Knowledge Management in Supply Chains: The ... - Wiley Online Library

Journal of Business Logistics, 2014, 35(2): 121–135 © Council of Supply Chain Management Professionals

Knowledge Management in Supply Chains: The Role of Explicit and Tacit Knowledge Tobias Schoenherr1, David A. Griffith2, and Aruna Chandra3 1

Michigan State University Lehigh University 3 Indiana State University 2

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e theorize, building on the knowledge-based view and the theoretical distinction between explicit and tacit knowledge, that knowledge management capability across the supply chain manifests itself in explicit and tacit knowledge, which in turn effectuates supply chain performance. The model is tested with survey data from 195 small- and medium-sized enterprises reporting on their primary supply chain. The results indicate that the supply chain’s knowledge management capability manifests itself in both explicit and tacit knowledge, with the latter being influenced more strongly. Moreover, it was found that while both explicit and tacit knowledge influence supply chain performance, the latter exerts a significantly greater impact than the former. Exploratory post hoc analyses add robustness to these findings and investigate mechanisms inherent to the transformation of tacit into explicit knowledge. Overall, this research contributes to academic theory development in logistics and supply chain management by the dichotomization of knowledge types and the demonstration of their differential magnitude of effects, and to managerial practice by providing important guidance for logistics managers structuring their knowledge management efforts across supply chains. Keywords: supply chain knowledge management; knowledge-based view; explicit and tacit knowledge; supply chain performance

INTRODUCTION In today’s competitive and dynamic marketplace firms need to leverage the strengths of their supply chains to remain competitive (e.g., Kahn et al. 2006). This has led to the adage of supply chains competing against supply chains. Within this setting, key aspects of competitiveness are encapsulated within the knowledge of logistics and supply chain partners, making knowledge management within the supply chain an important area of study (Craighead et al. 2009). Knowledge management is crucial for managerial decision making in logistics and supply chain management due to the fundamental nature of knowledge for problem solving and ensuing strategy development (e.g., Kahn et al. 2006). Despite considerable research on the creation and management of knowledge (e.g., Fugate et al. 2009; Anand et al. 2010), the field has been described as still being in an embryonic stage (Linderman et al. 2010) within the domains of logistics and supply chain management (Grawe et al. 2011). Within this context, supply chain knowledge can be defined as the use of knowledge resources obtained from supply chain members for economic gain (Craighead et al. 2009). It is the objective of the present research to contribute to this emerging and increasingly important domain so as to advance academic theory and provide substantive managerial guidance. Specifically, employing the literature on knowledge generation (Alavi and Leidner 2001) and the knowledge-based view (KBV) (Grant 1996), we contend that the presence of supply chain knowledge management capability (SCKMC) manifests itself in

Corresponding author: Tobias Schoenherr, Department of Supply Chain Management, Broad College of Business, Michigan State University, North Business College Complex, 632 Bogue St., Room N370, East Lansing, MI 48824, USA; E-mail: [email protected]

the two knowledge types of explicit and tacit knowledge. Drawing from Gold et al. (2001), SCKMC is conceptualized as a comprehensive and integrative set of knowledge management competencies consisting of knowledge acquisition, knowledge conversion, knowledge application, and knowledge protection. We further theorize the impact of explicit and tacit knowledge on supply chain performance, with tacit knowledge exerting a stronger influence than explicit knowledge. Our contentions are tested with a sample of small- and medium-sized enterprises (SMEs), a context which provides a unique opportunity to study knowledge management dynamics (Durst and Edvardsson 2012). SCKMC may be especially valuable for SMEs (Narula 2004), due to their often limited resources in developing specialized expertise in-house (Lu and Beamish 2001). While both explicit and tacit knowledge generated among supply chain members are important, the distinction between knowledge types is critical as they may have varying effects on key supply chain outcomes. Grawe et al. (2011) therefore encourage researchers to examine various knowledge types, and Anand et al. (2010) call for investigations into the “missed opportunities that may result from ignoring tacit knowledge” (p. 304). Given the need for a further understanding of knowledge in the supply chain, and particularly the types of explicit and tacit knowledge, the present study works to provide a deeper understanding of these two types of knowledge generated within a supply chain setting. As such, we contribute to logistics and supply chain management research and practice in three specific ways. Our first contribution lies in the investigation of how SCKMC manifests itself in two types of knowledge, an area left uninvestigated in extant research. While prior studies emphasize an evolutionary view of knowledge generation (Alavi and Leidner 2001), past empirical research has seldom conceptualized this framework consisting of knowledge acquisition, knowledge conversion, knowledge application, and knowledge protection as

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forwarded by Gold et al. (2001); rather, literature provides a limited perspective of this important aspect in logistics and supply chain management (Molina et al. 2007). By viewing SCKMC as a set of interconnected, operant resources (Smith et al. 2005), this work responds to calls for such extensions of knowledge management as indicated by Madhavaram and Hunt (2008). Furthermore, while other operationalizations of knowledge management capability exist (Wong and Wong 2011), they seldom have been subject to empirical testing, thereby not answering prior research calls (Freeze and Kulkarni 2007). By capturing the knowledge management construct with such an encompassing conceptualization, based on the seminal work of Gold et al. (2001), we are able to better understand how knowledge management manifests itself in different types of knowledge. Further, this contribution moves us from viewing knowledge on a continuum ranging from tacit to explicit (cf., Craighead et al. 2009), to a conceptualization that accounts for the simultaneous existence of both types of knowledge. Second, by conceptualizing explicit and tacit knowledge independently we are able to theorize and test the differential effects of knowledge types on supply chain performance, and contribute to extant research by the empirical investigation of these relationships via a large-scale survey. Supply chain performance, which assesses the supply chain’s competitiveness, business volume, profitability and competitive growth (Gunasekaran et al. 2004), was chosen due to its theoretical and practical relevance in today’s supply chain environment (cf., Griffis et al. 2007), and since it offers an integrative assessment of a supply chain’s competitiveness (e.g., Kahn et al. 2006). By theorizing and demonstrating the effects of knowledge types on supply chain performance we specifically address a limitation of the literature identified by Craighead et al. (2009), who state that “little is known about the performance enhancement offered by supply chain knowledge” (p. 405). The implications of our findings thus provide important guidance for practitioners, optimizing various knowledge generation and management aspects of their organizations, and illustrate the ensuing influences on supply chain performance. Third, by conducting a series of exploratory post hoc analyses we are able to scrutinize our theoretical model to alternate configurations, adding further insight and robustness to this work. Specifically, we investigate the differential influence of SCKMC on explicit and tacit knowledge, with the results suggesting a stronger influence on the latter knowledge type. We further assess the robustness of the SCKMC construct by considering the influence of its individual dimensions on knowledge, rather than in its hypothesized aggregate form. The results provide support for a dynamic capabilities view of SCKMC. In addition, we explore the conversion of tacit into explicit knowledge, also considering the moderating roles of the investigated knowledge management competencies.

