the evolution of risk in information systems offshoring - CiteSeerX

Hahn et al./Evolution of Risk in IS Offshoring

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THE EVOLUTION OF RISK IN INFORMATION SYSTEMS OFFSHORING: THE IMPACT OF HOME COUNTRY RISK, FIRM LEARNING, AND COMPETITIVE DYNAMICS1 By: Eugene D. Hahn Information and Decision Sciences Salisbury University Salisbury, MD 21801 U.S.A. [email protected] Jonathan P. Doh Department of Management Villanova School of Business Villanova University Villanova, PA 19085 U.S.A. [email protected] Kraiwinee Bunyaratavej Business Administration and Accounting Wesley College Dover, DE 19901 U.S.A. [email protected]

Abstract Information systems offshoring has emerged as a significant force in the global political economy, an important source of firm-specific competitive advantage, and a focal point for debates over the benefits and costs of globalization. As

1

This paper was recommended for acceptance by Associate Guest Editor Erran Carmel.

worldwide competition exerts increasing pressure on the IS function of firms to become geographically unbundled, and IS services are dispersed among increasingly distant and unfamiliar locations, the issue of risk emerges as a significant factor in decisions about where to locate offshore facilities. Drawing from prior research in IS outsourcing/offshoring and theoretical perspectives from international strategy and multinational management, we examine the determinants of risk firms bear in their offshoring decisions. In particular, the current paper explores firm-level and environment-level “push” factors that drive firms to accept increasingly greater degrees of host country risk. We predict that firm-level risk outcomes for locating IS offshore facilities will be influenced by prior firm-specific experience, the relative gap between home and host country risk levels, and the overall movement by IS offshore services providers toward increasingly riskier locations. We test these hypotheses on a proprietary data set of more than 850 information technology and software offshoring projects in 55 host countries worldwide during the period 2000 through 2005. We find that firm-specific experience and the core “risk gap” between home and host country are predictive of companies pursuing progressively riskier locations, but that their effects dissipate as environment-wide experience is incorporated into our model. Our analysis suggests that broader dynamics in the competitive environment are powerful contributors to the overall observation that IS offshoring is moving to increasingly high-risk locations. This trend has implications for the management, security, and global integration of information systems. Our study contributes to the literature on risk and IS offshoring in providing the first worldwide empirical examination of the determinants of actual firm IS offshoring behavior with respect to offshoring location risk.

MIS Quarterly Vol. 33 No. 3, pp. 597-616/September 2009

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Keywords: IS offshoring, risk, FDI, offshore outsourcing, firm experience, empirical, longitudinal

Introduction Over just the past decade, an ever-widening sphere of service activities are being offshored, from accounting functions such as tax preparation (e.g., Shamis et al. 2005) to architectural design and medical imaging diagnostic interpretation (UNCTAD 2004). These more recent offshoring trends owe their existence to the first mass mover of services offshoring: IS offshoring. The worldwide number of IS services jobs that could be offshored elsewhere in the globe was estimated by McKinsey & Co. to be a considerable 2.8 million in 2003 (Aspray et al. 2006). The origins of the growth in IS offshoring can be traced to Y2K conversion-based needs in Western countries in the years leading up to 2000 (Qu and Brocklehurst 2003). With demand for programming greatly outstripping supply, U.S. and European firms turned to offshoring to accomplish goals in required timeframes. Pleased with the results from this first wave of offshoring, firms began seeing increasingly more general opportunities for IS offshoring. Other contributing factors to the IS offshoring trend include spiraling IS costs in the 1990s as well as difficulties in clearly documenting the contribution of IS investment to bottom-line firm financial considerations (Fish and Seydel 2006). Perhaps most significantly, technological advances in information technology have contributed substantially to the growth of IS offshoring. Indeed, it would be difficult to conceive of the contemporary scope of offshoring without the Internet and associated global high-speed transfer of information. Highly-offshored IS activities to date have included data-center operations, application maintenance, network management, and user support (Barthélemy 2001). Looking ahead, IT professionals predict that the next several years are likely to be characterized by robust growth in offshoring of applications development and maintenance, data center operations, telecommunications/LAN activities, and systems development and maintenance (Fish and Seydel 2006). However, high client-contact activities like project management are being retained in-house, rendering them more immune to offshoring, at least for the present time (Fish and Seydel 2006; Serapio 2005). In undertaking IS offshoring, firms must make many decisions; however, the determination of the appropriate country in which to offshore has especially critical ramifications. From an IS industry perspective, consultants at the Gartner Group coined the phrase “country before company” to describe the precedence of country location selection over

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MIS Quarterly Vol. 33 No. 3/September 2009

vendor selection in strategic IS offshoring decisions (Zatolyuk and Allgood 2004). One of the more salient aspects of IS offshoring and the global relocation of internal firm functions is risk. Risk has, therefore, been a natural (and perhaps even customary) starting point for research on the benefits and costs of IS offshoring (Aspray et al. 2006; Erber and SayedAhmed 2005; Farrell 2004; King 2005; Tafti 2005). Indeed, if one adopts Bahli and Rivard’s (2003) managerial definition of risk as being uncertainty about negative outcomes, risk is a common theme in the offshoring and outsourcing literatures. For example, Dhar and Balakrishnan (2006) provide a recent review of the literature on IS offshoring risk factors and identify risk associated with transition costs, switching costs, contract negotiations, litigation, reliability and service quality, cost escalation and hidden costs, loss of core competencies, vendor opportunism, and security breach to be among the risks discussed in the literature. Kliem (2004) takes a different perspective and classifies risks more along firm functional area lines to discern financial, technical, managerial, behavioral, and legal risks of offshoring. Despite the focus that IS offshoring risk has received from conceptual and practitioner perspectives, and the development of some typologies of offshoring risks, there is very little empirical work that has examined actual firm-level investment decisions in IS offshoring in the face of country-level risk. This leads us to the following key research question: What factors drive firms to accept increasingly greater levels of destination (host country) risk in the location of IS services offshoring projects? Addressing this gap in the literature, we develop theories of intra-firm and across-firm (competitive environment-level) determinants of the risk levels firms tolerate in the country-specific siting of IS offshoring projects (see Figure 1). We test our theories using a global database of IS offshoring projects covering the years 2000 through 2005. We find support for our theoretical predictions regarding intra-firm and across-firm determinants of countryspecific location risk levels of IS offshoring projects.

Literature Review and Hypotheses Development In this section, we develop our theoretical perspectives on risk in IS offshoring. We begin by reviewing relevant literature in IS outsourcing and IS offshoring. In doing so, we highlight major themes, describe existing theories, and identify a substantial gap in the empirical research to date. We then turn to the literature in international risk, international strategy, and multinational management to further inform our analysis. We focus on research that has explored firm responses to country-


Firm-Level: • Firm Experience

Country-Level: • Home Country GDP • Home Country Risk Level IS Offshoring Destination (Host Country) Risk Level Project-Level: • Sector

