Supply chain integration and firm financial performance: A meta-analysis of positional advantage mediation and moderating factors

European Management Journal xxx (2015) 1e14

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Supply chain integration and firm financial performance: A metaanalysis of positional advantage mediation and moderating factors Woojung Chang a, *, Alexander E. Ellinger b, Kyoungmi (Kate) Kim b, George R. Franke b a b

College of Business, Illinois State University, Campus Box 5590, Normal, IL, 61790-5590, USA Culverhouse College of Commerce, University of Alabama, P.O. Box 870225, Tuscaloosa, AL 35487-0225, USA

a r t i c l e i n f o

a b s t r a c t

Article history: Received 2 December 2014 Received in revised form 5 November 2015 Accepted 20 November 2015 Available online xxx

Supply chain integration (SCI) is recognized as strategic process management that can be instrumental for creating positional advantages associated with improved firm performance. However, despite rigorous execution, recent meta-analyses derive different conclusions about the benefits of SCI. We propose that these inconsistencies may be associated with selection bias, failure to consider the mediating routes by which SCI affects financial performance, and lack of investigation of moderators. To address these issues, we apply positional advantage theory and the resource-based view, and focus on mitigating the potential selection bias by aggregating findings from 170 previous investigations in a comprehensive meta-analysis, to examine how discrete dimensions of SCI enhance firm financial performance through three types of intermediate firm performance. The moderating effects of time, relationship quality, and national culture are also assessed. The findings confirm that each dimension of SCI indeed improves financial performance. However, contrary to expectations, relational and strategic types of intermediate performance associated with superior customer value positional advantage have stronger mediating effects than operational performance associated with lower cost positional advantage. In addition, time, relationship quality, and collectivist national culture strengthen the associations between some dimensions of SCI and firm performance. Our study findings are reconciled with those from recent meta-analytic studies, and implications arising from our conclusions that may inform practice about how to effectively leverage SCI are presented. © 2015 Elsevier Ltd. All rights reserved.

Keywords: Supply chain integration Internal integration Firm performance Meta-analysis Resource-based view Positional advantage

1. Introduction Firms' strategic efforts to create positional advantages in marketplaces and achieve better performance by improving the efficiency and effectiveness of supply chain activities and processes are heavily dependent on supply chain integration (SCI). SCI is a firm's strategic collaboration and coordination with its suppliers and customers and the management of internal and external organizational processes. The essence of SCI is that streamlining core business processes within and between firms yields advantages over competitors through cost reduction or superior customer value creation that are associated with superior firm performance (Leuschner, Rogers, & Charvet, 2013; Mackelprang, Robinson, Bernardes, & Webb, 2014). Research that examines performance

* Corresponding author. E-mail addresses: [email protected] (W. Chang), [email protected] (A.E. Ellinger), [email protected] (K. Kim), [email protected] (G.R. Franke).

benefits associated with SCI has proliferated in the past twenty years, and two recent meta-analyses conducted by Leuschner et al. (2013) and Mackelprang et al. (2014) present empirical generalizations on the SCI-firm performance relationship. Despite their rigorous execution, the two meta-analytic consolidations of the extant research “derive different conclusions pertaining to the overall ‘value proposition’ of SCI” (Autry, Rose, & Bell, 2014, p. 275). Autry et al. (2014) contend that the inconsistent findings are largely due to the application of different definitions, operationalizations, and levels of analysis for SCI. However, in addition to differences in definitions and operationalizations of key constructs highlighted by Autry et al. (2014), other issues such as selection bias, failure to consider the mediating routes by which SCI affects firm financial performance, and lack of investigation of moderators may have constrained the researchers from coming to comprehensive and generalized conclusions about the benefits of SCI. To develop a more complete understanding of the SCI-firm performance linkage by taking into account these other issues, we apply the resource-based view (RBV) and positional advantage

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Please cite this article in press as: Chang, W., et al., Supply chain integration and firm financial performance: A meta-analysis of positional advantage mediation and moderating factors, European Management Journal (2015), http://dx.doi.org/10.1016/j.emj.2015.11.008

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theory (PAT) to conduct a meta-analysis that aggregates 170 samples representing a total of 39,495 observations. Our study extends current knowledge about the benefits of SCI in three ways. First, our comprehensive meta-analysis contributes to accomplishing empirical generalization while avoiding potential selection bias. Selection bias refers to “[t]he tendency of meta-analytic authors to select particular studies” (Eisend & Tarrahi, 2014, p. 317). Eisend and Tarrahi (2014) contend that biased estimates of relationships of interest caused by selection bias are critical threats to the rigor of meta-analytic studies. Selection bias can arise from the limitation of the literature search to a few electronic databases or particular journal outlets (e.g., leading journals) or the exclusion of unpublished work (i.e., publication bias) (Borenstein, Hedges, Higgins, & Rothstein, 2009; Eisend & Tarrahi, 2014). In the context under examination, Leuschner et al.’s (2013) restriction of the search process to one electronic database and exclusion of unpublished papers, Mackelprang et al.’s (2014) limitation of the search process to two databases, and the associated relatively small sample sizes in both studies (80 and 35 respectively) collectively increase the possibility that selection bias may have affected their study findings. To minimize the eventuality of generating conclusions that may be influenced by selection bias, the extensive dataset generated for our study comprises 170 independent samples from multiple databases and includes unpublished papers. Based on the application of a rigorous protocol for avoiding selection bias, our meta-analysis is expected to yield more generalizable and theoretically consistent conclusions. Second, our application of RBV and PAT provides a more holistic assessment of how, and indeed whether SCI affects firm financial performance – the ultimate bottom-line concern for all firms. The proposed theoretical framework and associated study hypotheses investigate the direct effect of SCI on firm financial performance as well as the indirect mediating effects by which SCI enhances financial performance through improved intermediate types of performance associated with lower cost and superior customer value positional advantages. Failure to consider both direct and indirect effects may also result in potentially misleading conclusions. For example, Leuschner et al.’s (2013) finding that SCI is not positively related to financial performance is based purely on the direct association between the two variables. However, reporting this insignificant direct relationship may mislead mangers by generating perceptions that financial returns associated with SCI are not large enough to warrant implementation. Therefore, in order to provide a more complete understanding of SCI's contributions to firms' bottom lines, our framework captures the direct effect as well as the indirect mediating influences of three types of intermediate firm performance (i.e., operational, relational, and strategic performance) between SCI and firm financial performance. Lastly, in response to Mackelprang et al.’s (2014, p. 92) call for research that identifies “potential unknown moderators” that may affect the SCI-performance linkage, we further explore the nuances of the relationship by examining the moderating effects of time, relationship quality, and national culture. Recent meta-analyses provide insights into how the effects of SCI on firm performance differ contingent on dimensions of SCI (e.g., internal, supplier, and customer integration) or types of firm performance (e.g., financial, market, operational, and relational performance). However, substantial or sample-specific moderators beyond operationalizationrelated moderators may provide additional explanations of why the SCI-firm performance association varies (Leuschner et al., 2013). We therefore seek to advance theory and practice by proposing and testing the moderating effects of time, relationship quality, and national culture on the SCI-firm performance linkage. The remainder of the paper is organized as follows. Before