THEORETICAL FOUNDATION Supply chain knowledge management capability Unlike prior research, which focused on single knowledge management elements within an individual organization (e.g., Cui

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et al. 2005), we consider knowledge management capability across a specific set of supply chain partners, as perceived by the focal firm. It is our belief that knowledge management, viewed at the level of a supply chain, can lead to an increased understanding of knowledge as a competitive resource, as under this view a supplier is not only relied upon to provide products and services, but is viewed as a key repository for knowledge and the source of unique capabilities. This is consistent with arguments in Cohen and Levinthal (1990) who consider outside knowledge (i.e., from supply chain partners in our context) critical to innovation, as well as the concept of knowledge-sharing networks (Dyer and Nobeoka 2000). Drawing from cognitive psychology (Neisser 1967), we argue that this approach facilitates the unique combination of stimuli emanating from individual supply chain members, realizing synergies and benefits in the processing of the stimuli that would not have been possible otherwise. In this view, each supply chain member can be regarded both as contributing to the knowledge of the supply chain, and as scaffolding and elevating it to a potentially unprecedented level of sophistication (cf., Brown and Duguid 2001). As such, SCKMC can serve as a dynamic capability, contributing to a specific supply chain’s “ability to integrate, build, and reconfigure internal and external competences to address rapidly changing environments” (Teece et al. 1997, 516). Within this context, SCKMC can provide for a valuable dynamic capability facilitating managerial decision making in turbulent environments. Such capability can be particularly valuable when knowledge is obtained through weak ties in the firm’s supply network, which should make the knowledge less redundant (Levin and Cross 2004). In these instances, SCKMC should be especially valuable due to its ability to harness disparate external knowledge and transform it to be used internally. This parallels the notion in cognitive psychology that new stimuli need to be processed into knowledge via appropriate mechanisms. As such, SCKMC represents an organizational routine able to generate organizational memory (Linderman et al. 2010) from external supply chain partners, with explicit and tacit knowledge then representing the actionable manifestations of SCKMC. In addition, following the approach in Smith et al. (2005), SCKMC can be classified as interconnected, operant resources. To conceptualize SCKMC, we draw on the aspects of knowledge acquisition, knowledge conversion, knowledge application, and knowledge protection developed by Gold et al. (2001). Knowledge acquisition refers to approaches aimed at knowledge accumulation (Lyles and Salk 1996), which is the basis for the enhancement of core capabilities (Leonard 1995). Knowledge conversion considers the processing of the acquired knowledge into usable formats, which is especially crucial in a supply chain relationship due to the disparate structure of knowledge among supply chain members (Roy et al. 2004). Knowledge application refers to approaches charged with the utilization of such supply chain knowledge to solve problems or develop strategies, which requires the active sharing of knowledge between supply chain partners (Kogut and Zander 1992). Knowledge protection concerns the approaches dealing with shielding the obtained knowledge from outside dissemination (Norman 2004), an issue especially relevant in a supply chain setting due to its multiple touchpoints. Together, these integrative aspects can be referred to

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as a capability (Amit and Schoemaker 1993), and we therefore refer to it as SCKMC. Explicit and tacit knowledge Research contends that knowledge can be present in the form of both explicit and tacit knowledge (Polanyi 1966). Explicit knowledge is codified and can be easily communicated and transferred (Nonaka 1994; Anand et al. 2010). Explicit knowledge can be in the form of manuals, blueprints, procedures, policies, forecasts, inventory levels, production schedules, market intelligence data, etc. In contrast, tacit knowledge is implicit, hard-to-conceptualize and subjective, and is part of an individual’s experiences; it is evidenced in behavior or actions, and is often highly ambiguous (Venkitachalam and Busch 2012). This type of knowledge has an important cognitive dimension, and includes mental models, beliefs, and perspectives. It develops interactively over time through shared experience, and the inherent “know how” is reflected in individual skills that result from learning-by-doing (Mooradian 2005). The philosopher Polanyi (1966) describes tacit knowledge as knowing more than we can tell or as knowing how to do something without thinking about it. The knowledge-based view The KBV (Grant 1996) rests on the idea that firms should be analyzed based on their knowledge resources. Drawing on the KBV's foundations in the resource-based view (Barney 1991), if knowledge is valuable, rare, inimitable, and nonsubstitutable, it can be considered a resource capable of establishing a competitive advantage (Grant 1996). A commonly applied theoretical framework in logistics and supply chain management (Defee et al. 2010), this firm level view of resources has been extended to include external actors, such as suppliers and buyers (Dyer 1996). We thus contend that knowledge generated through the interaction of specific supply chain members has the potential to improve the interface between parties via better integration, enabling more efficient and effective supply chain processes. This perspective of knowledge as a resource for a supply chain is consistent with extant supply chain literature (Defee et al. 2010). In fact, the generation and exploitation of such knowledge has been considered by some to be a driver for the pursuit of supply chain relationships themselves (Lanier et al. 2010). Especially when knowledge is not merely copied (which may make it Figure 1: Research model.

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redundant), but when it is elevated to new levels, can it serve as a valuable resource (Hamel 1991); SCKMC as a dynamic capability can aid in this endeavor. The next section builds on these theoretical foundations to develop our hypotheses, which are summarized in Figure 1.

HYPOTHESIS DEVELOPMENT Supply chain knowledge management capability, and explicit and tacit knowledge Based on the prior discourse and tenets inherent to the KBV, SCKMC may be described as a valuable (the generation of explicit and tacit knowledge ensues from SCKMC), rare (the structured and comprehensive approach is unique to each supply chain), inimitable (a special company climate may be needed to successfully implement the approach), and nonsubstitutable (the distinctive result may not be able to be replicated by alternate mechanisms) resource. As such, SCKMC’s four aspects can be viewed as a cohesive cognitive development in a sequence of states (Ericsson and Hastie 1994). That is to say, SCKMC provides a mental model (Johnson-Laird 1983; Gorman 2001) and structure, serving as a foundation for the generation of explicit and tacit knowledge across a specified set of supply chain members. SCKMC thus serves as an evolving capability, dynamically transforming external knowledge into a usable internal resource (cf., Hamel 1991). Past research showed that explicit knowledge has the potential to be generated when available knowledge among members to understand each other’s requirements is exchanged (Samuel et al. 2011), relationships and procedures are formalized, and existing knowledge repositories are shared and reused (Voelpel et al. 2005). Within this context, SCKMC can effectuate the effective accumulation, conversion, and application of knowledge among supply chain members, yielding easily understood explicit knowledge. The structured approach may aid especially in the formalization and documentation of knowledge among supply chain members, which may possess unique, nonredundant intelligence for the benefit of the firm. Common activities constituting SCKMC to generate such explicit knowledge include the conduct of structured meetings, the definition of contract specifications, and the archiving of documents (Dyer and Hatch 2004; Roy et al. 2004; Samuel et al. 2011). These initiatives have the

