Antecedents of Business Process Outsourcing in Manufacturing Plants

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1 Antecedents of Business Process Outsourcing in Manufacturing Plants Indranil R. Bardhan Jonathan Whitaker Sunil Mithas The University of Texas at Dallas University of Michigan University of Maryland Abstract This research explores the relationship between investments in information technology, manufacturing plant strategies and their impact on business process outsourcing (BPO) in manufacturing plants. Using survey data from US manufacturing plants, we develop a theoretical framework for studying the antecedents of BPO at the plant level. Our analysis suggests that the level of information technology (IT) investments, as a percentage of plant sales, is positively associated with the outsourcing of plant production activities. We also find that plants with higher levels of in-house software infrastructure are less likely to outsource plant support business processes. Furthermore, we find that plant manufacturing strategies and other plant-specific characteristics, such as production volume and the degree of worker unionization, also have a significant impact on the types of business processes that are outsourced. Managerial and research implications of our research into the antecedents of BPO are summarized. Keywords: Business process outsourcing, IT infrastructure, plant strategy, plant performance. 1.0 INTRODUCTION The debate on outsourcing has widened from a narrow focus on IT outsourcing to a broader focus on outsourcing of business processes and knowledge work. Prior research on IT outsourcing has primarily focused on the governance issues and consequences of outsourcing. However, despite a large body of literature on these issues, the antecedents and impacts of outsourcing specific business processes remains unclear (Dibbern et al., 2004). We study two important questions in the context of US manufacturing plants: 1) What are the antecedents of BPO for plant production and supporting business processes, and 2) Does information technology have an impact on the degree of outsourcing observed in plant business processes? We conduct an empirical study across a broad cross section of US manufacturing plants. We draw on the prior research on transaction cost economics and the business value of IT literature to develop our theoretical model. We empirically validate our theoretical model by using survey data from a large cross section of US plants. This study is relevant for both research and practice. From a research perspective, there is a need to identify the conditions under which plants outsource their business processes. An understanding of the antecedents of BPO will provide guidance on antecedents of other types of outsourcing and help build cumulative knowledge (Dibbern et al., 2004). Most prior previous research on outsourcing address the outcomes of information systems (IS) outsourcing at the firm level, while ignoring the antecedents of outsourcing and their relationship with organizational strategies and IT capabilities. Furthermore, there are no studies of BPO at the plant- or business unit-level. We also study the role and the impact of plant contextual variables, including different types of plant manufacturing strategies, on the degree of outsourcing of plant business processes. From a practice perspective, it is important for IT executives and plant managers to understand the antecedents of BPO. This will help managers evaluate whether their plants are well-prepared to engage in BPO, and will also help them to understand the potential outcomes of BPO as they consider critical outsourcing decisions. The rest of the paper is structured as follows. In section 2, we review the background literature, develop our conceptual research model and frame our research hypotheses. In section 3, we describe our survey research design, variable operationalization, and the econometric estimation model. In section 4, we present our statistical estimation results and discuss the significance of our findings with respect to our research hypotheses. Section 5 outlines the key results and implications of our study for future research and practice /06/$20.00 (C) 2006 IEEE 1

2 2.0 RESEARCH MODEL In this section, we briefly review and synthesize two streams of literature - transaction cost economics and business value of IT - that inform our research and present the theory underlying our hypothesized research model 2.1 IT Infrastructure and Business Process Outsourcing Transaction cost economics (TCE) provides one theoretical perspective to evaluate outsourcing considerations. TCE argues that firm boundaries are determined by a tradeoff between the production cost advantages of outside procurement in market relationships and the transaction cost advantages of internal production within hierarchies (Williamson, 1975). Markets and hierarchies entail different levels of production and transaction costs. Outsourcing may offer lower production costs through economies of scale or specialization, but these advantages come at the expense of higher transaction costs. Transaction costs include finding and contracting with a reliable supplier, monitoring and enforcing the contract, and coordinating with the supplier during the contract term. Transaction costs are presumed to increase with an increase in asset specificity. As asset specificity increases, more complex governance structures are required to ease costly bargaining over profits from specialized assets (Williamson, 1985). High transaction costs may overwhelm the savings in production costs from a market transaction, which