Outsourcing customer support: The role of provider customer focus

Accepted Manuscript Title: outsourcing customer support: the role OF PROVIDER CUSTOMER FOCUS Author: Stefan Wuyts Aric Rindfleisch Alka Citrin PII: DOI: Reference:

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Please cite this article as: outsourcing customer support: the role OF PROVIDER CUSTOMER FOCUS, Journal of Operations Management (2014), http://dx.doi.org/10.1016/j.jom.2014.10.004 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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May 20, 2014

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OUTSOURCING CUSTOMER SUPPORT: THE ROLE OF PROVIDER CUSTOMER FOCUS

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Stefan Wuytsa a

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Koç University & Tilburg University Koç University, Rumelifeneri Yolu, 34450 Sariyer, Istanbul, Turkey Tel. +90 212 338 1376; Email: [email protected]

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Aric Rindfleischb

b University of Illinois College of Business, 383 Wohlers Hall, 1206 South Sixth Street, Champaign, IL 61820 Email: [email protected]

Alka Citrinc

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Georgia Institute of Technology Ernest Scheller Jr. College of Business, 800 West Peachtree Street NW, Atlanta, Georgia 30308 Email: [email protected]

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OUTSOURCING CUSTOMER SUPPORT: THE ROLE OF PROVIDER CUSTOMER FOCUS ABSTRACT

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An increasing number of firms are outsourcing customer support to external service providers. This creates a triadic setting in which an outsourcing provider serves end customers on behalf of its clients. While outsourcing presents an opportunity to serve customers, service providers differ in their motivation and ability to fulfill customer needs. Prior research suggests that firms with a strong customer focus have an intrinsic motivation to address customer needs. We suggest that in an outsourcing context, this intrinsic motivation does not suffice. Using a MotivationOpportunity-Ability framework, we posit that the effect of a provider's customer focus will be moderated by a set of relational, firm, and customer characteristics that affect its ability to serve end customers. We test our conceptualization among 171 outsourcing clients from the Netherlands and then validate these results among 135 Indian outsourcing providers. The findings reveal that customer-focused providers achieve higher levels of customer need fulfillment but this effect is contingent on their ability to serve end customers. In particular, customer-focused providers more effectively fulfill customer needs when clients and providers share close relational ties, when clients also have a high level of customer focus, and when end customer needs exhibit a low degree of turbulence. In addition, we find that, in turbulent markets, equipment-related services offer greater opportunity for effective customer need fulfillment than other outsourced services.

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Keywords: outsourcing, service triads, customer support, customer focus

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1. INTRODUCTION In order to lower costs and enhance competitiveness, an increasing number of firms are outsourcing a variety of customer support services traditionally conducted internally, including

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equipment services such as installation, maintenance, and repair, distribution services such as logistics and transportation, and other client services such as training and system integration. This

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trend has attracted considerable attention from the popular press, which has identified

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outsourcing as one of the most important economic developments of this century (Economist, 2013; Friedman, 2005; Gottfredson et al., 2005). Outsourcing is experiencing tremendous growth

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due to the rapid advances in information technology and an increasing pool of educated workers across a number of developing countries, including India, China, and Malaysia (Garten, 2004;

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Lohr, 2006). In particular, the market for outsourced customer support services is growing steadily and is expected to reach $81.3 billion by 2018 (IDC, 2014). Several well-known

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companies such as IBM, Barclays, and T-Mobile have recently outsourced customer service

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functions to external providers (Cellular News, 2008; ComputerWeekly, 2011; Raassens et al., 2014; Time, 2009).

The phenomenon of customer support outsourcing is intrinsically triadic in nature: an outsourcing provider delivers support to end customers on behalf of a client firm. Unfortunately, outsourcing firms appear to have difficulty managing the complexities of triadic exchange and often focus on immediate cost savings while overlooking outsourcing’s hidden costs (Ren and Zhou, 2008). On the academic side, supply chain, operations management, and marketing scholars have identified triadic exchange as a topic of considerable academic interest (e.g., Choi and Wu, 2009; Gunawardane, 2012; Wathne and Heide, 2004; Wuyts et al., 2004). Thus, the triadic nature of outsourcing is an important topic for both scholars and practitioners. Prior

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studies have examined the triadic nature of customer service outsourcing and have developed theoretical expositions of these service triads primarily inspired by agency theory (Gunawardane, 2012; Tate et al., 2010; van der Valk and van Iwaarden, 2011). According to this perspective,

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outsourced customer support creates an agency situation: the provider (agent) acts on the client’s (principal) behalf, which carries the risk of moral hazard.

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We seek to enrich and extend this body of research, starting from the baseline expectation

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that an outsourcing provider’s degree of customer focus reduces this motivational hazard, and hence, enhances customer need fulfillment. We define customer need fulfillment as an

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outsourcing provider’s performance in terms of addressing end customer needs through information provision, service support, and problem solution. We then hypothesize that even a

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motivated provider is not necessarily able to fulfill end customer needs. We also examine the degree to which certain services provide a better opportunity for improved customer need

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fulfillment through outsourcing. Our conceptual lens is informed by the Motivation-Ability-

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Opportunity (MOA) framework and employs a contingency perspective (Boudreau et al., 2003; MacInnis et al., 1991). In brief, we suggest that while an outsourcing arrangement provides an opportunity for effective customer need fulfillment, the degree to which this opportunity is realized depends upon both a provider’s motivation and ability to serve its client’s customers. Specifically, we argue that customer-focused service providers are better motivated to serve end customers than service providers that lack this focus (Deshpandé et al., 1993; Franke and Park, 2006). Customer focus, the central element of a market orientation, refers to the institutionalization of firm-based processes that strategically leverage information about customers (Kohli and Jaworski, 1990, p. 3). These “customer-focused” processes include the

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collection, interpretation, analysis, and dissemination of customer information and reflect a provider’s intrinsic motivation to address customer needs (Cadogan and Diamantopoulos, 1995). We also suggest that the strength of the relationship between a provider’s customer focus

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and customer need fulfillment is contingent on the provider’s ability to serve end customers. Our key assertion is that customer-focused service providers are better able to serve end customers if

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they have access to customer insight. We propose that this ability is a function of (1) the

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relational tie between provider and client (which enhances the accessibility of customer insight), (2) the degree to which a client itself is customer-focused (which reflects the availability of

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customer insight), and (3) market turbulence (which increases the obsolescence of customer insight). In sum, we expect that relational tie, a client’s customer focus, and market turbulence

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moderate the relationship between provider customer focus and customer need fulfillment. In addition, as a follow-up analysis, we explore the possibility that certain services provide greater

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opportunity for successful customer need fulfillment via outsourcing. In particular, we

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distinguish between equipment-related services (i.e., installation, maintenance, and repair) versus other types of support services.

We test our conceptual model via a survey study among 171 outsourcing clients from the Netherlands, a country where many firms have outsourced customer service activities to external providers (Computerworld, 2004; Mol, 2007). The results of this study confirm our expectation that provider customer focus is associated with higher levels of customer need fulfillment and provide strong support for our MOA framework and contingency perspective. The results are robust across alternative methods and various model specifications. As a means of providing added verification, we conduct a validation study among 135 outsourcing providers in India. The results of this study lend additional support for our conceptualization.

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Theoretically, our research contributes to the literature on service triads by developing and validating a conceptual framework, grounded in the Motivation-Ability-Opportunity framework, which encompasses characteristics of outsourcing clients, providers, and end

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customers, and distinguishes between different types of services. Among our insights, we find that customer-focused providers deliver better service toward end customers if their upstream tie

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to a client firm is strong. We also find that end customers benefit less from a provider’s customer

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focus under conditions of higher market turbulence, however this moderating effect does not apply for clients outsourcing the installation, maintenance or repair of equipment, as these

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physical assets appear rather immune to market fluctuations. Our research also contributes to the market orientation literature by examining the effect of customer focus on customer need

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fulfillment in a triadic outsourcing setting where relevant customer insight resides with a client firm rather than the service provider. Managerially, our research provides outsourcing firms with

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a set of actionable recommendations regarding partner selection and relationship management.

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For example, our results suggest that a customer-focused provider can more effectively address customer needs when its client firm is also customer-focused.

2. CONCEPTUAL FRAMEWORK

2.1 Customer Support Outsourcing

We define outsourcing as the external delivery of a business activity that a firm used to (or could have) perform(ed) internally. Firms have outsourced a variety of activities such as advertising and production dating back to the dawn of the industrial era (Davis, 2004; Lonsdale and Cox, 2000). In recent years, outsourcing has expanded considerably beyond these traditional domains, as rapid and significant technological advances in communications (e.g., satellites, fiber optics, email, instant messaging, and teleconferencing) have reduced the barriers of geographic 5

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distance (Liu et al., 2011; Metters and Verma, 2008). Our focus is on the outsourcing of customer support services (e.g., installation, maintenance, transportation, user training, technical support, etc.), which are increasingly being conducted by firms in developing economies such as China

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and India due to both their large educated workforces and favorable labor costs (Hagel, 2004; Metters and Verma, 2008).