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potential to capture, structure, codify, and institutionalize knowledge across the supply chain, leading to the generation of explicit knowledge (Lee and Van den Steen 2010). Outcomes can consist of joint forecasts, consolidated market data, and production schedules. As such, we expect SCKMC to manifest itself in explicit knowledge. The multitude of parties involved, as well as their respective unique knowledge repositories, backgrounds, and insights into the specific supply chain, also provide great potential for tacit knowledge to be developed (Li and Tsai 2009). Tacit knowledge can be facilitated by methods such as brainstorming and the nominal group technique (Anand et al. 2010), aspects involved in knowledge acquisition and conversion of SCKMC, which seek to create an environment in which negative psychology is avoided (all supply chain partners can voice their ideas freely first, enabling higher levels of thinking) and positive synergies are generated (ideas can be built on by other supply chain members). These methods are consistent with arguments by Hamel (1991) and Badaracco (1991), who attribute tacit knowledge to social relations, and Zack (1999), who refers to communities of practice (consistent with the SCKMC element of knowledge application). As such, SCKMC may provide the structure necessary to tease out this type of knowledge resident in supply chain members. Formally: H1: Supply chain knowledge management capability is positively associated with explicit knowledge in a supply chain. H2: Supply chain knowledge management capability is positively associated with tacit knowledge in a supply chain. The impact of explicit and tacit knowledge on supply chain performance Explicit knowledge represents the knowledge within the supply chain that can be easily articulated. The effective exchange and usage of this readily available explicit knowledge in a supply chain promises great potential for enhancing the efficiency of the supply chain, since codified knowledge at one supply chain entity can be easily shared with another supply chain member (Dyer and Hatch 2004), yielding performance improvements. As such, the collaborative use of knowledge can improve the interface between supply chain members resulting in enhanced supply chain integration and its associated benefits (Song and Swink 2009). For example, the formal specification of the manners of acting and operating within a supply chain, as well as the open sharing of knowledge (without much elaboration and loss of integrity; Dyer and Hatch 2004) resulting in enhanced visibility, allows for an increase in the overall performance of the supply chain. Theoretical substantiation offers the KBV and the notion that explicit knowledge can be a valuable, rare, inimitable, and nonsubstitutable resource. This is especially true when such knowledge is derived from a firm’s supply chain members, enabling managers to develop competitiveness-enhancing strategies. We therefore contend that the ease of communication and transfer of explicit knowledge among supply chain members is associated with supply chain performance (reflected in H3a).

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Tacit knowledge is not only difficult to transfer among members of the supply chain, but may be unique to the specific supply chain and difficult for others to replicate (Grant 1996; Dooley 2000), due to its propensity to develop in relational interactions (Kahn et al. 2006). Tacit knowledge focuses on cognitive elements, or what Johnson-Laird (1983) calls “mental models,” in which individuals construct analogies, schemata, paradigms, viewpoints, beliefs, and perspectives in their minds to make sense of available information in complex realities; in this setting, new meanings can be created (Nonaka 1994). It is because of these aspects that Nonaka and Takeuchi (1995) label tacit knowledge as the primary source for innovation, new product development, and the conception of new business models. This proposed connection between tacit knowledge and supply chain performance is further grounded in KBV’s notion of tacit knowledge derived from supply chain members serving as a valuable, rare, inimitable, and nonsubstitutable resource. Parallels can be seen in Hall (1999) and Spekman et al. (2002), who describe tacit knowledge as the resident fabric of the firm (or in this case a specific supply chain). We therefore expect a positive association of tacit knowledge with supply chain performance (reflected in H3b). We further believe there to be differential effects of the knowledge types on supply chain performance. Specifically, we suggest the association of tacit knowledge with supply chain performance to be greater than the association of explicit knowledge with supply chain performance (reflected in H3c), based on the following theoretical notions derived from the KBV. While explicit knowledge is likely to benefit performance benchmarks, the codified nature of this type of knowledge might allow competitive differentiation to only a limited degree (as explicit knowledge might transfer to competitors easily, due to its codified state). While explicit knowledge is valuable, one could argue that the inimitability property of explicit knowledge is threatened due to the ease with which such knowledge can be transferred, and therefore copied/imitated by competitors, and thus be made redundant. Consequently, while explicit knowledge is certainly expected to be beneficial, what may be observed is that this type of knowledge allows for a smaller enhancement of a supply chain’s performance. Support for this argumentation is also found in Gorman (2001), who notes that explicit and documented knowledge alone does not suffice for the complex task of technology transfer, a key element for competitive advantage. In contrast, since tacit knowledge is socially complex, usually requiring significant organizational learning, the tacit knowledgebase developed can be expected to serve as a source of sustainable competitive advantage, thus leading to enhanced supply chain performance (assessed relative to the competition). It is tacit knowledge, which is difficult to imitate by competitors, that yields competitive differentiation. Parallels can be seen in the work by Swink (2006) on the development of collaborative innovation capability grounded in knowledge, and the literature in new product development that stresses the importance of tacit knowledge (Goffin and Koners 2011). Further, the differential impact of explicit and tacit knowledge on supply chain performance comports well with the KBV’s view of resources being heterogeneous and imperfectly mobile, decreasing their tendency to become redundant. Within this context it can be argued that tacit knowledge is characterized by being more imperfectly mobile, thus offering a

Knowledge Management in Supply Chains

more valuable, rare, inimitable, and nonsubstitutable resource. These properties are enhanced by knowledge’s generation from supply chain members, who may offer additional intelligence for the firm’s enhanced competitive performance. Therefore, while a positive impact of both explicit and tacit knowledge on supply chain performance is expected, we theorize that it is the hard-toconceptualize property of tacit knowledge that can generate greater supply chain performance. This contention is due to the learning-by-doing component of tacit knowledge, which provides a greater application foundation for supply chain performance as conceptualized in this study. H3: Explicit (a) and tacit (b) knowledge in the supply chain are positively associated with supply chain performance, with the impact of tacit knowledge on supply chain performance being greater than the impact of explicit knowledge on supply chain performance (c).

METHODOLOGY

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Respondents were senior executives (71.7%), followed by general managers (17.3%), owners of the company (7.5%), and frontline management of the firm (3.5%). Producer goods were the most commonly reported on product (58.8%), followed by consumer durables (18.2%), capital goods (13.3%), and consumer nondurables (9.7%). On average, respondents had 25 years of experience. Sales revenue for the majority of the respondents’ firms ranged between $1.01 and $5 million (36.80%), between $5.01 and $10 million (22.40%), and between $10.01 and $20 million (20.00%). We estimated nonresponse bias by comparing early (first 50) and late (last 50) respondents on the key variables utilized in this study, with the late respondents serving as a proxy for nonrespondents (Armstrong and Overton 1977). Since our research approach involved a total of five mailings to each addressee, as well as phone calls to the nonresponding firms with an encouragement to respond to our survey, we believe that the responses received later in the data collection represent a valid proxy for nonrespondents. Independent sample t-tests yielded nonsignificant results (p > .05), suggesting that early and late respondents do not differ on the key constructs under study. As such, nonresponse bias was considered to not be of serious concern.