would in turn lead the firm to vertically integrate the activity and produce internally. The information systems literature builds on transaction cost theory by suggesting that an increase in the use of IT would lead to increased outsourcing and a shift in firm boundaries for three primary reasons. First, IT reduces coordination costs. Malone, Yates and Benjamin (1987) note that IT reduces the time and cost of communicating information, which leads to a tighter coupling of processes that create and use information. They argue that the widespread use of IT decreases the unit costs of coordination, which will lead to the increased use of market transactions between firms. Second, IT mitigates transaction risk by enabling a firm to better monitor vendors (Clemons, Reddi and Row, 1993). This would encourage firms to invest in IT for interfirm coordination, and lead to increased outsourcing of IT intensive activities. Zenger and Hesterly (1997) concur that innovations in IT ease monitoring and communications across firms and facilitate the disaggregation of firms. Third, IT assets are increasingly standardized and not relationshipspecific (Clemons, Reddi and Row, 1993), alleviating the opportunistic behavior typically associated with asset specificity in transaction cost economics. Segal-Horn (1991) also notes that IT increases a firm s ability to coordinate its activities with business partners located nationally and internationally. As of 2001, manufacturing represents an industry sector with significant outsourcing activities, with durable goods manufacturers accounting for 39% of all outsourcing activity and non-durable manufacturing plants accounting for about 25% (Zhu et al., 2001). Although previous research has found support for conjectures relating to the effect of IT on firm boundaries, there is no empirical work that examines the effect of IT on BPO. Brynjolfsson et al. (1994) found that IT investment was significantly associated with a subsequent decrease in the average size of firms. In a separate study, Hitt (1999) also found that IT caused a decrease in vertical integration. We extend this stream of work by testing the implications of TCE in the BPO context. We hypothesize that plants with greater IT investments are more likely to outsource plant business processes. H1: Plants with greater IT investments are more likely to engage in business process outsourcing of plant processes H1a: Plants with greater IT investments are more likely to outsource production activities. H1b: Plants with greater IT investments are more likely to outsource plant support and service processes. Since plant IT investments includes investments in software, hardware, training, telecommunications and peripherals, and personnel, it does not allow us to isolate the specific impact of software applications on plant processes (Mukhopadhyay et al., 1997). Software applications enable plants to outsource business processes and maintain tight linkages with outsourcing vendors by providing the IT-enabled capabilities necessary for inter-organization integration of business processes. For example, web-enabled EDI-enabled applications and product design software enable cross-functional teams from manufacturing plants and outsourcing providers to collaborate effectively. Since IT investment decisions are often made at the application level, we examine the impact of the plant software infrastructure which allows us to trace the impact of advanced software-enabled capabilities on the extent of business process outsourcing. Hence, we hypothesize that plants with greater levels of IT infrastructure, as measured by their implementation of enterprise software, are more likely to outsource plant business processes. 2

3 H2: Plants with greater IT infrastructure are more likely to engage in business process outsourcing H2a: Plants with greater IT infrastructure are more likely to outsource production activities. H2b: Plants with greater IT infrastructure are more likely to outsource plant support processes 2.2 Plant Strategy and Business Process Outsourcing The marketing literature asserts that firms follow one of three primary strategies cost reduction, revenue expansion or both simultaneously (Rust, Moorman and Dickson, 2002). The strategy literature over the past 30 years concurs with this view. The early Miles-Snow (1978) typology of strategic organizations includes firms that compete primarily based on price (defenders), firms that meet new market opportunities (prospectors), and intermediate firms (analyzers). Porter (1980) also discusses the cost leadership and differentiation strategies. A cost leadership strategy requires comparison of the cost of value chain activities with the costs achieved by competitors (Porter 1985). On the other hand, a differentiation strategy focuses on customer judgments of relative product utility and satisfaction (Day and Nedungadi, 1994). Prior research in IS has examined the relationship between firm strategy and outsourcing of the IS function. Teng, Cheon and Grover (1995) note that the strategic orientation of a firm may affect its propensity to outsource the IS function. Cheon, Grover and Teng (1995) draw from Conner (1991) to note that product cost and differentiation are tied directly to resources used to produce the product, including the supporting IS resources. Several IS researchers have found that cost reduction is a primary motivation for IS outsourcing. Loh and Venkatraman (1992) found empirically that a higher business cost structure is associated with an increased propensity to outsource the IS function, and Lacity and Hirschheim (1994) found that senior