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The phenomenon of outsourcing customer support services differs fundamentally from

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outsourcing other services such as IT or advertising, as it creates a triadic situation where a client firm calls upon an external agent to deliver customer support to end customers. This setting

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entails significant risk: if customers are dissatisfied with a provider’s service delivery, they may develop an unfavorable perception about the client firm, engage in negative word-of-mouth, and

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possibly terminate their relationship (Thelen and Shapiro, 2012). Hence, it is crucial that an outsourcing service provider is both motivated and able to effectively fulfill end customer needs.

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2.2 Motivation, Opportunity, and Ability as an Organizing Framework

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The MOA perspective has served as a useful organizing framework for understanding knowledge-sharing and information-processing behaviors across a variety of organizational settings (e.g. Argote et al., 2003; Boudreau et al., 2003; MacInnis et al., 1991; Siemsen et al., 2008). According to this theoretical perspective, motivation captures willingness to act, opportunity refers to the contextual factors that surround an action, and ability represents skills or knowledge bases related to an action (Boudreau et al., 2003; Siemsen et al., 2008). The MOA framework has been used to explain not only individual behavior, but also organizational action (e.g., Clark et al., 2005; Wu et al., 2004). While our theory development and hypotheses focus on motivation and ability, we also explore the impact of opportunity as part of our analysis. Specifically, we propose that a

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provider’s customer focus is an indicator of its motivation to serve end customers. However, the effect of provider customer focus upon customer need fulfillment is contingent upon a provider’s ability to serve end customers. This contingency approach is congruent with prior application of

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the MOA framework in other information processing contexts. For example, MacInnis et al. (1991) adopted the MOA framework to better explain differences among consumers in their level

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of processing of brand information. They argue that the availability and accessibility of relevant

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knowledge structures “provide the foundation for processing ability” (p. 34). In accord with this perspective, we identify contingency variables that are specifically related to the provider’s

2.3 Customer Focus and Contingency Variables

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availability and accessibility of customer-related insights.

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While most firms acknowledge the importance of their customers, firms with a strong customer focus have strongly-held institutionalized processes and procedures directed toward

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understanding customers and addressing their needs. We extend previous work on the importance

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of customer focus in inter-organizational settings (e.g., Langerak, 2001; Rindfleisch and Moorman, 2003; Saparito et al., 2004; Siguaw et al., 1998) by adopting a triadic (outsourcing) perspective. The involvement of a third party (i.e., a provider firm) creates a triadic context which generates the question: How does a provider’s (rather than a client’s) focus on end-customers impact the quality of its service delivery? We seek to answer this question by first formulating the baseline hypothesis that a provider’s customer focus increases customer need fulfillment. The key argument for this hypothesis is that a customer-focused provider is better motivated to serve end customers. Then we proceed with a set of contingency hypotheses and introduce moderators that influence a provider’s ability to access relevant customer insight.

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Due to the embedded nature of customer insights and the difficulty of transferring tacit customer knowledge (Gebhardt et al., 2006; Kohli and Jaworski, 1990; Li and Calantone, 1998), provider customer focus is highly dependent upon the nature of its relationship with a client firm.

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The relational dimension of customer focus is a manifestation of Afuah’s (2000) notion of critical knowledge resources residing with external network partners. In a similar vein, Min et al. (2007)

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discuss the importance for market-oriented firms to access information that resides with supply

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chain partners. According to both Afuah (2000) and Burt (2000), accessibility, availability, and imperishability determine the value of external knowledge resources. These three key

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characteristics serve as the basis for the selection of our three contingency variables: the collaborative nature of the client-provider tie is indicative of the accessibility of customer insight;

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client customer focus is indicative of the availability of customer insight; and market turbulence

2.4 Hypotheses

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is indicative of its perishability. Figure 1 visualizes our conceptual framework.

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2.4.1 The Main Effect of Provider Customer Focus Since the early 1990s, a sizeable body of research suggests that firms with a strong customer focus engender high levels of customer satisfaction (Kirca et al., 2005). The fact that these firms have institutionalized processes for acquiring and disseminating information about end customers indicates their intrinsic, strategic motivation to address customer needs (Deshpandé et al., 1993; Kohli and Jaworski, 1990; Moorman, 1995). Thus, customer focus should be a highly desirable trait when selecting service providers for outsourced customer support, as their intrinsic motivation to serve customers likely translates into better customer need fulfillment. Hence, we offer the following baseline hypothesis: Hypothesis 1. Provider customer focus is positively related to customer need fulfillment.

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2.4.2 Moderators of this Baseline Relationship Relational Tie. Although a customer focus may motivate a service provider to deliver effective customer service, it does not guarantee that a provider is able to effectively serve a

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client’s customers. An external service provider needs to adapt its processes to the particular needs and preferences of the end customers of each client that it serves. Therefore, the impact of

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a provider’s customer focus upon customer need fulfillment is likely to depend upon its upstream

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tie to its client firm. Handley and Benton (2009) identified the creation of a close relational tie through collaboration and commitments as a crucial building block for effective outsourcing

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relationship management. Likewise, the organizational knowledge literature has demonstrated that relational ties facilitate the generation and sharing of knowledge, such as market insight

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(Argote et al., 2003; Uzzi and Lancaster, 2003).

A growing number of studies in the inter-organizational relationship domain suggest that

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relational norms and feelings of interconnectedness stimulate cooperation across a wide array of

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contexts (e.g., Hansen, 1999; Johnson et al., 2004; Rindfleisch and Moorman, 2001). Relational ties also enhance expectations of mutual disclosure, and thus, facilitate knowledge transfer (Hansen, 1999; Szulanski, 1997; Wuyts et al., 2004). For example, Darr et al. (1995) find that embedded know-how is more easily transferred between firms that share relational ties. The disclosure and transfer of embedded knowledge provides a basis for an outsourcing provider to learn about its client’s customers. In addition, a strong relational tie also facilitates new knowledge creation (Argote et al., 2003). Strong relational ties between client and provider may also help generate new market insight through the combination of the provider’s marketplace experiences and the client’s knowledge on customer needs and preferences.

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In summary, relational ties increase the provider’s ability to fulfill customer needs because they facilitate the creation and transfer of customer insight. In keeping with the multiplicative nature of the MOA framework (Blumberg and Pringle, 1982), customer-focused

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providers (who are intrinsically and strategically motivated to address customer needs) should be more effective at customer need fulfillment for triadic service relations characterized by strong

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relational ties between client and provider versus those with weaker relational ties. Thus, we

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suggest that:

Hypothesis 2. The relational nature of the client-provider tie strengthens the positive effect of provider customer focus on customer need fulfillment.

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Client Customer Focus. Thus far, our conceptualization has assumed that a client firm is motivated and equipped to interact and share customer insight with its outsourcing service

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provider. In reality, client firms vary in terms of their motivation to serve customers and in their

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level of embedded customer knowledge. Much like providers, client firms differ in terms of their

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degree of customer focus. Customer-focused client firms continuously gather detailed information about customers and adapt their internal processes to better suit customer needs (Franke and Park, 2006). Gradually, these processes becomes tacitly embedded into an organization’s culture, belief systems, and decision-making processes (Deshpandé et al., 1993; Gebhart et al., 2006). We suggest that because an outsourcing provider typically lacks this type of embedded knowledge about its client’s customer base, it is strongly dependent upon its client to serve as its coach and mentor. Client firms that lack a strong customer focus are likely to have low levels of embedded customer knowledge and routines, and hence, are unlikely to effectively enact this role. In essence, providers are better able to realize effective customer need fulfillment if their client firm is customer-focused since customer-focused clients can be a source of

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embedded customer knowledge. Again in line with the multiplicative nature of the MOA framework, we suggest that: Hypothesis 3. A client’s customer focus strengthens the positive effect of provider customer focus on customer need fulfillment.

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Market Turbulence. Market turbulence refers to the “rate of change in the composition of

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customers and their preferences” (Jaworski and Kohli, 1993, p. 57). Rapidly changing end

customer preferences can quickly turn previously acquired customer insights obsolete (Danneels

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and Sethi, 2011). Hence, while a customer-focused provider is intrinsically motivated and institutionally equipped to accumulate and disseminate customer insight, a high level of market

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turbulence can greatly reduce the value of previously acquired customer knowledge. Danneels and Sethi (2011) find that market scanning and customer need prediction are considerably less

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effective under volatile customer environments. Likewise, the organizational learning literature

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suggests that managers view volatile environments as harder to analyze and tend to interpret

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external information in a more ad hoc manner (Daft and Weick, 1984). Institutionalized processes for gathering and disseminating customer insight mirror what Gnyawali and Stewart (2003) refer to as the “informational mode of learning,” which, they argue, allows for fine-tuning preexisting knowledge but reinforces existing schemas. Hence, learning activities, such as the processes inherent to a customer focus, are likely to be less effective in volatile environments where accumulated knowledge is less valuable. Environmental volatility thus reduces a provider’s ability to effectively deploy its internal processes to address customer needs. Since effective customer need fulfillment is contingent on the provider’s motivation as well as ability to address customer needs, the baseline effect of the provider’s customer focus on customer need fulfillment should be weaker under conditions of high market turbulence. Hence, we hypothesize:

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Hypothesis 4. Market turbulence weakens the positive effect of provider customer focus on customer need fulfillment.

