improving the operational performance of the relationship. Based on a dyadic ... performance; resource-based view; relational view; dyadic data; mediation; structural equation modeling ...... public schools. Organization .... Automotive. 5. 8. 8.
A Dyadic Investigation of Collaborative Competence, Social Capital, and Performance in Buyer‐ Supplier Relationships Judith M. Whipple, Robert Wiedmer, and Kenneth K. Boyer Article Information: Judith M. Whipple, Robert Wiedmer, and Kenneth K. Boyer (2015), “A Dyadic Investigation of Collaborative Competence, Social Capital, and Performance in Buyer‐Supplier Relationships,” Journal of Supply Chain Management, Vol. 51 No. 2. Abstract As supply chains become increasingly complex, the management of buyer‐supplier relationships is essential for achieving superior performance. In order to enhance external collaborative relationships, many firms are investing in the development of internal relationship management skills. We propose that the development of internal collaborative process competence (CPC) is an important component for improving external collaborative relationships through the creation of social capital. Our research examines the potential for social capital, which is modeled as a second order factor consisting of structural, cognitive, and relational capital, to mediate the relationship between CPC and operational performance. Our findings provide insights as to whether internal competence alone is sufficient for improving the operational performance of the relationship. Based on a dyadic comparison of buyers and suppliers, we find that investment in internal CPC without building external social capital, does not lead to any improvement in operational performance. However, investment in CPC is beneficial in cases where buyers and suppliers have also built a high level of social capital, which, in turn, leads to desired operational performance for the relationship. Keywords: buyer‐supplier relationships; collaborative competence; social capital; operating performance; resource‐based view; relational view; dyadic data; mediation; structural equation modeling
INTRODUCTION Firms continue to recognize the need for strong relationships with their supply chain partners as a means for managing the complexities involved in today’s competitive global markets. In particular, collaborating with suppliers is a key element of many firms’ strategic approaches to achieving competitive advantage. Such collaboration often takes the form of formal supplier integration initiatives. Supplier integration has been defined as “the combination of internal resources of the buying firm with the resources and capabilities of selected key suppliers through the meshing of intercompany business processes to achieve competitive advantage” (Wagner, 2003, p. 4). When deciding whom to collaborate with, it is important to find key suppliers that offer new knowledge and complementary competencies (Zacharia, Nix, & Lusch, 2011). In this sense, supplier integration can lead to enhancing both parties’ supply chain competencies while offering the potential for competitive advantage. Dyer and Singh (1998) referred to such advantages as the attainment of relational rents. To attain relational rents, Bowersox, Closs and Stank (2003) indicate that various competencies (e.g., supplier integration, internal integration, etc.) are required to integrate a firm’s internal capabilities with its external partner’s capabilities. As such, successful inter‐organizational collaboration often requires significant intra‐organizational investments, namely the ability to collaborate with external partners. Unfortunately, “few companies are organized properly to implement and manage collaborative relationships” (Bowersox, Closs & Stank, 2003, p. 19). In order to build successful inter‐ organizational relationships, firms must build internal resources and management capabilities (Wagner, 2003). Zacharia et al. (2011) use the term collaborative process competence (CPC) to describe the intra‐ organizational skills needed to successfully manage inter‐organizational collaborative initiatives. However, companies are lacking in the development of strategies that enable the effective management of supply chain collaborations. One way to address this challenge is to invest in programs that facilitate CPC skills. The value of such programs is often difficult to quantify. In fact, Zimmermann
2
and Foerstl (2014) indicate that purchasing and supply management (PSM) practices that are internal‐ facing, such as enabling skill development, are not well understood from a research standpoint. Therefore, the question becomes, “Does the development of internal collaborative skills pay off?” Firms expect developed competencies will result in management techniques that generate successful supply chain relationships, but it is unclear whether or not the presence of intra‐organizational CPC alone is sufficient to produce successful inter‐organizational relationships. As indicated by Dyer and Singh (1998, p. 675) “a relational capability is not a sufficient condition for realizing relational rents.” Zimmermann and Foerstl (2014) also acknowledge the relationships among internal‐facing