Competitive Environment: • Risk Assumed by Overall IS Offshoring Participants

Figure 1. Conceptual Model of Key Variables Related to Risk and IS Offshoring Destinations

level risk, and the question of whether firms tend to pursue a self-reproducing approach to their offshore location decisions (e.g., continue to reinvest in the same locations) or engage in a learning process in which they gradually assume higher levels of risk in their investments as they and their competitors learn more about these risk-prone environments. Specifically, we explore whether the country risk of IS service offshoring projects is (1) determined primarily by selfreproducing behaviors that reflect past experience whereby firm learning does not change firm proclivity toward risk, (2) conditioned on firms gaining knowledge and experience that influences the evolution of their subsequent investment strategies toward riskier conditions, (3) characterized by the “oligopolistic reaction” hypothesis in which firms imitate and mimic leading competitors, gaining knowledge from the progression of the overall competitive environment. Figure 2 graphically depicts these varying perspectives and their potential implications for earlier and later entrants into a geographic location under conditions of risk. The first two rows present opposing views of the motivations behind firm entry decisions under these circumstances. The first row reflects the notion that, all other things equal, firms tend to repeat past actions in their entry location decisions. The second row reflects a firm-specific learning perspective and the expectation that firm location decisions will evolve based on their past experience. The third row reflects the notion that firms will also evolve and adjust their location decisions based on the overall movement of industry competitors. In

total, we expect that firms learn from their own and competitor behavior and incorporate that information into their decision making. This perspective underscores the dynamic view of market entry under conditions of risk and the importance of firm and competitor action and reaction to understanding location decisions in such environments. In our analysis, we incorporate these differing perspectives in developing specific hypotheses related to firm learning and firm reaction to the overall movements of their competitive environment. We also evaluate whether the difference between the risk profile of the home and host country influences the propensity of investors to take on increasing risk

IS Outsourcing, Offshoring, and Risk Selected perspectives in the IS outsourcing literature form a useful starting point for our analysis. This is because domestic outsourcing can be viewed as an important special case of global offshoring wherein many sources of risks to firms such as country-level governmental/political risk, currency fluctuations, differences in legal systems, and the extent of institutional barriers to business and/or differences in corruption are held constant or simply nonexistent. Susarla et al. (2003) identified two major thrusts of the IS outsourcing literature, one examining antecedents and the other focusing on outcomes. In regard to the former, Loh and


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Early Moves Self-Reproducing Behavior Firm A

Later Moves

Country X

Self-Reproducing Behavior Firm A

Lower Risk

Country X Lower Risk

Move Toward Risk with Experience Firm

Move Toward Risk with Experience Firm

A

A

Country X Lower Risk

Environment Dynamic Firms B C

Country Y

Environment Dynamic Firms B

Country Y

A

Higher Risk

Country Y

C

Higher Risk

D

Higher Risk

D

Figure 2. Competing Explanations of Firm Risk Pursuit Strategies

Venkatraman (1992) appear to have been the first to have empirically established that the degree of outsourcing was related to firm-level cost structures as well as IS performance, while Ang and Straub (1998), using a transaction cost perspective, found that production cost advantages and lowered transaction costs led to increases in the degree of outsourcing. Factors contributing to IS offshoring success include positive experiences with provider performance and absence of disconfirmation of performance expectations (Grover et al. 1996; Lee and Kim 1999; Susarla et al. 2003). More recent works in this tradition include Levina and Ross (2003), who empirically identified complementarities in vendor-client competencies with respect to market conditions as an outsourcing success factor, and Lin et al. (2005), who indicate that effective knowledge transfer should improve benefits for outsourcers and vendors. At the individual level, Koh et al. (2004) documented the importance of careful management of person-to-person relationships between customers and suppliers in IS outsourcing. Presumably this becomes more important (and more challenging) in IS offshoring because of cultural differences, language considerations, and large geographic distances. Ang and Cummings (1997) utilized the lens of organizational theory to examine IS outsourcing in the banking industry. Building on institutional theory, they

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adopted a “variance” theoretic perspective. They tested the homogenous influence of both competitor banks and federal regulators on IS outsourcing, finding that banks respond to institutional pressures, but that response varies depending on the source of the pressure. Members of transnational IS teams may have profoundly different expectations as to what constitutes effective individual work habits or appropriate power relations among individuals (Krishna et al. 2004; Walsham 2002), further complicating successful execution of business task requirements in IS offshoring. Hence, expectation alignment appears important in enhancing offshoring success. More managerially oriented accounts of best practices for facilitating offshoring success have been provided by Farrell (2004) and Rottman and Lacity (2006). We observe that implicit in these discussions of “success factors” for IS outsourcing is the converse outcome of failure. Indeed, given that risk is closely aligned with the likelihood of negative outcomes (Bahli and Rivard 2003), the aforementioned success factors could also be considered IS outsourcing risk mitigation factors since by fostering success they reduce risk. However, this link between IS offshore success factors and risk mitigation rarely comes into sustained focus in the literature (for possible exceptions, see Carmel and Agarwal 2002; Grover and Teng


1993) and more in-depth examinations of the relationships between success factors and risk would be a particularly useful extension of the IS offshoring literature as it begins to mature. One of the most commonly used frameworks in the IS outsourcing literature is transaction cost theory (Williamson 1985). As pointed out by Qu and Brocklehurst (2003), transaction cost theory is typically associated with manufacturing as opposed to services. However, it has recently been extended to services by Murray and Kotabe (1999) and Wang (2002). Transaction cost theory posits that costs can be divided into production costs and transaction costs. Production costs involve the cost of creating a good (or providing a service) and hence they include labor costs, raw material costs and capital costs. Transaction costs, by contrast, are the costs of overseeing, coordinating, enforcing, and managing an enterprise or undertaking. In the offshoring context, firms will offshore if the production cost savings due to offshoring IS functions exceed the additional transaction costs associated with offshoring. Qu and Brocklehurst find that these additional transaction costs in IS offshore outsourcing may be quite substantial and variable, and hence a key source of risk. Multinational enterprises face a range of economic, financial, institutional, and political risks as they enter and operate in various environments around the world. These risks can include country risks such as macroeconomic and other financial shocks, as well as political risk (Kobrin 1979). In addition, some researchers distinguish between overall economic and political risks and a country’s governance infrastructure—its political, institutional, and legal environment—as an important determinant of foreign direct investment (FDI) (Globerman and Shapiro 2002). Other researchers argue the corruption constitutes an important and independent source of risk, especially in the emerging markets context (Uhlenbruck et al. 2006). Transaction costs associated with particular investments stem from the political and institutional environments in which both the government and private investor operate. Hence, these environments may be viewed as a set of parameters, changes in which will elicit shifts in the comparative costs of governance (Williamson 1999). Indeed, recent research has shown country-level risks increase transaction costs and cause firms to avoid such environments or alter their entry mode or governance structure (Delios and Henisz 2003; Henisz and Delios 2001; Henisz and Macher 2004). While political risk researchers have noted that instability does not equal political risk (Kobrin 1978) and that not all risks affect firms in the same way (Oetzel 2005; Robock 1971),2 overall

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We appreciate the insights of an anonymous reviewer in pointing this out, a consideration which we carry forward to our empirical analysis.

country risk appears to be an important consideration for offshoring investment. Carmel and Nicholson (2005) examined the strategies that small firms use to overcome the large transaction costs associated with offshoring. Using a sample of nine small firms that had recently offshored, they adopted a semi-structured qualitative interview methodology, finding that gaining experience through trial and error and persevering through multiple offshoring project failures was a common strategy. In doing so, these small firms were able to gain economies of experience, which allowed them to subsequently lower transaction costs and conduct successful offshoring. Hence, the amount of firm experience with IS offshoring appears to be an important factor allowing firms to operate in riskier locations while still maintaining a reasonable likelihood of success. In their work with 13 major U.S. firms, Carmel and Agarwal (2002) found that companies go through four stages of an offshoring experience curve in which increasing amounts of work are transferred offshore. Again, this implies the importance of firm experience as an influential factor in IS offshoring.