3. EMPIRICAL STUDY AMONG DUTCH OUTSOURCING CLIENTS

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We empirically tested our conceptual framework among outsourcing clients (across several manufacturing industries) in the Netherlands. We selected the Netherlands as the context

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for our inquiry because it is a prototypical example of a high-income country where firms

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increasingly outsource business activities (Mol, 2007). Indeed, previous research has found that the outsourcing trend in the Netherlands, and the factors driving it, is highly similar to other

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countries (Kotabe and Mol, 2009; Mol, 2007). A focus on one country also minimizes potential biases due to unobserved extraneous factors (such as culture, regulation, or macro-economic

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conditions). In the remainder of this section, we detail our data collection, measurement, and

3.1 Method

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3.1.1 Subjects and Procedures

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psychometric assessment. We then explain our analysis and discuss our results.

The subjects for our study were carefully selected using a multistep approach. Our first task was to construct a list of potential participants. Prior research suggests that many customer service-outsourcing clients reside in the manufacturing sector (Lei, 2007). Thus, to reduce unobserved heterogeneity and enhance comparability, we focus on manufacturing industries (SIC codes 29-33). We restrict this study to the Netherlands to further control for extraneous influences and increase comparability across the firms in our study. We began our sampling procedure by examining the Reach database of Bureau van Dijk, which provides industry, contact, and other information for over 400,000 Dutch firms (e.g., Wuyts, 2007). From this database, we composed a list of all Dutch firms that are active in SIC codes 2933 and have at least 10 employees. We selected this cutoff because firms with fewer than 10 12

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employees are typically entrepreneurial start-ups that are less likely to be engaged in strategic outsourcing. This cutoff level is in line with the classification by the European Union which distinguishes these very small firms as a separate category (i.e., “micro-firms”1). We then

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contacted the resulting 1186 firms to identify eligible respondents and seek their participation. To be eligible for this study, a firm had to affirm that it outsources at least a portion of its customer

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support services (and the respondent must be responsible for managing this outsourced service).

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Through this process, we identified 713 eligible respondents. Each of these eligible respondents received an introductory letter and an email that contained the link to an on-line survey. As an

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incentive, we promised to donate €5 to a charity of the respondent’s choice.

Of the 713 eligible respondents, 181 participated, for a response rate of 25%. This

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response rate compares favorably with prior surveys on related topics such as customer orientation and organizational capabilities (e.g., Johnson et al., 2004; Rindfleisch and Moorman,

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2003). Ten respondents were excluded because of excessive missing data, leaving a final sample

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of 171 client firms. To verify the efficacy or our key informant approach (Campbell, 1955), we measured the respondent’s knowledge on the outsourced customer service on a 7-point knowledge scale: the average score was 5.5 and the scale exhibited little variance (Std Dev = 1.2). Our sample characteristics also suggest that our approach was successful, as over 80% of our respondents occupied senior management positions such as managing director or chief executive officer and over 80% of the responding firms outsourced installation, maintenance and repair, system integration, and/or logistics services (see Table 1 for more details on the sample firms). In addition, we compared participating firms with non-participating firms on several firm descriptors reported in the Reach database (such as firm size and financial measures) but found

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http://europa.eu/legislation_summaries/other/n26001_en.htm

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no significant differences (p > .05). Thus, we believe that our respondents were well qualified and that non-response bias is not a significant concern. 3.1.2 Measures

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Our survey began by asking respondents to identify a specific customer support activity for one of their major products or services currently outsourced to an external provider.

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Respondents were then asked to evaluate the primary outsourcing provider of this activity across

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a broad number of dimensions, including their degree of customer focus and customer need fulfillment. Because this survey was directed at respondents located in the Netherlands, it was

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administered in Dutch. In order to ensure proper translation, we first drafted our survey in English, translated it into Dutch, and then back-translated it into English using two native Dutch

discrepancies, which were easily rectified.

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speakers who are fluent in English (Brislin, 1970). This procedure revealed a few minor wording

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We assessed our dependent variable, customer need fulfillment, using a new four-item

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scale. The first two items are adapted from Kumar et al.’s (1992, p. 252) measure of customer satisfaction with service delivery (reflecting a provider’s provision of information and solutions to end customers); we then created two additional items that reflect the provider’s success in terms of addressing customer needs. Since customer focus is the central tenet of a market orientation (Kohli and Jaworski, 1990; Narver and Slater, 1990), we assessed provider (client) customer focus using a subset of four items from Kohli et al.’s (1993) market orientation scale. In accordance with our conceptualization, we selected items that relate to the generation and dissemination of customer knowledge. We measure client-provider relational tie using an adapted four-item version of Rindfleisch and Moorman’s (2003) relational embeddedness scale. Finally,

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we measured market turbulence using three items proposed by Jaworski and Kohli (1993) that assess customer preference changes across time. We also control for a number of variables. First, we control for a provider’s resources to

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support service delivery by assessing its level of financial (availability of funds), human (employee knowledge, technical support), physical (infrastructure), and technological

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(technology to support service delivery) resources. Prior research suggests that these types of

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resources influence a firm’s ability to develop and deploy its capabilities (Eisenhardt and Martin, 2000). Second, we control for the propensity of client and provider firms to share information on

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operational issues (i.e., operational information sharing). Sharing operational information and solving operational problems as they occur could contribute to the quality of customer service

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delivery and may be correlated with our relational tie measure. Third, we control for clients’ strategic outsourcing objectives by asking how important such factors as accessing new skills and

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enhancing the quality of service delivery were in their decision to outsource. Fourth, we control

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for the size of the client firm (measured by the log of the number of employees). Fifth, to control for potential unobserved differences caused by the location of outsourcing providers, we include a set of provider country dummy variables2. Sixth, we control for the nature of the outsourced customer support services (identified by the respondents) by categorizing them into equipmentrelated services (i.e., services for industrial machinery such as installation, maintenance, and repair) versus other types of client support services (e.g., training and software support, system integration, logistics).

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The provider firms are located in the following countries (because of missing observations, this list may not be exhaustive): Argentina, Belgium, China, Czech Republic, Germany, Hungary, India, Ireland, Italy, the Netherlands, South-Korea, Spain, Taiwan, United Kingdom, and United States. For reasons of parsimony, we only retained the significant country dummy variables in the reported models.

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3.1.3 Psychometric Assessment We began the psychometric assessment of our key measures by first assessing their dimensionality. We formed a CFA model that specified each item as loading on its intended

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latent construct while allowing these constructs to co-vary. The fit indexes for this model met or exceeded recommended levels (CFI = .90, NNFI = .90, RMSEA = .06), and all items displayed

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strong loadings on their latent constructs (average loadings: provider customer focus = .74; client

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customer focus =.72; customer need fulfillment = .78; relational tie = .77; market turbulence = .71; provider resources = .68; operational information sharing = .71; strategic objective = .76). In

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addition, the Average Variance Extracted (AVE) calculations for all of these latent constructs were at or above the recommended level of .50. We also calculated the Cronbach Alpha

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reliability measures for each construct and found that all exceeded the recommended standard (range: .75 to .86). See Measurement Appendix A for the Cronbach Alpha values per construct

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for our key measures.

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and the loadings for each individual item. See Table 2 for descriptive statistics and correlations

We tested the discriminant validity of these measures by employing Fornell and Larcker’s (1981) test of shared variance between pairs of latent constructs. The results of this test reveal that the squared correlations between construct pairs do not exceed the average variance extracted for any single latent construct. Thus, our key measures display adequate discriminant validity. We assessed potential common method variance (CMV) using the marker variable technique recommended by both Lindell and Whitney (2001) and Malhotra et al. (2006). Essentially, this approach attempts to control for CMV bias by identifying a marker variable that is theoretically unrelated to at least one variable in the study. In our survey, we included the item “price competition is the hallmark of our industry,” which is theoretically unrelated to the

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provider’s degree of customer focus (a key construct in our conceptual framework). This item was measured on a 7-point Likert scale identical to the scales used for our key constructs and was embedded in the survey instrument, in-between items that measured market turbulence and client

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customer focus. Its correlation with provider customer focus was .03. We discounted this level of correlation from all of the dataset’s other correlations, adopting the formula and corresponding t-

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statistic proposed by Malhotra et al. (2006, p.1868):

is the adjusted correlation coefficient,

is the correlation of the marker variable (price

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where

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and

competition) with the theoretically unrelated construct (provider customer focus), and

is the

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uncorrected (observed) correlation. A comparison of the adjusted correlations and the uncorrected correlations did not indicate any changes in significance. For example, the full

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correlation between provider customer focus and customer need fulfillment was .43 (p < .01),

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while the discounted correlation was .42 (p < .01). Hence, it appears that our results are not confounded by CMV bias3. This finding is congruent with a growing body of evidence that suggests that the degree of CMV bias is often quite low in organizational research (e.g., Doty and Glick, 1998; Malholtra et al., 2006; Rindfleisch et al., 2008). In order to further validate our perceptual measure of customer need fulfillment, we asked respondents to report two objective indicants of effective customer need fulfillment (i.e., the number of monthly customer complaints received about the service provider and the average time the service provider takes to respond to a customer request). Both objective indicants were 3

We also considered an alternative marker variable, namely an item measuring the ease with which a competing firm’s products can be imitated by others in the client’s industry. This marker variable is conceptually unrelated to our control variable “provider resources” (i.e., the quality of the provider’s resources relative to other potential providers). The observed correlation is .02. Following the same procedures described above, we arrive at a similar conclusion: the pattern of significant adjusted correlations is identical to the pattern of significant observed correlations.

