Irwin transformation on the dependent variable permits the coefficients of logged independent variables (e.g., the b2, b3, b4, b7 and b8 coefficients) to have a percentage change interpretation. As mentioned previously, Englishi is a dummy variable that takes the value 1 if English is an official or primary national language or national lingua franca for country i, and 0 otherwise. Hence a log transformation of this independent variable is unnecessary. Similarly, Home Country is a dummy variable taking the value 1 if the project’s home country is the US and 0 if it is the UK. Finally, we did not take the log of Political Risk as there was no need to transform the variable due to skewness, and given the relativistic level of measurement, the percentage change interpretation of the coefficient would be potentially debatable. Hence we left the variable untransformed. In Model 1 the data from the 595 projects appear as 148 rows of count information, so the sample size for Model 1 is N¼148. In Model 2 we examine the data in more detail by obtaining results on a by-sector basis using a multinomial logit model. In this model, we adopt the perspective of a hypothetical firm and investigate the choice of initiating an offshore FDI project in one of four sectors (shared services centers, call centers, IT centers, or regional headquarters). Hence the sample size is N¼595. Model 2 retains the same predictor variables as Model 1 but focuses on the sector choices that firms make, assuming they have made the entry decision to invest in a particular country. In particular, we used the right-hand-side variables of Model 1 to predict which of the four sectors the firm invests in. We chose shared services as our reference group for the analysis.

þ b3 logðEducationi =EducationHome Country Þ þ b4 logðICT Infrastructurei =ICT InfrastructureHome Country Þ

Results

þ b5 Englishi þ b6 Home Countryi þ b7 logðDistancei Þ

Overall model: Model 1. Table 3 presents variable summary statistics and Pearson correlations, including those for the transformed dependent

þ b8 logðGDPi Þ þ b9 Political Riski ð2Þ




936

Table 3

1 2 3 4 5 6 7 8

Descriptive statistics and Pearson correlations for Model 1

Number of projects Log ratio wage Log ratio education Log ratio ICT infrastructure English Log distance Log GDP per capita Risk

Mean

s.d.

1

2

3

4

5

6

7

8

0.890 1.010 1.720 0.393 0.203 8.105 9.218 77.098

1.007 0.952 1.521 0.322 0.403 1.002 1.284 11.807

— 0.133 0.120 0.249* 0.459* 0.071 0.155 0.001

— 0.422* 0.176* 0.019 0.350* 0.799* 0.823*

— 0.021 0.049 0.050 0.489* 0.504*

— 0.145 0.077 0.208* 0.101

— 0.131 0.039 0.143

— 0.259* 0.315*

0.677*

—

N ¼148; *correlation significant at the po0.05 level.

variable involving the number of FDI projects. We examined the parametric regression assumptions associated with Model 1 by conducting a Shapiro– Wilk test (Rahman and Govindarajulu, 1997) for the normality of the residuals. Here, although we were unable to reject the null hypothesis that the residuals were normal using the Eichengreen and Irwin transformation (W ¼ 0.99, n.s.), the Shapiro– Wilk test strongly rejected the hypotheses of normality of residuals (W ¼ 0.69, po0.0001) if the dependent variable was left untransformed. Hence we used the Eichengreen and Irwin transformation on the dependent variable. We investigated the presence of heteroscedasticity using the Breusch– Pagan test, and we could not reject the null hypothesis of regression homoscedasticity (BP ¼ 12.34, n.s.) To examine the issue of multicollinearity, we calculated VIFs (see Table 4). None of the VIFs exceeded 10, the value that is typically taken to be the threshold for excessive multicollinearity. We first report on the overall analyses associated with Model 1. The regression coefficients appear in Table 4. The coefficient of the log wage ratio was 0.373 (po0.05), supporting Hypothesis 1a. For education, the coefficient was 0.285 (po0.001), supporting Hypothesis 1b. The coefficient of the log infrastructure ratio was 0.190 (n.s.), failing to support Hypothesis 1c. For English, the coefficient (1.16) is significant (po0.001), indicating that English-speaking countries possess an advantage in attracting FDI projects over non-English-speaking countries ceteris paribus consistent with Hypothesis 1d. The coefficient for political risk (b ¼ 0.039, po0.001) indicated that as political risk decreased (higher score), the expected number of offshore projects increased, supporting Hypothesis 1e. In terms of the controls, log GDP per capita was not significant; however, the US dummy was significant because, as noted above, overall US firms engage in a