Imitation and Experience in International Investment Research related to the international strategies of multinational firms has placed increased attention on the location of firms entering foreign markets and the factors that influence the sequence, proximity, and competitive dynamics between and among firms in these choices (Chang and Park 2005; Delios and Henisz 2003; Shaver and Flyer 2000). Literature on imitative and mimetic behavior in managerial strategy has focused on how firms learn from their own experiences and/or those of leading competitors (Caves and Mehra 1986; Gatignon and Anderson 1988; Gomes-Casseres 1989, 1990; Hennart 1991; Kogut and Singh 1988; Zejan 1990). The recent growth in international offshoring of IS services provides an interesting laboratory to test these competing hypotheses in an environment characterized by risk and change. Further, while this industry shares some features with other knowledgeintensive sectors, it also possesses some unique characteristics that may be relevant in evaluating firm and industry tolerance for risk. Finally, the fact that we are likely in a relatively early stage of international offshoring may be reflected in the types of evolutionary behavior that are observed. Firm-Level Experience and International Investment Research in the internationalization process of firms has long focused on questions of past experience in international loca-


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tion and entry mode choice. Johanson and Vahlne (1977) were perhaps the first to examine the sequential internationalization process that distinguishes specific stages of gradually increasing foreign involvement that firms follow as they internationalize. Their model emphasizes incremental internationalization through acquisition, integration, and use of knowledge concerning foreign markets. The firm enters new markets with increasing psychic distance, defined as aspects of language, culture, business practices, and industrial development that tend to reduce the efficiency of information flows between the market and the firm with the effect that transaction costs are increased. Tallman (1992, pp. 462-463) explicitly discusses the importance of past decision-specific experience in multinational corporations’ (MNC) organizational structure decisions, by noting that The MNE (multinational enterprise) may reduce its uncertainty in a given situation by attempting to imitate either its own previously successful strategies and structures or those of its competitors in the new market. At the same time, however, greater general international/host country experience may also enable the firm to deal effectively with the costs and uncertainties of doing business in riskier markets, and accepting less familiar, more challenging locations and competitive environments (Padmanabhan and Cho 1996). The role of learning in MNC internationalization decisions suggests that an effective organization continuously develops new knowledge and incorporates that learning into strategic management decisions (Senge 1990). The ability of an MNC to learn from experience in foreign markets and then transfer that knowledge to other markets is consistent with a range of research streams, especially studies of the organizational management of multibusiness, multinational firms and their subsidiaries (Prahalad and Doz 1987; Stopford and Wells 1972). International expansion by IT firms, especially in a new form of investment (services offshoring) places demands on firms that are dissimilar to home country experiences and require capabilities that are specific to those investment forms. Some researchers propose that the prior experience provides the bases for accumulations of the necessary skills to facilitate entry and operation in a given country (Barkema et al. 1996). Greater experience in a given or similar country, or with a specific type of investments, gives investors an opportunity to overcome so-called liabilities of foreignness—the social, economic, political and cultural challenges that present particular problems for foreign firms (Zaheer 1995). Experience developed through a sequence of investments that is specific to a

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host country can provide the requisite background and increase success in ever more challenging markets (Chang 1995; Chang and Rosenzweig 2001; Henisz and Delios 2001; Kogut 1988). Regarding the question of whether firms are likely to continue investing in their first or early investment locations or use that learning to take on increasing risk (and opportunities), Davidson (1980) found that while companies are likely to invest in countries in which they already have a presence, firms with experience tend to give investment priority to countries that have a relatively high level of uncertainty. Delios and Henisz (2003) found that companies that had international experience were less sensitive to uncertain policy environments on investment. From a more practitioner perspective, the most recent Duke/Archstone (2005) consulting survey found that companies lacking in offshoring experience perceive higher risks in offshoring than do companies with such experience. Again, these observations are consistent with a transaction cost perspective. IS offshoring has inherently greater levels of risk than do similar activities in the domestic setting (Aspray et al. 2006; Dhar and Balakrishnan 2006; Erber and Sayed-Ahmed 2005; Farrell 2004; King 2005; Kliem 2004; Tafti 2005). As such, offshoring projects (whether in risky countries or in safe countries) are undertaken with substantially elevated levels of risk in comparison to home country investments. Hence, prior experience with such projects constitutes a reflection of experience with this form of risk, and this experience in turn may serve to partially mitigate the risk of subsequent offshoring activities. This assumption is consistent with the findings of the Duke/Archstone research, which notes that “companies with no offshoring experience perceive higher risks than companies with experience” (p. 9). Given the relative nascent stage and the relatively underdeveloped knowledge base regarding IS offshoring investment, we expect IS offshoring firms with greater experience to be open to subsequent investments in riskier environments. In particular, we expect that firm-specific experience would help lower transaction costs such as switching costs and contract negotiation costs, as well as lessen the likelihood of asset/intellectual property expropriation by governments, mitigate vendor opportunism, and reduce the likelihood of security breach, risks identified in the IS offshoring literature (Dhar and Balakrishnan 2006). H1: As firm-specific experience with risky IS offshoring investments increases, firms will assume increasing levels of host country risk in subsequent offshoring projects.


Home Country Risk Level Another mechanism by which firms might develop higher risk tolerance for foreign investment is through their experiences in their home market. For example, firms based in countries that feature risk profiles closer to a potential country destination for investment may be more open to investments in that country than would be their counterparts in countries with a risk profile that is quite unlike that of the destination country. The logic for this expectation is quite similar to that of foreign country experience described above (e.g., experience and learning under similar conditions can help offset the challenges and transaction costs associated with investment in unfamiliar and risky environments) and to research on the influence of cultural distance on international investment location choice and form. Several researchers have examined how psychic distance affects international investment decisions and their studies have found that locating in countries that are psychically close reduces the levels of uncertainty and the perceptual barriers that result (Johanson and Vahlne 1992). Kogut and Singh (1988) were the first to evaluate psychic distance and internationalization, using a cultural distance index that provided a quantitative measure that has since been used to predict the location and mode choice of foreign investment. Kogut and Singh found that the greater the cultural distance between the home and host country, the more likely a firm would choose a joint venture entry mode to reduce risk and uncertainty and to gain knowledge about the market. Gatignon and Anderson (1988) echoed this finding, arguing that sociocultural distance causes uncertainty for firms, and concluding that cultural distance was the most important variable affecting location and control of foreign ventures. The existence of similarities in culture between a host country and home country provides many benefits to a firm. In a more similar culture, firms will likely be able to reduce transaction costs that might occur from training and acquiring information. Moreover, although the point of production of services could be physically located far from consumers, it is incumbent on the service providers that they should make consumers feel that the services originate close to home. We draw an analogy between host/home cultural distance and distance in the degree of country-level risk. Indeed, Kogut and Singh characterized psychic distance as the degree to which firms are uncertain about a foreign market, a term that explicitly conjures up risk and lack of predictability. Hence, we believe the risk gap between home and host country will be an important contributor to the location choice of offshore IS investment.

H2: The greater the risk of the country in which a firm is headquartered, the greater the level of host country risk that will be associated with the foreign location of offshoring investment projects undertaken by that firm.

Competitive Environment: Firm Imitation, Mimicry, and the Oligopolistic Reaction Knickerbocker (1973) introduced the concept of oligopolistic reaction to explain patterns in foreign direct investments (FDI). He explained that firms (followers) are likely to match the foreign investment moves of rivals (leaders) by investing in the same countries. Although Knickerbocker suggested that these patterns were more likely to emerge in concentrated industries, many researchers have confirmed this imitative pattern in a range of industry settings and environments, showing that organizational actions such as international entry moves by industry actors often exhibit macrolevel clustering (Gimeno et al. 2005; Henisz and Macher 2004; Nachum and Zaheer 2005; Schelling 1978). Among the competitive dynamics that urge firms to follow the overall moves of competitors, some researchers suggest that follower firms react out of concern that early entrants could gain “first-mover” type advantages from the additional information in a market (Knickerbocker 1973; Lieberman and Montgomery 1988). Other researchers have suggested this calculus is less objective and is more likely characterized as herd behavior (Abrahamson and Rosenkopf 1993). Head et al. (2002) showed that Knickerbocker’s prediction relies on risk aversion and Carmel and Agarwal (2002) also documented an imitative bandwagon effect in IS offshoring (see also Lacity and Hirschheim 1993) as well as the need to be sustainable in competitive global markets. Henisz and Delios (2001) found that the previous number of entries by firms in the same industry had an impact on the probability of locating a plant in a given country. In their analysis of the motivations for international investment among knowledge-intensive industries, Nachum and Zaheer (2005) hypothesized that competitive pressure, as measured by imitative and mimetic investment patterns, would be a stronger driver of FDI in the information-intensive world (in both its linear and quadratic forms) than in noninformationintensive industries, although this was not supported. They conjectured that this finding may have resulted from the open systems that characterize many information-intensive industries, creating a more cooperative dynamic.