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negatively correlated with our measure of customer need fulfillment (monthly complaints: r = -.20, p < .05; response time: r = -.32, p < .01). This pattern of results provides additional (criterion-related) validity for this measure. These objective indicants are unrelated to the

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provider’s customer focus, which underscores the conceptual and empirical difference between provider customer focus (motivation to perform) and customer need fulfillment (performance).

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3.2 Findings

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We tested our hypotheses by employing a nested series of multiple regression analyses (Narasimhan et al., 2013; Stouthuysen et al., 2012). For each of these models, our dependent

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variable is our measure of customer need fulfillment. For this as well as our other multi-item measures, we employed their factor scores in order to account for differences across individual

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item loadings upon their underlying construct4. To verify the normality of the dependent variable, we conducted a Kolmogorov-Smirnov test, which did not reject the null-hypothesis of normality.

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We compared the fit of each of these nested regression models using their corresponding F-

Ac ce pt e

statistics (see Table 3). Model 1 only includes an intercept and the control variables. Model 2 adds the main effect of provider customer focus, which does not significantly improve model fit (p > .05). However, Model 3, which also includes the main effects of the four moderator variables resulted in a significant improvement in model fit (p < .01). Adding the hypothesized interaction effects between the three moderating variables and provider customer focus (Model 4), further improves model fit (p < .01). In summary, results across the various models are quite consistent, and Model 4 (the proposed model) is superior to the more constrained Models 1, 2, and 3. Thus, our findings reported below are based on the results of Model 4. We tested for multi-collinearity in Model 4 and observed a maximum variance inflation factor of 2.04, which is

4

Using item averages rather than factor scores did not alter signs and significance levels of the reported findings.

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well below the threshold value of 10 (Mason and Perreault, 1991). We also assessed an additional model (Model 5), which we discuss in Section 3.3.3. We now turn to the interpretation of the regression results reported in Model 4.

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Interestingly, the main effect of provider customer focus on customer need fulfillment is not significant (β = .02, p > .10), thereby rejecting Hypothesis 1. However, we find that the effect of

cr

provider customer focus is contingent upon the relational nature of the client-provider tie, in

us

support of Hypothesis 2. As predicted, strong relational ties enhance the positive effect of provider customer focus on customer need fulfillment (β = .09, p < .05). The first graph in Figure

an

2 portrays this effect. Following Yan and Dooley (2013), we set the “low” and “high” levels of the moderator (in this case, relational tie) at its extreme values observed in the sample (thereby

M

assuring that the reported simple slopes are within the boundaries of the sample). To determine the significance of the simple slopes, we calculate their standard errors according to the

d

procedures outlined by Aiken and West (1991). We observe that provider customer focus

Ac ce pt e

increases customer need fulfillment at high levels of relational tie, but decreases customer need fulfillment at low levels of relational tie5. We also find support for Hypothesis 3 since the effect of a provider’s customer focus on customer need fulfillment is stronger when the client firm also has a strong customer focus (β = .20, p < .01). As shown in the second graph in Figure 2, provider customer focus increases customer need fulfillment at high levels of client customer focus but decreases customer need fulfillment at low levels of client customer focus. Finally, the findings support Hypothesis 4, as the effect of provider customer focus is weakened under conditions of higher market turbulence (β = -.12, p < .05). As displayed in the third graph in

5

All six simple slopes calculated according to the extreme-value approach outlined above, and visualized in Figure 2, are significant at 5% level (using two-sided tests).

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Figure 2, at high/low levels of market turbulence, a high degree of provider customer focus is harmful/beneficial. In terms of the direct effects of the moderating variables, we only find a positive effect for

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relational tie (β = .25, p < .01). Among the control variables, we find a marginally significant effect for provider resources (β = .11, p < .10), and significant effects for operational information

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sharing (β = .20, p < .01) and strategic objective (β = .15, p < .05). Finally, compared to service

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providers from other countries, service providers from China, the Czech Republic, and Italy appear significantly less successful at fulfilling end customer needs whereas providers from

an

Ireland and the UK are significantly more successful. 3.3 Additional Analyses

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Below we report on the robustness of our findings across alternative model specifications as well as an estimation using structural equations modeling. In addition, we extend Model 4,

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which is based on motivation and ability arguments, by incorporating variation in terms of

Ac ce pt e

opportunity across different types of outsourcing services. 3.3.1 Alternative Model Specifications with Additional Covariates We examined the robustness of our findings by re-estimating our regression analysis after including a number of additional covariates. First, given the transactional nature of outsourcing, we included transaction-related covariates such as environmental uncertainty, behavioral uncertainty, and transaction-specific investments and then re-estimated the model (Williamson, 1985). The results of this re-specified model closely matched the results reported above. Second, we considered the possibility that operational information sharing (one of our controls) may mediate our hypothesized moderation effects. Formal mediation analyses indicate that this variable partially mediates the interaction between provider customer focus and relational tie but

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not the other two ability-based interactions. Moreover, the resulting direct effect of the interaction of provider customer focus and relational tie remains significant (and the size of the interaction effect is reduced by only 17%) after including operational information sharing in the regression.

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Third, we re-estimated our model by including industry fixed effects but none of these effects were significant. Fourth, we re-estimated our model by including the length (log-transformed) of

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the client-provider relationship (in terms of months). Because of missing values, we could only

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estimate this model for 140 observations. The results closely match the results for our full sample analysis (i.e., Model 4). We also specified a model that added two-way interaction effects

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between relationship length and both provider and client customer focus as well as the three-way interaction effect between relationship length and provider and client customer focus. None of

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these interaction effects were significant.

In summary, none of these alternative specifications (1) flow directly from our developed

d

theory, (2) alter our key findings, or (3) provide additional insights. Thus, our baseline results

Ac ce pt e

(i.e., Model 4) appear to demonstrate a considerable degree of empirical robustness. 3.3.2 Structural Equations Model

As an additional validation assessment, we also tested our conceptual framework using a Structural Equations Modeling (SEM) approach following the procedures developed by Mathieu et al. (1992) and detailed by Cortina et al. (2001). Specifically, this approach tests moderated relationships in SEM by adjusting the observed predictor variables (i.e., provider customer focus, relational tie, client customer focus, and market turbulence) and their interactions by the square root of their reliabilities. These adjusted variables were entered into a model in which customer need fulfillment was the dependent variable, along with the control variables employed in our regression analysis. In addition, this model accounted for item error by estimating the loadings of

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the items for customer need fulfillment, provider resources, operational information sharing, and strategic objective. As in our regression analysis, we employed a nested approach by first estimating a model that included only the direct effects and controls (i.e., Model I) and then

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estimating a second model that added the moderation effects (Model II). The chi-square for Model II (χ2(df =360) = 1074) is significantly lower than the chi-square for Model I (χ2(df =363)

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= 1096). This result is congruent with the results of our nested regression analysis and provides

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additional support for our moderated model in general. The detailed results for Model II are displayed in Appendix C and replicate all results found in our regression model. In sum, the SEM

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analysis provides added validation for the pattern of findings reported in regression Model 4. 3.3.3 Variation in Opportunity: Distinguishing between Customer Support Services

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Our conceptual framework focused on variations in motivation (i.e., provider customer focus) and ability (i.e., relational tie, client customer focus, and market turbulence). As noted

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earlier, outsourcing customer support to an external provider can be viewed as an opportunity to

Ac ce pt e

enhance customer need fulfillment. Thus, as a means of exploring the role of opportunity, we analyzed potential differences in customer need fulfillment across variations in customer support services6. Specifically, we divided our sample into two types of outsourced services: (1) equipment-related services and (2) all other customer support services. In contrast to other types of outsourced services, equipment-related services such as installation, maintenance, and repair are strongly tied to physical assets (Goffin and New, 2001). Thus, these services are less likely to be susceptible to changing customer preferences. Hence, outsourcing equipment-related services may offer client firms a stronger opportunity, relative to other customer support services, especially under conditions of high market turbulence.

6

We are grateful to the review team for suggesting this analysis.

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In accord with the multiplicative nature of the MOA framework (Blumberg and Pringle, 1982), we estimated a three-way interaction between provider customer focus (motivation), market turbulence (ability), and equipment-related services (opportunity). The results of this

ip t

analysis are reported as Model 5 in Table 3: the three-way interaction effect is significant and positive. In addition, the improvement in model fit compared to baseline Model 4 is significant.

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Hence, we conclude that the constraining effect of market turbulence is less pronounced when the

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outsourced customer support service entails installation, maintenance, and repair of equipment. This finding provides additional support for our MOA perspective by indicating that opportunity-

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related factors also influence the effectiveness of a provider’s customer need fulfillment.