PSM practices, supplier‐ facing PSM practices and performance are under‐researched. Given this research gap, we propose that firms must actively demonstrate their internal collaborative competence in order to enhance external collaborative relationships. For example, buyers cannot lure suppliers into collaborating and then revert to adversarial behaviors, such as extreme cost pressures, while expecting suppliers to still believe the relationship is collaborative. Social capital provides valuable resources for the involved parties (Nahapiet & Ghoshal, 1998), which can enhance inter‐organizational relationships. Social capital refers to the “relational resources attainable by individual actors through networks of social relationships” (Tsai, 2000, p. 927). Social connections, characterized by trust, information exchange and shared vision, play an important role in achieving superior performance, which can result in added value for firms engaged in collaboration (Autry & Griffis, 2008; Cousins, Handfield, Lawson, & Peterson, 2006; Krause, Handfield, & Tyler, 2007). Following this line of argument, social capital is viewed as a tacit resource residing in relationships. The literature has paid only limited attention to social capital within a supply chain management context (Krause et al., 2007). Further, “the tension between a strong desire to combine complementary competencies for distinctive advantage and the persistent inability to build relational advantage suggests a need to take a closer look inside the ‘black box’ of collaboration” (Fawcett, Fawcett, Watson,
3
& Magnan, 2012, p. 45). This tension is well noted and illustrated by the challenge that many firms face when trying to manage supply chain relationships (Nyaga, Lynch, Marshall, & Ambrose, 2013). Regarding this challenge, recent literature emphasizes the importance of effectively managing supply chain resources to ensure supply chains function and perform (Crook & Esper, 2014). Despite the importance of managing supply chain resources, Van Weele and Van Raaij (2014) acknowledge a lack of research focused on the “strategies and competences to manage external resources” (p. 60). Specifically, we examine the impact of intra‐organizational CPC on inter‐organizational social capital, and the resulting impact on operational performance in buyer‐supplier relationships. This is important given that research examining the relationship between supply chain integration and performance has shown inconsistent results, giving rise to the need to study integration and performance more comprehensively (Flynn, Huo, & Zhao, 2010). In addition, our research addresses the internal‐facing/external‐facing practices gap discussed by Zimmermann and Foerstl (2014). We test whether a mediating effect exists (i.e., CPC on operational performance through social capital) in order to understand whether CPC alone is sufficient for improving performance in buyer‐supplier relationships. In other words, are both intra‐ and inter‐organizational competencies needed for successful collaboration to occur? Further, to understand if the model is consistent regardless of channel position, the proposed model considers both buying and supplying firms’ perspectives. Given that the vast majority of supply chain studies rely on data from a single party in the relationship, understanding the impact of channel position is an important consideration as only a limited number of multi‐stakeholder studies have been conducted (Ellram & Hendrick, 1995; Carter, 2000; Cheung, Myers, & Mentzer, 2010; Nyaga et al., 2013). The focus on different stakeholders is especially important in cases where external resources, such as social capital, are developed collaboratively. In that regard, many relationships are lacking relationship‐ specific investments, which describe an ongoing issue between supply chain partners (Paulraj, Chen, &
4
Lado, 2012). We also address the question of how such investments need to be made by both partners. The next section examines the proposed model and hypotheses supported by theoretical perspectives and the literature. The research design and methodology are then presented, followed by our results and conclusions. THEORETICAL FRAMEWORK The resource‐based view (RBV) of the firm and the relational view (RV) provide a theoretical lens for examining the processes that firms use to develop internal and external strategic resources, which, in turn, create competitive benefits (Zacharia et al., 2009). The RBV is a theoretical framework for explaining how a firm can achieve competitive advantage through intra‐organizational resources and competencies whereby a firm is able to create value that is unique and difficult for competitors to duplicate (Penrose, 1959; Barney, 1991). Day and Wensley (1988) describe advantages as resulting from superior skills and resources where superior skills represent the “distinctive capabilities of personnel that set them apart from personnel of competing firms” (p. 2‐3) and where superior resources represent the “tangible requirements for advantage that enable a firm to exercise its capabilities” (p. 3). Superior skills and resources can be integrated resulting in distinctive competencies (Day & Wensley, 