executives expected IS outsourcing to reduce costs. More recently, IS researchers have observed that the motivations for IS outsourcing extend beyond cost. McFarlan and Nolan (1995) note that general managers concerns about quality also drive the IS outsourcing decision. DiRomulado and Gurbaxani (1998) note that the motivation for IS outsourcing is evolving from a primary focus on cost reduction to other factors, such as improvements in business performance. The issues addressed by IS researchers for IS outsourcing remain relevant in a business process outsourcing context. Some practitioners assert that firms engage in BPO primarily for cost-cutting reasons (Madigan and Mandell 2003), while others argue that BPO can enable high quality, innovation and value creation (Engardio et al. 2005; Prahalad and Krishnan 2004). So an early view, within the developing BPO literature, is that cost reduction and quality would both be associated with BPO. We extend the prior IS literature by testing the relationship between plant strategy and outsourcing in a BPO context. H3: Plant that focus on cost reduction strategies are more likely to outsource plant processes. H3a: Plant that focus on cost reduction strategies are more likely to outsource production processes. H3b: Plant that focus on cost reduction strategies are more likely to outsource supporting processes. H4: Plant that focus on high quality strategies are more likely to outsource plant processes. H4a: Plant that focus on high quality strategies are more likely to outsource production processes. H4b: Plant that focus on high quality strategies are more likely to outsource supporting processes. We present our conceptual research model in Figure 1, which provides an integrative snapshot of our research hypotheses which we test with empirical data in the next section. 3.0 RESEARCH DESIGN AND METHODOLOGY Empirical data for this research was obtained from the Industry Week-Manufacturing Performance Institute (MPI Group) survey of U.S. manufacturing plants conducted during The survey was designed to collected information about manufacturing trends, best practices, and specific manufacturing performance metrics using a mail-in survey. The survey consisted of a questionnaire mailed to plants, with two-digit North American Industrial Classification System (NAICS) codes from 31 to 33. Plant managers provided data on a broad range of manufacturing practices, strategies, types of information systems in use, and the types of business processes that were outsourced within each plant. Data on plant-specific characteristics, such as plant age, size, nature of manufacturing, degree of 3

4 worker unionization, and product mix and volume, were provided by plant controllers and are based on archival records. 1 The survey was mailed to approximately 20,000 plant managers and controllers from IndustryWeek s database of manufacturing plants. A total of 925 plants responded to the questionnaire. Our usable sample contains 332 plants which provided complete responses to the variables of interest. The overall response rate of 1.65% is comparable to large plant operations surveys as reported in Roth and van der Velde (1991) and Stock, Greis and Kasarda (2000). 3.1 Variable Definition Key dependent and independent variables in our research model are defined as follows: Production Outsourcing (PRODOUT): A three-item summative index indicating the extent of outsourcing of production processes within the plant. The three plant production processes are: Fabrication and/or processing, Assembly, and Staging and/or packaging. The degree of outsourcing within each process was measured based on a 0-1 variable, where zero indicates that the process was not outsourced, and one indicates that the process was outsourced. Service/Support Outsourcing (SVCEOUT): A seven-item summative index indicating the extent of outsourcing of support/service processes within the plant. The seven service processes are: Warehousing and/or distribution, Information Technology, Maintenance/asset management, Purchasing, Transportation, Design and/or R&D, and customer service. Similar to the production processes, the degree of outsourcing for supporting process was measured based on a 0-1 variable. Extent of Outsourcing (EXTENTOUT): A ten-item summative index indicating the extent of outsourcing of production and support processes within the plant. This index measures the degree of outsourcing across all plant operations, including production and supporting processes IT Investment (ITINVPC): Level of plant spending on information technology as a percentage of 2004 plant sales. IT Infrastructure (ITINFRA): A sixteenitem summative index indicating the deployment of 1 Due to space constraints, we have not provided a copy of the survey. However, it is available upon request from the authors. IT systems in the plant. The enterprise software covered by this factor are specified as follows: enterprise resource planning (ERP), ERP II, material resource planning (MRP), MRP II, manufacturing execution systems, financial management systems, asset management, warehouse management systems, transportation management systems, demandplanning/forecasting systems, design systems, product data management, customer relationship management, product lifecycle management, electronic data interchange, online purchasing, online selling, and mobile management. Each of the sixteen items was measured as a 0-1 variable, where zero indicates not in use and one indicates that the system is currently in use. Furthermore, we note that IT infrastructure is quite different from IT investment, to the extent that the former represents