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4. VALIDATION STUDY AMONG INDIAN OUTSOURCING PROVIDERS Our Netherlands study sought to minimize extraneous influences by examining outsourcing

d

firms within a single country. However, this focused approach limits the external validity and

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generalizability of our findings. Therefore, we conducted a validation study in a context which differs from our Netherlands study in three key aspects. First, all respondents are located in India, a country with a large number of firms that provide customer services for western clients (Friedman, 2005; Thottam, 2004). Second, the respondents are managers at provider rather than client firms. Third, the scope of inquiry of this study focused on our first two hypotheses. 4.1 Method

4.1.1 Subjects and Procedures.

To create our sampling frame, we compiled various publicly available business directories. Given our focus on customer support services, we examined directories that listed the functions performed by member firms. We identified five specific databases (NASCCOM, JUST

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Dial, BPOIndia, Invest India, and Accent). We then hired a well-known Indian research firm, TNS, to pre-contact these firms to confirm that they provide customer support services. This resulted in a list of 259 Indian outsourcing providers for our sampling frame. Prior research

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suggests that firms in developing nations such as India seldom respond to mail surveys (Hoskisson et al., 2000). Thus, we employed a structured interview technique. This technique,

cr

which orally elicits respondents’ reactions to survey items by employing trained interviewers (in

us

our case, TNS), has been employed in prior studies of firms located in developing nations (e.g., Grewal and Tansuhaj, 2001; Li and Atuahene-Gima, 2001). Of the 259 firms contacted, 135

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provided complete responses, for an effective response rate of 52%.

Following the key informant approach (Campbell, 1955), we targeted individuals who

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were knowledgeable about their firm’s outsourcing services. Our sample characteristics suggest that this approach was successful, as over 90% of our respondents were either the owner or

d

managing director of their firm. As an additional check, we asked respondents to report their

Ac ce pt e

degree of familiarity (on a 7-point scale where 1= extremely low and 7 = extremely high) with their firm’s outsourcing activities. The mean response to this item was 6.37, which suggests that we were successful in locating knowledgeable informants. The service providers in our sample provide various customer support services, with an emphasis on customer service via telephone and email. We did not observe any meaningful differences between the 135 responding firms vs. the 124 other firms in our sampling frame. We pretested the questionnaire among four Indian outsourcing providers to ensure that the questions were well understood and reflected the constructs we intended to measure. This pretesting provides initial evidence of the conceptual equivalence of our constructs. We provide further evidence of equivalence below.

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4.1.2 Measures This study was conducted in English because the English language is widely employed in Indian commerce and Indian outsourcing providers serve a large number of American and British

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clients. As in the Netherlands study, we used existing measures whenever possible. Our key measures are reported in Measurement Appendix B. We did not include measures of the client’s

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customer focus and market turbulence in this survey; hence, this second study focuses on

us

validating H1 and H2. We assessed provider customer focus using a similar scale as in our Netherlands study (Kohli et al., 1993). While we would have preferred to obtain a performance

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measure from the client firms or their end customers, respondents were unwilling to reveal the identities of their clients due to confidentiality agreements (previous research has reported similar

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constraints, cf. Carson, 2007). Hence, we constructed a new scale to assess our respondents’ perceptions of their own performance in terms of serving end customers. This measure, which we

d

refer to as “provider performance,” captures the provider’s performance in terms of

Ac ce pt e

responsiveness and service delivery. We used a similar scale as in the Netherlands study to measure relational ties. As in the Netherlands study, we control for operational information sharing and for provider resources to support customer service delivery. Finally, we include a dummy variable separating large firms (value 1 = more than 1000 employees) from small and medium size firms (value 0 = less than 1000 employees).7 4.1.3 Psychometric Assessment

We tested our measurement model following similar steps as in the Netherlands study. We established a CFA model that specified each item as loading on its intended latent construct while allowing these constructs to covary (Gerbing and Anderson, 1988). On the basis of factor loadings, we had to make a few adjustments in the items of provider customer focus, relational 7

In the Netherlands study, 5% of client firms had > 1000 employees; In the India study, this percentage equals 51%.

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tie, and the control variable provider resources. Appendix B reports the constructs and corresponding items after these refinements. The fit indices for the resulting measurement model met or exceeded recommended levels (CFI = .94, IFI = .94, RMSEA = .06), and all retained

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items displayed acceptable loadings (average loading = .70) on their respective constructs. Appendix B also reports the Cronbach Alpha reliabilities (all exceed recommended levels, i.e. α

cr

> .70) and individual item loadings. In addition, the Average Variance Extracted (AVE)

us

calculations for all of these latent constructs were at or above the recommended level of .50, with the exception of the control variable operational information sharing (.46). Table 4 provides

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descriptive statistics and correlations for our key measures. We tested the discriminant validity of our multi-item measures by employing Fornell and Larcker’s (1981) test of shared variance

M

between pairs of latent constructs. The results of this test reveal that the squared correlations between these pairs do not exceed the AVE for any single latent construct. Thus, our key

d

measures display adequate discriminant validity. Finally, we assessed potential common method

Ac ce pt e

variance (CMV) bias by employing a modified marker variable analysis (Lindell and Whitney, 2001; Malhotra et al., 2006). Essentially, this approach controls for CMV bias by estimating a dataset’s second lowest bivariate item-to-item correlation (.04 in our dataset) and then discounting this level of correlation from all of the dataset’s other item-to-item correlations. Following this discounting, the overall correlations are then re-estimated and compared with the original correlations. We found that the observed and discounted correlations are closely matched in terms of sign and significances. For example, the observed correlation between provider customer focus and provider performance was .32 (p < .01), while the discounted correlation was .29 (p < .01). Thus CMV bias does not appear to be a concern.

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As an additional psychometric assessment, we also estimated the degree of measurement equivalence (i.e., invariance) for the constructs that overlap between this validation study (India) and our main study (Netherlands). According to measurement scholars, there are multiple forms

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of measurement equivalence (e.g., configural, metric, scalar) and the type of equivalence assessed should match the goals of the research (Rungtusanatham et al., 2008; Steenkamp and

cr

Baumgartner, 1998). Specifically, for a study that simply seeks to validate findings in a different

us

context, configural invariance (i.e., the degree to which measures across different groups have the same factor structure) is the most critical (Steenkamp and Baumgartner, 1998). Following

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Steenkamp and Baumgartner (1998), we assessed configural invariance by specifying a multigroup (i.e., Netherlands = 1, India = 2) CFA model for the four constructs (i.e., provider customer

M

focus, relational tie, provider resources, operational information sharing) employed in both studies. As noted earlier, our psychometric assessment of the India dataset resulted in a pruning

d

of a few items across these various constructs. Thus, our equivalence assessment focused on the

Ac ce pt e

items common to both studies. In this model, the factor loadings across these two countries were allowed to freely vary. The results of this model suggest that these four measures display a high degree of configural invariance; the overall fit statistics are strong (i.e., CFI = .92, IFI = .93, RMSEA = .06) and average item loadings for all four factors exceeded .70. Hence, it appears that the shared items across these four constructs are equivalent in terms of their factor structure8.

8 As an additional test of the equivalence of our measures across both countries, we also conducted a test of metric invariance (i.e., the degree to which items have the same factor loadings across different groups). This form of equivalence is most commonly used when pooling data across groups or making cross-group comparisons (Steenkamp and Baumgartner, 1998). Since the goal of the India study is mainly validation, we neither pool data nor make direct comparisons to the Netherlands study. Nonetheless, assessing metric invariance provides an additional test of the robustness of our measures. We constrained the factor loadings for all items across both countries to equality and then compared the difference in the chi-square statistic between this constrained model (χ2(df = 131) = 272) vs. the unconstrained model (χ2(df = 118) = 228). This difference is significant (p < .01), which indicates that the items across these two studies do not display full metric invariance (Rungtusanatham et al., 2008). However, as noted by Steenkamp and Baumgartner (1998), full metric invariance is fairly uncommon in cross-cultural studies. Thus, they recommend that researchers assess partial metric invariance by allowing one or more item loadings per

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4.2 Findings We tested H1 and H2 by employing a series of four nested multiple regression analyses in

ip t

which provider performance served as the dependent variable, and provider customer focus and the interaction between provider customer focus and relational tie as the key predictor variables.

cr

In addition, these regressions included the main effect of relational tie, as well as operational

us

information sharing, provider resources, and client size as control variables. As before, we used factor scores for all multi-item measures.9 Across all four nested models, the maximum variance

an

inflation factor was 1.85, which is well below the threshold value of 10. This suggests that the results are not influenced by multi-collinearity (Mason and Perreault, 1991). The results for all

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four models are displayed in Table 5.

As shown in this table, the proposed model (Model 4) is superior to nested Models 1, 2, and

d

3. Hence, we focus on Model 4 as the test of our hypotheses. The results of this model indicate

Ac ce pt e

that provider customer focus is positively related to provider performance (Model 4: β = .15, p < .05), which provides support for H1. We also find support for the hypothesized contingency effect. In accord with Hypothesis 2, provider customer focus exerts a stronger positive effect on provider performance when client and provider share a stronger relational tie (β = .15, p < .01). In terms of the control variables, a relational tie (β = .37, p < .01) significantly increases provider performance. Also, provider performance is significantly better when serving large client firms (β = .29, p < .05). In sum, the results of this validation study provide additional support for our

factor to freely vary. We followed this recommendation by unconstraining one item per factor and then re-estimating the model. The chi-square statistic (χ2(df = 127) = 239) for this partially constrained model does not differ significantly from the chi-square for the unconstrained model (p < .61). Thus, the shared items across the Netherlands and India studies display partial metric invariance. In summary, the measures appear to have a high degree of equivalence. 9 Using item averages rather than factor scores did not alter signs and significance levels of the reported findings.