1988). Fawcett, Wallin, Allred, Fawcett, and Magnan (2011, p. 39) indicate that the RBV can be useful in examining how companies “organize and deploy resources to achieve advantage.” In addition to intra‐organizational resources and competencies, a firm can also achieve a competitive advantage by connecting with other firms in a manner that creates a unique inter‐ organizational value chain (Porter, 1991). A firm can enhance its core competencies by partnering with other firms (McHugh, Humphreys, & McIvor, 2003; Petersen, Handfield, Lawson, & Cousins, 2008). Expanding on the RBV, Dyer and Singh (1998) indicate that competitive advantage could result by developing relational resources that extend beyond an individual firm; thus, the Relational View (RV) proposes that relational rent between partners leads to improved benefits for all involved parties. One
5
of the key advantages of developing inter‐organizational relational resources is that it enables firms to combine “complementary, but scarce, resources and capabilities” (Dyer & Singh, 1998, p. 662). Relational rent can develop through collaborative initiatives such that “collaborating with other firms allows access to resources and skills that are difficult to duplicate and are not available from within the firm” (Zacharia et al., 2009, p. 103). Combining the RBV and RV provides a strong framework to examine the collective impact of internal and external collaborative efforts on organizational performance. Internal efforts, as measured by CPC, can be combined with external collaboration, as measured by social capital, to improve the strategic position of a firm and its supply chain. The following section examines the relationships among CPC, social capital, and operational performance and introduces the research hypotheses. The proposed relationships are shown in Figure 1. Insert Figure 1 here LITERATURE REVIEW AND HYPOTHESIS DEVELOPMENT Zacharia et al. (2011) argue that organizations can develop internal competencies that enable them to manage collaboration initiatives more efficiently and effectively. The authors use the term collaborative process competence (CPC) to reflect a company’s internal ability to “select appropriate partners, establish processes to monitor and manage the initiative, and resolve conflicts and difference of opinion as they arise” (Zacharia et al., 2011, p. 594). Paulraj, Lado, and Chen (2008, p. 46) similarly conceptualized relational competence as resulting when “a strategic intent then drives firms to acquire, access, or develop additional resources through cooperation”. Kale, Dyer, and Singh (2002) emphasized that managers could improve their relational competencies over time, leading to enhanced relationships. The various intra‐organizational skills that are posited to lead to CPC include: “(1) recognize, select, and negotiate with potential partners, (2) manage interactions such that roles and responsibilities are clear, (3) work with their partner to combine and synthesize complementary knowledge and resources, (4) resolve conflicts that arise as part of the
6
interaction, and (5) monitor the process and make adjustments if things are not moving in a positive direction” (Zacharia et al., 2011, p. 594). Hunt and Davis (2008) argue that competitive resources can reside both within an organization as well as external to an organization, and that external social resources are “more likely to produce a sustainable competitive advantage” than internal resources (p. 16). While CPC, as an intra‐ organizational resource, is available to one firm, social capital, as an inter‐organizational resource, can exist among firms. McCallum and O’Connell (2009, p. 164) conclude that active cultivation of social capital elements, such as developing trust, goodwill, and strong relationships, is crucial, stating that firms need to have not only “the knowledge, skills and abilities to operate effectively but also possess the relational capabilities to partner with others to realize their vision and goals.” Consequently, we propose that internal CPC plays an important role in achieving the desired level of social capital. While the supply chain literature acknowledges the importance of collaborative competence, there is no empirical evidence of the relationship between CPC and social capital. Recent research illustrates that a key barrier to successful collaboration is the unwillingness on the part of managers to adopt collaborative behavior (Fawcett et al., 2012). It is important to understand the role that internal relationship management skills, such as CPC, play on influencing inter‐organizational social capital as indicated by the following hypothesis: H1:
Collaborative Process Competence has a positive impact on Social Capital. Gained through mutual effort, social capital provides the individuals, who invest in such
relationships, positive economic and emotional returns (Gulati & Gargiulo, 1999). Value is derived from the collective capital embedded in relationships (Nahapiet & Ghoshal, 1998) and, as such, we consider social capital as an important measure of supplier integration. Nahapiet and Ghoshal (1998) conceptualized, but did not test, three aspects of social capital: the structural, the relational, and the cognitive dimensions. The structural dimension of social capital