the types of enterprise software implemented, while the latter also includes the hardware, telecommunications, support, and other peripheral services that are needed for operating the IT functions. The degree of correlation between these variables was not significant. Low Cost Strategy (LOWCOST): Indicates whether the plant s market strategy has low cost as one of the top three objectives (1 = yes, 0 = no). High Quality Strategy (HIQ): Indicates whether the plant s market strategy has low cost as one of the top three objectives (1 = yes, 0 = no). We also include additional variables to control for the impact of plant characteristics on BPO use. These variables include plant age (PLANTAGE), plant size measured in terms of number of plant employees (EMPNO), product mix (MIX), product volume (VOL), and nature of manufacturing operation (DISCRETE, PROCESS). Large plants are more likely to have the scale and financial resources required to justify adoption of BPO. Plant age is likely to play a significant role since older plants are less likely to adopt outsourcing and often fail to realize the impact of technology-enabled processes on plant performance. Product mix is measured as a binary variable based on low or high product mix. Product volume is measured as a binary variable based on low or high volume. We used binary variables DISCRETE (1 = discrete manufacturing process, 0 otherwise) and PROCESS (1 = process based manufacturing, 0 otherwise) to control for the type of manufacturing operation used by a plant. Plant Size (Size): Indicates the number of employees at the plant. The available responses were: less than 100, , , 500 1,000, and more than 1,000. 4

5 Age of Plant (Age): Indicates the number of years since plant start-up. The available responses were: less than 5 years, 5 10 years, years, and more than 20 years. Production Volume (VOL): Indicates whether the plant operations are high volume (versus low volume) (1 = yes, 0 = no). Product Mix (MIX): Indicates whether the plant product mix is characterized by high mix/variety of products (versus low mix) (1 = yes, 0 = no). Discrete Operations (discrete): Indicates whether the manufacturing operations for primary products at the plant are discrete (measured by numeric quantities) (1 = yes, 0 = no). Process Operations (process): Indicates whether the manufacturing operations for primary products at the plant are process (measured by weight or volume) (1 = yes, 0 = no). Union (union): Indicates the extent to which plant production workers are represented by a union. Available responses were: no union workers, some union workers, and all union workers. Table 1 provides descriptive statistics for our model variables while Table 2 provides a description of the extent of outsourcing observed for the ten types of plant production and service/support business processes. 3.2 Model Estimation Our estimation model can be conceptualized as follows: Extent of BPO = Constant + IT Investment + IT Infrastructure + Low Cost Strategy + High Quality Strategy + Plant Size + Plant Age + Volume+ Mix + Discrete + Process + Union + ε (1) Production BPO = Constant + IT Investment + IT Infrastructure + Low Cost Strategy + High Quality Strategy + Plant Size + Plant Age + Volume+ Mix + Discrete + Process + Union + ε (2) Support/Service BPO = Constant + IT Investment + IT Infrastructure + Low Cost Strategy + High Quality Strategy + Plant Size + Plant Age + Volume+ Mix + Discrete + Process + Union + ε (3) We used the ordinary least squares (OLS) approach to estimate the model of production and services BPO because the three dependent variables represent summative indices which are continuous measures of BPO use. Furthermore, we also tested separate probit models, for each of the ten outsourced processes as dependent, and found that the results were very consistent with the above model. 2 Table 3 shows the results of empirical estimation of the models in equations (1) and (2). We also tested for potential endogeneity between our dependent and independent variables, and found that endogeniety is not prevalent in our model. 4. RESULTS The results of empirical estimation of the models in equations (1) through (3) are shown in Table 3. For the linear regression models, we tested for the standard assumptions of linear regression. We tested for multi-collinearity by computing the condition numbers (Belsley, Kuh, and Welsch 1980). The highest condition index was lower than the threshold specified in the literature, indicating that multi-collinearity is not a serious concern in our analysis (Belsley, Kuh, and Welsch 1980). We also tested for heteroskedasticity using the Breusch-Pagan and Cook-Weisberg tests and we did not find evidence of heteroskedastic error distribution. 4.1 Impact of Information Technology on BPO Our results suggest that there is a positive and significant association between the level of IT investment and the degree of outsourcing of plant production processes. IT investment is positively and significantly associated with production BPO (β= 0.028, p<0.013). Hypothesis 1b predicts a positive association between IT investment and the incidence of support BPO. We did not find support for this hypothesis (β= , p<0.118). Hence, our results provide partial support for hypothesis H1 to the extent that they support H1a but not H1b. We found that the coefficient of IT software infrastructure is positive but not statistically significant for production BPO (β= 0.037, p<0.205). However, we do find that there is a negative association between IT infrastructure and the incidence of support/service BPO (β= , p<0.009). In other words, plants that exhibit greater usage of advanced enterprise software are less likely to outsource their service processes, such as customer 2 Due to the space constraint, we have not shown the probit results in this paper. 5