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general thesis regarding the contingency effect of a close client-provider tie, as a facilitator of the effect of a provider’s customer focus upon customer service provision.10

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5. DISCUSSION In recent years, supply chain, operations management, and marketing scholars have

cr

discussed the triadic nature of customer support outsourcing (Gunawardane, 2012; Tate et al.,

2010; van der Valk and van Iwaarden, 2011). Our study extends and enriches research on this

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important service triad both conceptually and empirically by adopting an interdisciplinary

an

perspective (Rindfleisch and Moorman, 2003; Saparito et al., 2004). Marketing scholarship suggests that a customer focus is a primary driver of effective customer need fulfillment.

M

However, our results indicate that the role of customer focus is rendered more complex due to the triadic nature of customer service outsourcing. Even though a customer-focused service provider

d

may be intrinsically motivated to serve end customers, due to the triadic nature of outsourcing it

Ac ce pt e

may lack the ability to do so successfully. We propose that characteristics of the client, the clientprovider tie, and end customers significantly affect a provider’s access to customer insight and, hence, moderate the effect of provider customer focus on customer need fulfillment. We examined our conceptual framework via a survey study conducted among 171 client firms located in the Netherlands and then validate these results among 135 outsourcing providers in India. Collectively, the results of these two studies provide broad support for our core thesis. In this section, we discuss theoretical and managerial implications of these findings.

10

We re-estimated this model by including relationship age (for which we had several missing variables) and the results were robust. We also tested if operational information sharing mediates the hypothesized effects but mediation analysis failed to show any mediating role for this variable. Thus our model appears quite robust.

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5.1 Theoretical Implications We believe that our findings contribute to the outsourcing literature, the interorganizational relationship literature, and the market orientation literature.

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Our research extends and enriches recent efforts in the outsourcing literature to understand the triadic nature of outsourcing and its performance consequences. More than a

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century ago, Simmel (1950 [1908]) took a fundamental theoretical leap by moving the scope of

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inquiry from the dyad to the triad. Recently, Choi and Wu (2009) took a similar leap in the context of supply chain networks by suggesting that triads are the fundamental building blocks of

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a network and proposing different triadic archetypes that represent unique dynamic interactions between buyers and suppliers. This triadic perspective has recently entered the outsourcing

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literature, as an increasing number of scholars have become interested in understanding the

the globe.

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client-provider-customer triads that underlie the growing number of outsourcing relations across

Ac ce pt e

As outlined in our introduction, most of this extant research is inspired by agency theory (Gunawardane, 2012; Tate et al., 2010; van der Valk and van Iwaarden, 2011). The key assumption of this perspective is that client firms face a moral hazard problem, which is intrinsically motivational in nature. We take this idea one step further. While we acknowledge the possibility of motivational problems in a triadic outsourcing setting, we offer a broader theoretical framework that also accounts for ability problems. In accord with the triadic nature of this phenomenon, we identify contingency factors that relate to the client firm, the client-provider tie, and end customers. Thus, our research expands the conceptual scope of outsourcing research beyond the proclivities of the outsourcing provider to the characteristics (i.e., abilities) of the broader triadic setting in which it is embedded. We also explored whether some services offer a

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greater opportunity for outsourcing under particular conditions. The combined findings of a positive main effect of provider customer focus on customer need fulfillment (enhanced motivation), the negative interaction effect of market turbulence (reduced ability), and the

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positive three-way interaction effect with equipment-related services (enhanced opportunity) illustrate the explanatory power of the MOA framework.

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Our findings also provide theoretical implications for research in the inter-organizational

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relations domain, of which outsourcing can be viewed as a particular manifestation (Raassens et al., 2012; van der Valk and van Iwaarden, 2011). This literature has devoted considerable

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attention to enhancing interfirm relations among existing partners but comparatively little attention to the characteristics that firms should consider in qualifying new partners. As noted by

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Wathne and Heide (2004), systematic upfront selection can proactively reduce potential relational problems before they arise. According to Stump and Heide (1996), firms should employ selection

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criteria that reflect the partner’s motivation, such as the partner’s “general customer practices and

Ac ce pt e

business philosophy” (p. 432). Our findings support these assertions within triadic service settings: a provider’s customer focus (as reflected in institutionalized processes for the generation, dissemination, and analysis of customer insight) enhances customer need fulfillment, and may hence serve as an effective selection criterion. However, since the actual deployment of a customer focus is highly contingent in nature (Gebhardt et al., 2006; Kohli and Jaworski, 1990; Li and Calantone, 1998), client firms need to do more than simply select customer-focused service providers; they need to back up this selection by fostering collaboration. Thus, our research suggests that inter-organizational scholars should devote more attention to the role of partner selection and how this selection process can be bolstered via close ties.

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In addition to providing new insights for outsourcing and inter-organizational research, we believe that our findings also have implications for market orientation scholarship. As noted earlier, a considerable portion of the market orientation literature focuses on a firm’s processes

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for acquiring, utilizing, and disseminating customer insights (Deshpandé et al., 1993; Kohli and Jaworski, 1990; Moorman, 1995). Marketing scholars have traditionally viewed customer focus

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as a strategic orientation (Gatignon and Xuereb, 1997; Voss and Voss, 2000), a set of

us

organizational processes (e.g., Jaworski and Kohli, 1993), or as an element of an organization’s culture (e.g., Narver and Slater, 1990). Our focus on a triadic setting where customer support is

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outsourced to a third party provides an alternative perspective.

Specifically, our research suggests that a strong customer focus does not guarantee

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effective customer need fulfillment when a customer is serviced by a third party. In other words, when institutionalized processes for understanding customers and addressing their needs reside

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with an external party, the mere presence of this orientation does not guarantee its successful

Ac ce pt e

deployment. Interestingly, the main effect of provider customer focus is insignificant in the Netherlands study (and only reaches significance once all moderators are removed). This provides strong support for the contingency framework: provider customer focus enhances customer need fulfillment, but only under particular contingency conditions that increase the provider’s “ability” to serve end customers. If these contingency conditions are not met, provider customer focus may be counter-productive. Although we do find a positive main effect for provider customer focus in the India study, this study only includes one of the three moderators and hence we cannot rule out the possibility that the positive main effect may be due to unspecified moderation effects.

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Thus, to understand the impact of customer focus on performance within triadic settings, a contingency perspective appears to be in order. Close collaboration between a provider and client transforms the outsourcing relationship from an arm’s-length transaction to a relationship

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with quasi-firm properties (Eccles, 1981). This transformation appears to enable a client firm with close relational ties to benefit from its provider’s customer focus in the form of enhanced

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customer need fulfillment. However, the selection of a customer-focused service provider does

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not exempt a client firm from being customer-focused. These findings are consistent with the argument that client firms should coach their providers and share the embedded customer insight

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necessary for effective deployment of a customer focus. Thus, the triadic nature of the outsourcing phenomenon presents a new relational perspective on the concept of customer focus

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and, by extension, on the theory of market orientation.

In addition to shedding light on market orientation’s relational nature, our research

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suggests that this orientation may also have a dark side. As shown by our simple slope analyses

Ac ce pt e

(see Figure 2), provider customer focus may actually reduce customer need fulfillment under particular conditions. Specifically, our findings suggest that when a client-provider tie is very weak, when a client firm lacks a customer focus, or when customer preferences change frequently, a customer-focused provider’s ability to fulfill customer needs is significantly impaired. Building on prior research (Danneels and Sethi, 2011), we conjecture that under such conditions customer-focused providers adhere to processes that are bound to be ineffective because customer insight is not available, cannot be accessed, or obsolesces quickly. Thus, a more improvisational and ad hoc approach may be a more effective means to deal with customer needs and preferences under these types of conditions (Daft and Weick, 1984).

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5.2 Managerial Implications Our research also presents a number of managerial implications for firms that outsource their customer service activities or engage in other forms of triadic service relationships. First,

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outsourcing firms may benefit from partner selection practices that include a provider’s customer focus. From the service provider’s perspective, signaling a strong customer focus and

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emphasizing the benefits of institutionalized processes to serve end customers may differentiate it

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from competing service providers. Client firms should be aware, however, that the mere selection of a customer-focused service provider is not a guarantee of success. They must back up this

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selection by making efforts to establish close ties with their provider.

Second, although clients may outsource customer support, they should remain customer-

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focused. Our results demonstrate that a client firm benefits more from outsourcing customer support to a customer-focused service provider if the client itself is customer-focused. This

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finding may indicate synergistic effects between the client’s accumulated idiosyncratic market

Ac ce pt e

experiences and the provider’s customer-focused internal processes, in pursuing the shared goal of addressing customer needs. Hence, a client should nurture the creation and sharing of customer insight with its service provider.

Third, the nature of end customers also determines a customer-focused provider’s success at addressing customer needs. When customer needs and preferences are subject to change (across time and across customers), service providers face more difficulties deploying their customer focus on the client’s behalf. Thus, client firms that outsource service functions in rapidly changing markets should not be surprised if their outsourcing providers face considerable difficulty fulfilling customer needs. The impact of customer dynamics appears to have received relatively little attention from both outsourcing scholars and practitioners and may be one reason

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why firms such as Dell have decided to repatriate some of their outsourcing activities (Li and Choi, 2009). However, our results also suggest that the impact of market turbulence varies according to the nature of the service being outsourced, as the ability of providers to support

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equipment-related services is less susceptible to changing markets as compared to other types of services. Thus, firms that outsource physical-asset activities such as the installation, maintenance,

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or repair of equipment should have greater confidence in the ability of their outsourcing providers

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to serve customer needs in turbulent markets.

