Knowledge Management in Supply Chains: The Role of Explicit and Tacit Knowledge

Knowledge Management in Supply Chains: The Role of Explicitand Tacit KnowledgeTobias Schoenherr1, David A. Griffith2, and Aruna Chandra3

1Michigan State University2Lehigh University3Indiana State University

W e theorize, building on the knowledge-based view and the theoretical distinction between explicit and tacit knowledge, that knowledgemanagement 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. Theresults indicate that the supply chain’s knowledge management capability manifests itself in both explicit and tacit knowledge, with the latterbeing influenced more strongly. Moreover, it was found that while both explicit and tacit knowledge influence supply chain performance, thelatter exerts a significantly greater impact than the former. Exploratory post hoc analyses add robustness to these findings and investigate mech-anisms inherent to the transformation of tacit into explicit knowledge. Overall, this research contributes to academic theory development inlogistics and supply chain management by the dichotomization of knowledge types and the demonstration of their differential magnitude ofeffects, and to managerial practice by providing important guidance for logistics managers structuring their knowledge management effortsacross 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 toleverage the strengths of their supply chains to remain competi-tive (e.g., Kahn et al. 2006). This has led to the adage of supplychains competing against supply chains. Within this setting, keyaspects of competitiveness are encapsulated within the knowl-edge of logistics and supply chain partners, making knowledgemanagement within the supply chain an important area of study(Craighead et al. 2009). Knowledge management is crucial formanagerial decision making in logistics and supply chain man-agement due to the fundamental nature of knowledge for prob-lem solving and ensuing strategy development (e.g., Kahn et al.2006). Despite considerable research on the creation and man-agement of knowledge (e.g., Fugate et al. 2009; Anand et al.2010), the field has been described as still being in an embryonicstage (Linderman et al. 2010) within the domains of logisticsand supply chain management (Grawe et al. 2011). Within thiscontext, supply chain knowledge can be defined as the use ofknowledge resources obtained from supply chain members foreconomic gain (Craighead et al. 2009). It is the objective of thepresent research to contribute to this emerging and increasinglyimportant domain so as to advance academic theory and providesubstantive 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 chainknowledge management capability (SCKMC) manifests itself in

the two knowledge types of explicit and tacit knowledge. Draw-ing from Gold et al. (2001), SCKMC is conceptualized as acomprehensive and integrative set of knowledge managementcompetencies consisting of knowledge acquisition, knowledgeconversion, knowledge application, and knowledge protection.We further theorize the impact of explicit and tacit knowledgeon supply chain performance, with tacit knowledge exerting astronger influence than explicit knowledge. Our contentions aretested with a sample of small- and medium-sized enterprises(SMEs), a context which provides a unique opportunity to studyknowledge management dynamics (Durst and Edvardsson 2012).SCKMC may be especially valuable for SMEs (Narula 2004),due to their often limited resources in developing specializedexpertise in-house (Lu and Beamish 2001).

While both explicit and tacit knowledge generated among sup-ply chain members are important, the distinction between knowl-edge types is critical as they may have varying effects on keysupply chain outcomes. Grawe et al. (2011) therefore encourageresearchers to examine various knowledge types, and Anandet al. (2010) call for investigations into the “missed opportuni-ties that may result from ignoring tacit knowledge” (p. 304).Given the need for a further understanding of knowledge in thesupply chain, and particularly the types of explicit and tacitknowledge, the present study works to provide a deeper under-standing of these two types of knowledge generated within asupply chain setting. As such, we contribute to logistics andsupply chain management research and practice in three specificways.

Our first contribution lies in the investigation of how SCKMCmanifests itself in two types of knowledge, an area left uninvesti-gated in extant research. While prior studies emphasize an evolu-tionary view of knowledge generation (Alavi and Leidner 2001),past empirical research has seldom conceptualized this frame-work consisting of knowledge acquisition, knowledge conver-sion, knowledge application, and knowledge protection as

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

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

forwarded by Gold et al. (2001); rather, literature provides a lim-ited perspective of this important aspect in logistics and supplychain management (Molina et al. 2007). By viewing SCKMC asa set of interconnected, operant resources (Smith et al. 2005),this work responds to calls for such extensions of knowledgemanagement as indicated by Madhavaram and Hunt (2008). Fur-thermore, while other operationalizations of knowledge manage-ment capability exist (Wong and Wong 2011), they seldom havebeen subject to empirical testing, thereby not answering priorresearch calls (Freeze and Kulkarni 2007). By capturing theknowledge management construct with such an encompassingconceptualization, based on the seminal work of Gold et al.(2001), we are able to better understand how knowledge manage-ment manifests itself in different types of knowledge. Further,this contribution moves us from viewing knowledge on a contin-uum ranging from tacit to explicit (cf., Craighead et al. 2009), toa conceptualization that accounts for the simultaneous existenceof both types of knowledge.