presenting the study hypotheses, the dimensions of SCI, types of firm performance, and theoretical foundations for this study are briefly reviewed. Next, the meta-analytic method, results and implications of the study findings are discussed. Finally, a future research agenda is proposed to facilitate a better understanding of how firms can effectively leverage SCI to achieve performance objectives, and to identify additional moderating factors that may affect the SCI-firm performance relationship. 2. Background SCI and firm performance are both recognized as complex, multi-faceted constructs. Flynn, Huo, and Zhao (2010, p. 58) therefore argue that “to fully understand SCI and its relationship to performance, there is a need to examine … how individual dimensions of SCI are related to different dimensions of performance.” Given that inconsistent findings on the value of SCI come from differences in definitions and operationalization of key constructs (Autry et al., 2014), we first explicitly define each dimension of SCI and type of firm performance. 2.1. Dimensions of SCI As pointed out by Autry et al. (2014), the literature on SCI has developed from divergent and often inconsistent perspectives. For example, Leuschner et al. (2013) focus on SCI characteristics and activities by classifying SCI as information integration that refers to the coordination of information and the availability of supporting information technology among firms in the supply chain (e.g., Hill & Scudder, 2002; Holweg, Disney, Holmstrӧm, & Småros, 2005), operational integration that focuses on the collaborative joint activities and work processes among firms (e.g., Ireland & Webb, 2007; Saeed, Malhotra, & Grover, 2005), and relational integration that emphasizes a strong connection between firms in the supply chain based on trust, commitment, and long-term orientation (e.g., Chen, Paulraj, & Lado, 2004; Johnson, 1999). In contrast, and consistent with the predominant stream of recent empirical research studies (e.g., Flynn et al., 2010; Zhao, Huo, Flynn, & Yeung, 2008; Zhao, Huo, Selen, & Yeung, 2011), Mackelprang et al. (2014) aggregate characteristics of SCI like information, technology, process, and relationship to classify SCI into the three dimensions of internal, supplier, and customer integration. Internal integration refers to a firm's coordination and collaboration of its organizational information, processes, and behaviors within a firm. Supplier and customer integration are forms of external SCI and refer to a firm's coordination and collaboration of inter-organizational information, processes, and behaviors with its key supply chain members (customers and suppliers). Thus, consistent with conceptualizations of SCI that emphasize aggregate strategic integration rather than characteristics and activities, we define SCI as the collaborative and coordinated management of intra- and inter-organizational information, processes, and behaviors to create maximum value, which comprises three dimensions of internal, supplier, and customer integration (Mackelprang et al., 2014; Zhao et al., 2008, 2011). 2.2. Types of firm performance SCI research categorizes firm performance into three types: operational, financial, and strategic performance (Fabbe-Costes & Jahre, 2008). Operational performance has long been recognized as a complex, multidimensional, hierarchical construct that involves the improvement of supply chain-related organizational measures including logistics cost reduction, on-time delivery, inventory turnover, and cycle time reduction. Financial performance

Please cite this article in press as: Chang, W., et al., Supply chain integration and firm financial performance: A meta-analysis of positional advantage mediation and moderating factors, European Management Journal (2015), http://dx.doi.org/10.1016/j.emj.2015.11.008

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is the improvement of economic goals based on revenue minus cost-based measures such as profitability, return-on-investment, and return-on-sales. Based on types of firm performance identified in the strategic management and marketing literature (e.g., Chenhall & Langfield-Smith, 2007; Morgan, 2012; Vorhies & Morgan, 2003), we further distinguish between relational and strategic performance. Relational performance is the improvement of customer-oriented measures such as customer satisfaction, customer loyalty, and customer retention. Strategic performance is the improvement of market goals that is assessed with purely revenue-based measures such as sales, market share, and growth in sales and market share. 3. Theory and hypotheses 3.1. Theoretical foundations This meta-analysis is grounded in the resource-based view (RBV) (Barney, 1991; Wernerfelt, 1984) and positional advantage theory (PAT) (Day & Wensley, 1988). Drawing on the work of Penrose (1959), RBV proposes that firms possess different bundles of resources. Resource heterogeneity across firms implies that “some firms are more skilled in accomplishing certain activities, because they possess unique resources” (Kozlenkova, Samaha, & Palmatier, 2014, p. 3) and that firms can achieve superior performance by successfully exploiting their own particular bundles of resources (Rumelt, 1984; Wernerfelt, 1984). When a firm's bundle of resources and skills are valuable, rare, and inimitable, the benefits associated with exploiting heterogeneous resources persist over time and can be transformed into sustainable competitive advantage (Barney, 1991; Kozlenkova et al., 2014). Competitive advantages associated with a firm's ability to integrate activities and processes within and across firms are wellestablished conceptually (Barney, 2012; Chen, Daugherty, & Roath, 2009; Stevens & Johnson, 2016) and empirically (Ellinger et al., 2011; Ellinger et al., 2012). SCI is valuable because the associated process-based resources facilitate information exchange and efficient flows of products/services that enable firms to effectively address the changing needs of supply chain members. Moreover, SCI encompasses complex, intangible process resources that may be difficult and costly for competitors to quickly imitate (Chen et al., 2009). In consequence, relatively few firms achieve high levels of SCI, and SCI can serve as valuable heterogeneous resources that differentiate firms in the marketplace and elevate firm performance (Barney, 2012; Leuschner et al., 2013). As such, RBV provides a theoretical foundation for the association between SCI as valuable heterogeneous resources and firm performance. Our application of RBV is complemented by PAT that provides the foundation for the proposed mediating framework on which this meta-analysis is based. The essence of PAT is that a firm's unique resources and skills generate two differential types of positional advantages in the marketplace (i.e., lower relative costs advantages and superior customer value advantages), that, in turn, enhance performance outcomes (Day & Wensley, 1988; Porter, 1991). Following the PAT framework complemented with RBV, we view SCI as a driving source of a firm's positional advantage that can create both lower relative costs and superior customer value positional advantages. Firms with superior SCI can achieve lower relative costs positional advantage via efficiency improvements by coordinating and streamlining the information, activities, and processes in supply chains. Cost advantages created by SCI are directly reflected by operational performance improvements that yield cost reductions associated with purchasing, distribution and inventory turnover efficiencies (Christopher, 1993).

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SCI can also generate superior customer value positional advantages through service augmentation and by effectively meeting supply chain partners' specific needs (Christopher, 1993). Superior customer value positional advantages are captured more precisely by relational and strategic performance improvements. Because SCI enables a firm to provide supply chain members with additional and more customized services that better satisfy their specific needs, the members may perceive greater customer value from the firm, and show higher satisfaction with the relationship and loyalty associated with superior relational performance. Furthermore, supply chain partners tend to purchase more products/services from and switch their businesses to firms that offer superior customer value via the provision of more customized services (Chen & Dubinsky, 2003). Consequently, a firm's superior customer value positional advantages lead to increased sales and market share associated with superior strategic performance. In summary, drawing upon PAT, SCI yields two different types of positional advantages over competitors. The realization of the two types of positional advantages can be captured respectively by intermediate types of firm performance (i.e., operational, relational, and strategic performance), that, in turn, culminate in superior financial performance. We do not claim that the intermediate types of performance depicted in our mediating model are equal to positional advantages per se. However, consistent with the overarching principles of PAT, we use intermediate types of firm performances pertaining to each positional advantage as actualized positional advantages. As a preview and summary of the study hypotheses, Fig. 1 depicts the proposed mediating relationships of types of intermediate firm performance that reflect the realization of two types of positional advantages as well as the proposed moderating effects of time, relationship quality, and national culture. 3.2. Mediating hypotheses Drawing on PAT, Hypothesis 1 investigates the overall mediating mechanism by which each dimension of SCI leads to two types of positional advantages that are directly captured by three intermediate types of firm performance, that ultimately improve financial performance. Internal integration enables firms to facilitate information sharing among departments, reduce redundancy of interfunctional tasks, and achieve rapid and effective internal decision-making and efficient implementation (Wong, Boon-Itt, & Wong, 2011). Because internal integration maximizes the efficiency of a firm's internal activities and processes, it improves operational performance like on time delivery and cycle time reduction to achieve lower relative cost advantage in the marketplace (Christopher, 1993; Christopher & Gattorna, 2005). Internal integration is also associated with superior customer value advantage. Internal integration facilitates thorough market information sharing among departments, helps firms closely meet and respond to customer requirements, and promotes responsiveness through functionally coordinated actions among departments (Hult, Ketchen, & Slater, 2005; Shapiro, Rangan, & Sviokla, 2004). Greater perceived customer value associated with internal integration positively affects relational performance by increasing customer satisfaction, loyalty, and retention as well as strategic performance such as sales and market share by encouraging customers to purchase more or switch more of their business over to the firm (Hult et al., 2005). Since improved operational, relational, and strategic performances associated with internal integration influence firms' financial returns through cost reduction and revenue expansion, internal integration consequently improves financial returns.

Please cite this article in press as: Chang, W., et al., Supply chain integration and firm financial performance: A meta-analysis of positional advantage mediation and moderating factors, European Management Journal (2015), http://dx.doi.org/10.1016/j.emj.2015.11.008

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Fig. 1. Conceptual model. Note: The bolded lines represent hypotheses pertaining to the relative strength of relationships between dimensions of SCI and types of firm performance.