7
represents the linkages between people or the pattern of connections, which determine how people reach others within a network (Nahapiet & Ghoshal, 1998). In this way, structural social capital can be operationalized by the frequency of interaction and the existence of multiple connections across diverse hierarchical levels and functions between parties, such as buyers and suppliers. Lawson, Tyler and Cousins (2008) describe the relational dimension as representing the assets that are gained through and embedded within a relationship. Kale, Singh, and Perlmutter (2000) propose five items to measure the relational dimension of social capital: close interpersonal interactions, trust, friendship, respect, and reciprocity. This dimension focuses on how relationships develop and strengthen over time. A lack of trust, for example, has been shown to negatively impact performance (Kwon & Suh, 2004). After describing the more mechanical structure and the personal attachment between individuals in relationships, Tsai and Ghoshal (1998) discuss the cognitive dimension as the existence of a common code of understanding. Most studies describe this dimension as shared vision (e.g., Carey, Lawson, & Krause, 2011; Nahapiet & Ghoshal, 1998). A shared vision enables buyers and suppliers to have similar perceptions in order to create integrated knowledge through “collective goals and aspirations” (Inkpen & Tsang, 2005, p. 157). Autry and Griffis (2008) propose that higher social capital leads to a higher propensity to invest in relationships. Cousins et al. (2006) found that socialization processes enhance relational social capital. Carey et al. (2011) found social capital positively impacted innovation and cost from a buyer’s perspective. As such, we consider social capital as an inter‐organizational resource that leads to superior performance in buyer‐supplier relationships. Only a few empirical studies have applied all three dimensions of social capital as proposed by Nahapiet and Ghoshal (1998) let alone examined the supplier’s perspectives of social capital. Krause et al. (2007) showed a significant link between all three dimensions of social capital and operational
8
performance, but for buying firms only. Further, Carey et al. (2011) conclude that the “relationships among these three dimensions of social capital within strategic buyer‐supplier relationships have been relatively underexplored in the literature” (p. 278). Tsai and Ghoshsal (1998), in examining social capital among business units in one firm, emphasized the interdependence among these three different dimensions. While there is some evidence with respect to the linkage between social capital and performance, we extend previous research by testing this causal relationship for both buyer and supplier firms as well as by examining social capital as a higher order factor comprised of structural, relational, and cognitive dimensions. As such, we propose that: H2:
Social Capital (represented as a higher order factor) has a positive impact on Operational Performance. Further, our conceptual model analyzes the relationship between CPC, social capital, and
operational performance. As this relationship has not been directly examined in the literature, the third hypothesis is based on additional findings from the RBV and RV literature. The literature shows evidence that internal competencies lead directly to improved performance. Researchers recognize the importance of internal capabilities to provide an organization with operational strength and competitive performance (Flynn & Flynn 2004). Barney (2012) argues that firms may gain competitive advantages from internally created supply chain management and purchasing capabilities. Additional research illustrates the impact of successful buyer‐supplier relationships on performance from an RV perspective. For example, Daugherty, Richey, Roath, Min, Chen, Arndt, and Genchev (2006) found that collaborative relationships led to improved operational performance, while Gligor and Autry (2012) found that personal relationships among logistics outsourcing partners (as framed in a social capital perspective) positively impacted business performance through enhanced communication. Lawson et al. (2008) illustrated that social capital improved the buyer’s performance in
9
collaborative buyer‐supplier relationships. This supports Hunt and Davis’ (2008) argument that complex, socially‐driven resources are more likely to generate a competitive advantage. Figure 1 illustrates that the positive effect of CPC on operational performance is mediated by social capital. We hypothesize that the existence of an intra‐organizational competence alone does not lead to improved performance. Rather, when managers apply their skills and knowledge in an integrative manner in order to build stronger external relationships, performance is enhanced. As such, we propose that CPC leads to social capital, which, in turn, leads to better operational performance. We thus put forth the following hypothesis: H3:
Social Capital mediates the effect of Collaborative Process Competence on Operational Performance.