Data collection and sample Construct measures The hypotheses were tested with data collected from importers operating in the manufacturing industry. Importers were chosen due to their coordinating role in supply chains, yielding respondents that are familiar with both suppliers and customers. Respondents were asked to report on a single, specific supply chain relationship encompassing their primary supplier and the corresponding key customer receiving outputs from the supplier relationship. As such, the unit of analysis in our survey was the focal firm reporting on its primary supply chain. We restricted our sample to firms with 50 employees or less so as to enhance key informant quality, as smaller firms usually have a general manager responsible for a majority of the firm’s activities (key informant quality was enhanced due to these general managers possessing intimate knowledge of the firm’s primary linkages with critical suppliers and customers). Borrowing this approach from the marketing literature (e.g., Lusch and Brown 1996), we suggest its value also for logistics and supply chain management research in aiding in respondent expertise. At the same time, the sampling approach enabled us to focus on a key but often neglected sector of the worldwide economy, SMEs, which provided a unique opportunity to study knowledge management dynamics. Firm contact information was drawn from the Journal of Commerce database. We started with a systematic random sample of 3,000 U.S. addresses in 19 four-digit standard industrial classification codes in manufacturing. The sample was then restricted to firms with fewer than 50 employees, resulting in a final sample size of 900 firms to whom the survey package was sent following Dillman’s (2000) tailored design method; an executive summary of the results was offered to motivate participation. A total of four additional mailings were conducted to increase the response rate. In addition, phone calls were made to the nonresponding firms, resulting in a sample of 204 survey responses. Upon close examination of the data, nine records were deleted due to missing values. The final sample thus consisted of 195 records, representing a response rate of 21.7%.

To ensure content validity, measures were developed based on established scales following guidelines by Spector (1992), and adapted to our supply chain context. Refinement took place in pilot tests involving practitioners and academics, confirming the content domain of each construct and its corresponding measurements, as well as the cohesiveness, precision, and logic of our definitions. Questions consisted of statements to which the respondent was asked to indicate their degree of agreement on a 7-point Likert scale, anchored at strongly disagree (value = 1) and strongly agree (value = 7) (Appendix). Supply chain knowledge management capability was modeled as a formative second-order construct (cf. Johnson et al. 2006; Ruiz et al. 2008) consisting of four-first-order reflective constructs drawn from Gold et al. (2001), adapted to the supply chain context: knowledge acquisition, knowledge conversion, knowledge application, and knowledge protection. This formulation is based on theoretical considerations and criteria for the choice of formative versus reflective second-order models provided in Jarvis et al. (2003), Podsakoff et al. (2003b), and Gligor et al. (2013). Specifically, we consider the four knowledge management aspects as forming the underlying SCKMC construct (i.e., greater knowledge acquisition, conversion, application, and protection generate greater overall SCKMC, and not the reverse; Diamantopoulos and Siguaw 2006). Explicit knowledge was measured by items adapted from Zander (1991) and Bresman et al. (1999), and tap into the notion of formalization of processes via manuals and documents (cf., Smith 2001). Tacit knowledge was measured by items drawn from Simonin (1999), and tap into the importance of first-hand experience for tacit knowledge (cf., Lord and Ranft 2000). Both knowledge dimensions were adapted to the supply chain context (Schoenherr et al. forthcoming). Supply chain performance measurement items were drawn from Zou et al. (1998) and were adapted to the supply chain context.

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MODEL AND HYPOTHESIS TESTS Measurement model The measurement model consists of seven multiitem constructs, four of which are used in a later step to constitute SCKMC. For the assessment of the psychometric properties the seven constructs were considered individually (cf., Fugate et al. 2009). All constructs were modeled with reflective measurement items. To refine the measurement model, the constructs were subjected to confirmatory factor analysis. Items were removed in an iterative process, one at a time, based on cross-loadings and weak loadings on the underlying construct (Anderson and Gerbing 1988). Final measurement items, including their mean, standard deviation, completely standardized loading, t-value, standard error, and R2 are summarized in Table 1. Validity and reliability of the constructs were assessed based on recommendations by Anderson and Gerbing (1988). Specifically, content validity was provided by the structured and literature-based development and design of the questionnaire and its measurement items, benefiting from the involvement of practitioners and academics knowledgeable in the content domain. Convergent validity was assured by each indicator’s estimated standardized coefficient loading on its associated construct; as can be seen from Table 1, each coefficient is greater than twice its standard error. Discriminant validity was assessed by examining the square roots of the average variance extracted (AVE) for each construct, which was greater than the corresponding correlation coefficient, establishing discriminant validity (Fornell and Larcker 1981). Some of the four constructs associated with

SCKMC did not fulfill this criterion, suggesting a weak discrimination between each other, which was expected due to their second-order conceptualization. The uni-dimensionality of the constructs was established by confirmatory factor analysis, with all items loading well above the suggested threshold of .30 (O’Leary-Kelly and Vokurka 1998). Reliability was ensured by Cronbach alpha values above .70 (Table 1). Finally, construct validity was established by satisfactory content validity, unidimensionality, reliability, and convergent and discriminant validity (O’Leary-Kelly and Vokurka 1998). Correlations and the AVE values are provided in Table 2. The measurement model exhibited good fit to the data (comparative fit index = .981; incremental fit index = .982). Further support was provided by the root mean square error of approximation (= .049) and the v2/df ratio (332.979/209 = 1.593). Based on these evaluations, the measurement of the constructs was judged to be acceptable. We aimed to minimize the potential for common-method bias in the questionnaire administration via several means. First, we interspersed the dependent and independent variables, which may have an impact on the retrieval cues minimizing commonmethod bias (Podsakoff et al. 2003a). Second, we followed a rigorous approach for informant selection, and restricted our sample to firms with 50 employees or less. This enhanced key informant quality, as smaller firms usually have a general manager responsible for a majority of the firm’s activities, who therefore possess intimate knowledge of the firm’s primary linkages with critical suppliers and customers. Thus, our respondents were credible (Phillips 1981). And third, while common-method bias is of greater importance in research related to issues of social

Table 1: Final construct measurement items Construct Knowledge acquisition (a = .710) Knowledge conversion (a = .887)

Knowledge application (a = .819) Knowledge protection (a = .892) Tacit knowledge (a = .739) Explicit knowledge (a = .711) Supply chain performance (a = .868)

Variable

Mean

SD

Loading

t-value

SE

R2

acqk1 acqk2 acqk3 convk1 convk2 convk3 convk4 appk1 appk2 appk3 protk1 protk2 protk3 tactk1 tactk2 tactk3 explk1 explk2 explk3 p1 p2 p3 p4

4.815 4.897 4.979 4.600 4.533 4.821 4.472 5.241 5.103 4.836 4.600 4.713 4.821 4.908 4.769 4.692 4.113 3.338 4.056 4.949 4.785 4.651 4.928

1.402 1.335 1.432 1.419 1.451 1.390 1.337 1.192 1.243 1.333 1.581 1.482 1.426 1.301 1.386 1.319 1.365 1.546 1.534 1.157 1.278 1.277 1.216

.665 .635 .716 .845 .871 .840 .703 .822 .805 .716 .824 .886 .865 .710 .626 .762 .576 .877 .576 .752 .877 .783 .745