External integration with suppliers and customers also creates lower relative costs and superior customer value positional advantages. Collaborative coordination with supply chain members helps firms to perform activities at a lower cost than competitors by minimizing inefficient resource utilization and by eliminating nonvalue adding activities (Christopher & Gattorna, 2005; Esper, Ellinger, Stank, Flint, & Moon, 2010). Streamlined collaboration and information sharing with suppliers and customers facilitate transactional efficiencies by allowing suppliers and customers to efficiently take charge of their proper tasks in the supply chain. Such collaborative coordination puts into practice Wernerfelt's (2014, p. 22) contention that “firms in a supply chain should not all perform the same tasks. Instead, each task should be performed by the firm that does it best.” Thus, supplier and customer integration positively impact a firm's operational performance as a consequence of transactional efficiencies throughout entire supply chains, that help firms to do more with less to achieve superior financial performance (Davis & Spekman, 2004; Marn & Rosiello, 1992). External integration also generates superior customer value positional advantage by finding new and better ways to serve customers (Ittner & Larcker, 1998; Reichheld, 2001). Interorganizational information sharing, communication, and cooperation enable firms to better and more rapidly understand and address customer needs in dynamic markets (Jüttner, Christopher, & Baker, 2007) to promote service delivery innovation (Hammer, 2004; Srivastava, Shervani, & Fahey, 1999) and differentiation through segmented service offerings based on specific customer needs (Mentzer, Flint, & Hult, 2001). Consequently, SCI-driven customer value advantage is appropriately captured by improved customer satisfaction and loyalty (i.e., relational performance) and increased sales and market share (i.e., strategic performance), which can favorably influence financial performance. Based on the above logic, and consistent with PAT, types of intermediate firm performance that reflect the achievement of positional advantage are expected to mediate the relationships between dimensions of SCI and financial performance: Hypothesis 1. (a) Internal, (b) supplier and (c) customer integration increase firm financial performance through operational, relational and strategic performance.

As hypothesized in Hypothesis 1, all three types of intermediate performance are expected to mediate the associations between each dimension of SCI and financial performance. However, the magnitude of each mediating route may differ contingent on the particular dimension of SCI because each dimension of SCI has a distinctive emphasis and role (Flynn et al., 2010). To further explore the proposed mediating model, the next three hypotheses seek to identify the relative strength of the mediating routes or positional advantages through which the different dimensions of SCI influence firm financial performance. Internal integration involves breaking down traditional departmental silos to promote collaborative interaction between different functional areas within firms (Ellinger, 2000; Esper et al., 2010; Swink & Schoenherr, 2015). Previous studies consistently link cross-functional collaboration and interfunctional coordination with operational (e.g., Flynn et al., 2010), relational (e.g., Rinehart, Cooper, & Wagenheim, 1989) and strategic (e.g., Narasimhan & Kim, 2002) firm performance. However, internal integration primarily involves streamlining and synchronizing customer order fulfillment-related activities within the firm (Flynn et al., 2010). Once functions and activities within the firm are integrated, customer orders flow efficiently within the organization without any delay because functional areas share information and work together (Flynn et al., 2010). Thus, the explicit benefits of internal integration are realized as the achievement of lower relative cost positional advantage reflected by superior operational performance. Consistent with this line of reasoning, researchers report a positive relationship between internal integration and operational performance (Flynn et al., 2010; Saeed et al., 2005). Mackelprang et al.’s (2014) meta-analysis also provides support for the particularly strong influence of internal integration on cost savings associated with superior operational performance. The study reports that internal integration significantly improves operational performance (e.g., manufacturing costs) but does not improve strategic performance (e.g., sales growth and market share). In summary, the SCI literature indicates that internal integration is more strongly associated with improvements in operational performance. We therefore propose that internal integration enhances financial performance more through lower cost advantage reflected by improved operational performance than through

Please cite this article in press as: Chang, W., et al., Supply chain integration and firm financial performance: A meta-analysis of positional advantage mediation and moderating factors, European Management Journal (2015), http://dx.doi.org/10.1016/j.emj.2015.11.008

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customer value advantage: Hypothesis 2. Operational performance has a stronger mediating effect than relational and strategic performance in the internal integration-firm financial performance relationship. Working closely with key suppliers has long been regarded as a means for better serving target markets. Such integration can generate positional advantage through new product development, responsiveness, process innovation and operational excellence (Mentzer, Min, & Zacharia, 2000). Moreover, joint problem solving with key suppliers and synergistic utilization of unique complementary resources and capabilities facilitate the identification of opportunities for achieving lower relative cost as well as superior customer value positional advantages (Eltantawy, Giunipero, & Fox, 2009; Lado, Paulraj, & Chen, 2011). However, positional advantages associated with supplier integration are largely actualized as superior operational performance (Wong et al., 2011). With a low level of supplier integration, a firm is more likely to receive inaccurate or distorted supply information, which results in poor production plans, high levels of inventory and poor delivery reliability (Lee, Padmanabham, & Whang, 1997; Wong et al., 2011). On the other hand, a high level of supplier integration promotes mutual understanding and facilitates task coordination, which reduce wastage and redundancy in managing supply chain activities between the firm and its suppliers (Swink, Narasimhan, & Wang, 2007; Wong et al., 2011). Furthermore, as with internal integration, the SCI literature consistently emphasizes that most benefits from supplier integration are manifested in the form of cost savings associated with operational performance improvements (Leuschner et al., 2013; Madhok & Tallman, 1998). Thus, although supplier integration can generate both lower relative cost and superior customer value positional advantages, we hypothesize that: Hypothesis 3. Operational performance has a stronger mediating effect than relational and strategic performance in the supplier integration-firm financial performance relationship. The relationship between customer integration and firm performance is a pervasive theme in the strategic marketing literature. Customer integration facilitates customer linking, market sensing and channel bonding capabilities that differentiate the performance of market-driven organizations (Day, 1994, 2011). As a result of building strong bonds with customers and having a deeper understanding of customer needs and changes in demand and market opportunities, customer integration achieves superior customer value positional advantage that makes customers more satisfied and prevents customers from switching to competitors (Hult et al., 2005; Jaworski & Kohli, 1993; Narver & Slater, 1990). Empirical research also indicates that customer integration strongly influences customer satisfaction both directly and indirectly through its relationship with product development and innovation (Flynn et al., 2010; Homburg & Stock, 2004; Koufteros, Vonderembse, & Jayaram, 2005). Thus, customer integration is expected to directly improve relational performance. Customer integration also positively influences operational performance by increasing flexibility in the distribution process and by introducing process innovation, and improves strategic performance that generates new opportunities for sales (i.e., revenue expansion) based on a better understanding of customers and markets (Fabbe-Costes & Jahre, 2008; Mackelprang et al., 2014). However, Mackelprang et al. (2014) report that customer integration does not significantly impact cost savings although it contributes to other aspects of operational performance such as flexibility. Moreover, the benefits of customer integration on

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strategic performance may take longer to realize than the effect of customer integration on relational performance because improvement of strategic performance may be driven partly by improved relational performance (Guo, Kumar, & Jiraporn, 2004). Thus, based on the well-accepted business principle that cultivating appropriate relationships with customers is associated with relational firm performance benefits, our final mediating hypothesis proposes that: Hypothesis 4. Relational performance has a stronger mediating effect than operational and strategic performance in the customer integration-firm financial performance relationship.

3.3. Moderating hypotheses The next three hypotheses investigate the moderating influences of time, relationship quality, and national culture. The moderating hypotheses are tested with firm performance that combines all types of firm performance (i.e., operational, relational, strategic, and financial performance) as the outcome variable due to limited sample sizes for the moderators of interest. The first moderating hypothesis suggests the premise that the relationships between dimensions of SCI and firm performance strengthen over time. Pioneering integration research reports that the majority of respondent organizations were at the early stages of SCI. These empirical findings are consistent with Hammer's (2001, p. 85) observation that many firms were having difficulty “making the SCI concept a reality.” Thus, although positive linkages between integration and firm performance are found in the relatively few empirical studies published during the 1990s (e.g., Daugherty, Ellinger, & Gustin, 1996; Ellinger, Daugherty, & Gustin, 1997), overall low levels of SCI may have precluded the realization of substantial associated performance improvements. However, as the strategic importance and payoffs associated with SCI became more apparent, many firms progressed from early to more advanced stages of SCI (Fawcett & Magnan, 2002; Stevens & Johnson, 2016). Furthermore, an amalgamation of notable business trends has progressively intensified the need for SCI. These trends include the increasing prevalence and complexity of global supply chains, the shift from competition between firms to competition between supply chains, increasing market turbulence, technological advancements that facilitate SCI and, perhaps most significantly, burgeoning customer expectations for consistently high levels of service. Thus, as the supply chain management (SCM) concept steadily gained credence after its introduction by management consultants in the early 1980s, and more firms achieved advanced stages of SCI, firm performance-related benefits may have become greater. This proposition is consistent with Flynn et al.’s (2010) suggestion that there may be a tipping point for SCI, such that improvements in SCI may not significantly influence performance when levels of SCI are relatively low. However, once a threshold level of SCI is achieved, even small increases in integration may significantly enhance performance. Based on these ideas, we hypothesize that the relationships between dimensions of SCI and firm performance become significantly stronger over time: Hypothesis 5. The relationships between (a) internal, (b) supplier and (c) customer integration and firm performance strengthen over time. Our second moderating hypothesis examines the influence of relationship quality on the relationships between dimensions of SCI and firm performance. Relationship quality is a composite measure