Table 4

Parameter estimates and regression results for Model 1

Intercept Log ratio wage Log ratio education Log ratio ICT infrastructure English Home (US) Log distance Log GDP Risk R2 Adjusted R2 Omnibus F-test N

Coefficient

s.e.

t-value

VIF

0.567 0.373* 0.285*** 0.118 1.159*** 1.348*** 0.282*** 0.024 0.039*** 0.523*** 0.496*** 19.08*** 148

1.312 0.143 0.057 0.223 0.156 0.181 0.077 0.081 0.010

0.433 2.606 5.034 0.527 7.438 7.428 3.667 0.295 3.782

— 5.349 2.134 1.486 1.137 2.299 1.703 3.088 4.214

*po0.05; ***po0.001.

greater number of offshoring projects worldwide than do UK firms. Log distance was also significant, indicating that ceteris paribus firms preferred to locate in countries that were nearer.

By-sector model: Model 2. The results for the bysector analyses associated with Model 2 appear in Table 5. The three project sectors are IT service centers, shared services centers, and call centers, with a total sample size of 595 projects. Shared services centers constitute the reference group for the multinomial logit model. We estimated this multinomial logit model using the maximum likelihood approach: the associated asymptotic standard errors and z statistics from the maximum likelihood estimation appear in Table 5 along with the coefficients. As for the results involving wages, Table 5 indicates that as wages in a particular country increase, MNEs are more likely to choose to invest



937

Table 5

Parameter estimates and multinomial logit results for Model 2 (reference group – shared services centers)


Intercept Log ratio wage Log ratio education Log ratio ICT infrastructure English Home (US) Log distance Log GDP Risk

IT service centers

Coefficient

s.e.

z

Coefficient

s.e.

z

19.198 1.211* 0.200 2.599*** 0.632** 1.218*** 0.754*** 0.182 0.124**

— 0.494 0.139 0.438 0.243 0.363 0.178 0.190 0.046

— 2.449 1.440 5.932 2.594 3.353 4.237 0.959 2.689

12.892 1.953** 0.178 0.259 0.269 1.396** 0.190 0.001 0.136*

— 0.630 0.168 0.593 0.316 0.524 0.226 0.259 0.055

— 3.102 1.060 0.437 0.853 2.666 0.842 0.003 2.464

N ¼ 595. *po0.05; **po0.01; ***po0.001.

abroad through a call center (b ¼ 1.211, po0.05) or an IT services center (b ¼ 1.953, po0.01) as opposed to a shared services center. This indicates that shared services centers are particularly wage costsensitive, more so than call centers and IT services centers. We therefore find support for Hypothesis 3 but not for Hypothesis 4. Turning to infrastructure, as host-country infrastructure expenditures increased, MNEs favored call centers as opposed to shared services centers (b ¼ 2.599, po0.001), providing support for Hypothesis 2. In English-speaking host countries, MNEs also favored constructing call centers as opposed to shared services centers (b ¼ 0.726, po0.001), again supporting Hypothesis 2. Finally, as political risk decreases, MNEs were more likely to choose to invest abroad via a shared services center as opposed to a call center (b ¼0.124, po0.01) or an IT services center (b ¼0.136, po0.05), providing support for Hypothesis 3. US firms were significantly more likely than UK firms to choose to invest abroad via a call center (b ¼1.218, po0.001). Moreover, the US firms were significantly more likely to develop an IT services center than were UK firms (b ¼ 1.396, po0.01). Similarly, as (log) distance from the home country increased, MNEs were more likely to choose to invest through a shared services center (b ¼0.754, po0.001) than a call center. Thus ceteris paribus call centers tended to be located closer to the home country than were other types of services offshoring ventures. Firms do not appear to choose locations in response to host-country (log) GDP per capita ceteris paribus. Table 6 summarizes the principal findings of our sector analyses.