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In addition to the competitive dynamics described above, another set of related forces that may draw firms to colocate in a country or region are agglomeration economies: the positive externalities that benefit firms that locate in close geographic proximity. For resource-seeking investors who are reliant upon information and human capital, externalities associated with colocation in a specific geography may be a powerful draw. Shaver and Flyer (2000) and Chung and Song (2004) found that Japanese firms located their manufacturing facilities in states where many other Japanese firms were located and suggested that externalities might explain this pattern of agglomeration. Similarly, in their study of firms entering China, Chang and Park (2005) conclude that Korean firms are more inclined to invest in areas where other Korean firms have located. As Shaver and Flyer point out, there are at least two related sources of positive externalities associated with colocation: information spillovers that help firms “keep tabs” on industry developments and competitive dynamics, and human resource externalities that create larger demand for qualified workers to enter the labor force and upgrade their skills to qualify them for positions in industry. Both of these factors and the associated reduction in transaction costs can be used to explain the emergence of industry clusters in various regions of the world, including potentially clusters of IS offshore facilities in places like Bangalore, India. The Duke/Archstone offshoring study (2005) found that competitive pressure was cited as an offshoring driver by 71 percent of executives surveyed. In sum, we expect that the competitive environment, as represented by the move by all participants toward country locations with greater risk, will create a climate in which firms feel pressure to pursue those same riskier locations. We believe this is especially likely in newly internationalizing and emerging industries. In addition to competitive dynamics, firm clustering in a given industry likely mitigates some of the risks posed to assets by creating a large and growing constituency that can leverage its influence through creation of trade associations, such as the National Association of Software and Service Companies in India, a trade association that represents foreign (and domestic) firms, many of which are involved in IS offshoring. Further, as the presence of an industry becomes more significant, multiple companies from a given home country can call upon the governmental representatives of that country to pressure host governments to protect the increasing presence of their IS offshore firms. H3: The greater the host country risk assumed by the IS services offshoring environment overall, the greater the level of host country risk firms will accept in subsequent offshoring project locations.

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Data, Research Methods, and Findings Data We use three main sources of data for our empirical analysis. The first is drawn from a database of over 36,000 worldwide, foreign, direct-investment projects called the LOCOmonitor database as developed by OCO Consulting. The FDI project information in the LOCOmonitor database is derived from nearly 9,000 media sources using daily data collection via search strings. From this database, we extracted IT and software FDI projects for the years 2000 (the beginning of the data) to 2005 (the last full year of data). For our analysis, it was important to consider only services-based projects and thus to exclude FDI projects involving manufacturing and other non-services-based activities. Hence, we utilized projects involving the four major types or sectors of services offshoring as identified by UNCTAD (2004). These were customer support centers (e.g., help desks, customer technical support, information services, and customer relationship management), IS services centers (e.g., software development, software design, and applications testing), shared services centers (e.g., data processing, transaction processing, and claims and payroll processing), and regional headquarters (e.g., regional IS management coordination centers for activities such as enterprise application software, information management software, Web software infrastructure and data management software). There were 881 such projects worldwide in the period 2000–2005.3 Descriptive statistics for the variables used in the models to be described appear in Table 1. Figure 3 shows the distribution4 of the top 10 home countries and the top 15 host countries in our sample (including the United States). It is interesting to compare our distribution of host countries with that reported by Carmel and Agarwal (2002, p. 75). They had identified that more than

3

The host countries for offshoring for which we had complete data were as follows: Argentina, Australia, Austria, Belgium, Brazil, Canada, China, Colombia, Costa Rica, Czech Republic, Denmark, Dominican Republic, Egypt, Estonia, Finland, France, Germany, Honduras, Hong Kong, Hungary, India, Indonesia, Ireland, Israel, Italy, Japan, Jordan, Latvia, Lebanon, Luxembourg, Malaysia, Mexico, Morocco, Netherlands, New Zealand, Oman, Pakistan, Philippines, Poland, Portugal, Romania, Russia, Singapore, South Africa, South Korea, Spain, Sweden, Switzerland, Thailand, Turkey, United Arab Emirates, United Kingdom, Uruguay, United States, and Venezuela. The home countries were Australia, Belgium, Canada, China, Colombia, Denmark, Egypt, Finland, France, Germany, Hong Kong, India, Ireland, Israel, Italy, Japan, Netherlands, New Zealand, Norway, Philippines, Russia, Singapore, South Africa, South Korea, Spain, Sweden, Switzerland, United Arab Emirates, United Kingdom, and United States. 4

The pie charts in Figure 3 can be read clockwise alphabetically beginning with Australia at the 12 o-clock position.


Table 1. Summary Statistics and Pearson Correlations

HostRisk CumulProj HomeGDP

Mean

Host

Cumul

Home

Home

Home

Home

Home

(S.D.)

Risk

Proj

GDP

Risk Agg

Pol St

Gov Eff

Corrup

80.573 (6.712) 0.000 (3.352) 0.000 (8.006)

HomeRisk

0.000

Aggregate

(4.260)

HomePol

0.000

Stability

(0.552)

HomeGovt Effective HomeCorruption Year RRA CustSvc Center SharedSvc Center TechSupport Center

0.000 (0.366) 0.000 (0.452) -0.364 (1.741) 0.000 (1.292) 0.000 (0.350) 0.000 (0.281) 0.000 (0.331)

CustSvc Shared Year

RRA

Center

SvcCtr

--0.148

--

0.001

0.114

--

0.141

-0.161

0.188

--

0.164

-0.166

0.181

0.898

--

0.047

-0.029

0.602

0.543

0.555

--

0.051

-0.020

0.565

0.565

0.559

0.953

--

-0.269

0.284

0.042

-0.470

-0.597

-0.174

-0.139

--

0.271

-0.275

-0.028

0.469

0.594

0.163

0.129

-0.976

--

-0.172

0.076

0.028

-0.043

-0.022

0.058

0.062

0.005

-0.006

--

-0.210

0.137

0.013

-0.073

-0.094

-0.014

-0.010

0.064

-0.061

-0.126

--

-0.197

0.270

0.048

-0.112

-0.118

-0.062

-0.051

0.309

-0.296

-0.154

-0.116

Absolute correlations > 0.068 are significant at the 0.05 level assuming i.i.d. sampling.