Comparing Buyer‐Supplier Perspectives Social capital is rooted in social exchange theory, and, in part, is predicated on norms of reciprocating behavior (Blau, 1964). Without reciprocity, firms may decide to behave differently than expected, particularly when they perceive the reward for collaboration is not clear (Nyaga et al., 2013). For example, Corsten and Kumar (2005) found that suppliers felt greater inequity associated with collaborative benefits than their retail counterparts. Even in cases where reciprocity exists, buyers and suppliers may view collaborative relationships differently. For example, buyers’ and suppliers’ perceptions have been shown to be more similar concerning structural, as opposed to relational, issues (John & Reve, 1982). Given the potential for differences in perceptions between buyers and suppliers, it is important to include both parties’ perceptions when studying collaborative efforts. When constructs of interest entail multiple‐stakeholders (as is the case with social capital), dyadic data provide benefits over single sided (or non‐dyadic) research, given that both parties’ perspectives on the relationship can be considered (Roh, Whipple & Boyer, 2013). Dyadic research can view the dyads side‐by‐side (i.e., dyadic
10
data are collected but analyzed as two separate groups) or as matched dyads analyzed as dyadic units (Roh et al., 2013). Side‐by‐side comparisons enable each group (i.e., buyer and supplier) to articulate their individual perceptions relative to the shared relationship while matched dyads evaluate the level of agreement and symmetry within each dyad. Each approach offers value, but varies according to the research purpose and unit of analysis. For our research, we selected a dyadic, 2‐sided comparison approach. We collected dyadic data, but treat these matched sets of buyers and suppliers as independent informants for each dyad – in other words, we do not assume consensus among dyadic partners in an approach similar to Cheung et al. (2010). We argue that both buyers and suppliers will exhibit similar causal relationships between the variables of interest (i.e., CPC, social capital and operational performance). RESEARCH DESIGN A survey instrument was developed after a thorough literature review, including literature focused on collaborative relationships, RBV and RV theory, and social capital. Given few empirical studies testing all three dimensions of social capital exist in the supply chain literature, we relied on empirical research studying both intra and inter‐organizational social capital. Proven constructs and items were used to ensure greater convergent and discriminant validity. Items used, for example, to study intra‐organizational shared values were adapted to an inter‐organizational context for our survey. A pretest was conducted with industry and academic representatives familiar with buyer‐supplier relationships. Based on the pretest, minor changes were made to the survey to improve question clarity. The survey was designed to be applicable to both buying and selling firms. The goal of the survey was to collect matched sets of buyer‐supplier data in order to evaluate both parties’ perceptions of the same relationship. We began the data collection effort by identifying 30 manufacturing firms from various industries and firm sizes in order to increase generalizability of the results. We contacted a senior executive, generally responsible for procurement, within each firm and
11
asked for the firm to participate by providing us with a list of purchasing managers/buyers within the firm that would have intimate knowledge of a relationship with at least one supplier. Of the 30 firms contacted, 21 firms agreed to provide us with contact information for at least one purchasing manager/buyer (a total of 182 names were provided). A survey was sent to each name provided. Buying participants were asked to answer the survey focusing on a particular supplier relationship and to provide contact information for that supplier representative. Once buyers returned completed questionnaires, the identified suppliers were provided a mirror‐image questionnaire to complete. Of the 182 purchasing managers/buyers we sent surveys to, 108 respondents completed the survey for a response rate of 59.3% (note: at least one participant from 19 of the 21 manufacturing firms completed the survey; no surveys were completed from participants in 2 of those firms). Table 1 summarizes industry information for the 19 buying firms that actually participated in the research. While the names of the participating firms are concealed, short industry descriptors are provided for the primary product line of each of the buying firms. The size of the buying firms in our sample ranged from $500 million to $10 billion in annual sales. We did not want to bias the level of collaboration exhibited in the supplier relationships selected. As such, we did not qualify the level of collaboration warranted for supplier selection. Instead, we asked buyers to select a supplier providing one of two types of products purchased: functional products or innovative products. Functional products were defined as products with long life cycles, available substitutes, low margin of error for forecasts, and lower stock out rates, while innovative products were defined as products with unique engineering/service requirements, generally short life cycles, limited or no substitutes available, high margin of errors for forecasts, and the potential for high stock out rates (note: product type descriptions were based on Fisher, 1997). Buyers could complete the survey, separately, for up to two supplier relationships (i.e., one functional and one innovative supplier relationship).