DISCUSSION, LIMITATIONS, AND CONCLUSIONS Recent debates over whether the IB research agenda is ‘‘running out of steam’’ prompt reconsideration of the relevance of the core research thrusts (Buckley, 2002; Peng, 2004; Shenkar, 2004). Buckley (2002) highlights a range of issues that the IB research community has largely overlooked, including knowledge management and geography/ location, suggesting that each represents a strong candidate for important new research agendas. Peng (2004) echoes these sentiments, noting that new aspects of IB, such as emerging economies, have received inadequate exploration. We add to this critique by highlighting the inadequacy, to date, of IB research to fully incorporate the decoupling of the services value chain and the commensurate increase in the mobility of administrative and technical services facilities. Findings and Implications Our findings suggest that, overall, wages, education, language, and risk constitute important factors that firms use to consider where to offshore their services. Although popular press citations and government policy exchanges consistently identify cost reduction as the most powerful rationale for offshoring (e.g., Farrell, 2005), in our study wages emerge as one of several important contributors to offshore location decisions. In addition to wages, offshore projects gravitate to countries that have a large educated workforce, a finding consistent with recent anecdotal examples of offshore services firms identifying education as one of their principal considerations when examining offshore locations (Duke University CIBER/Archstone Consulting,




938

Table 6 services

Summary of overall and relative impact of host-country factors on offshore location of different administrative and technical

Overall sample: aggregate

Shared services centers (reference group)


IT services centers

Wages

Important

Wages relatively more important

Wages relatively less important

Wages relatively less important

Education

Important

n.s. (no significant difference among three groups)



ICT infrastructure

n.s.

ICT infrastructure relatively less important

ICT infrastructure relatively more important

ICT infrastructure relatively less important

English

Important

English relatively less important

English relatively more important

n.s. (no significant difference from reference group)

Risk

Important

Risk relatively more important

Risk relatively less important

Risk relatively less important

2005; Manning et al., 2008). Surprisingly, telecommunications and IT infrastructure emerged as a nonsignificant factor in the aggregate (however, we do find evidence that IT infrastructure serves as a draw for some offshore sectors). This may result from the fact that offshoring firms increasingly rely on their own infrastructure to facilitate transactions, or that the particular types of infrastructure on which offshoring relies are ubiquitous. The role of the English language emerged as another significant factor in determining the location of US/UK-based offshore facilities: not surprisingly, English competency appears to be central to the human resource needs of these offshoring firms, particularly for certain sectors. Political risk was also significant in influencing the location of facilities, reflecting perhaps offshoring firms’ sensitivity to deploying their IT and telecom infrastructure in politically risky environments for fear of unexpected fiscal policy and regulatory changes, economic development policy changes, or other disruptions. Given the heavier weighting in our political risk measure of overall government stability, socio-economic and investment profile, and internal/external conflict, firms’ reactions are likely to correspond to these overarching conditions rather than to any specific aspect of government behavior. Moreover, given the extremely high negative correlation between wages and risk, many firms appear to be implicitly


assessing the costs of risk and strategically trading off risk mitigation against higher wages. Shared services centers, because of their routine and repetitive nature, are more numerous in countries with lower wages. Call contact centers were more numerous in countries with more highly developed infrastructure, reflecting perhaps the significant infrastructure demands of call center operation. Call centers locate in English-speaking countries, confirming the natural expectations that personnel in these centers must be able to interact with customers in fluent (and sometimes properly accented) English. We also found that MNEs were more likely to choose to invest abroad by building a shared services center as political risk decreased. Our finding regarding education in Models 1 and 2 implies that the presence of a potential workforce with high levels of educational attainment serves as a draw across our three categories of services. Conventional wisdom would suggest that an abundance of highly educated workers would be essential to higher value-added functions that require more sophisticated skills, but not so essential to more routine or repetitive activities. These results imply that all of the offshoring activities considered here depend on an educated workforce, and that government policy-makers and managers should consider this in their decision-making.