Projects by Home Country

Projects by Host Country: Top 15 + United States Australia Canada China

Australia

France

Canada

Germany

France

Hong Kong

Germany

India

India

Ireland

Ireland

Malaysia

Israel

Netherlands

Japan

Singapore

UK

Spain

USA

UAE

Other

UK USA Other

Figure 3. Distribution of Projects by Home Country and Host Country


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95 percent of U.S. offshore IT sourcing activity is taking place in the following nations: Australia, Brazil, Canada, China, “EU nations (most),”5 India, Israel, Japan, Mexico, Philippines, Russia, and Singapore. Hence, there is considerable concordance between the distribution of offshoring locations in our sample and theirs, especially considering our top 15 host countries plus the United States does not account for 95 percent of the total global sourcing activity in the data. Our second source of data is drawn from the International Country Risk Guide (ICRG) country risk ratings maintained by the PRS Group. We obtained yearly composite risk rating data on 62 countries for the period 2000–2005. The composite risk ranking for country m is formed by PRS through a weighted average of country m’s risk ratings on political risk (weight = 50 percent), financial risk (weight = 25 percent), and economic risk (weight = 25 percent). Components of a country’s political risk rating include the country’s governmental stability, investment profile, corruption, and democratic accountability. Financial risk rating components include the country’s exchange rate stability and foreign debt as a percentage of GDP, while economic risk rating components include the country’s annual inflation rate, real GDP growth rate, and budget balance as a percentage of GDP. We comment here that the risk ratings are defined on an index basis that ranges from 0 to 100, with 0 being the most risky and 100 being the least risky. It will be worthwhile to remember the definition of this scale in the subsequent discussion of the results as a high value on the scale indicates low risk and vice versa. The ICRG is a definitive source of country risk ratings, and is used extensively in scholarly and practitioner research across multiple disciplines, including strategy, economics, international business, and finance (e.g., Buch et al. 2006; La Porta et al. 1997; Oxley and Yueng 2001; Uhlenbruck et al. 2006). Since research has suggested that different types of risk may affect investment differently (Oetzel 2005; Robock 1971), that political instability does not necessarily equate to risk (Kobrin 1978), and that features of host country institutional governance—such as corruption and governmental effectiveness (Globerman and Shapiro 2002; Uhlenbruck et al. Eden 2006)—can also have important effects on investment, we wanted to ensure that results obtained via our overall risk measure would be consistent with more specific measures of risk. We were also interested in whether more specific measures of risk in the home country would influence the patterns of project risk level at the host country level. Hence, our third data source was drawn from the Worldwide Governance

Indicators (WGI) compiled by the World Bank. The WGI contains aggregate indicators of six dimensions of governance. The indicators are constructed using an unobserved components methodology that relies on 31 sources, including surveys of enterprises and citizens, and expert polls, gathered from 25 different organizations around the world. These provide data derived from hundreds of questions on governance. Each question is mapped to one of the six dimensions of governance before the aggregation is carried out. The six governance indicators are measured in units ranging from about -2.5 to 2.5, with higher values corresponding to better governance outcomes. Details on the concepts measured by each indicator, its components, and the interpretation of the point estimates and standard errors can be found in the many papers that have been used to evaluate governance (see Kaufmann et al. 2006). The six indicators included in the WGI are voice and accountability, political stability and absence of violence, government effectiveness, regulatory quality, rule of law, and control of corruption. We selected three of these—political stability, government effectiveness, and control of corruption—based on literature that has distinguished between these measures of stability and institutional governance and also research that suggests they would be less highly correlated to overall risk, and could independently capture specific risk types or transaction costs firms face. Our overall data set was created by merging the data sets described above. Composite risk rating data was unavailable for a few small countries (Antigua, Iceland, Mauritius, Slovakia, and Ukraine). Hence, it was necessary to drop the 13 projects associated with these countries. There was also one geographic entity, the territory of Puerto Rico, that is sometimes classified as a separate entity and other times treated as part of the United States. Because of the multiple classification issue and the fact that there were no composite risk ratings for Puerto Rico, we dropped the three projects in Puerto Rico. In accounting for host country GDP (see control variables below), sporadic incomplete data as well as data unavailability for Taiwan caused 10 more observations to be unaccounted for. The final sample thus consisted of P = 855 projects. Dependent Variable and Explanatory Variables For a given project, the dependent variable consisted of the host country’s aggregate risk rating.6 Our first explanatory independent variable of interest was the experience level of a 6

5

Given the 2002 publication date, this would mean the pre-enlargement European Union.

606


We also estimated an additional three sets of models in which the three more specific variables from the WGI were respectively used as the dependent variable. In sum, no substantive differences were found with these alternative measures. These results are available from the authors upon request.


particular firm as operationalized by the number of offshore FDI projects undertaken prior to the current one. For example, for a firm’s first FDI project, the independent variable takes on the value zero; for a firm’s second project, the independent variable takes on the value one, and so on. Numerous firms had only one FDI project during the time period. From our sample of N = 624 unique firms, 98 of them had two or more FDI projects during 2000–2005. Our second explanatory variable of interest is the risk ranking of the home country. This continuous variable was derived from the same source (PRS) as was our dependent variable (except when we respecified our model, substituting the more specific risk measures). Our third explanatory variable was designed to capture the hypothesized driver of firm imitative activities— namely, the overall appetite for risk in the IS services environment. To capture this characteristic of the broader trans-firm direction regarding risk, we calculated the running risk average (RRA) of host country entries over time in the sample. Specifically, we first sorted the projects by their calendar date of occurrence from oldest to most recent. In considering the kth project, let t index the projects from the first (oldest) project to the current project k. Then, we calculated

new markets or new products. Again, this perspective is consistent with the Duke/Archstone Offshoring Study (2005) in which “competitive pressure” (i.e., market activities) was cited as an offshoring driver by 71 percent of executives surveyed. Hence, we expect our RRA variable to be related to year but perhaps a more accurate predictor of IS offshoring risk outcomes. Although we would also be interested in various firm-level characteristics such as sales volume, market capitalization, or number of employees (e.g., Loh and Venkatraman 1992), identifying information was unavailable for the firms in our database; hence, we had no way of including such variables and consider these as areas for future research. However, we did examine controlling for other relevant firm-level factors such as firm-specific experience with country risk.8 Additionally, at the project level, we controlled for the aforementioned sectors, and at the country level, we controlled for home country GDP per capita, home country political stability, government effectiveness, and corruption.

Research Methods 1 k -1 RRAk =  HostCountryRiskt k - 1 t =1 so that the value of RRA for project k is equal to the average of the host-country risk ratings of the previously conducted projects up to and including that of project k – 1. This measure is shown in Figure 4 with the calendar date of the project on the x-axis. We see, for example, that the average of the host country risk ratings of all IS offshoring projects occurring before January 2001 was about 83.9.7 Here, RRA is undefined for the first project and this project was dropped. Control Variables We also include year as a control variable. Based on our discussion above, it is possible that there is an overarching temporal trend such that over time firms are increasingly likely to locate in riskier countries. However, time in isolation is more a control variable in the statistical sense as it is firm decisions and market activities, not time per se, that drives entries into particular countries or other arenas such as

Conceptually, our models center on the drivers of the risk firms are prepared to tolerate when locating offshore projects. As such, we focus on identifying those “push” factors that contribute to these outcomes (see UNCTAD 2006); this orientation is distinct from research on “pull” factors such as country-level wages and education (see Bunyaratavej et al. 2007; UNCTAD 2006) and a distinct aspect of this particular research. Additionally, our approach enables us to assess the determinants of the level or degree of risk assumed; something that can at most be only more indirectly assessed with count-based pull methodologies. Our data set consists of P = 855 projects associated with N = 624 firms. Hence, for firms that have more than one project, we have longitudinal data on a firm’s IS offshoring project history. Clearly, however, this is unbalanced longitudinal data in that a firm may have numerous IS offshoring projects (up to 28 in our sample) but each firm’s quantity of offshoring projects varies from firm to firm. Random effects models are panel data models (see Greene 1997) that are especially flex-

7

To ensure that our findings were robust across different levels of country risk, in addition to the alternate models described above, we divided our sample at the median level of country risk and generated variables for the cumulative number of projects undertaken in risky countries and those undertaken in safe countries and reran the model with the cumulative risk variable derived from the “risky” versus “safe” sample, respectively. Our findings were substantively identical to the overall model.