9.786 9.239 10.719 14.276 14.966 14.129 10.933 13.487 13.078 11.087 13.584 15.152 14.596 9.928 8.578 10.768 7.534 11.017 7.530 11.767 14.739 12.472 11.630

.095 .092 .096 .084 .084 .083 .086 .073 .077 .086 .096 .087 .085 .093 .101 .093 .096 .087 .085 .074 .076 .080 .078

.443 .403 .513 .714 .758 .705 .495 .676 .648 .513 .680 .785 .748 .504 .392 .580 .332 .769 .332 .565 .769 .613 .556

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Table 2: Correlations and average variance extracted

Knowledge acquisition (KAC) Knowledge conversion (KCO) Knowledge application (KAP) Knowledge protection (KPR) Explicit knowledge (EK) Tacit knowledge (TK) Supply chain performance (SCP)

KAC

KCO

KAP

KPR

EK

TK

SCP

.453 .862 .899 .530 .176 .609 .664

.668 .864 .577 .353 .460 .516

.612 .673 .185 .552 .622

.738 .115 .552 .367

.478
.021 .246

.492 .435

.626

Values for the average variance extracted are printed in the diagonal.

desirability, our research focuses on specifics and does not have social desirability components attached to it, further minimizing the potential for common-method bias. In addition, following recommendations provided in Podsakoff et al. (2003a), common-method bias was empirically assessed. First, Harman’s one-factor test was conducted (McFarlin and Sweeney 1992). The one-factor model exhibited significantly worse fit than the measurement model, suggesting that commonmethod bias is not of serious concern (Podsakoff and Organ 1986). Second, we employed the marker-variable approach (Malhotra et al. 2006), where a marker variable is included in the model that is not theoretically expected to be related to the constructs under study. The marker variable (gender) did not have significant influences on the model constructs, providing further evidence of the minimization of common-method concerns. Third, to ensure that halo effects were not influential in our data (e.g., respondents anchoring their responses on performance and thus rating all independent variables highly), we used cross tabs to examine the dispersion of the dependent performance variable across the range of independent variables in the raw data. Our analysis showed considerable dispersion among the independent and dependent variables. Structural model The hypotheses were tested with partial least squares (Hair et al. 2013). The partial least squares approach was chosen, since it has been commonly employed when testing second-order formative constructs (Diamantopoulos et al. 2008; Oh et al. 2012; Peng and Lai 2012). Three control variables were included in the model: firm size, to account for different resource endowments of firms due to their size; years of experience with the supply chain relationship, to account for greater knowledge that may have been accumulated over a longer period of time; and industry type, to account for different dynamics inherent in industries. The controls were not found to have a significant influence (firm size: b = .013, p > .1; years of experience with the supply chain relationship: b =
.025, p > .1; industry type: b = .214; p > .05). The model explained 24.3% in the variance of supply chain performance, 25.5% in the variance of tacit knowledge, and 13.2% in the variance of explicit knowledge. Since the partial least squares approach does not provide a commonly accepted measure to assess the appropriateness of the model (Chin 1998; Wetzels et al. 2009), we computed a number of fit indices for robustness. Specifically, we calculated Tenenhaus et al.’s (2005)

“goodness of fit” (GoF) criterion to assess the model’s global fit. This measure has received application and confirmation in recent research (e.g., Perols et al. 2013; Sawhney 2013). The criterion can take on values between 0 and 1, with higher values indicatqffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ing better fit. Applying the formula GoF ¼ communality  R2 , we receive a value of .576. This is greater than the suggested cut-off value of .36 for large effect sizes of R2 (Perols et al. 2013), indicating our model to be of very good fit. Hypothesis tests H1 argued that SCKMC is positively associated with explicit knowledge in the supply chain. The results support H1 (b = .363; p < .001). H2 theorized that SCKMC is positively associated with tacit knowledge in the supply chain. The results support H2 (b = .505; p < .001). H3a theorized that explicit knowledge is positively associated with supply chain performance. The results support H3a (b = .281; p < .01). H3b theorized that tacit knowledge is positively associated with supply chain performance. The results support H3b (b = .317; p < .001). H3c suggested the impact of tacit knowledge on supply chain performance being greater than the impact of explicit knowledge on supply chain performance. To assess whether a significant difference exists, we conducted a Ztest. The results confirmed a stronger link between tacit knowledge and supply chain performance than the link between explicit knowledge and supply chain performance (Z =
2.025; p < .01), providing support for H3c. To supplement these findings, a series of exploratory post hoc analyses were conducted, which are described next. Post hoc analyses The differential influence of SCKMC on explicit and tacit knowledge We aimed to bring greater specificity and insight into the differential influence of SCKMC on the two knowledge types in an exploratory post hoc test. Specifically, we assessed whether the impact of SCKMC on tacit knowledge is greater than the impact of SCKMC on explicit knowledge. We conducted this test based on the belief that the supply chain setting provides an environment that is especially amenable for tacit knowledge to be created. Tacit knowledge has been said to provide great promise (Dooley 2000), but due to its more subtle state, has been

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underutilized in many instances. While explicit knowledge is also an outcome of the approach underlying SCKMC, based on each supply chain member contributing to the specific and codified knowledge of the supply chain, we believe that the interaction between supply chain members is especially prone to generate tacit knowledge. Our arguments rely on the properties of tacit knowledge, which can be described as complex, context dependent, difficult to imitate and codify, and not easily transferable (Kogut and Zander 1992). It represents the type of knowledge that may be known, but which cannot be readily expressed verbally or in writing (Polanyi 1966). As such, the methodical approach inherent in SCKMC may be able to provide structure, and thus facilitate this more elusive type of knowledge. This is consistent with arguments underlying the KBV, specifically when considering SCKMC as a valuable bundle of resources offering unique advantages for the firm. Such differentiation may be more likely to be derived from tacit knowledge, due to its more intangible nature, its greater difficulty for transfer, and its likely property of being less redundant. In contrast, explicit knowledge, due to its more transferrable nature, is likely to be generated also via less complex approaches, and may be more prone to becoming redundant. Our reasoning builds on Nonaka’s (1991) notion of the supply chain representing a complex “living organism” through which more elevated and sophisticated knowledge can be developed. Based on the multiple interactions among supply chain partners, the synergies likely to ensue are suggested to elevate tacit knowledge to a greater degree. This contention is also supported by organizational theorists (Granovetter 1973), who view explicit knowledge as the outcome of arms-length transactions, whereas tacit knowledge is more susceptible to be created in collaborative relationships. To assess whether a significant difference exists, we conducted a Ztest. The results confirm a stronger association of SCKMC with tacit knowledge than with explicit knowledge (Z =
2.120; p < .01), providing support for our theoretical arguments. The robustness of the SCKMC construct To assess the robustness of our model conceptualizing SCKMC as a second-order formative construct, we constructed a competing model that removes SCKMC entirely and tests the direct relationships of the individual first-order constructs on explicit and tacit knowledge. The calculation of Tenenhaus et al. (2005) “goodness of fit” criterion yielded a value of .417 for the model’s global fit. This is smaller than the GoF value derived for our proposed model (.576), suggesting the competing model to be inferior (Perols et al. 2013). This finding bolsters our conceptualization of SCKMC as a second-order formative construct, and by implication, the theoretical disposition of SCKMC as a valuable bundle of complex operant resources offering unique advantages for the firm. Considering the four aspects of knowledge acquisition, conversion, application, and protection, and the continued evolution of these capabilities, the result is suggestive of our arguments of SCKMC as constituting a dynamic capability able to combine, transform, or renew resources as markets evolve. In addition, the notion of SCKMC being greater than the sum of its parts was supported. We thus substantiated, also from a statistical viewpoint, SCKMC as representing a comprehensive and integrative set of knowledge management competencies as part of the KBV.