939

Although not part of our hypotheses, US firms were more likely to sponsor call centers than were UK firms, reflecting, in part, the relatively larger demands of the US market for call center services. Call centers gravitate closer to the home country than other types of services offshoring ventures, reflecting, perhaps, the need for close oversight and control, an issue that has emerged as a managerial challenge of offshoring. In addition, earlier investments were more likely to be in call centers and later investments in shared services and IT centers.

Contributions and Limitations We respond to the paucity of empirical studies on FDI in services generally, and on offshoring of administrative and technical work in particular, and contribute to IB research generally, and scholarship on services and offshoring in particular, by showing that different types of services respond to locations with different mixes of qualities and resources. As far back as 1986 Boddewyn, Halbrich, and Perry called for additional research in IB and services. Doh and Pearce (2003: 74) argued for additional research on IB services that explores ‘‘practical implications for government and business.’’ Lewin (2005) proposed that the IB research community has an important role to play to unraveling the complex forces at work in offshoring. Our research contributes to this call for both scholarly and practical approaches to questions surrounding location decisions for services offshoring. We have also sought to respond directly to Manning and Lewin’s (2007: 4) observation that ‘‘the complex dynamics of offshoring remain to be understood’’ by exploring more fine-grained aspects of offshoring, namely the differences in offshore location choice among different offshoring activities. To date, research on services has emphasized the distinctions between services and manufacturing (Bessom & Jackson, 1975; Hill, 1977, 1999). Similarly, research in international services and offshoring has tended to emphasize the unique attributes of services and offshoring generally (Bunyaratavej et al., 2007; Doh, 2005; Eden, 2005; Farrell, 2005; Graf & Mudambi, 2005; Kotabe & Murray, 2004), with little exploration of how differences among services tasks influence offshore location choice. Hence we extend the literature by investigating the qualitatively different activities within the broad universe of services offshoring. In so doing, we find that different offshore services characteristics – interactivity, repetition,

and innovativeness – influence the location of specific services activities. The examination of the particular characteristics of services, and the location requirements and demands they imply, constitute the principal contribution of our study to the IB and FDI literature. Our results provide a somewhat complex set of findings for governmental policy-makers. Low wages, often thought to be the primary draw for offshoring activities, appear to be just one of several factors that play a role in offshoring services facilities from the US and UK. Rather, education and English language, and to a lesser extent political risk, also represent important variables that attract services investment. Lewin (2005) has stressed the importance of human capital in offshoring; our observations regarding the importance of education in the offshoring decisions appear to corroborate this view. Hence government investment in education would appear to be an obvious normative implication of our research. Our findings suggest that practitioners and policymakers focus too narrowly on the draw of wages as a rationale for offshoring, and miss broader opportunities for access to knowledge capabilities. As in any study, our research has a number of limitations. First, as a ‘‘count’’ variable, our dependent variables do not discriminate among different sizes or values of services facilities. It could be that services facilities in some of our countries are substantially larger than in others, and this could influence our results. Other limitations include our inability to determine with absolute certainty whether the projects in our database are organized as internal or external initiatives of the parent firms, and the imperfect nature of classification of offshoring investments more generally. In particular, our data set limited our ability to incorporate firm-level explanatory or control variables: our analysis therefore focuses more on the ‘‘demand’’ side of the offshoring equation, and leaves the ‘‘supply’’ side relatively unexplored.

Suggestions for Future Research Our research offers directions for future empirical investigation as follows. Future research could build on our study by following the location changes in services offshoring over longer periods of times (10 years or more). Also, future research could extend our study to focus specifically on higher valueadded activities, such as pharmaceuticals or R&D. In addition, we incorporated geographic distance as a control variable; given our findings that certain