Please cite this article in press as: Chang, W., et al., Supply chain integration and firm financial performance: A meta-analysis of positional advantage mediation and moderating factors, European Management Journal (2015), http://dx.doi.org/10.1016/j.emj.2015.11.008

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of relationship closeness and strength between exchange partners (Palmatier, Dant, Grewal, & Evans, 2006). Good relationship quality is characterized by mutual trust, commitment, and long-term relationship (Lahiri & Kedia, 2011; Srinivasan, Mukherjee, & Gaur, 2011). As relational paradigms have transitioned from armslength, independent transactions to strategic, long-term, mutually beneficial relationships (Davis & Spekman, 2004; Morgan & Hunt, 1994), strong relationships between partners are recognized as a strategic, complementary relational resource (Srinivasan et al., 2011). Since high relationship quality promotes specific behaviors pertinent to internal and external SCI (e.g., communication, information sharing and joint coordination of processes) and reduces risks like opportunistic behaviors, performance-related payoffs can be maximized for exchange partners with good relationship quality (Crosby, Evans, & Cowles, 1990; Palmatier et al., 2006; Srinivasan et al., 2011). In the current context, the quality of relationships plays a critical role in determining the effectiveness of collaborative interactions between supply chain partners (Chen, Preston, & Xia, 2013; Eltantawy et al., 2009; Lado et al., 2011). Strong, close relationships based on mutual trust and commitment encourage openness in knowledge sharing, reduce monitoring costs for opportunistic behaviors and increase supply chain partners' willingness to adapt systems to benefit the overall supply chain. Moreover, supply chain members with high relationship quality are likely to be more familiar with each other's objectives, expectations, and knowledge base (Srinivasan et al., 2011) that helps firms realize the benefits of SCI. Following this rationale, we predict that relationship quality positively moderates the relationships between discrete dimensions of SCI and firm performance: Hypothesis 6. The relationships between (a) internal, (b) supplier and (c) customer integration and firm performance strengthen as relationship quality increases. Our final moderating hypothesis proposes the influence of national culture. International business literature differentiates between individualistic western cultures that are characterized by loosely-knit social networks and the collectivist values of Asian cultures where group interests dominate (Hofstede, 2001). Western cultures are viewed as more individualistic and shortterm oriented (Troy, Hirunyawipada, & Paswan, 2008), while in Asian cultures based on collectivist values, “living and working as a group, with mutual responsibilities and group accountability” is regarded as an active norm (Troy et al., 2008, p. 136). SCI research indicates that integration effectiveness can hinge on cultural context (Zhao et al., 2008, 2011). Zhao et al. (2008, p. 371) point out that, “[t]he essence of a collective culture is a constant concern for belongingness, dependency and reciprocity.” Thus, firms in collectivist cultures are more likely to maintain relationships with partner firms, exhibit higher levels of trust and commitment and provide preferential treatment to their in-group supply chain partners (Chen, Ellinger, & Tian, 2011; Zhao et al., 2011). Consequently, the higher levels of collectivism in the Asian cultures are expected to maximize the positive influence of SCI on firm performance by significantly minimizing the costs of monitoring opportunistic behaviors by partner firms, reducing uncertainty in the supply chain, and creating superior performance outcomes by working collaboratively to prioritize group interests. In light of the advantages associated with collectivist culture (in comparison to individualistic western culture) we expect: Hypothesis 7. The relationships between (a) internal, (b) supplier and (c) customer integration and firm performance are stronger in the Asian cultures than in the Western cultures.

4. Method 4.1. Identification and collection of studies Meta-analyses increase power and generalizability relative to individual studies by producing estimates of effect sizes based on large numbers of studies reported over time. To maximize the power, reliability, and generalizability of meta-analyses and avoid selection bias, researchers should endeavor to collect as much of the relevant available findings that can be meaningfully compared (Eisend & Tarrahi, 2014). Thus, although Leuschner et al.’s (2013) and Mackelprang et al.’s (2014) recent SCI meta-analytic studies make substantial initial contributions, the generalizability of their findings may be interpreted with caution because, as mentioned earlier, the literature searches and resulting number of studies analyzed are limited. To present a more comprehensive meta-analytic assessment of the complex links between SCI and firm performance, the following steps were undertaken. First, a series of searches of multiple electronic databases were performed to identify English language empirical SCI studies available through April 2015. Using the terms “integration,” “collaboration,” “cooperation,” “coordination,” “internal integration,” “external integration,” “supplier integration,” and “customer integration,” as well as combinations of these key terms with “supply chain” and “performance,” relevant studies were identified through ABI/Inform, Business Source Premier, ProQuest Digital Dissertations and Google Scholar. These four databases collectively provide access to a wide array of SCI studies published in various business areas as well as unpublished dissertations and proceedings. This exhaustive identification process helps to mitigate selection bias, including publication bias that is one of the serious biases in meta-analytic studies (Eisend & Tarrahi, 2014; Rosenthal, 1979). Second, as each database search was completed, references in the identified studies were examined to identify additional relevant studies. Finally, tables of contents and abstracts for all issues of leading SCM journals (Journal of Business Logistics, Journal of Operations Management, Journal of Supply Chain Management, International Journal of Logistics Management, International Journal of Physical Distribution & Logistics Management, and Supply Chain Management: An International Journal) that publish papers on SCI were manually searched from January 1995 to April 2015. We chose 1995 as a starting point for this additional manual search because the first empirical studies on SCI were published in the mid-1990s (e.g., Daugherty et al., 1996). Once the literature search was completed, two of the authors narrowed down the articles based on the following criteria. First, consistent with our definition of SCI, we included studies that address firms’ strategic behaviors for collaboratively managing intra- and/or inter-organizational activities and processes in supply chain contexts. In the process, individual scale items were thoroughly assessed to ensure that the authors of the papers used measures of the constructs of interest that had face validity. Second, studies of integration in other contexts (e.g., inter-functional interaction in new product development) were excluded. Third, to ensure consistency in performance outcomes, the analyses focused on papers that assess the effect of SCI on focal firm performance. Therefore, studies that measure only supply chain partner performance or overall supply chain performance were excluded. Finally, to be considered usable, the articles had to report inter-construct zero-order correlations or other information that could be converted into correlations (Borenstein et al., 2009). In total, we ended with a final dataset of 170 independent samples from 164 sources including 139 articles in 45 different journals, 15 dissertations, 7 proceedings, 2 working papers and 1 book chapter representing a total of 39,495 observations. In

Please cite this article in press as: Chang, W., et al., Supply chain integration and firm financial performance: A meta-analysis of positional advantage mediation and moderating factors, European Management Journal (2015), http://dx.doi.org/10.1016/j.emj.2015.11.008