8

We created a country-specific experience variable that took on the value 1 if the firm had previously located a project in the particular country, and zero otherwise. This control variable did not have a significant relationship with the dependent variable, nor did its utilization impact conclusions regarding our hypotheses. Hence, these results are omitted.


607

(lower risk)


86 85

RRA

84 83

(higher risk)

82 81 80 Jan-00 Jan-01 Jan-02 Jan-03 Jan-04 Jan-05

Time

Figure 4. IT Services FDI Project Running (Overall) Risk Average: 2000–2005

ible with regard to unbalanced data (Laird and Ware 1982; Mikulich et al. 1999). Here we use the random intercepts model (see Pinheiro and Bates 2000). This model can be written in matrix notation as follows: y i = X i β + bi + g i , gi ~ Normal(0, σ ),

bi ~ Normal(0, τ).

(1) (2) (3)

We see from (1) and (2) that the model follows the linear regression form with coefficients of β and the residuals being normally distributed with mean zero and standard deviation σ. However, we also include random intercepts where each firm in our sample (firm i where i = 1, …, N) has its own intercept, bi. These intercepts share a common normal distribution with mean of zero and standard deviation of τ as is seen in (3). Note that yi is a vector of length Pi where Pi is the total number of projects for firm i. Hence we may write the elements of yi as yij where j = 1, …, Pi. This similarly applies to Xi and gi. An additional aspect of the random effects model is that we can examine whether any unobserved firm-specific effects that are constant across a firm’s projects (e.g., management structure, CEO style, or market position) may have altered the findings obtained. Phrased differently, a Hausman test can reveal whether there is an omitted variables problem, or conversely whether we have estimated β consistently in the face of omitted variables. The results for Model 1 through Model 5 appear in Table 2.

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Model 1 In Model 1, we examined Hypothesis 1 by including the firm experience variable while controlling for home country differences in GDP and home country risk level. The particular specification that follows from Equations (1) through (3) was as follows: HostRiskij = β0 + β1CumulProjij + β2HomeGDPij + β3HomeRiskij + bi + gij, bi ~ Normal(0, τ). Here, each project constitutes an observation. HostRiskij, the dependent variable, is the degree of aggregate risk in the host country of the jth project of firm i. CumulProjij is the corresponding cumulative number of projects previously completed by the firm at the time of the jth project, HomeGDPij is the corresponding GDP per capita of the home country of the offshoring firm, and HomeRiskij is the risk rating of the host country for the jth project of firm i. We centered independent variables around the mean as is customary in hierarchical modeling (Raudenbush and Bryk 2002). In Model 1, we find that as firm IS offshoring project experience increases, firms increasingly seek out more risky host countries (β = -0.304, p = 0.0009) after controlling for differences in home country risk levels and home country GDP. Here the negative slope indicates that as the number of projects completed increases, firms seek host countries with lower risk ratings that corre-


Table 2. Parameter Estimates and Regression Results Model 1

Model 2

Model 3

Model 4

Model 5

80.581

80.579

80.266

80.608

80.527

CumulProj

-0.304 (0.0009)

-0.298 (0.001)

-0.162 (0.0692)

-0.169 (0.0551)

0.049 (0.5618)

HomeGDP

-0.001 (0.9624)

0.027 (0.4705)

0.024 (0.4213)

0.021 (0.4847)

0.033 (0.2253)

HomeRisk (Aggregate)

0.182 (0.0012) 2.087 (< 0.0001)

-0.134 (0.8006)

-0.117 (0.8229)

-0.394 (0.4225)

1.294 (< 0.0001)

1.103 (< 0.0001)

Intercept

HomePol Stability HomeGovt Effective

-1.534 (0.4781)

Home Corruption

0.129 (0.9389) -0.958 (< 0.0001)

Year RRA CustSvc Center

-4.779 (< 0.0001)

SharedSvc Center

-5.844 (< 0.0001)

TechSupport Center

-5.287 (< 0.0001)

RRA*Cust SvcCenter

1.500 (0.0029)

RRA*Shared SvcCenter

2.034 (0.0016)

RRA*Tech SupportCtr

-0.634 (0.4083) 2.100

τ

6.258

σ AIC

5669.0

2.038 6.259 5657.8

1.787 6.206 5632.7

1.732 6.217 5631.2

1.373 5.857 5513.5

Note: p values in parentheses.

spond to host countries with greater risk. Hence, we find support for H1. We also find that home country risk level ceteris paribus is positively related to the dependent variable (β = 0.182, p = 0.0012), supporting H2. Here the positive slope indicates that firms from lower risk countries (higher risk ratings) seek out host countries with lower levels of risk (higher risk ratings) after controlling for differences in firm IS offshoring experience and home country GDP. Hence, Model 1 suggests that, internationally, IT firms seek relative risk similarities in that firms in low-risk countries tend to seek somewhat lesser-risk hosts.

Standard results for random effects models show that the intra-firm R2 of firms’ project risk is ρ = τ 2/(τ 2 + σ 2). Here ρ is 0.10, suggesting it would be erroneous to use OLS regression and treat a firm’s projects as independent. We can confirm this through a likelihood ratio (LR) test for the random effects component. This test rejected the null hypothesis that τ = 0 (LR = 6.57, p = 0.0052) and as this test similarly rejected the null for all remaining models, we do not report on it further. A nonsignificant Hausman test (W = 0.69, p = 0.8754) supported the random effects formulation and indicated that the model did not suffer from inconsistency due


609


to omitted variables.9 We also note the significant result from the LR test indicates that specification tests designed for the OLS regression scenario are not valid for this model.

Model 3

Model 2

HostRiskij = β0 + β1CumulProjij + β2HomeGDPij + β4HomePoliticalStabilityij + β7Yearij + bi + gij.

In Model 2, we provide a more fine-grained analysis of the impact of home country risk by replacing our aggregate risk measure, HomeRisk, with three more specific measures of risk described previously. As such our model was as follows: HostRiskij = β0 + β1CumulProjij + β2HomeGDPij + β4HomePoliticalStabilityij + β5HomeGovtEffij + β6HomeCorruptionij + bi + gij. The random effects, bi, are again taken to be normally distributed with mean zero and standard deviation τ (since this specification does not change throughout, we omit it for brevity). Model 2 indicates an important driver of firm risk considerations is home country political stability (β = 2.087, p < 0.0001) ceteris paribus. This finding supports H2 in a more specific way by identifying a key driver for H2. We also find that H1 is again supported even with the more finegrained characterization of risk (β = -0.298, p = 0.001). We compare the model fit of Models 1 and 2 by using the Akaike Information Criterion (AIC) of the respective models. AIC is a model comparison measure that favors models that both fit well and are parsimonious. We see that the AIC of Model 1 is 5669.0 while that of Model 2 is 5657.8. Model 2’s lower AIC indicates it is preferred over Model 1. Here, using three specific risk measures10 instead of the aggregate measure leads to a notable fit improvement, with the utility of home country political stability being particularly relevant. We therefore utilized this latter variable instead of the aggregate measure in subsequent models.11 The latter two risk components have contradictory signs which may render the conclusions less satisfying. However, a partial explanation for this may well be the extreme collinearity (r = 0.953) exhibited by these two components in excess of the |r| > 0.9 collinearity threshold of Hair et al. (1995).