940

offshoring activity occurs closer to ‘‘home’’, future research in this area may yield potentially interesting insights. Recent IB research incorporates concepts from economic geography into models of FDI location. Specifically, agglomeration economies – the positive externalities that benefit firms that locate in close geographic proximity – can explain the location of foreign facilities, as do ‘‘mimetic’’ effects – the apparent bunching of foreign facilities from the same countries (Nachum, 2000; Shaver & Flyer, 2000). Shaver and Flyer (2000) and Chung and Song (2004) found that Japanese firms located their manufacturing facilities in states where many other Japanese firms had located, and that externalities might explain this pattern of agglomeration. Nachum (2000: 375) examined FDI in the United States in the professional services industry, concluding that ‘‘agglomeration economies and location advantages together shape the location choice of FPS TNCs in the US.’’ Given evidence that certain locations appear to be preferred settings for a given type of offshoring, as the temporal record of the location of offshoring of administrative and technical services becomes more established, examination of the mimetic and agglomeration effects would likely augment the observation derived from our study. In addition, our data set did not permit examination of more micro firm-level characteristics that undoubtedly affect the location and mode of offshoring projects. Firm-level resources and capabilities (Barney, 1991) such as size, previous international experience, and others may well add additional explanatory power to our analysis.

ACKNOWLEDGEMENTS We thank special issue editors Tom Murtha, Silvia Massini, and Martin Kenney and three anonymous reviewers for their feedback and guidance. NOTES In all there were 45 countries represented, with a total of 595 FDI projects. The country locations represented were Argentina, Australia, Austria, Belgium, Brazil, Canada, Chile, China, Colombia, the Czech Republic, Denmark, Finland, France, Germany, Greece, Hong Kong, Hungary, India, Indonesia, Ireland, Israel, Italy, Japan, Malaysia, Mexico, the Netherlands, New Zealand, Norway, Pakistan, the Philippines, Poland, Portugal, Romania, Russia, Slovakia, South Africa, South Korea, Spain, Sweden, Switzerland, Thailand, Turkey, the United Kingdom, the United States, and Venezuela. We include the UK 1


as a host country for FDI projects initiated by US companies and vice versa. 2 As Figure 1b illustrates, India is the largest country destination, representing more than 25% of the total projects in our sample. Because of India’s large presence, as an additional robustness check we reran our models without India in the sample. The results for Model 1 were identical to those with India included, with the exception that t values were somewhat lower. Results for Model 2 were nearly identical, except that for call centers wage no longer was a significant predictor of call center location vs the reference group of shared services centers, and for IT service centers English became significant in comparison with the reference group. Given the importance of India as a destination of offshore projects, we retained India in the sample. 3 As an additional robustness check, we re-ran the model with the inclusion of Germany and Japan, the next two largest country sources of offshoring projects. The substantive results were the same as those reported in the Results section. 4 These are government stability, socio-economic conditions, investment profile, internal conflict, external conflict, corruption, military in politics, religious tensions, law and order, ethnic tensions, democratic accountability, and bureaucracy quality. In the overall composite the first five factors are allocated 12 points, the next 6 points (half the weight of the first five), and the final component 4 points. 5 As an additional robustness check, and in order to ensure that there were no time-dependent effects, we estimated Model 1 with a year dummy: substantively identical conclusions were reached regarding the above coefficients, and the dummy variables were not significant. 6 To check for potential differences in our sample attributable to the dispersion of developing and developed countries, using the World Bank classification of higher- and lower-income countries, we created a dummy variable that took the value 1 if the host country was high income (i.e., developed), and 0 otherwise We then re-estimated Model 4 including the new variable. The principal difference in this model from the one reported in the Results section is that Log Ratio Wage becomes marginally significant, as opposed to significant. This is understandable in light of the fact that we would expect wages and country income levels to be substantially correlated. Indeed, we found the dummy variable to have a correlation of 0.849 with our Log Ratio Wage variable. For these reasons, we retained and report our original model with the full sample.



941

7

We recognize that each of these categories shares some combination of the three characteristics we propose. For example, in addition to being interactive, some outbound call centers provide a service that is highly similar (repetitive) from one producer– consumer relationship to another (telephone solicitation), such that the more this act is repeated, the more routine and undifferentiated it becomes. Even inbound call centers receiving inquiries about computer problems all begin with a common routine (‘‘Have you

turned the computer on and off? Have you checked that the computer is plugged into an outlet?’’) that is driven by scripts and pre-programmed troubleshooting decision trees. However, on balance, we consider interactivity to be the defining feature of call centers. Hence, while we locate the three services sectors in Figure 2 to reflect that they are not fully discrete, we evaluate and classify each to the extent that we can identify its principal defining feature. 8 For small values of y, the semilog interpretation holds.