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comparison to Leuschner et al.’s (2013) and Mackelprang et al.’s (2014) studies that analyze the SCI-performance linkage with 80 and 35 samples respectively, the 170 samples in our meta-analysis provide a broader dataset that mitigates the possibility for selection bias and yields greater generalizability and reliability. Furthermore, 48.9% of the studies in this dataset (i.e., 68 out of 139 published articles) come from the six leading SCM journals mentioned above, and 77.7% of the papers (i.e., 108 out of 139) are published in journals included in the master list of the Social Sciences Citation Index. Therefore, the quality of the studies that provide the correlations for this meta-analysis is considered high. A list of the studies is available on request. 4.2. Coding of the studies Prior to coding the studies, all authors reached consensus on the conceptual and operational definitions for each dimension of SCI and type of firm performance. With the agreed definitions, two of the authors independently coded the studies to reduce bias, and any discrepancies in coding were resolved through discussion. The two coders thoroughly assessed the scale items of dimension of SCI and type of firm performance in each paper to determine whether the scales are consistent with any of the definitions in our study. If more than 75% of the items in each construct closely matched our definition, we categorized the construct into the relevant dimension of SCI or type of performance (Hunter & Schmidt, 2004). Then, relevant empirical studies were coded by extracting correlations between the variables of interest (Borenstein et al., 2009). The two raters’ initial independent codings of the correlations in this metaanalysis were compared with each other. Out of the 539 coded correlations of each pair of variables of interest, we found 68 differences, yielding an inter-rater reliability of 87.4% ((539e68)/539 codings) that exceeds the generally accepted threshold of 80% and indicates good level of agreement among raters (Gwet, 2010). When studies provided multiple estimates of the same relationship, a single composite correlation was computed as discussed by Hunter and Schmidt (2004, pp. 435e430), rather than averaging them or treating them as independent observations. This approach is stronger psychometrically and gives a more accurate indication of the amount of information reported in the literature (Hunter & Schmidt, 2004). For cases where zero-order inter-construct correlations are not reported in a study, we reproduced the correlations of interest using other statistical information that could be converted into correlations (Borenstein et al., 2009). If constructelevel correlations are reported, we divided the correlation coefficients by the product of the square root of the reliabilities of the two constructs to adjust for measurement error (Hunter & Schmidt, 2004). When reliability data were not available for measurement error correction, we substituted an average of the reported reliabilities (Leuschner et al., 2013; Mackelprang et al., 2014). To avoid including the same results obtained from the same dataset repeatedly, multiple studies based on the same sample were treated as a single study. However, independent samples from a single study were treated as multiple observations (Hunter & Schmidt, 2004). 4.3. Meta-analytic procedures To calculate the meta-analytic mean correlations for the relationships between dimensions of SCI and different types of firm performance, we multiplied the observed correlation coefficients by a correction factor suggested by Olkin and Pratt (1958). That is, rather than summarizing the reported correlation coefficients r, we used an adjusted r þ r(1 e r2)/2(n e 3), where n is the sample size. The adjustment is generally very small, but Schulze's (2004) Monte

7

Carlo simulations indicate that this approach gives better estimates of the true population effect size than the raw r. Since larger samples should give more accurate estimates, the adjusted correlations are weighted by their sample sizes to find the overall mean (Schulze, 2004). We also calculated the Q-statistic to test the heterogeneity in effects of studies in the meta-analysis (Borenstein et al., 2009). A significant Q-value provides evidence that the true effects vary from study to study and therefore subsequent investigation of the moderating effects is required. Publication bias was evaluated using fail-safe N which refers to “the number of nonsignificant, unpublished (or missing) studies that would need to be added to a metaanalysis to reduce an overall statistically significant observed result to non-significance” (Rosenberg, 2005, p. 464). A large fail-safe N suggests that the likelihood of publication bias is not substantial. 4.4. Mediation and moderation analysis procedures Hayes (2013) describes procedures for analyzing mediation models using tools implemented in PROCESS, a set of macros for the SPSS and SAS statistical packages (available at http://www.afhayes. com/introduction-to-mediation-moderation-and-conditionalprocess-analysis.html). When raw data are not available for analysis, as in meta-analytic research, PROCESS can use Monte Carlo procedures to get robust estimates of the confidence intervals (C.I.) around mediated effects. Because PROCESS is a regression-based procedure, it does not estimate relationships between variables while taking measurement error into account. Thus, we estimated the disattenuated correlations that would be observed if the variables were measured without error (Hunter & Schmidt, 2004). Then, we generated normally-distributed random data exactly reproducing the disattenuated correlations, using the harmonic mean of the sample sizes across the estimated correlations (N ¼ 3322; Viswesvaran & Ones, 1995). With the generated random data, we then ran separate regressions for each dimension of SCI (i.e., internal, supplier, and customer integration) to estimate direct and indirect effects of our proposed mediating model in Fig. 1. The analyses allow us to examine the significance of the mediating indirect effects through operational, relational, and strategic performance from each dimension of SCI to firm financial performance, and to compare the relative magnitude of the indirect effects through three different mediators as proposed in Hypotheses 1e4. We tested the moderating effects of time, relationship quality, and national culture between dimensions of SCI and firm performance in Hypotheses 5e7 using a mixed-effects model. Moderator analyses in meta-analysis allow researchers to test whether variation among studies in effect size is associated with differences in moderators (Borenstein et al., 2009). In this paper, we examined whether the variation of the correlations of the dimensions of SCIfirm performance linkage in our meta-analysis is related to time, relationship quality, and/or national culture by regressing the correlations between dimensions of SCI and firm performance on time, relationship quality, and national culture. As mentioned above, for the moderating hypotheses, firm performance that combines all types of firm performance was used rather than each type of firm performance due to limited sample sizes. For such analyses, a potential problem is that multiple correlations on different performance dimensions from a single study may be included repeatedly. Mixed-effects models solve the problem by controlling for the inclusion of multiple effect sizes from a single study. Specifically, the PROC MIXED procedure in SAS was employed for mixed-effects models to control for the problem (Sheu & Suzuki, 2001). To test Hypothesis 5, the publication year for each paper is used as the proxy indicator for time. The proxy indicator is intended to

Please cite this article in press as: Chang, W., et al., Supply chain integration and firm financial performance: A meta-analysis of positional advantage mediation and moderating factors, European Management Journal (2015), http://dx.doi.org/10.1016/j.emj.2015.11.008

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W. Chang et al. / European Management Journal xxx (2015) 1e14

capture perceptions of a firm's SCI at a particular point in time. Using the publication year in each paper, we can test whether the relationships between dimensions of SCI and firm performance are stronger in more recent years (i.e., in studies with later publication dates) when SCI has become more commonplace, than twenty years ago (i.e., in studies with earlier publication dates) when SCI was still in its infancy. We analyzed the moderation impact of time with the PROC MIXED procedure in SAS while including all types of performance simultaneously. Similar procedures were applied to examine Hypotheses 6 and 7. When coding relationship quality for each dimension of SCI, we ensured that each paper involves relationship quality specifically with suppliers or customers or between functional areas. Thus, for example, for supplier integration, we included only papers that assess relationship quality between suppliers and a focal firm. Studies in this meta-analysis provide sample means for relationship quality based on consistent measures such as trust and commitment that allow them to be divided into low (coded as 0) and high (coded as 1) relationship quality studies based on median splits. Then, the moderating effects of relationship quality between dimensions of SCI and firm performance were tested. Finally, the studies in this meta-analysis were divided into Asian collectivist (coded as 1) and Western individualistic cultures (coded as 0) based on the country where the sample for each study was collected. Both studies whose samples were collected from multiple countries that mixed Western and Asian countries and studies that did not provide information about where the data were collected were excluded for testing Hypothesis 7. 5. Results 5.1. Direct effects between dimensions of SCI and types of firm performance We first evaluated the direct relationships between dimensions of SCI and types of firm performance based on our extensive dataset that was generated with the objective of avoiding selection bias. Table 1 presents the mean correlations between the three dimensions of SCI and each type of firm performance, the number of estimates (i.e., the number of correlations found for each

relationship), and the corresponding sample sizes (i.e., total sample size from all studies included in the relationship analysis). As shown in Table 1, our comprehensive meta-analysis indicates that internal integration positively relates to all types of firm performance, ranging from 0.19 (p < 0.01) for strategic performance to 0.35 (p < 0.01) for operational performance. Supplier integration is also positively associated with each type of firm performance, ranging from 0.19 (p < 0.01) for financial performance to 0.30 (p < 0.01) for operational performance. Finally, customer integration is positively related with each type of firm performance, ranging from 0.21 (p < 0.01) for strategic performance to 0.39 (p < 0.01) for relational performance. For completeness, we also calculated combined effects and sample sizes for all dimensions of SCI and types of firm performance. For example, the results between overall SCI and operational performance in Table 1 merged all the correlations between internal (39 correlations), supplier (70 correlations) and customer (45 correlations) integration and operational performance. The overall SCI numbers also incorporated correlations between SCI and operational performance (27 correlations) from studies that measure SCI using a mix of different SCI dimensions that cannot be classified into one of the three discrete dimensions of integration. As a consequence, we found the mean correlation of 0.31 (p < 0.01) between overall SCI and operational performance based on 181 estimates and 46,710 observations. Similarly, the mean correlations between overall SCI and each type of performance are 0.30 (p < 0.01) for relational performance, 0.24 (p < 0.01) for strategic performance, and 0.24 (p < 0.01) for financial performance. Thus, the overall SCI findings represent the broadest available empirical generalizations between SCI and the various types of firm performance, and provide robust confirmation that each dimension of SCI favorably affects each type of firm performance. 5.2. Mediating effects between dimensions of SCI and financial performance Another contribution of this meta-analysis is the application of PAT that enables a holistic examination of how dimensions of SCI translate to a firm's bottom-line, i.e., financial performance. This is achieved by considering both direct and indirect mediating effects.