Second, by conceptualizing explicit and tacit knowledge inde-pendently we are able to theorize and test the differential effectsof knowledge types on supply chain performance, and contributeto extant research by the empirical investigation of these relation-ships via a large-scale survey. Supply chain performance, whichassesses the supply chain’s competitiveness, business volume,profitability and competitive growth (Gunasekaran et al. 2004),was chosen due to its theoretical and practical relevance intoday’s supply chain environment (cf., Griffis et al. 2007), andsince it offers an integrative assessment of a supply chain’s com-petitiveness (e.g., Kahn et al. 2006). By theorizing and demon-strating the effects of knowledge types on supply chainperformance we specifically address a limitation of the literatureidentified by Craighead et al. (2009), who state that “little isknown about the performance enhancement offered by supplychain knowledge” (p. 405). The implications of our findings thusprovide important guidance for practitioners, optimizing variousknowledge generation and management aspects of their organiza-tions, and illustrate the ensuing influences on supply chainperformance.

Third, by conducting a series of exploratory post hoc analyseswe are able to scrutinize our theoretical model to alternate con-figurations, adding further insight and robustness to this work.Specifically, we investigate the differential influence of SCKMCon explicit and tacit knowledge, with the results suggesting astronger influence on the latter knowledge type. We furtherassess the robustness of the SCKMC construct by consideringthe influence of its individual dimensions on knowledge, ratherthan in its hypothesized aggregate form. The results provide sup-port for a dynamic capabilities view of SCKMC. In addition, weexplore the conversion of tacit into explicit knowledge, also con-sidering the moderating roles of the investigated knowledge man-agement competencies.

THEORETICAL FOUNDATION

Supply chain knowledge management capability

Unlike prior research, which focused on single knowledge man-agement elements within an individual organization (e.g., Cui

et al. 2005), we consider knowledge management capabilityacross a specific set of supply chain partners, as perceived by thefocal firm. It is our belief that knowledge management, viewedat the level of a supply chain, can lead to an increased under-standing of knowledge as a competitive resource, as under thisview a supplier is not only relied upon to provide products andservices, but is viewed as a key repository for knowledge andthe source of unique capabilities. This is consistent with argu-ments in Cohen and Levinthal (1990) who consider outsideknowledge (i.e., from supply chain partners in our context) criti-cal to innovation, as well as the concept of knowledge-sharingnetworks (Dyer and Nobeoka 2000).

Drawing from cognitive psychology (Neisser 1967), we arguethat this approach facilitates the unique combination of stimuliemanating from individual supply chain members, realizingsynergies and benefits in the processing of the stimuli thatwould not have been possible otherwise. In this view, each sup-ply chain member can be regarded both as contributing to theknowledge of the supply chain, and as scaffolding and elevatingit to a potentially unprecedented level of sophistication (cf.,Brown and Duguid 2001). As such, SCKMC can serve as adynamic capability, contributing to a specific supply chain’s“ability to integrate, build, and reconfigure internal and externalcompetences to address rapidly changing environments” (Teeceet al. 1997, 516). Within this context, SCKMC can provide fora valuable dynamic capability facilitating managerial decisionmaking in turbulent environments. Such capability can be partic-ularly valuable when knowledge is obtained through weak tiesin the firm’s supply network, which should make the knowledgeless redundant (Levin and Cross 2004). In these instances,SCKMC should be especially valuable due to its ability to har-ness disparate external knowledge and transform it to be usedinternally. This parallels the notion in cognitive psychology thatnew stimuli need to be processed into knowledge via appropriatemechanisms. As such, SCKMC represents an organizational rou-tine able to generate organizational memory (Linderman et al.2010) from external supply chain partners, with explicit and tacitknowledge then representing the actionable manifestations ofSCKMC. In addition, following the approach in Smith et al.(2005), SCKMC can be classified as interconnected, operantresources.

To conceptualize SCKMC, we draw on the aspects of knowl-edge acquisition, knowledge conversion, knowledge application,and knowledge protection developed by Gold et al. (2001).Knowledge acquisition refers to approaches aimed at knowledgeaccumulation (Lyles and Salk 1996), which is the basis for theenhancement of core capabilities (Leonard 1995). Knowledgeconversion considers the processing of the acquired knowledgeinto usable formats, which is especially crucial in a supply chainrelationship due to the disparate structure of knowledge amongsupply chain members (Roy et al. 2004). Knowledge applicationrefers to approaches charged with the utilization of such supplychain knowledge to solve problems or develop strategies, whichrequires the active sharing of knowledge between supply chainpartners (Kogut and Zander 1992). Knowledge protection con-cerns the approaches dealing with shielding the obtained knowl-edge from outside dissemination (Norman 2004), an issueespecially relevant in a supply chain setting due to its multipletouchpoints. Together, these integrative aspects can be referred to

122 T. Schoenherr et al.

as a capability (Amit and Schoemaker 1993), and we thereforerefer to it as SCKMC.

Explicit and tacit knowledge

Research contends that knowledge can be present in the form ofboth explicit and tacit knowledge (Polanyi 1966). Explicitknowledge is codified and can be easily communicated andtransferred (Nonaka 1994; Anand et al. 2010). Explicit knowl-edge can be in the form of manuals, blueprints, procedures, poli-cies, forecasts, inventory levels, production schedules, marketintelligence data, etc. In contrast, tacit knowledge is implicit,hard-to-conceptualize and subjective, and is part of an individual’sexperiences; it is evidenced in behavior or actions, and is oftenhighly ambiguous (Venkitachalam and Busch 2012). This type ofknowledge has an important cognitive dimension, and includesmental models, beliefs, and perspectives. It develops interactivelyover 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 tacitknowledge as knowing more than we can tell or as knowing how todo something without thinking about it.