6 service, IT, procurement, distribution, and transportation/logistics functions. Our results imply that plants which greater investments in IT are more likely to outsource their production processes. On the other hand, plants that exhibit greater IT infrastructure capabilities, as measured by the usage of enterprise software, are less likely to outsource their support processes, and instead, are likely to develop these service capabilities in-house. The lack of support for H2b could be attributed to the possibility that greater software infrastructure may, in fact, inhibit plants from outsourcing their service and support business processes because the enterprise software may be tightly integrated with other plant business processes (e.g., accounting, finance, human resources) and possibly with the ERP systems of the parent company. In other words, while it may be easier to parcel out only the production activities, the enterprise software may impede companies from outsourcing their supporting processes such as product R&D, logistics, maintenance, and IT, that are more tightly integrated to other company processes. Hence, we find partial support for both hypotheses H1 and H2. Our results support hypotheses H1a but do not support H1b. Similarly, our results do not provide support for H2a and we reject hypothesis H2b. 4.2 Impact of Plant Strategies on BPO Our results further indicate that plants with low cost market strategies are less likely to outsource their production processes (β= 0.146, p<0.048). On the other hand, such plants are also more likely to outsource their service/support business functions (β= 0.271, p<0.010). Our results imply that plants with low cost strategies are more likely to outsource their service/support business processes in order to realize their cost-control objectives. Interestingly, these plants are more likely to keep their production functions in-house rather than outsource production to a third-party vendor as a means to lower production costs. Hence, our results reject hypothesis H3a but support H3b Hypothesis 4 predicts a positive relationship between high quality plant strategy and production and service/support BPO. We find partial support for this hypothesis to the extent that plants with high quality market strategies are more likely to adopt business process outsourcing. However, a closer analyses of the results indicates that such plants are more likely to outsource their plant production functions (β= 0.118, p<0.068). Our results suggest that the association between high-quality strategy plants and outsourcing of service/support processes is positive but not statistically significant (β= 0.079, p<0.228). Hence, our results provide partial support for hypothesis H4, to the extent that they support H4a but do not support H4b. Our results indicate that, at the mean level of IT infrastructure, a low cost strategy does not have a statistically significant effect on BPO. At the higher level of IT infrastructure, plants with low cost strategies are more likely to outsource their business processes. Among our disaggregated analyses, these results hold for the outsourcing of individual processes, such as IT and purchasing processes. These results show that the effect of IT infrastructure on BPO varies depending on the plant s strategy. Whereas IT accentuates use of BPO for plants with low cost strategies, manufacturing plants with high quality strategies and greater IT sophistication may be less likely to use BPO. 4.2 Impact of Plant-Specific Characteristics on BPO The results related to the effect of plant control variables on BPO also provide interesting insights. First, a more unionized plant workforce is associated with increased production BPO and increased incidence of support BPO (β= 0.110, p<0.032; β= 0.189, p<0.010). We can think of two potential explanations for this finding. One is that union personnel may generally be less specialized, in terms of their training and available skills for specific plant processes, which may lead plants to outsource these processes outside their capabilities. Another potential explanation is that union constraints in work rules or compensation may lead management to outsource production and support processes to reduce costs or increase workforce flexibility. Another interesting insight is that high volume plants are less likely to outsource production and support processes (β= , p<0.078; β= , p<0.087). Furthermore, discrete manufacturing plants are more likely to outsource their production activities (β= 0.332, p<0.000), and process manufacturing plants are likely to outsource their support processes (β= 0.259, p<0.064). These results indicate that plant-specific characteristics play important roles in determining the outsourcing of critical plant business processes. Our results also indicate that plant size and age do not have a significant impact on the incidence of BPO. 5. DISCUSSION AND CONCLUSIONS 5.1 Research implications 6