Table 1 Overview of relationships between SCI and firm performance. Relationships

Internal integration Operational performance Relational performance Strategic performance Financial performance Supplier integration Operational performance Relational performance Strategic performance Financial performance Customer integration Operational performance Relational performance Strategic performance Financial performance Overall SCI Operational performance Relational performance Strategic performance Financial performance

Number of estimates

Sample size

Mean correlation

Range

95% Confidence interval

Q

Fail safe N

39 16 12 19

9487 3001 2425 4653

0.35* 0.30* 0.19* 0.28*

0.17 0.06 0.10 0.05

0.75 0.57 0.33 0.48

0.32 0.26 0.15 0.25

0.37 0.33 0.23 0.30

225.50* 61.89* 25.90* 90.87*

2230 1050 237 1548

70 14 22 26

18,012 2722 5552 6690

0.30* 0.28* 0.26* 0.19*

0.07 0.08 0.08 0.03

0.78 0.56 0.58 0.58

0.29 0.24 0.24 0.16

0.31 0.31 0.29 0.21

543.81* 96.92* 129.82* 163.99*

5172 747 2405 1840

45 10 10 13

12,386 2300 1956 3676

0.26* 0.39* 0.21* 0.22*

0.05 0.17 0.02 0.06

0.64 0.70 0.81 0.58

0.24 0.35 0.17 0.19

0.28 0.43 0.26 0.25

216.63* 164.64* 143.16* 158.73*

9730 1119 283 606

181 51 59 72

46,710 10,980 13,318 18,465

0.31* 0.30* 0.24* 0.24*

0.05 0.08 0.10 0.06

0.78 0.70 0.81 0.75

0.30 0.28 0.22 0.23

0.32 0.32 0.25 0.26

1436.60* 475.40* 383.76* 602.71*

3549 3391 2571 1867

*p < 0.01.

Please cite this article in press as: Chang, W., et al., Supply chain integration and firm financial performance: A meta-analysis of positional advantage mediation and moderating factors, European Management Journal (2015), http://dx.doi.org/10.1016/j.emj.2015.11.008

W. Chang et al. / European Management Journal xxx (2015) 1e14

To test the proposed mediating effects in Hypotheses 1e4, the disattenuated meta-analytic correlations presented in Table 2 were used to generate random data to employ Hayes's (2013) PROCESS. The results are summarized in Table 3. The mediating results show that internal integration has a significant direct effect (0.13, 95% C.I.: 0.10e0.16) and total indirect effect through the three intermediate types of firm performances (0.20, 95% C.I.: 0.18e0.23). Because both C.I.s for the direct and indirect effects do not include zero, the two effects are significant. For supplier integration, the direct effect on financial performance is small but significantly negative (direct effect: -0.03, 95% C.I.: 0.06 ~ 0.00), whereas the total indirect effect through the three mediator performance variables is significant and positive (total indirect effect: 0.26, 95% C.I.: 0.24e0.28). These findings imply that supplier integration directly deteriorates focal firm financial performance due to the high costs of integrating with suppliers and the longer time required to realize the returns of supplier integration, but that supplier integration ultimately contributes to financial performance by favorably influencing operational, relational, and strategic performance. In contrast to the direct effects tested previously, our theoretically-supported mediating findings more realistically and comprehensively explicate the influence of supplier integration on firm financial performance. Specifically, Leuschner et al.’s (2013) analysis considers only the direct effect of SCI on firm financial performance (without taking into account indirect effects) concluding that SCI makes limited contributions to firm financial performance. However, our inclusion of mediating types of firm performance reveals that supplier integration actually improves a firm's financial returns through enhanced intermediate types of performance. Finally, although customer integration does not directly affect financial performance (0.01, 95% C.I.: 0.02e0.04), the study findings indicate that the three intermediate firm performance variables significantly mediate the relationship between customer integration and financial performance (0.25, 95% C.I.: 0.23e0.28). Moreover, as presented in the mediation effects column of Table 3, the results of our analysis demonstrate that the three proposed mediating variables of operational, relational and strategic performance significantly mediate the relationships between discrete dimensions of SCI and firm financial performance. Thus, Hypothesis 1 is supported. However, as hypothesized in Hypotheses 2e4 and shown in the comparisons of mediation effects column in Table 3, the study findings suggest that the magnitudes of the mediation effects for the three types of intermediate firm performance are significantly different. Hypothesis 2 predicts that operational performance has the strongest mediating effect in the internal integration-financial performance association. Yet, in contrast to expectations, the mediating routes through relational and strategic performance are

9

significantly stronger than through operational performance (comparison effect between operational and relational performance: -0.04, 95% C.I.: 0.06 ~ 0.02; comparison effect between operational and strategic performance: -0.06, 95% C.I.: 0.08 ~ 0.03). However, the magnitudes of the mediating effects through relational and strategic performance are not significantly different (comparison effect: -0.01, 95% C.I.: 0.03e0.00). Consequently, our results indicate that internal integration enhances firm financial performance more through improved relational and strategic performance (i.e., through superior customer value positional advantage) than through operational performance. Similarly, the study findings reveal that the effect of supplier integration on firm financial performance is improved more by strategic performance than by operational performance. As shown in Table 3, the mediation effect through strategic performance is significantly stronger than through operational performance (comparison effect: -0.08, 95% C.I.: 0.10 ~ 0.06) and through relational performance (comparison effect: -0.05, 95% C.I.: 0.07 ~ 0.03). Thus, the study findings do not provide support for Hypotheses 2 and 3. Finally, our mediator analyses reveal that the mediation effects of relational and strategic performance are significantly greater than that of operational performance in the customer integrationfinancial performance relationship (comparison effect between operational and relational performance: -0.07, 95% C.I.: 0.09 ~ 0.04; comparison effect between operational and strategic performance: -0.06, 95% C.I.: 0.08 ~ 0.04). However, the magnitudes of the mediation effects through relational and strategic performance are not significantly different (comparison effect: 0.01, 95% C.I.: 0.01 ~ 0.03). Accordingly, the study findings provide partial support for Hypothesis 4. In summary, the study findings about the relative magnitudes of the mediating effects indicate that all three types of intermediate firm performance significantly mediate the relationships between dimensions of SCI and firm financial performance. However, with reference to the relative magnitudes of these mediation effects, relational and strategic performance associated with superior customer value positional advantage have stronger mediating influences than operational performance related to lower relative cost advantage. 5.3. Moderating effects on the relationships between dimensions of SCI and firm performance All significant Q statistics in Table 1 confirm the need for moderation tests due to the existence of substantial variances in effects between SCI and firm performance. Hypothesis 5 assesses whether the effects of discrete dimensions of SCI on firm

Table 2 Meta-analytic correlation matrix.

1. 2. 3. 4. 5. 6. 7.

II SI CI OP RP SP FP

1

2

3

4

5

6

7

0.81 0.42* 0.42* 0.35* 0.30* 0.19* 0.28*

0.52* 0.80 0.48* 0.30* 0.28* 0.26* 0.19*

0.52* 0.60* 0.80 0.26* 0.39* 0.21* 0.22*

0.44* 0.38* 0.33* 0.78 0.44* (16; 3415) 0.35* (17; 4149) 0.35* (18; 5319)

0.36* 0.34* 0.48* 0.54* 0.84 0.30* (8; 1617) 0.38* (5; 828)

0.23* 0.32* 0.26* 0.43* 0.36* 0.84 0.46* (15; 3809)

0.33* 0.23* 0.26* 0.42* 0.44* 0.54* 0.88

(29; (28; (39; (16; (12; (19;

6589) 6771) 9487) 3001) 2425) 4653)

(40; (70; (14; (22; (26;

10,710) 18,012) 2722) 5552) 6690)

(45; (10; (10; (13;

12,386) 2300) 1956) 3676)

Notes: Values below the diagonal are mean correlations; values above the diagonal are disattenuated using the average reliabilities. Diagonal entries are the average reliability for each construct. Values in parentheses are the number of estimates and corresponding sample size. II: Internal integration, SI: Supplier integration, CI: Customer integration, OP: Operational performance, RP: Relational performance, SP: Strategic performance, FP: Financial performance. *p < 0.01. N ¼ 3322.