In Model 3, we include the temporal control variable using the following specification:

Model 3 includes the year (2000–2005) in which the jth project of the ith firm was undertaken. We subtracted the value of 2003 from the year variable in order to approximately mean-center the variable. In Model 3, we find that, after controlling for home country’s risk level, home country GDP, and year, there continues to be a relationship between greater firm IS offshoring project experience and firms seeking out more risky host countries ceteris paribus. However, this relationship is only marginally significant in Model 3 (β = -0.162, p = 0.0692), so we have marginal support for H1. By contrast, we find that home country risk level (as measured by our more specific home political stability risk measure) is no longer significantly related to host country risk level when year is entered into the model (β = -0.134, n.s.), contrary to H2. Finally, we find that there is a negative relationship between year and project host country risk level and that this relationship is significant (β = -0.958, p < 0.0001). This negative relationship indicates that as the years pass from 2000 to 2005, firms increasingly seek out riskier host countries for their projects. Our control for home country GDP is again nonsignificant. According to AIC, Model 3 explains the data better than the previous models, suggesting the relevance of temporal factors and/or environment evolution effects in IS offshoring project decisions. The question remains, however, as to whether this is merely temporal drift or whether it is evolution in the competitive environment resulting from firm competitive/imitative behavior such as that predicted by the oligopolistic reaction theory. In Model 4, we examine these two competing explanations for shifts in IS offshoring patterns and compare them to each other. Model 4 In Model 4, we include the RRA measure using the following specification:

9

Hausman tests for all models were nonsignificant and are hence not reported further.

HostRiskij = β0 + β1CumulProjij + β2HomeGDPij + β4HomePoliticalStabilityij + β8RRAij + bi + gij.

10

As suggested by an anonymous reviewer.

11

As noted above, we additionally conducted three sets of analyses in which each one of the three specific risk measures was individually used as the dependent variable in the place of the aggregate risk variable, HostRiskij. The conclusions reached were substantively identical to those described here; hence, these analyses are omitted.

610


Model 4 incorporates the running risk average variable as described previously. We centered RRA around its mean for interpretational purposes. In Model 4, we find that ceteris paribus there continues to be a marginally significant relation-


ship between firm IS offshoring project experience and firms seeking out more risky host countries (β = -0.169, p = 0.0551). Hence we again have marginal support for H1. We again find that contrary to H2 our specific measure of home country risk level is no longer significantly related to host country risk level ceteris paribus (β = -0.117, n.s.). The relationship between risk and RRA is significant (β = 1.294, p < 0.0001). The positive relationship between RRA and the dependent variable indicates that as the broader competitive environment assimilates experience with higher risk countries, firms increasingly seek out higher risk IS offshoring destinations, further accelerating the trend in IS services offshoring. We also note that the AIC for Model 4 indicates that it provides a better fit than Model 3. This provides evidence for the inclusion of RRA as opposed to Year. Model 5 In Model 5, we explore the possibility of differences attributable to projects’ association with one of four IS services sectors discussed previously: customer services centers, shared services centers, IT technical support centers, and regional headquarters. The headquarters sector was used as the reference group and the projects in the remaining sectors were dummy-coded with a “1” on the respective dummy variable if the project was of the respective type. We also explore the possible interaction between sector-level effects and the competitive environment risk construct, RRA. Competitive environment trends toward greater risk may accelerate firm decisions to locate offshoring projects in riskier countries for certain sectors more than others. For example, some sectors may be extremely competitive such that broader trends toward risk become highly magnified, while other sectors may be relatively less affected by such trends. Accordingly, Model 5 was specified as follows: HostRiskij = β0 + β1HomeGDPij + β2HomeGDPij + β3HomePoliticalStabilityij + β6RRAij + β7CustSvcCenterij + β8SharedSvcCenterij + β9TechSupportCenterij + β10RRAijCustSvcCenterij + β11RRAijSharedSvcCenterij + β12RRAijTechSupportCenterij + bi + gij. With regard to the central theoretical development of the paper, H3 remained significant after controlling for possibly different sector-level reactions to broader risk trends (β = 1.103, p < 0.0001), while H1 (β = -0.049, n.s.) and H2 (β = -0.394, n.s.) again did not. Moreover, we see that projects in the sectors of customer services centers (β = -4.779, p < 0.0001), shared services centers (β = -5.844, p < 0.0001), and

IT technical support centers (β = -5.287, p < 0.0001) are all associated with more risky country locations than are those in the headquarters sector. It is also interesting to note that the interaction term was positive and significant for both customer service centers (β = 1.500, p = 0.0029) and shared services centers (β = 2.034, p = 0.0016). Firms generating projects in these two sectors appear to have an accelerated response to broader trends toward risk such that they pursue host countries with riskier locations more than do firms generating projects in the headquarters sector. Also, while the dummy variable for technical support centers remains significant (β = -5.287, p < 0.0001), the corresponding interaction term is nonsignificant (β = -0.634, n.s.). One interpretation of this finding is that the relatively more sophisticated work in technical support centers appears to be somewhat less affected by environment trends toward risk than does the relatively less sophisticated work associated with call centers and backoffice support. Hence, technical support centers are similar to headquarters in being somewhat more resistant to trends toward risk.

Discussion and Conclusions The emergence and growth of IS offshoring has generated significant interest in the business community, stimulated considerable political debate, and begun to capture the attention of academic researchers. In this paper, we have explored the determinants of IS offshoring risk in terms of firm-, environment-, sector-, and country-level factors that push firms to consider progressively riskier IS offshoring destinations.

Implications for Research and Practice Much of research in offshoring to date has been practitioneroriented (as described above) or conceptual (Doh 2005; Graf and Mudambi 2005), with the relatively smaller number of IS empirical studies taking either a more case-study-based approach (e.g., Carmel and Nicholson 2005; Choudhury and Sabherwal 2003; Dhar and Balakrishnan 2006; Levina and Ross 2003), or relying on survey research in relatively small, focused populations (Fish and Seydel 2006; Susarla et al. 2003). Our study complements the insights drawn from these works by providing what appears to be the first comprehensive empirical assessment of the determinants of the risk levels borne by firms in global IS offshoring activities. The study integrates theories of the firm with regard to riskseeking behavior, derives specific propositions for the IS offshoring environment, and empirically examines actual firm behavior as it pertains to risk.


611


Our findings support the view that firms are able to learn from their own experiences and those of their peers, and process that learning in a way that positions them to explore more challenging and difficult environments. At the outset of this research, we would not have anticipated that environmentlevel effects trends would be so powerful, and that directly acquired, hard-won firm experience in the inherently risky arena of global IS offshoring would play a somewhat more minor role by comparison. We have found that the imperative of following the broader competitive environment irrespective of prior offshoring experience is quite pronounced in this early phase of global IS offshoring. This is in spite of the fact of very real risks of failure. For example, Doh (2005) describes how Dell ultimately had to backshore its call center operations from India to the United States due to problems with quality, while Carmel and Nicholson (2005) described individual firms enduring multiple rounds of IS offshoring failures. One possible explanation for this may be that while the literature has tended to focus on the risks of offshoring, firms by contrast may have given considerable weight to the risks of not offshoring, and the loss of competitiveness that that would entail, and hence pursued offshoring more defensively as opposed to offensively. Interestingly, we also find the relative level of home country risk, and the implied risk gap between home and host countries, begins to dissipate as these learning and experience effects are fully incorporated into our model. Often empirical studies are cross-sectional, existing at a particular point in time. In such studies, it is often difficult to discern whether the results may be due to a particular convergence of events at a point in time, or whether the results are indicative of a more long-standing, generalizable phenomenon. Our use of a worldwide longitudinal panel data set ensures that the findings are indicative of continuing firm trends in global IS offshoring. Our findings empirically demonstrate an ongoing trend toward risk, one that seems to be driven by broader trends in the competitive environment, and more modestly by increasing firm capabilities and experience. Our study also has implications for the management of information systems. IS offshoring is a dynamic phenomenon that will continue to evolve over time. Already in China and India, labor markets are tightening dramatically, with wages rapidly escalating, skilled workers in short supply, and executives describing being in a “war for talent" (Johnson and McGregor 2006). Our research shows that firms are powerfully enticed by broader trends toward higher risk countries. Yet by following the latest trend in the competitive environment, firms may find themselves in a position where there is little time to recoup their investments before the market cost

612


structure itself changes. Newer services sourcing developments such as home shoring, in which untapped domestic labor pools such as stay-at-home family members or rurally located individuals are utilized, may possess higher initial costs but potentially lower transaction costs and have less cost escalation over time, thereby becoming more competitive with offshore outsourcing.