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The conversion of tacit into explicit knowledge The domain of converting tacit into explicit knowledge has been gaining increasing attention, especially with heightened employee mobility. In an exploratory post hoc analysis, we investigated whether SCKMC helps firms convert tacit knowledge into explicit knowledge by including a path between these two constructs in a competing model. The path turned out to not be statistically significant (b =
.068, p > .1). In a further competing model we assessed the moderating role of SCKMC on the relationship between explicit and tacit knowledge. The interaction term was also not supported by our data (b =
.061, p > .1). While SCKMC did not significantly moderate this relationship, we explored whether one of its underlying dimensions did. In a last set of exploratory post hoc analyses, we thus tested the potential individual moderating impact of the four constructs constituting SCKMC on the link between tacit and explicit knowledge. To do so, we considered the prior model which linked all four individual knowledge management competencies to both tacit and explicit knowledge, and added the link between tacit and explicit knowledge. The four ensuing interaction terms, testing for the moderating role of each of the four dimensions on the relationship between tacit and explicit knowledge, were not significant. This suggests that the transformation of tacit into explicit knowledge follows different pathways than the ones specified herein. While the data did not support our knowledge transformational expectations, we have brought greater clarity to the interworkings of SCKMC in its ability to influence both tacit and explicit knowledge. While our data did not support these relationships, the results have to be treated with caution. Data collection was conducted without the objective to test these relationships in mind, and was therefore exploratory. Nevertheless, we thought it important to investigate the conversion of knowledge types in this exploratory post hoc analysis. Future research is encouraged in this domain, to specifically study the dynamics inherent in knowledge conversion (Nonaka 1994), and to extend the studies by Kahn et al. (2006) and Anand et al. (2010).

DISCUSSION This research contributes to extant literature in logistics and supply chain management by enhancing our understanding of the ability of SCKMC to influence explicit and tacit knowledge, and by the investigation of the differential outcome effects resulting from explicit and tacit knowledge. Our findings offer significant insights into knowledge within supply chains, advance academic understanding, and provide important implications for managers. Overall, our study is important from both a theoretical and a practical perspective, enhancing the value of our research (Fawcett et al. 2011). Theoretical implications The findings offer important insight for logistics and supply chain management scholars interested in the investigation of knowledge and its potential within a supply chain context. Specifically, we demonstrate that SCKMC is positively associated

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with knowledge present within a supply chain. We introduced the concept of SCKMC, and conceptualized it as a comprehensive approach consisting of knowledge acquisition, knowledge conversion, knowledge application, and knowledge protection, representing a set of interconnected, operant resources. Our work demonstrates the value of this framework, borrowed from the information systems literature, in the domain of supply chain management. By operationalizing knowledge management capability within a supply chain setting, we work to address the calls by Freeze and Kulkarni (2007) and Wong and Wong (2011), who encouraged a greater understanding of knowledge. Our findings show that SCKMC manifests itself in explicit and tacit knowledge within the supply chain. This is an important extension to literature as it demonstrates not only the importance of knowledge management capability as a bundle of valuable resources, but its ability to generate both explicit and tacit knowledge among supply chain partners. We have thus effectively tied the SCKMC construct to the KBV and its value propositions inherent in valuable, rare, inimitable, and nonsubstitutable resources. Since the generation of explicit and tacit knowledge ensues from SCKMC, it can be viewed as valuable, and since a structured and comprehensive approach for SCKMC occurs within a unique supply chain context, it can be considered as rare. The inimitability property derives from a special climate that may be needed to successfully implement the approach, and the nonsubstitutability property rests in the distinctive result of SCKMC that may not be replicated by alternate mechanisms. In our dichotomization of the two knowledge types, we relied on the most prominent classifications in the knowledge management literature. The importance of this distinction and the potential ensuing differential effects were recently noted and encouraged in the logistics and supply chain management literature (e.g., Anand et al. 2010; Grawe et al. 2011). In addition, the results provide evidence for the value of a formal approach in facilitating cognitive processing, and demonstrate the value of interconnected, operant resources. SCKMC can thus be viewed as an interorganizational routine and a dynamic capability able to yield beneficial outputs. We further found in an exploratory post hoc analysis that SCKMC was more effective in influencing tacit knowledge than it was in influencing explicit knowledge within a supply chain. This result substantiates our theorization of the differential influence, specifically our view of SCKMC as a valuable bundle of resources offering unique advantages for the firm. Support was found for tacit knowledge’s more intangible nature and greater difficulty for transfer, as was for explicit knowledge’s more transferrable nature, and its likelihood to be more redundant and to be generated also via less complex approaches. We demonstrated the conduciveness and potential of a collaborative supply chain to generate the more elusive type of tacit knowledge. Our rationale, which argued for the unique pairing of partners and their respective skills, yielding a higher and more holistic type of knowledge, was confirmed, as was Nonaka’s (1991) notion of viewing supply chains as living organisms. Our results further advance the literature by not only conceptualizing explicit and tacit knowledge as outcomes of SCKMC separately but more importantly by demonstrating the effects of each knowledge type on supply chain performance. Specifically,