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RP vs. SP

W. Chang et al. / European Management Journal xxx (2015) 1e14 Table 4 Moderating effects of time, relationship quality, and national culture on the SCIeperformance link.

-0.01 (0.03~0.00) -0.05 (0.07~-0.03)* 0.01 (0.01~0.03)

10

Time Internal integration H5a

OP vs. SP

-0.06 (0.08~-0.03)* -0.08 (0.10~-0.06)* -0.06 (0.08~-0.04)*

Customer integration H5c Relationship quality Internal integration H6a Supplier integration H6b

-0.04 (0.06~-0.02)* -0.03 (0.05~-0.01)* -0.07 (0.09~-0.04)*

National culture Internal integration H7a Supplier integration H7b Customer integration H7c

Notes: Values in parentheses are the 95% confidence intervals that are estimated using 5000 bootstrap samples in Hayes's (2013) PROCESS macro. *p < 0.05.

OP vs. RP

0.09 (0.08~0.11)* 0.13 (0.11~0.15)* 0.10 (0.09~0.12)* 0.08 (0.06~0.09)* 0.08 (0.07~0.10)* 0.11 (0.09~0.13)* 0.33 (0.30~0.36)* 0.23 (0.19~0.26)* 0.26 (0.23~0.30)* II / FP SI / FP CI / FP

OP

0.04 (0.02~0.05)* 0.05 (0.04~0.06)* 0.04 (0.03~0.05)* 0.20 (0.18~0.23)* 0.26 (0.24~0.28)* 0.25 (0.23~0.28)*

Total effect

0.13 (0.10~0.16)* -0.03 (0.06~-0.00)* 0.01 (0.02~0.04)

RP

SP

Customer integration H6c

Relationship

Table 3 Results of mediation analysis.

Direct effect

Indirect effect

Mediation effects through

Comparisons of mediation effects

Supplier integration H5b

Effect

Estimate

p-value

Intercept Time Intercept Time Intercept Time

0.22 0.01 0.30 0.00 0.20 0.01

0.00 0.01 0.00 0.57 0.00 0.19

Intercept Relationship quality Intercept Relationship quality Intercept Relationship quality

0.09 0.15 0.04 0.21 0.10 0.13

0.71 0.39 0.80 0.04 0.91 0.83

Intercept National culture Intercept National culture Intercept National culture

0.45 0.09 0.47 0.08 0.41 0.08

0.00 0.03 0.00 0.08 0.00 0.17

performance increase over time. As shown in Table 4, the effect of internal integration on firm performance significantly increases over time (b ¼ 0.01, p < 0.01), lending support for Hypothesis 5a. However, contrary to expectations, our findings do not provide support for Hypotheses 5b and H5c. Both trends are nonsignificant over time (supplier integration: b ¼ 0.00, p > 0.10; customer integration: b ¼ 0.01, p > 0.10). However, the support for Hypothesis 5a reinforces the growing impact of internal integration on firm performance that is reported in Flynn et al.’s (2010) empirical research study. Hypothesis 6 proposes that relationship quality moderates the associations between discrete dimensions of SCI and firm performance. As shown in Table 4, the effects of supplier integration on firm performance are greater when a focal firm has good relationship quality with suppliers (b ¼ 0.21, p < 0.05). Although the study findings provide support for Hypothesis 6b, contrary to expectations, Hypothesis 6a (internal integration: b ¼ 0.15, p > 0.10) and Hypothesis 6c (customer integration: b ¼ 0.13, p > 0.10) are not supported. However, the nonsignificant results need to be interpreted with caution because they may be a function of the very small number of estimates available for internal and customer integration. The extant literature includes a substantial number of papers that simultaneously measure supplier integration, firm performance and relationship quality. In contrast, only six such correlations were identified for internal and only four correlations for customer integration. Thus, lack of available data precludes a thorough investigation of the proposed moderating effect of relationship quality on the relationships between internal integration, customer integration, and firm performance. As additional empirical studies accrue, we anticipate that, consistent with theory, relationship quality will also be found to favorably moderate these associations. Finally, consistent with Hypotheses 7a and 7b, the study findings indicate that the effects of internal and supplier integration on firm performance are stronger in Asian collectivist than in Western individualistic cultures (internal integration: b ¼ 0.09, p < 0.05; supplier integration: b ¼ 0.08, p < 0.10). The stronger effects for internal integration are logical in that different functional areas within organizations are likely influenced by the same culture, whereas, even though the focal firm may have a collectivistic culture, their suppliers may have either collectivistic or individualistic

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cultures. In contrast, the effect of customer integration on firm performance did not significantly differ depending on national culture (customer integration: b ¼ 0.08, p > 0.10). The nonsignificant result may come from the considerable variability of the effects of customer integration in Asian contexts, because many firms in Asian contexts are still in the process of advancing customer integration (Tian, Ellinger, & Chen, 2010). 6. Discussion 6.1. Theoretical and managerial implications Our comprehensive meta-analysis provides clarity and consensus about the significant positive relationships between discrete dimensions of SCI and various types of firm performance that are not completely apparent based on the inconsistent findings from recent meta-analyses. More specifically as pointed out by Autry et al. (2014), while Leuschner et al. (2013) report limited benefits of SCI by showing several nonsignificant correlations between SCI and types of firm performance, Mackelprang et al.’s (2014) analyses reveal more consistent benefits. Going beyond the differences in definitions and operationalizations of key SCI constructs that Autry and colleagues suggest are potential reasons for the inconsistent conclusions in the two previous meta-analytic studies, our study focuses more on minimizing the potential for tainted results due to selection bias and failure to consider direct and indirect as well as moderating effects. Some of our study findings are indeed markedly different from those reported in the previous studies. For example, Leuschner et al. (2013) report a nonsignificant association between SCI and financial performance based on 18 effect sizes, and conclude that the weak link is not surprising due to SCI's limited contributions to revenue generation. However, our findings based on 72 effect sizes in a comprehensive dataset that was gathered based on a rigid protocol for avoiding selection bias reveal that, consistent with general expectations and a myriad of anecdotes from practice, SCI actually does enhance firm financial performance. As Autry et al. (2014) note, there is a possibility that the conflicting findings on the effect of SCI on firm financial performance come from differences in definitions and operationalizations of key constructs. Beyond these possibilities, our meta-analysis suggests that Leuschner et al.’s (2013) conclusion that SCI does not contribute to a firm's bottom line may have been influenced by their restriction of sample selection to one database, omission of non-peer-reviewed articles, or limited power of their restricted dataset. Therefore, as recommended by Eisend and Tarrahi (2014), future meta-analysis researchers would be well-advised to pay more attention to reducing the possibility of selection bias. Furthermore, as our mediating model indicates, SCI, especially supplier integration, may be associated with high costs of implementation that may even have negative direct effects on financial performance. Leuschner et al.’s (2013) finding that the direct effect of supplier integration on firm performance is not significant may also result from high implementation costs. However, by taking into consideration direct effects as well as the indirect mediating effects of intermediate types of firm performance, the intuitive beneficial influence of supplier integration on firm financial performance becomes apparent. Thus, our meta-analytic assessment provides a more holistic explication of the relationship between SCI and firms’ ultimate goal, financial performance. Furthermore, the more comprehensive consideration of the association between SCI and financial performance afforded by our comprehensive aggregation of the extant literature may encourage managers to more confidently invest organizational resources on SCI. Another noteworthy contribution of our meta-analysis is to