7 This research constitutes one of the first studies to provide empirical evidence on the antecedents of business process outsourcing in the manufacturing industry. Unlike prior research, which has primarily focused on the business value of outsourcing at the firm-level, we study the relationships between plant strategies, IT infrastructure investments, and the incidence of business process outsourcing in manufacturing plants. Furthermore, we separate the incidence of BPO into two parts: outsourcing of core production activities and the outsourcing of support/service activities which are ancillary functions to the core plant processes. The contribution of our research is twofold: (a) development of a theoretical framework to study the antecedents of BPO in manufacturing plants, and (b) empirical validation of the role of IT investments, IT infrastructure, and plant manufacturing strategies on the incidence of BPO. Our study indicates that plant information systems have a significant impact on the incidence of BPO in manufacturing plants. Specifically, plants with greater IT investments are more likely to outsource their core production processes. These results imply that IT investments enable plants to develop the enterprise integration capabilities required to outsource production processes to third-party suppliers. Our results also indicate that plants with greater IT infrastructure capabilities are less likely to outsource their support processes. Instead, they are likely to leverage their internal IT capabilities to keep such processes in-house, since these processes could potentially provide a source of competitive advantage. Furthermore, our results suggest that plant market strategies also play critical roles in guiding the outsourcing of plant business processes. Our findings suggest that plants with low cost strategies are more likely to outsource non-core support processes, such as logistics, IT, procurement, and plant maintenance functions. However, plants with high quality strategies are more likely to outsource their core production processes, perhaps as a path to gain access to core competencies of external suppliers who have greater access to technological resources for delivery high quality products. Our results suggest that plant size and age do not play significant roles in terms of their impact on BPO. Other plant-specific characteristics, such as plant unionization, and the nature of the manufacturing processes have a significant impact on the incidence of BPO. For example, our study shows that discrete manufacturing plants and plants with greater levels of worker unionization are more likely to oustource their production and service/support business processes. 5.2 Limitations and Future Research Our study has several limitations. First, since the scope of the survey was limited to US plants, our findings may not be generalizable to plants outside the US. Second, there is a need to validate these perceptual measures of BPO and IT infrastructure by collecting detailed archival data from plant records through more industry-specific field studies. Third, because of the cross-sectional nature of our data, we can only show associations in our discussion of the relationships between plant characteristics, IT investment and BPO. A potential area for future research may include extending these models to evaluate relationships between BPO and plant performance. Future research may also include collection of time-series data so that we can evaluate the lagged impact of new technologies and manufacturing capabilities on the incidence of BPO and plant performance. This will be of fundamental importance to the extant research in information systems and operations management which has thus far been limited by the unavailability of objective plant-level data that spans a significant time horizon. Since the R 2 values of our regression models are relatively low, it is possible that there may be other plant- and industry-specific factors that may affect outsourcing decisions within manufacturing plants. For instance, the degree of standardization of product components may vary from one industry to another. Consequently, an industry with greater product standardization may be more likely to outsource its production processes compared to another industry where components are customized for different firms. Hence, future research may include studies of such industry-specific factors to better understand the drivers of plant outsourcing. REFERENCES Achrol, R. S. (1991), "Evolution of the Marketing Organization: New Forms for Turbulent Environments," Journal of Marketing, 55 (October), Anderson, M. C., R. D. Banker, and S. Ravindran (2003), "The New Productivity Paradox," Communications of the ACM, 46 (3),

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10 Table 2: Extent of Outsourcing of Plant Business Processes in Manufacturing Plants Production Processes Fabrication and/or processing Outsource Do Not Outsource 37% 63% Assembly 16% 84% Staging and/or 5% 95% packaging Support/Service Processes Warehousing and/or distribution 16% 84% Information 19% 81% technology Maintenance/asset 6% 94% management Purchasing 4% 96% Transportation 60% 40% Design and/or R&D 11% 89% Customer service 3% 97% Table 3: Antecedents of Business Process Outsourcing in Manufacturing Plants (1) (2) (3) Extent of Production Support/ Outsourcing BPO Service BPO IT Investment (0.251) 0.028** (0.013) (0.118) IT Infrastructure * (0.087) (0.205) *** (0.009) Low Cost Strategy (0.163) ** (0.048) 0.271*** (0.010) High Quality Strategy 0.197* (0.057) 0.118* (0.067) (0.228) Plant Size (0.445) (0.193) (0.204) Plant Age (0.436) (0.324) (0.281) Volume ** (0.018) * (0.078) * (0.087) Product Mix (0.340) (0.247) (0.493) Discrete Manufacturing 0.406*** (0.002) 0.332*** (0.000) (0.217) Process Manufacturing (0.246) (0.113) 0.259* (0.064) Unionization 0.300*** 0.110** 0.189*** (0.002) Constant 0.861*** (0.006) (0.032) (0.355) (0.010) 0.768*** (0.006) Observations Prob > F R-squared Robust one-tailed p values are shown in parentheses * significant at 10%; ** significant at 5%; *** significant at 1%. 10