REFERENCES Amsden, A. 1989. Asia’s next giant: South Korea and late industrialization. New York: Oxford University Press. Anderson, J. E. 1979. A theoretical foundation for the gravity equation. American Economic Review, 69(1): 106–116. Baker, S., & Kripalani, M. 2004. Software: Will outsourcing hurt America’s supremacy? Business Week, March 1: 84–91. Barney, J. 1991. Firm resources and sustained competitive advantage. Journal of Management, 17(1): 99–120. Bessom, R. M., & Jackson, D. W. 1975. Service retailing: A strategic marketing approach. Journal of Retailing, 51(2): 75–84. Blinder, A. 2006. Offshoring: The next industrial revolution? Foreign Affairs, 85(2): 113–128. Boddewyn, J., Halbrich, M. B., & Perry, A. C. 1986. Service multinationals: Conceptualization, measurement and theory. Journal of International Business Studies, 17(3): 41–57. Buckley, P. J. 2002. Is the international business research agenda running out of steam? Journal of International Business Studies, 33(2): 365–373. Buckley, P. J., & Casson, M. 1976. The future of the multinational enterprise. London: Holmes & Meier. Bunyaratavej, K., Hahn, E. D., & Doh, J. P. 2007. International offshoring of services: A parity study. Journal of International Management, 13(1): 7–21. Bunyaratavej, K., Hahn, E. D., & Doh, J. P. 2008. Multinational investment and host country development: Location efficiencies for services offshoring. Journal of World Business, 43(2): 227–242. Central Intelligence Agency (CIA). 2006. The world factbook. Washington, DC: CIA. https://www.cia.gov/library/publications/ the-world-factbook/index.html. Accessed 7 May 2006. Chung, W., & Song, J. 2004. Sequential investment, firm motives and agglomeration of Japanese electronics firms in the United States. Journal of Economics and Management Strategy, 13(3): 539–560. Cole, R. E. 1994. Reengineering the corporation: A review essay. Quality Management Journal, 1(4): 77–85. Corbett, M. F. 2004. The outsourcing revolution: Why it makes sense and how to do it right. Chicago, IL: Dearborn Trade Publishing. Davidson, W. 1980. The location of foreign direct investment activity: Country characteristics and experience effects. Journal of International Business Studies, 11(2): 9–22. Doh, J. P. 2005. Offshore outsourcing: Implications for international business and strategic management theory and practice. Journal of Management Studies, 42(3): 695–704. Doh, J. P., & Pearce, J. A. 2003. Revising our understanding and expectations of the international trade in services. Journal of Transnational Management, 9(1): 59–78. Doh, J. P., & Ramamurti, R. 2003. Reassessing risk in developing country infrastructure. Long Range Planning, 36(4): 337–353.

Dossani, R., & Kenney, M. 2003. Went for cost, stayed for quality? Moving the back office to India, Mimeo, Berkeley and Stanford University. Dossani, R., & Kenney, M. 2007. The next wave of globalization: Relocating service provision to India. World Development, 35(5): 772–791. Drezner, D. W. 2004. The outsourcing bogeyman. Foreign Affairs, 83(3): 22. Duke University CIBER/Archstone Consulting. 2005. Second bi-annual offshore survey results. https://offshoring.fuqua. duke.edu/pdfs/2ndBiannualOffshoringHighlights.pdf. Accessed 17 May 2006. Dunning, J. H. 1981. International production and the multinational enterprise. London: George Allen & Unwin. Dunning, J. H. 1988. Explaining international production. London: Unwin Hyman. Dunning, J. H. 1993. Multinational enterprises and the global corporation. New York: Addison-Wesley. Dunning, J. H. 1998. Location and the multinational enterprise: A neglected factor? Journal of International Business Studies, 29(1): 45–66. Eden, L. 2005. Went for cost, priced at cost? An economic approach to the transfer pricing of offshored business services. Transnational Corporations, 14(2): 1–53. Eichengreen, B., & Irwin, D. A. 1995. Trade blocs, currency blocs and the reorientation of world trade in the 1930s. Journal of International Economics, 38(1/2): 1–24. Erramilli, M. K., & Rao, C. 1993. Service firms’ international entry mode choice: A modified transaction-cost analysis approach. Journal of Marketing, 57(3): 19–38. Farrell, D. 2005. Offshoring: Value creation through economic change. Journal of Management Studies, 42(3): 675–683. Florida, R. 2002. The rise of the creative class: And how it’s transforming work, leisure and everyday life. New York: Basic Books. Gadfrey, J., & Gallouj, F. 1998. The provider–customer interface in business and professional services. Service Industries Journal, 18(2): 1–15. Graf, M., & Mudambi, S. M. 2005. The outsourcing of ITenabled business processes: A conceptual model of the location decision. Journal of International Management, 11(2): 253–268. Grossman, G., & Helpman, E. 1990. Comparative advantage and long-run growth. American Economic Review, 80(4): 796–815. Grossman, G., & Helpman, E. 1991. Innovation and growth in the global economy. Boston: MIT Press. Hill, T. P. 1977. On goods and services. Review of Income and Wealth, 23(4): 315–338. Hill, T. P. 1999. Tangibles, intangibles and services: A new taxonomy for the classification of output. The Canadian Journal of Economics, 32(2): 426–446.