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the data supported our KBV-based arguments for the impact of explicit and tacit knowledge on supply chain performance, recognizing the two knowledge types as valuable, rare, inimitable, and nonsubstitutable resources. The collaborative development of explicit and tacit knowledge via interactions with supply chain partners is an asset that can yield competitive differentiation for the firm. More importantly, while prior research has established the value of knowledge for performance (e.g., Craighead et al. 2009; Fugate et al. 2009; Grawe et al. 2011), we demonstrate that it is only when explicit and tacit knowledge are separately considered that the intricacies of their effects are understood. This contention was derived based on the KBV’s interpretation of resources as being heterogeneous and imperfectly mobile, and the notion that different types of resources exist that possess differing degrees of ability in effectuating an outcome. At this point it is worth noting that a mere copying and internalizing of knowledge from supply chain partners already present within their organizations may yield redundant resources, reducing their overall effectiveness in influencing competitive performance (Hamel 1991; Levin and Cross 2004). Our conceptualization of SCKMC as a dynamic capability elevates external resources to higher levels, demonstrating their ability to generate explicit and tacit knowledge for the firm, which in turn enables competitive performance. In this research we considered different types of knowledge resources. Specifically, our results indicate that it is tacit knowledge, characterized by applied skills and learning-by-doing that exhibits a greater impact on a supply chain’s competitive performance. The findings provide substantiation for our contention that due to its more elusive and complex nature, tacit knowledge represents a resource that is more imperfectly mobile, thus offering a more valuable, rare, inimitable, and nonsubstitutable resource for the firm under the KBV. Tacit knowledge was shown to also provide a greater application foundation for supply chain performance as conceptualized in this study, and as such was confirmed to be more effective. In contrast, explicit knowledge, due to its ease of communication and transfer, exhibits a lesser ability to impact supply chain performance (although it is important to note that we did find support for the codified nature of this type of knowledge to allow for competitive differentiation to a degree). We note here that our performance measure was designed to test for this specific outcome, in that it assesses competitive performance (Kahn et al. 2006). As such, we do not postulate that explicit knowledge is less valuable per se, but that it is less valuable to differentiate the firm from competition. Explicit knowledge is less able to retain the characteristics needed to meet the requirements for being a resource under the KBV, since, due to its codified nature, this knowledge may be easily transferred to competitors. This observation is indicative of the more obvious disposition of explicit knowledge, being less context dependent and more easily to transfer among competitors. No support was found in our exploratory post hoc analyses for the link between tacit and explicit knowledge, as was for the moderating role of SCKMC and its individual elements. Knowledge conversion (i.e., the conversion of tacit into explicit knowledge) follows different pathways than specified in this research, and future studies are encouraged to delve deeper into this domain.

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Overall, through the specification of differential knowledge effects on supply chain performance, this work contributes to our understanding of the complex influence of explicit and tacit knowledge within supply chain relationships. As such, it answers calls in the literature for a greater understanding of knowledge management dynamics (Craighead et al. 2009). For example, Hult et al. (2004) stress the lack of attention that has been paid to the link between knowledge and supply chain outcomes, Ferdows (2006) encourages supply chain management to not take a passive role in knowledge management research, and Craighead et al. (2009) argue that there is still much to be learned about performance improvement possible via supply chain knowledge. Most recently, Grawe et al. (2011) encourage the study of knowledge synthesis mechanisms, as well as the consideration of different knowledge types. This research answers these calls while extending one of the most prominent classifications in the knowledge management literature, that is, the differentiation between explicit and tacit knowledge, a distinction also fundamental to the theory of knowledge creation (Polanyi 1966). This research further contributes to the field via its contextual focus on SMEs. Limited research exists within the domains of logistics and supply chain management research that taps into this sector (Tokman et al. 2007; Bode et al. 2011). We focused on SMEs since SCKMC may be especially valuable for them (Narula 2004), due to their often limited resources in developing specialized expertise in-house (Lu and Beamish 2001). By investigating knowledge management in an SME setting, we also answered the call by Durst and Edvardsson (2012) for more specific insight in this context. Managerial implications As competition increases, the importance of knowledge management capability for logistics and supply chain management as a competitive foundation is likely to increase as well. Against this reality, managerially, the results provide important insights, especially for SMEs. First, our findings clearly demonstrate the importance of SCKMC. We confirmed that it is the establishment of SCKMC that leads to knowledge within a specific supply chain, and the ultimate enhancement of supply chain performance. As such, our results provide a stimulus for managers to invest in SCKMC incorporating specific key supply chain partners. Our concept of SCKMC drew on its encapsulation in the four supply chain knowledge management aspects identified, to which managerial attention should be devoted. This finding should be especially valuable for SMEs, which have been described as lagging behind in knowledge management endeavors (McAdam and Reid 2001). This observation may be attributed to the short distance between executive and functional levels in SMEs, and the ensuing perception that a formal knowledge management system may not be necessary. As such, knowledge sharing in SMEs primarily occurs informally (Durst and Edvardsson 2012). The more formal approach, as presented herein, was demonstrated to be effective in SMEs, and can thus provide a template for SMEs to enhance their knowledge management capability. Second, pursuing SCKMC promises the generation of internal knowledge, which has been said to be limited in SMEs (Lu and Beamish 2001). The structured approach can thus provide guid-

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ance for SMEs on how to harness knowledge distributed across the supply chain, strengthening internal expertise. While SCKMC is certainly able to influence both explicit and tacit knowledge, it is the finding that the approach is especially amenable to create tacit knowledge that has implications for practicing managers. As such, this result provides further impetus for firms to focus on what they can do best, that is, their core competencies, and to rely on outside partners for the remaining tasks. The findings point to the fact that besides the physical product that the firm is receiving from suppliers, the potential for knowledge transfer and generation cannot be neglected, and can be, in some instances, even more valuable than the physical product. Similar value can be placed on knowledge obtained through customers, which may provide the firm unique insight into market developments, trends, and changing preferences. Managers are thus provided with the advice to nurture the supply chain knowledge capability among suppliers and customers, as it is through this process that higher levels of knowledge within the supply chain can be achieved, which will benefit the firm. These findings illustrate that all knowledge does not have to be generated internal to the firm (which may consume significant resources), but can be harnessed from interactions with suppliers and customers in the supply chain. This offers SMEs a unique opportunity to enhance their knowledge repositories by tapping into these entities. Third, the findings of this study suggest that managers must carefully consider the type of knowledge fostered within a specific supply chain. While both explicit and tacit knowledge are important, it is tacit knowledge that can provide greater competitive differentiation. If the improvement of such performance metric is the objective, the generation of tacit knowledge should be emphasized, due to its greater impact in influencing competitive performance. The distinctive intelligence derived from supply chain members, especially the insights that are imperfectly mobile, as encapsulated in tacit knowledge, represent a more valuable asset in generating competitive disparity. This provides valuable guidance for SMEs, which are often constrained in their resources devoted to knowledge management.

LIMITATIONS AND FUTURE DIRECTIONS Although this study made several advances to the literature, limitations must be noted. While our approach to focus on firms with 50 employees or less is consistent with prior research (e.g., Lusch and Brown 1996), provides advantages for the identification of the key informant, and offers unique insight into the context of SMEs, our study is limited as it may not be generalizable to larger firms and the supply chains in which they may operate. Future research should thus seek the replication of the present study among a sample of larger companies. We also restricted our survey to firms in the manufacturing industry. Although this is a frequent practice in empirical logistics and supply chain management research, it also limits the generalizability of our results, suggesting a need for future research to expand this work to other industries. Our research is further limited by a single respondent completing the survey. An alternative approach would have involved multiple respondents in the supply chain relationship, including the focal firm as well as their most impor-