Limitations and Future Research In this paper, we find evidence that firm-level and environment-level learning leads to firms’ increasing tolerance of riskier locations for IS offshoring. These findings are dependent upon the particular conditions and specifications of our model. However, we did undertake a number of additional specifications with different measures of risk (see footnote 5) and different samples of riskier versus less risky countries (see footnote 6) and our findings were quite consistent across these various specifications. Our macro-level analytical perspective would be well-complemented by additional microlevel research examining the details of the individual decision-making dynamics of top management teams as they consider in detail the trade-offs between alternatives of various risk levels in IS offshoring decisions. Moreover, while we have found that learning leads to pursuit of increased risk, it is still unknown whether such factors lead to appropriate pursuit of risk such that the odds of success are increased. Future research should consider both firm- and environmentlevel factors and examine both risk and success/failure. Here we did not have access to data regarding the ultimate outcome of the project investments that constitute our main data source. Furthermore, while the longitudinal perspective adopted here does provide unique insights, the data we used only comprises the years 2000 through 2005. As such, the findings described here may not capture the dynamics of the Y2K IS offshoring era in the years preceding 2000 or the current expansion of onshore outsourcing by foreign companies in the year 2006 and beyond. Since authors such as Carmel and Agarwal (2002), Carmel and Nicholson (2005), and Erber and Sayed-Ahmed (2005) indicate that pre-2000 IS offshoring was of a considerably smaller magnitude than post2000 offshoring, and possibly of a more limited Y2K-centric scope (as indicated by Qu and Brocklehurst 2003), we do not believe that the time frame causes serious concerns. It may be difficult to disentangle the effects of firm familiarity with a location, or offshoring, or with familiarity with risk. Research on firm choices to invest or reinvest in particular locations as a function of these factors would be an interesting area for additional research. Furthermore, a summary mea-


sure of risk may not capture all of the nuances of country risk factors, especially considering that there may be intra-country local determinants of country-level risk. Nonetheless, we note executives ultimately must aggregate the many nuances of country risk down to a single overall go/no-go decision, providing considerable corroboration of the use of a summary measure.12 We note that project level risk cannot be separated from returns (or losses) and that country factors do not actually result in actual realized risk unless there are losses to the firm.13 However, executives must make decisions about the course of action of future events in advance of actual returns (or losses) to the firm. The current work centers on the examination of push factors that drive the acceptance of risk levels at time t. Hence, future research should examine what the project’s financial returns and losses were in actuality in subsequent years. Regrettably, such data was unavailable to us. Additional research could also examine the outcome correlates of the level of host country risk as other kinds of country-level or business outcomes may go along with that of differing levels of host country risk. Another fascinating possible line of inquiry suggested to us would be to assess a firm’s overall risk in its other endeavors, both domestic and abroad, so as to formulate research in terms of a more comprehensive risk portfolio approach. It may be difficult to disentangle the relationship between the influence of the competitive environment versus time qua time. While a definitive answer is left to future research, it is our contention that it is market events and human decisions, inventions, and desires that drive business phenomena such as offshoring. For example, it is widely accepted that (human) developments such as the Internet and advances in information/communication technology made offshoring possible, while the “competitive pressures” driver identified in the Duke/Archstone study makes it clear that executives are responding to their competitors rather than time itself. Future research should also consider additional firm-level characteristics such as market capitalization or extent of global operations as potentially directly impacting risk-related behavior, or possibly moderating the impact of other factors. Future research should also continue to incorporate environment-level trends such as examined here with our RRA variable. A promising area may be to segment environmentlevel effects by other factors such as country of origin to uncover the possible existence of differences in risk perceptions between Western-based and non-Western-based firms.

12

Indeed, organizations such as PRS exist precisely to inform the very type of executive decision making we seek to evaluate here. 13

As suggested by an anonymous reviewer.

Finally, future research should also explicitly consider the security risks associated with IS offshoring. As has been pointed out by Swartz (2004), large volumes of sensitive consumer information have been distributed abroad through offshoring, making it beyond the control of the consumer and sometimes even the firm that collected it. Swartz reports thriving black markets for consumer bank account information on the streets of developing countries.

Conclusions Although some have documented the maturation of offshoring (Carmel and Agarwal 2002), it may be that we are witnessing only the first wave of IS offshoring, yet our study shows that dynamic patterns are already emerging regarding where to invest. What will subsequent waves suggest in terms of location and types of offshoring? There are many unknowns and uncertainties in offshoring; as such, offshoring presents new challenges to researchers, policy makers, and practitioners. In this paper, we have sought to capture one interesting and important set of dynamics that pushes firms to accept increasing degrees of risk in the location of offshore facilities. We view our principal contributions in terms of an initial round of theory-building and theory-testing for IS offshoring in the heretofore unexplored area of firm activities in the context of offshoring host country risk, and regard these kinds of contributions as being important developments for the enrichment and sustainability of the emerging, though highly important, area of offshoring research. We note that the intricacies associated with the construct of risk undoubtedly leave room for additional research above and beyond the empirical explorations of risk in IS offshoring undertaken here. Future research will undoubtedly reveal other dynamics, and through this accumulated record, we should be able to provide a more sophisticated and complete understanding of this very important phenomenon that is reshaping information systems management.

Acknowledgments The authors would like to acknowledge the helpful comments of the anonymous editorial team and reviewers, as well as those of editors Bill King and Reza Torkzadeh.

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About the Authors Eugene D. Hahn is an 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 has published or has articles in press in journals such as Decision Sciences, Strategic Management Journal, Journal of the Royal Statistical Society (Series A), European Journal of Operational Research, Organizational Research Methods, and Organizational Behavior and Human Decision Processes among others and has also published book chapters. He was recently peer-elected into membership in the International Statistical Institute. Eugene has consulted for organizations such as the U.S. Bureau of the Census, Booz-Allen & Hamilton, and the Corporation for Public Broadcasting. He received his Ph.D. in Information and Decision Systems from the George Washington University. Jonathan Doh is the Herbert G. Rammrath Endowed Chair in International Business, an 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 has published in leading journals such as Academy of Management Review, Journal of International Business Studies, Organization Science, Sloan Management Review, and Strategic Management Journal. He is coeditor of Globalization and NGOs and Handbook on Responsible Leadership and Governance in Global Business; and coauthor of International Management: Culture, Strategy, and Behavior, the best selling international management text, and Multinationals and Development (Yale University Press). He has served as a consultant or executive instructor for ABB, Anglo American plc, Bosch, China Minsheng Bank, Deloitte Touche, HSBC, Medtronic, and Shanghai Municipal Government. He received his Ph.D. from the George Washington University in strategic and international management. Kraiwinee Bunyaratavej is an assistant professor in the Department of Business Administration and Accounting at Wesley College. Kraiwinee obtained her Ph.D. from the Department of International Business at the George Washington University. She also has an M.B.A. in Finance and a post-M.B.A. certificate in International Finance from the George Washington University. Kraiwinee has articles published in or in press at journals such as Journal of International Business Studies, Journal of International Management, Journal of World Business, and the ASEAN Economic Journal. Her research interests center on offshoring, regional economic convergence, monetary unions, and financial crises. She also conducted funded research in conjunction with the European Union Research Center at the George Washington University.