11

highlight SCI's critical role in generating superior customer value positional advantage. In contrast to expectations that operational performance would be the most influential mediator, the mediation tests reveal that relational and strategic performance have stronger mediating effects on the relationships between internal integration and supplier integration and firm financial performance. Previous literature on SCI is replete with an overwhelming emphasis on lower cost positional advantage reflected by superior operational performance (Leuschner et al., 2013; Madhok & Tallman, 1998). Indeed, Leuschner et al. (2013, p. 46) mention that “most of the benefits from SCI are expected to be in the form of cost savings.” However, our findings imply that the gains of SCI on financial performance through lower costs positional advantages are present but that the magnitude is not as pronounced as posited in previous studies. Contrary to the general sense of the aforementioned plethora of studies, the stronger mediating influences of relational and strategic performance identified in our analyses suggest that SCI serves as a differentiator in the marketplace through superior customer value creation. These results may reflect that advances in technology, and the availability of third party logistics providers and integrators help supply chain participants to relatively easily accomplish appropriate levels of operational and process efficiency, which makes it more difficult to achieve superior low cost advantages over competitors through SCI. In contrast, achieving superior customer value advantages by providing customized products/services by means of collaborative integration between supply chain partners continues to be a differentiating strategic approach due to the ongoing lower levels of collaborative integration consistently reported in the literature. In consequence, SCI may improve firm financial performance more through the creation of superior customer value positional advantage. These ideas are consistent with Mackelprang et al.’s (2014) discovery that the contributions of supplier and customer integration on cost reduction are not significant, and with Leuschner et al.’s (2013) conclusion that cost improvements are not a significant outcome of SCI. Accordingly, our findings can serve to reinforce the premise that SCI can be proactively employed as an effective strategic resource to generate new revenues based on superior customer value creation rather than a mere mechanism for cost reduction. We also expand current understanding of the complex associations between SCI and firm performance by responding to Mackelprang et al.’s (2014) call for assessments of moderating influences. Our analysis reveals that the effects of internal integration on firm performance have become significantly stronger over time. Moreover, the study findings indicate that over the past two decades, internal integration has developed a stronger influence on firm performance than external integration. Identifying these trends is particularly notable because researchers contend that SCM practice has traditionally placed far more emphasis on external integration with suppliers and customers than on integrating internal processes (Ellinger, 2000; Shapiro et al., 2004). In fact, some seminal studies on SCI strategy do not even consider internal integration (e.g., Frohlich & Westbrook, 2001). However, our findings are in line with Flynn et al.’s (2010) discovery that internal integration has a greater influence on firm performance than external integration. The results are also consistent with Leuschner et al.’s (2013) recent meta-analytic conclusions that while supplier and customer integration are not significantly associated with firm performance, internal integration has a significant positive correlation with firm performance. Our findings also reinforce earlier empirical studies that suggest internal integration provides the necessary platform for external integration (e.g., Stank, Keller, & Daugherty, 2001). The growing importance of internal integration revealed by our

Please cite this article in press as: Chang, W., et al., Supply chain integration and firm financial performance: A meta-analysis of positional advantage mediation and moderating factors, European Management Journal (2015), http://dx.doi.org/10.1016/j.emj.2015.11.008

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analysis of the moderating influence of time also has important managerial implications. Specifically, overly prioritizing external integration with customers and suppliers could result in firms missing out on opportunities to achieve competitive advantages by not sufficiently encouraging internal integration between traditionally uncooperative and sometimes antagonistic functional areas. Relatedly, RBV scholars argue that to effectively exploit outsidein capabilities, there must be a match with inside-out capabilities (Day, 1994; Kozlenkova et al., 2014). In the context under examination, internal integration as an inside-out capability can help firms to more effectively capitalize on the potential benefits of the outside-in capabilities of external supplier and customer integration. In consequence, based on our study findings, the potential payoffs associated with achieving superior levels of internal integration should not be under-estimated. We also add theoretical and managerial implications to the current literature by showing that the effect of supplier integration on firm performance strengthens as relationship quality with suppliers increases. The results highlight the critical role of relational aspects of SCI. As reported by Leuschner et al. (2013), operational integration is not significantly related to firm performance. Therefore, mere tactical integration of activities or processes may not be enough to fully leverage the benefits of SCI. However, as suggested by Mackelprang et al. (2014), when integration of activities, processes, information, technology, workflow, etc. among supply chain participants is complemented by relationships that are grounded in mutual trust and commitment, the benefits of SCI could be maximized. Finally, our moderator tests also imply that national culture plays a role in determining the magnitude of the returns associated with SCI. Our findings indicate that internal and supplier integration yield greater performance-related benefits in collectivist Asian cultures than in the individualistic Western cultures. Thus, managers in collectivist Asian cultures are encouraged to more actively leverage SCI as a strategic means of creating positional advantages. In particular, given that firms in developing Asian countries are at less advanced stages of SCI than their Western counterparts, firms operating in collectivist cultural contexts may have more opportunity to improve firm performance by focusing on SCI. We next discuss opportunities for future research including multiple ideas for identifying and testing other currently unknown moderating factors that may influence the SCI-firm performance linkage. 6.2. Opportunities for future research Our study begins to unravel the myriad of factors that influence the complex relationship between SCI and firm performance. However, a meta-analysis should not be seen as the culmination of a research stream (Dillard, 1998). Nor does the discovery of interesting findings mean that the “problem is solved, that no future research is needed” (Cooper & Hedges, 2009, p. 564). Given that there is ample consensus that SCI indeed enhances firm performance, future research should focus on identifying and testing the multiple potential conditions under which firms can most effectively exploit SCI to achieve positional advantages (Flynn et al., 2010; Mackelprang et al., 2014). Based on our finding that internal integration's influence on firm performance has significantly strengthened over time, an intriguing opportunity for future research would be to examine the complementarity of relationships between internal, supplier and customer integration in greater depth. As noted above, prior research suggests that internal integration enables external integration. In other words, simultaneous development and exploitation of internal and external integration may have a synergistic

effect on performance. Testing the synergistic effects of internal and external integration was not possible in this meta-analysis due to the absence of raw data. However, future research could better explicate potential interactive effects and help firms effectively configure and allocate resources to improve performance by measuring and testing interactions between supplier, customer and internal integration. Another opportunity for future research arises because this meta-analysis examines SCI and firm performance from the perspective of the single focal firm. However, effective value creating networks entail supply chain vs. supply chain (rather than firm vs. firm) competition (Ketchen, Rebarick, Hult, & Meyer, 2008; Spekman & Davis, 2016; Stevens & Johnson, 2016). Therefore, assessing the holistic impact of SCI on the performance of other supply chain participants, or even on the performance of entire supply chains, would provide richer insights about the distinctive value of SCI as a strategic competitive resource. Such research would provide opportunities to confirm the mutual benefits that SCI is believed to generate for participants in extended enterprises (Davis & Spekman, 2004; Spekman & Davis, 2016). To this end, future studies could link survey-based measures of exemplar focal firms and their upstream and downstream supply chain partners’ behaviors with operational and financial performance outcome data derived from Bloomberg, Compustat and other secondary sources. Scholars can also contribute towards developing a better understanding of the potential moderating factors that may influence the relationship between SCI and firm performance. For example, product or industry type may influence SCI-performance linkages (Holweg et al., 2005). Thus, firms with high margin innovative products with unpredictable demand at the early stages of the product life cycle may derive greater positional and competitive advantages from devoting resources to the development of collaborative SCI by increasing flexibility and responsiveness than firms with low margin functional products facing predictable demand at the maturity stage of the product life cycle. Similarly, as suggested by Wong et al. (2011), market and technological turbulence may further moderate relationships between SCI and firm performance. In turbulent markets where technology and customers’ wants and needs change quickly, obtaining new information directly from suppliers and customers, and collaboratively interacting with supply chain partners, may be more critical than in stable markets. Finally, as with most research areas, future researchers could develop more robust insights by examining the longitudinal effects of SCI. Such studies could directly test for differences across progressive stages of SCI to potentially identify evidence of performance-related tipping points in response to SCI improvements (Flynn et al., 2010). Whatever the direction of future research, ongoing empirical investigationseperhaps synthesized by future generations of meta-analytic summaryewill continue to help explicate the strategically important relationship between the complex multidimensional constructs of SCI and firm performance. References Autry, C. W., Rose, W. J., & Bell, J. E. (2014). Reconsidering the supply chain integrationeperformance relationship: In search of theoretical consistency and clarity. Journal of Business Logistics, 35(3), 275e276. Barney, J. B. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99e120. Barney, J. B. (2012). Purchasing, supply chain management and sustained competitive advantage: the relevance of resource-based theory. Journal of Supply Chain Management, 48(2), 3e6. Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2009). Introduction to meta-analysis. Cornwall, UK: John Wiley & Sons. Chen, H., Daugherty, P. J., & Roath, A. S. (2009). Defining and operationalizing supply

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