942

Isard, W. 1956. Location and space-economy: A general theory relating to industrial location, market areas, land use, trade and urban structure. Cambridge, MA: MIT Press. Johanson, J., & Vahlne, J.-E. 1977. The internationalization process of the firm: A model of knowledge development and increasing foreign market commitments. Journal of International Business Studies, 8(1): 23–32. Kobrin, S. 1979. Political risk: A review and reconsideration. Journal of International Business Studies, 10(1): 67–80. Kobrin, S. 1982. Managing political risk assessment. Berkeley, CA: University of California Press. Kotabe, M., & Murray, J. Y. 2004. Global sourcing strategy and sustainable competitive advantage. Industrial Marketing Management, 33(1): 7–14. Krugman, P. 1981. Trade, accumulation and uneven development. Journal of Development Economics, 8(2): 149–161. La Porta, R., Lopez de Silanes, F., Shleifer, A., & Vishny, R. W. 1997. Legal determinants of external finance. Journal of Finance, 52(3): 1131–1150. Lazear, E. P. 1999. Culture and language. Journal of Political Economy, 107(S6): S95–S126. Lenway, S. A., & Murtha, T. P. 1994. The state as a strategist in international business research. Journal of International Business Studies, 25(3): 513–532. Levy, B., & Spiller, P. T. 1996. Institutions, regulations, and commitment. Cambridge, MA: Cambridge University Press. Lewin, A. 2005. Letter from the editor. Journal of International Business Studies, 36(5): 489–491. Lewin, A., & Peeters, C. 2006. Offshoring work: Business hype or the onset of fundamental transformation? Long Range Planning, 39(3): 221–239. Lewin, A.Y., Massini, S., & Peeters, C. forthcoming. Why are companies offshoring innovation? The emerging global race for talent. Journal of International Business Studies, in press. Manning, S., & Lewin, A. 2007. The globalization of innovation: A dynamic perspective on offshoring. Paper prepared for the First Annual Research Conference and Workshop on Offshoring, Cary, NC, 15 April. Manning, S., Massini, S., & Lewin, A. 2008. A dynamic perspective on next-generation offshoring: The global sourcing of science and engineering skills. Academy of Management Perspectives, 22(3): 35–54. McCann, P., & Mudambi, R. 2004. The location behavior of the multinational enterprise: Some analytical issues. Growth and Change, 35(4): 491–524. McEvily, B., & Zaheer, A. 1999. Bridging ties: A source of firm heterogeneity in competitive capabilities. Strategic Management Journal, 20(12): 1133–1156. Murtha, T. P., & Lenway, S. A. 1994. Country capabilities and the strategic state: How national political institutions affect multinational corporations’ strategies. Strategic Management Journal, 15(Summer special issue): 113–129. Nachum, L. 2000. Economic geography and the location of TNCs: Financial and professional service FDI to the USA. Journal of International Business Studies, 31(3): 367–386. Peng, M. 2004. Identifying the big question in international business research. Journal of International Business Studies, 35(2): 99–108. Porter, M. E. 1990. The competitive advantage of nations. New York: Free Press. Porter, M. E. 2000. Location, clusters, and company strategy. In G. Clark, M. Gertler, & M. Feldman (Eds), Oxford handbook of economic geography: 253–274. Oxford: Oxford University Press. PRS Group. 2002 & 2004. International country risk guide. http://www.prsgroup.com/ICRG.aspx. Rahman, M. M., & Govindarajulu, Z. 1997. A modification of the test of Shapiro and Wilk for normality. Journal of Applied Statistics, 24(2): 219–235. Ramamurti, R. 2001. The obsolescing ‘‘bargaining model’’? MNC-host developing country relations revisited. Journal of International Business Studies, 32(1): 23–39.