12
From those completed buyer surveys, we received contact information for 176 suppliers (i.e., 68 of our buyers provided contact information for two supplier relationships). Of the 176 supplier contacts sent surveys, a total of 109 completed surveys were received for a response rate of 61.9%. After removing surveys with a high number of missing values, a total of 105 matched buyer‐supplier sets were used for testing the hypothesized model. The use of matched sets enables the hypotheses to be evaluated from both the buyer and supplier perspective. Insert Table 1 Here In order to achieve desired response rates, Dillman’s (2000) total design method was used to distribute the surveys to both the buyer and supplier respondents. Identified participants were sent an electronic copy of the survey, which could be completed and returned electronically or completed by hand and faxed/scanned for return. To evaluate nonresponse bias, we compared early versus late response waves across items and demographic variables as prescribed by Armstrong and Overton (1977). We conducted t‐tests for early and late responses and found no significant differences. Construct Measurement The collaborative process competence scale consists of 5 items as used in Zacharia et al. (2011) and originally developed based on the work of Spekman, Salmond, and Lambe (1997). As indicated previously, we conceptualized social capital as three dimensions similar to Nahapiet and Ghoshal (1998) and Tsai and Ghoshal (1998). Structural capital is measured as the manner in which firms are connected using four items from Ellinger, Daugherty, and Keller (2000). Relational capital examines close interpersonal interactions, which we measure as trust using three items adapted from Doney and Cannon (1997) and Moberg and Speh (2003). Cognitive capital examines the extent to which both parties in the relationship have a shared vision, and includes five items adapted from Leana and Pil (2006).
13
Operational performance is measured by five indicators, which are adapted from Knemeyer, Corsi, and Murphy (2003) and Dahlstrom, McNeilly, and Speh (1996). The operational performance measures we used are perceptual measures given the challenge of obtaining sensitive performance data in empirical surveys. However, perceptual measures of performance have been shown to correspond closely to objective performance data (Venkatraman & Ramanujam, 1986). All construct items were assessed based on a 7‐point Likert‐type scale (e.g., “1” = strongly disagree; “7” = strongly agree). To reduce the potential for common method bias, the questionnaire was structured in sections such that respondents read instructions and then proceeded to answer the questions in each section (Podsakoff, MacKenzie, Jeong‐Yeon, & Podsakoff, 2003). Additionally, we performed Harman’s one‐ factor test whereby all construct items were tested together using principal component factor analysis to determine whether a single factor would dominate (Podsakoff & Organ, 1986; Hult, Boyer, & Ketchen, 2007). The unrotated analysis yielded three factors with eigenvalues greater than 1.0 for both the buyer and supplier samples. In the buyer sample, the first factor accounted for 46% of the variance, the second factor accounted for 14% of the variance, and the third factor accounted for 9.5% of the variance. In the supplier sample, the first factor accounted for 48% of the variance, the second factor accounted for 15.8% of the variance, and the third factor accounted for 8.8% of the variance. These results indicate that common method bias is not a significant issue of concern for this data set. We used structural equation modeling (SEM) with Bentler’s EQS to test the hypothesized relationships between the constructs. The hypothesized model was run separately for the buyer and supplier samples. First, the measurement model was developed for each sample to assess unidimensionality, and convergent and discriminant validity. Unidimensionality and convergent validity were assessed from the initial CFA with model development. Any item that exhibited lower item‐to‐ construct correlations and/or items that did not load at an acceptable level were removed from the sample. Given the small number of constructs in the model, along with well‐determined factors with 3‐5
14
indicators per factor, and the high communalities of at least .50 for the buyer and supplier model, the sample size is sufficiently large for the specified measurement model (MacCallum, Widaman, Xhang, & Hong, 1999; Hair, Black Babin, & Anderson, 2010). Additionally, since 17 of the 19 manufacturing firms that actually participated had multiple buyers responding to our survey, there was potential for the buyer group to be nested as buyers within one company may behave similarly (e.g., culture within the firm may influence buyers’ responses and confound the results). In order to assess the possibility of a nested data structure, we tested a two‐level model using SEM in STATA 13.1 to determine if a firm‐level effect had a significant impact on CPC. The buying firm level effect contributes approximately 10% of the overall explained variance while the remaining variance is due to the supplier effect. As such, nesting is not a concern and we proceeded with single‐level (e.g., group) analysis. Table 2 illustrates the final items and factor loadings for each construct. The factor loadings are significant (p‐values