6. LIMITATIONS AND FUTURE RESEARCH DIRECTIONS

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Although our findings are based on two distinct studies with strong psychometric

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properties, we acknowledge that these studies are limited in terms of both their sample and measures. These limitations point toward promising directions for future research.

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We recognize that our findings are constrained by the fact that we were unable to obtain a

Ac ce pt e

matched sample of clients and providers due our respondents’ reluctance to identify their outsourcing partner firms (cf. Carson, 2007; Engardio et al., 2005). Matched dyads would have allowed us to examine the congruence (and deviations) of partners’ perceptions (Anderson and Weitz, 1992). These types of matched studies, however, are rare as they are extremely difficult to conduct11. As an alternative, our two studies provide an expansive examination of the role of customer focus across outsourcing settings and nations, covering a diverse array of customer service functions, including installation, maintenance and repair, system integration, logistics services, and customer problem-solving and support services via telephone or email. Moreover, in spite of the differences in organizational level (i.e., client vs. provider) and cultural setting (Europe vs. India), the results of our two studies display a high degree of congruence, enhancing 11

For exceptions, see Homburg and Furst, 2005; Johnson et al., 1996.

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the generalizability of our findings. Both studies reveal that the positive baseline effect of provider customer focus on customer need fulfillment is moderated by close ties. Although our findings likely generalize across a range of complex industrial services, they may be less

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applicable for more routine-like services, such as customer complaint handling or financial service transactions.

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In addition to the constraints of our sample, we also recognize the limitations of our

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measures. Perhaps most importantly, we would have preferred to obtain our measure of customer need fulfillment from end customers rather than from client firms (or from provider firms in the

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India study). Unfortunately, due to confidentiality concerns, we were unable to obtain this type of data. While we acknowledge this limitation, prior research suggests that managers often track

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customer perceptions and thus, have a fairly accurate understanding of their customers’ concerns (Im and Workman, 2004). As a validation step, we found that clients’ assessments of customer

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service delivery were negatively correlated with objective indicators that reflect poor service

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delivery (number of complaints and response time), strengthening confidence in our perceptual measure. Nevertheless, future research would be helpful in terms of validating our findings by collecting satisfaction measures (and other relevant metrics) at the level of the individual customer.

The inclusion of country variables highlights another interesting avenue for future research. For a decade, there has been considerable political debate about the negative implications of outsourcing operations to providers located in remote countries where labor is plentiful and cheap (Davis, 2004; Economist, 2013; Kinetz, 2003). Operations management scholars have recently formulated alternative arguments against offshoring practice. For example, Handley and Benton (2013) showed that the geographic distance between client and provider

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locations is associated with higher levels of control and coordination costs. Likewise, Stringfellow et al. (2007) argued that customer support services should not be offshored to distant locales due to the inherent need for localized knowledge and communication skills. To

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paraphrase Friedman (2005), the world has not yet flattened and there still appears to be some important distinction between Bangalore versus Bethesda. Our finding that outsourcing to China

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exerts a negative effect on customer need fulfillment is in line with these ideas. We also find,

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however, that outsourcing to Italy and Czech Republic negatively affects customer need fulfillment whereas outsourcing to Ireland or the UK enhances customer need fulfillment. Large-

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scale international research projects may help unveil the reasons for these differences. Finally, our findings open up other opportunities to expand our theoretical framework.

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For example, the homophily principle (the tendency to associate with similar others) in the study of triads and networks is a very robust empirical regularity (Kossinets and Watts, 2009). Even

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though we explicitly control for the client’s strategic objective to outsource (e.g. to enhance the

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quality of service delivery rather than simply cut costs), the interaction effect between provider and client customer focus may also be partially explained by the trust and cost reductions engendered by homophily. Siguaw et al.’s (1998) finding of a positive association between a supplier’s market orientation and a distributor’s market orientation as well as Langerak’s (2001) evidence of market orientation spillovers from manufacturers to individual salespeople are indicative of such a homophily mechanism at work. A thorough analysis of the role of homophily requires a longitudinal design since the selection of a similar other may result from individual preferences (i.e. active partner selection), from structural proximity (higher likelihood of encountering similar others), or, more likely, from the interplay between both mechanisms over time (Kossinets and Watts, 2009). It would be very interesting to build on these recent

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breakthroughs in sociology and introduce and examine such dynamic processes in the study of

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partner selection and collaboration in triads and larger business networks.

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FIGURE 1

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CONCEPTUAL FRAMEWORK

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FIGURE 2 VISUALIZATION OF CONTINGENCY EFFECTS12

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All simple slopes visualized in Figure 2 are significant at 0.05 significance level.

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TABLE 1

Industry Manufacturing products Manufacturing services

84% 16%

Outsourced Services Equipment-related services Other services

55% 45%

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30% 31% 39%

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Outsourcing Experience Less than 2 years 2-5 years 5+ years

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95% 5% 0%

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Firm Size (employees) Less than 1000 1000-5000 Over 5000

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SAMPLE DESCRIPTORS (NETHERLANDS STUDY)

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TABLE 2 DESCRIPTIVE STATISTICS AND CORRELATIONS (NETHERLANDS STUDY)

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Min Max Mean Stdev CNF PCF REL CCF TUR PRES INFO OBJ CFS Customer Need 1 7 4.17 1.34 .61 Fulfillment (CNF) Provider Customer 1 7 3.25 1.31 .43** .56 Focus (PCF) Relational Tie 1 7 4.86 1.19 .46** .52** .61 (REL) Client Customer 1 7 4.64 1.25 .23** .23** .19* .54 Focus (CCF) Market Turbulence 1 7 3.65 1.38 .10 .11 .02 .13 .50 (TUR) Provider Resources 2.2 7 4.40 .98 .38** .37** .40** .14 .20** .50 (PRES) Operational 1 7 4.30 1.37 .40** .47** .46** .25** .14 .23** .51 Information Sharing (INFO) Strategic Objective 1 7 4.01 1.53 .37** .52** .31** .06 .22** .34** .28** .57 (OBJ) Client Firm Size 15 5400 287.78 610.78 -.08 .05 -.06 .20* .05 .03 .01 -.02 NA (CFS) Note: the descriptives Min, Max, Mean, and Stdev are calculated on the basis of the original values rather than factor scores; the descriptives for Client Firm Size are calculated on the basis of the number of employees, before log-transformation; The AVEs (average variance extracted) for each construct are shown on the diagonal. * indicates significance at the .05 level; ** indicates significance at the .01 level.

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Hyp.

H1 (+) H2 (+) H3 (+) H4 (-)

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Constant Term Provider Customer Focus (PCF) PCF * Relational Tie PCF * Client Customer Focus PCF * Market Turbulence Relational Tie Client Customer Focus Market Turbulence Provider Resources Operational Information Sharing Strategic Objective Client Size Equipment-related Services (ERS) China Czech Republic Italy Ireland UK

MODEL 1 Parameter (st. error) .44 (.33)

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Variable

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TABLE 3 DRIVERS OF CUSTOMER NEED FULFILLMENT (NETHERLANDS STUDY)

.23 (.07) ** .37 (.07) ** .15 (.07) * -.08 (.06) -.06 (.13) -2.09 (.81) * -1.01 (.42) * -2.73 (.80) ** 2.08 (.80) ** .95 (.58)

MODEL 2 Parameter (st. error) .45 (.32) .13 (.08) †

MODEL 3 Parameter (st. error) .40 (.32) .04 (.08)

.21 (.07) ** .33 (.07) ** .11 (.07) -.08 (.06) -.05 (.13) -2.08 (.81) * -.94 (.42) * -2.58 (.81) ** 2.01 (.80) * 1.02 (.57) †

.22 (.08) ** .07(.07) -.04 (.06) .16 (.07) * .26 (.07) ** .13 (.08) † -.07 (.06) -.01 (.13) -1.98 (.80) * -1.10 (.42) ** -2.46 (.80) ** 1.95 (.78) * 1.04 (.57) †

MODEL 4 Parameter (st. error) .26 (.32) .02 (.08) .09 (.05) * .20 (.06) ** -.12 (.05) * .25 (.08) ** .09 (.06) -.04 (.06) .11 (.07) † .20 (.07) ** .15 (.07) * -.06 (.06) .00 (.12) -1.82 (.76) * -.96 (.40) * .-2.30 (.76) ** 1.98 (.74) ** .98 (.54) †

PCF * Market Turbulence * ERS PCF * ERS Market Turbulence * ERS R Square Adjusted Model Improvement: F-statistic

MODEL 5 Parameter (st. error) .31 (.32) -.05 (.10) .10 (.05) * .18 (.06) ** -.30 (.08) ** .24 (.08) ** .08 (.06) .13 (.10) .15 (.07) * .21 (.07) ** .14 (.07) ** -.07 (.06) -.04 (.12) -2.02 (.75) ** -.96 (.39) ** -2.31 (.75) ** 2.00 (.73) ** 1.06 (.53) * .28 (.11) ** .08 (.12) -.23 (.12) †

0.37

0.38 2.59

0.41 3.86 **

0.47 7.21 **

0.49 2.60 *

† indicates significance at the .10 level; * indicates significance at the .05; level ** indicates significance at the .01 level; significances of hypothesized effects are based on onesided tests, all other significances are based on two-sided tests.