Richardson, R., Belt, V., & Marshall, N. 2000. Taking calls to Newcastle: The regional implications of the growth in call centers. Regional Studies, 34(4): 357–369. Rogers, E. M. 1983. Diffusion of innovations, (3rd ed.). New York: Free Press. Shaver, J. M., & Flyer, F. 2000. Agglomeration economies, firm heterogeneity, and foreign direct investment in the United States. Strategic Management Journal, 21(12): 1175–1193. Shenkar, O. 2004. One more time: International business in a global economy. Journal of International Business Studies, 35(2): 161–171. Sethi, S., & Luther, K. A. N. 1986. Political risk analysis and direct foreign investment: Some problems of definition and measurement. California Management Review, 28(2): 57–68. Suder, G. G. S. (Ed.) 2004. Terrorism and the international business environment: The security-business nexus. Cheltenham: Edward Elgar. UNESCO Institute for Statistics (UIS). 2006. Global education digest. Montreal: UNESCO. Union Bank of Switzerland (UBS). 2003 & 2005. Prices and earnings: A comparison of purchasing power around the globe. http://www.ubs.com/economicresearch. Accessed 2 June 2005. Union Bank of Switzerland (UBS). 2006. Prices and earnings: A comparison of purchasing power around the globe. http:// www.ubs.com/economicresearch. Accessed 15 August 2006. United Nations Conference on Trade and Development (UNCTAD). 2004. World investment report. New York: United Nations. Weber, A. (1909 repr. 1928). Theory of the location of industries, Translated by C. J. Friedrich. Chicago: University of Chicago Press. Wessel, D., & Davis, B. 2007. Pain from free trade spurs second thoughts. Wall Street Journal, March 28: A1. Williamson, O. E. 1985. The economic institutions of capitalism. New York: Free Press. World Bank. 2006. World development indicators. Washington, DC: World Bank. www.worldbank.org/data. Accessed 23 April 2006. Zellner, A. 1962. An efficient method of estimating seemingly unrelated regression equations and tests for aggregation bias. Journal of the American Statistical Association, 57(298): 348–368.

ABOUT THE AUTHORS Jonathan P Doh ([email protected]) is the Herbert G. Rammrath Chair in International Business, Associate Professor of Management, and Director of the Center for Global Leadership at the Villanova School of Business. Jonathan’s research focuses on multinational strategy and global corporate responsibility. He received his PhD from the George Washington University in strategic and international management. Jonathan was born in and is a citizen of the US.

Kraiwinee Bunyaratavej ([email protected]) is an Assistant Professor in the Department of Business Administration and Accounting at Wesley College. Kraiwinee obtained her PhD from the Department of International Business at the George



943

Washington University. Her research interests center on offshoring, regional economic convergence, monetary unions, and financial crises. She was born in and is a citizen of Thailand.

Eugene D Hahn ([email protected]) is Associate Professor of Information and Decision Sciences

at Salisbury University. His research interests include international operations including offshoring and global supply chain management as well as management decision-making. He received his PhD in information and decision systems from the George Washington University. Gene was born in the US and holds both US and Irish citizenship.

Accepted by Thomas Murtha, Martin Kenney, and Silvia Massini, Guest Editors, 16 July 2008. This paper has been with the authors for four revisions.