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tant supplier and customer (a dyadic or even triadic survey design). In addition, our questions asked for the respondent to focus on their primary supply chain, averaging the potentially different knowledge processes across the two partners. An alternate approach would have involved the measuring of the knowledge processes with both customers and suppliers separately. Further, although our model is theoretically and empirically supported, the employment of a cross-sectional design does not allow for fully discerning causality. It could be argued that knowledge is both an antecedent and a consequence of knowledge management capability. In fact, in an ongoing supply chain relationship, SCKMC generates knowledge which can then serve as an input to SCKMC to generate additional knowledge. While we took the theoretical approach of SCKMC generating knowledge, the specificity of this relationship in terms of causation would provide further insights into this important area. Future research employing longitudinal data collection is needed to overcome these shortcomings. We also note that while we pilottested our questionnaire with knowledgeable practitioners and academics for the refinement of our scales, we did not conduct a formal pretest with a smaller subsample of our population. Future research is encouraged to not omit this important step, to be able to afford a more rigorous survey methodology. We also did not address nonresponse bias directly, but rather used late respondents as a proxy for nonrespondents. While commonly done, a more rigorous approach would have involved the comparison of respondents to actual nonrespondents. Data from these could have been obtained by approaching them after the end of the survey administration, with the request to answer a shorter survey. Last, while our operationalization and ensuing measurement of our two knowledge dimensions as separate constructs is an extension to current literature, future research is encouraged to refine and improve upon our measurement. Specifically, recent theoretical and conceptual advances, published after the data collection for this work (Venkitachalam and Busch 2012), could be taken into consideration in further advancing the measurement of explicit and tacit knowledge. We provide a first step toward this undertaking. The same applies to our SCKMC construct, which we based on the seminal work of Gold et al. (2001). Besides the aspects of knowledge acquisition, conversion, application and protection, additional aspects, such as knowledge exchange and dissemination across the supply chain, could be considered. While this study has limitations, the findings suggest several exciting avenues to extend this research. First, future work should conceptualize explicit and tacit knowledge as two separate constructs, rather than a uni-dimensional measure. In this vein, one could investigate how the length of the supply chain relationship influences the resource stocks of both explicit and tacit knowledge. Our expectation would be that the more mature the relationship becomes, the less explicit and the more tacit knowledge exists within the supply chain due to the nature of SCKMC building on prior knowledge to generate new knowledge and the development of implicit routines. Second, the creation of explicit and tacit knowledge could also be examined from the theory of knowledge creation (Nonaka 1994). Specific measures for each of the four mechanisms of combination, internalization, socialization, and externalization could be developed, similar to Kahn et al. (2006) and Anand et al. (2010), and their influence on types of knowledge could be

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examined. In addition, research is needed that provides greater specificity into what may constitute explicit and tacit knowledge in a supply chain context, such as the knowledge about supplier capabilities and capacities. Third, we found that SCKMC influences tacit knowledge to a greater degree than it influences explicit knowledge. We suggested that this result may stem from our setting of a collaborative supply chain, in which the unique interplay between the various supply chain members as part of SCKMC, can provide such potential. In-depth, longitudinal case studies tracking the development of both explicit and tacit knowledge generated from SCKMC in a specific supply chain could help to better understand this issue. Fourth, while we contributed to the literature by differentiating explicit and tacit knowledge and their unique influences on performance, we did not consider how the different knowledge types influence performance. Future research is therefore encouraged to identify and assess intermediate processes that are created by the two knowledge types, through which knowledge impacts performance. In addition, while we focused on one of the most prominent knowledge classifications in the literature and differentiated between explicit and tacit knowledge, other aspects of knowledge could be investigated, such as market or technological knowledge, and their differential impact on performance. Fifth, while our choice of supply chain performance as a dependent variable was theoretically substantiated and is managerially meaningful, the influence of the two knowledge types on other performance measures is needed, which will lead to a more holistic view of our framework; for example, cycle time could be examined. Last, researchers could also look at the diversity of the supply chain in terms of the unique knowledge assets and resources that individual members bring to the table. Our expectation would be that the more diverse and unique individual supply chain entities are within a specific supply chain, the greater the potential for knowledge generation, especially in terms of tacit knowledge.

APPENDIX Questionnaire items In answering the questionnaire, respondents were asked to focus on one of their primary supply chains, involving the firm’s primary supplier and the corresponding key customer in that supply chain. Respondents were asked to keep this supply chain in mind when completing the survey.

Knowledge acquisition Working with my supply chain partners, we have developed processes for. . . . . . acquiring knowledge about new products/services within our industry (acqk1). . . . generating new knowledge from existing knowledge (acqk2). . . . collaborating (acqk3).

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because they are straightforward and easily documented (explk3).

Working with my supply chain partners, we have developed processes for. . . Supply chain performance . . . integrating different sources and types of knowledge (convk1). . . . organizing knowledge (convk2). . . . replacing outdated knowledge (convk3). . . . filtering knowledge (convk4).

Knowledge application Working with my supply chain partners, we have developed processes for. . . . . . using knowledge to solve new problems (appk1). . . . taking advantage of new knowledge (appk2). . . . locating and apply knowledge to changing competitive conditions (appk3).

Knowledge protection Working with my supply chain partners, we have developed processes for. . . . . . protecting knowledge from inappropriate use outside the organization (protk1). . . . encouraging the protection of knowledge (protk2). . . . restricting access to some sources of knowledge (protk3).

Tacit knowledge Considering the supply chain, would you agree that: The market knowledge in our supply chain can only be learned through first-hand experience (tactk1). New employees in our supply chain could only learn their job by first-hand experience (tactk2). The knowledge used in our supply chain is highly complex and can only be gained through first-hand experiences (tactk3).

Explicit knowledge Considering the supply chain, would you agree that: The market knowledge in our supply chain is easily documented (explk1). New employees in our supply chain could easily learn their entire job from work manuals (explk2). A competitor would have relatively little difficulty copying the routines and processes that we use in our supply chain

Considering the supply chain, would you agree that the supply chain relationship has: ... ... ... ...

been very profitable (p1). generated a high volume of business (p2). helped us achieve rapid growth (p3). improved our competitiveness (p4).

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SHORT BIOGRAPHIES Tobias Schoenherr (PhD Indiana University) is Associate Professor in the Broad College of Business at Michigan State University. His research focuses on strategic supply management, including strategic sourcing and leveraging the supply base. He has more than 40 journal publications, which include publications in Management Science, Journal of Operations Management, Journal of Business Logistics, Production and Operations Management, Decision Sciences, and Journal of Supply Chain Management. He is an Associate Editor for the Journal of Operations Management and Decision Sciences, and serves on several Editorial Review Boards, including the Journal of Business Logistics and IEEE Transactions on Engineering Management. David A. Griffith (PhD Kent State University) is Chairperson and Professor of Marketing at Lehigh University. His research focuses on inter-firm governance and global marketing strategy and has appeared in the Journal of Marketing Research, Journal of Marketing, Journal of International Business Studies, Strategic Management Journal, Journal of Operations Management, and the Journal of Business Logistics. He has served as the John William Byington Endowed Chair in Global Marketing and Professor of Marketing at Michigan State University, and on faculty at the University of Hawai’i, the Japan-America Institute of Management Science, Wirtschaftsuniversit€at Wien, and the University of Oklahoma. Aruna Chandra (PhD Kent State University) is a Professor of Management at the Scott College of Business, Indiana State University. She holds doctoral degrees in Strategy/International Business and in English / Linguistics from Kent State University. Her research interests include small business / new venture strategies, innovation ecosystems and business framework conditions in emerging market contexts.