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TABLE 4 DESCRIPTIVE STATISTICS AND CORRELATIONS (INDIA STUDY)

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Min Max Mean Stdev PERF PCF REL PRES INFO Provider Performance 4 7 6.17 .68 .55 (PERF) Provider Customer 2.33 7 5.92 .86 .32** .54 Focus (PCF) Relational Tie 1 7 6.02 .88 .41** .47** .50 (REL) Provider Resources 1 7 6.00 .80 .22** .50** .45** .52 (PRES) Operational 2.33 7 5.79 .89 .37** .45** .58** .52** .46 Information Sharing (INFO) Note: the descriptives Min, Max, Mean, and Stdev are calculated on the basis of the original variables rather than factor scores; The AVEs (average variance extracted) for each construct are shown on the diagonal. ** indicates significance at the .01 level

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Hypothesis

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R Square Adjusted Model Improvement: F-statistic

MODEL 2 Parameter (standard error) .13 (.11) .21 (.09) *

MODEL 3 Parameter (standard error) 0.13 (.11) .15 (.09) †

.04 (.09) .34 (.09) ** .26 (.16) †

-.04 (.10) .29 (.10) ** .26 (.16) †

.24 (.10) * -.07 (.10) .19 (.10) † .25 (.16)

MODEL 4 Parameter (standard error) .079 (.10) .15 (.09) * .15 (.04) ** .37 (.10) ** .06 (.10) .15 (.10) .29 (.14) *

0.13

0.16 15.48 **

0.19 5.99 **

0.27 4.91 *

H1 (+) H2 (+)

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Constant Term Provider Customer Focus (PCF) PCF * Relational Tie Relational Tie Provider Resources Operational Information Sharing Client Size

MODEL 1 Parameter (standard error) .13 (.11)

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Variable

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TABLE 5 DRIVERS OF PROVIDER PERFORMANCE (INDIA STUDY)

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† indicates significance at the .10 level; * indicates significance at the .05; level ** indicates significance at the .01 level; significances of hypothesized effects are based on onesided tests, all other significances are based on two-sided tests.

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ACKNOWLEDGEMENTS

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This research was supported by grants from the Institute for the Study of Business Markets at Pennsylvania State University and the Center for International Business Education and Research at Georgia Institute of Technology. The authors thank Kersi Antia, Inge Geyskens, Christine Moorman, and Peter Verhoef for their helpful comments on earlier drafts of this paper. They also thank seminar participants at BI Norwegian Business School, Rotterdam School of Management, and Tilburg University. The first author acknowledges the support of the Netherlands Organization for Scientific Research.

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Reliability

Provider (Client) Customer Focus

α = .84 (α = .83)

Ac

Source: Adapted from Kumar et al., 1992

Relational Tie

α = .83

Source: Adapted from Rindfleisch and Moorman, 2003

Market Turbulence Source: Jaworski and Kohli, 1993

Based on your experiences with this service provider, please rate to what extent you agree with the following statements regarding the service provider’s (your firm’s) internal processes and routines. (1 = strongly disagree; 7 = strongly agree) 1. This (Our) firm has procedures in place to help us understand and address different types of customer support issues 2. This (Our) firm has processes to systematically analyze customer information 3. This (Our) firm has interdepartmental meetings at least once a quarter to discuss market trends and developments 4. Data on customer satisfaction are disseminated at all levels in this (our) firm on a regular basis

.82 (.63) .72 (.59) .61 (.95) .81 (.72)

pt α = .86

ce

Customer Need Fulfillment

Factor Loadings

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Source: Adapted from Kohli et al., 1993

Items

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Measure

an

APPENDIX A KEY MEASURES: NETHERLANDS STUDY

α = .75

The following questions focus on the degree to which the service provider attends to customer needs. Please indicate how strongly you agree or disagree with each statement. (1 = strongly disagree; 7 = strongly agree) 1. This firm helps solve customers’ problems. 2. This firm provides useful information to our customers. 3. This firm has shown itself proactive in addressing customer needs. 4. Customers seem generally pleased with the services provided by this firm.

Please rate the degree to which the following statements describe the current status of your firm’s relationship with this service provider. (1 = strongly disagree; 7 = strongly agree) 1. Our interactions with this organization can be defined as “mutually gratifying” 2. We expect to be interacting with this organization far into the future 3. We would be willing to make adjustments to help out our service provider when faced with special problems/circumstances 4. Our service provider would be willing to make adjustments to help out when we are faced with special problems/circumstances The following questions relate to your firm’s environment. Please indicate how strongly you agree or disagree with each statement. (1 = strongly disagree; 7 = strongly agree) 1. In our industry, customer preferences change quite strongly over time 2. Our customers are continuously on the lookout for new products 3. New customers have typically product related needs that deviate from those of our existing customers

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.73 .85 .76 .77

.76 .90 .57 .86

.71 .75 .67

cr us

Provider Resources

Evaluate the skills of this service provider as compared to other potential service providers in the following areas, based on your experiences with this service provider and your own assessment of the provider’s company. (1 = much worse; 7 = much better) 1. Technology to support service delivery 2. Employee knowledge of customer needs 3. Quality of technical support personnel 4. Availability of funds to pursue new developments in customer support delivery 5. Physical infrastructure and facilities

an

α = .83

α = .82

pt

Source: New Scale

α = .84

ce

Strategic Objective

Please rate the degree to which the following statements describe the current status of your firm’s relationship with this service provider/client firm. (1 = strongly disagree; 7 = strongly agree) 1. Our organizations regularly exchange information related to changes in the technology of this product or service 2. Our organizations exchange information about unexpected problems as soon as possible 3. It is common to establish joint teams with our outsourcing partner to solve operational problems in this relationship

ed

Operational Information Sharing

M

Source: Adapted from Eisenhardt and Martin, 1999

Ac

Source: Self-constructed

How important were each of the following factors in your decision to outsource this service? 1. Enhancing customer focus in the long run 2. Accessing new skills and technology 3. Improving quality of service delivery to customers 4. Exploring options to offer better service to customers

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.65 .50 .80 .69 .77

.66 .65 .82

.78 .65 .79 .80

cr us

Measure Provider Customer Focus

an

APPENDIX B KEY MEASURES INDIA STUDY

Reliability

α = .75

ce

Relational Tie

α = .74

Ac Source: New Scale

The next set of questions focus on your firm’s customer-related capabilities since you began providing the customer support activities for this particular client firm. (1 = strongly disagree; 7 = strongly agree) 1. In general, our customer-serving abilities are excellent 2. In general, our market-response abilities are excellent 3. We are able to offer services that help attract new customers Please rate the degree to which the following statements describe the current status of your firm’s relationship with this client. (1 = strongly disagree; 7 = strongly agree) 1. We expect to be interacting with this client far into the future 2. We would be willing to make adjustments to help out our client when faced with special problems/circumstances 3. Our client would be willing to make adjustments to help out when we are faced with special problems/circumstances Compared to your competitors, please rate your firm’s abilities in the following areas. (1 = much worse; 7 = much better) 1. Technology to support service delivery 2. Employee knowledge of customer needs 3. Quality of technical support personnel 4. Availability of funds to pursue new developments in customer support delivery Please rate the degree to which the following statements describe your interactions with this client firm. (1 = strongly disagree; 7 = strongly agree) 1. Our organizations regularly exchange information related to changes in the technology of this product or service 2. Our organizations exchange information about unexpected problems as soon as possible 3. It is common to establish joint teams with our client firm to solve operational problems in this relationship

pt

Source: New scale

Source: Adapted from Rindfleisch and Moorman, 2003 Provider Resources Source: Adapted from Eisenhardt and Martin, 1999 Operational Information Sharing

Please rate the degree to which the following statements describe your firm’s organizational practices (1 = strongly disagree; 7 = strongly agree) 1. We have procedures in place to help us understand and address different types of customer support issues 2. We systematically analyze customer support problems 3. We have interdepartmental meetings at least once a quarter to discuss market trends and developments

ed

Source: Adapted from Kohli et al., 1993 Provider Performance

Factor loadings

M

α = .75

Items

α = .79

α = .71

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.51 .77 .88

.76 .69 .70 .62 .80 .67

.70 .65 .72 .73 .57 .81 .64

.05 (.12) .16 (.11) † .30 (.11) ** -.19 (.10) * .34 (.15) * .10 (.10) -.06 (.10) .18 (.08) * .17 (.05) ** .13 (.05) * .00 (.13) -2.25 (.88) ** -1.15 (.44) ** -2.68 (.88) ** 2.39 (.88) ** 1.07 (.61) †

cr

H1 (+) H2 (+) H3 (+) H4 (-)

us

Parameter (st. error)

an

Constant Term Provider Customer Focus (PCF) PCF * Relational Tie PCF * Client Customer Focus PCF * Market Turbulence Relational Tie Client Customer Focus Market Turbulence Provider Resources Operational Information Sharing Strategic Objective Equipment-related Services China Czech Republic Italy Ireland UK

Hypothesis

M

Variable

ip t

APPENDIX C ESTIMATION RESULTS STRUCTURAL EQUATIONS MODEL – NETHERLANDS STUDY

Ac ce pt e

d

† indicates significance at the .10 level; * indicates significance at the .05; level ** indicates significance at the .01 level; significances of hypothesized effects are based on one-sided tests, all other significances are based on two-sided tests.

61

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