The knowledge-based view

The KBV (Grant 1996) rests on the idea that firms should beanalyzed based on their knowledge resources. Drawing on theKBV's foundations in the resource-based view (Barney 1991), ifknowledge is valuable, rare, inimitable, and nonsubstitutable, itcan be considered a resource capable of establishing a competi-tive advantage (Grant 1996). A commonly applied theoreticalframework in logistics and supply chain management (Defeeet al. 2010), this firm level view of resources has been extendedto include external actors, such as suppliers and buyers (Dyer1996). We thus contend that knowledge generated through theinteraction of specific supply chain members has the potential toimprove 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 chainis consistent with extant supply chain literature (Defee et al.2010). In fact, the generation and exploitation of such knowledgehas been considered by some to be a driver for the pursuit ofsupply chain relationships themselves (Lanier et al. 2010). Espe-cially when knowledge is not merely copied (which may make it

redundant), but when it is elevated to new levels, can it serve asa valuable resource (Hamel 1991); SCKMC as a dynamic capa-bility can aid in this endeavor. The next section builds on thesetheoretical foundations to develop our hypotheses, which aresummarized in Figure 1.

HYPOTHESIS DEVELOPMENT

Supply chain knowledge management capability, and explicitand tacit knowledge

Based on the prior discourse and tenets inherent to the KBV,SCKMC may be described as a valuable (the generation of expli-cit and tacit knowledge ensues from SCKMC), rare (the struc-tured and comprehensive approach is unique to each supplychain), inimitable (a special company climate may be needed tosuccessfully implement the approach), and nonsubstitutable (thedistinctive result may not be able to be replicated by alternatemechanisms) resource. As such, SCKMC’s four aspects can beviewed as a cohesive cognitive development in a sequence ofstates (Ericsson and Hastie 1994). That is to say, SCKMC pro-vides a mental model (Johnson-Laird 1983; Gorman 2001) andstructure, serving as a foundation for the generation of explicitand tacit knowledge across a specified set of supply chain mem-bers. SCKMC thus serves as an evolving capability, dynamicallytransforming external knowledge into a usable internal resource(cf., Hamel 1991).

Past research showed that explicit knowledge has the potentialto be generated when available knowledge among members tounderstand each other’s requirements is exchanged (Samuel et al.2011), relationships and procedures are formalized, and existingknowledge repositories are shared and reused (Voelpel et al.2005). Within this context, SCKMC can effectuate the effectiveaccumulation, conversion, and application of knowledge amongsupply chain members, yielding easily understood explicitknowledge. The structured approach may aid especially in theformalization and documentation of knowledge among supplychain members, which may possess unique, nonredundant intelli-gence for the benefit of the firm. Common activities constitutingSCKMC to generate such explicit knowledge include the conductof structured meetings, the definition of contract specifications,and the archiving of documents (Dyer and Hatch 2004; Royet al. 2004; Samuel et al. 2011). These initiatives have the

Figure 1: Research model.

Knowledge Management in Supply Chains 123

potential to capture, structure, codify, and institutionalize knowl-edge across the supply chain, leading to the generation of expli-cit knowledge (Lee and Van den Steen 2010). Outcomes canconsist of joint forecasts, consolidated market data, and produc-tion schedules. As such, we expect SCKMC to manifest itself inexplicit knowledge.

The multitude of parties involved, as well as their respectiveunique knowledge repositories, backgrounds, and insights intothe specific supply chain, also provide great potential for tacitknowledge to be developed (Li and Tsai 2009). Tacit knowledgecan be facilitated by methods such as brainstorming and thenominal group technique (Anand et al. 2010), aspects involvedin knowledge acquisition and conversion of SCKMC, which seekto create an environment in which negative psychology isavoided (all supply chain partners can voice their ideas freelyfirst, enabling higher levels of thinking) and positive synergiesare generated (ideas can be built on by other supply chain mem-bers). These methods are consistent with arguments by Hamel(1991) and Badaracco (1991), who attribute tacit knowledge tosocial relations, and Zack (1999), who refers to communities ofpractice (consistent with the SCKMC element of knowledgeapplication). As such, SCKMC may provide the structure neces-sary to tease out this type of knowledge resident in supply chainmembers. Formally:

H1: Supply chain knowledge management capability ispositively associated with explicit knowledge in a supplychain.

H2: Supply chain knowledge management capability ispositively associated with tacit knowledge in a supplychain.

The impact of explicit and tacit knowledge on supply chainperformance

Explicit knowledge represents the knowledge within the supplychain that can be easily articulated. The effective exchange andusage of this readily available explicit knowledge in a supplychain promises great potential for enhancing the efficiency of thesupply chain, since codified knowledge at one supply chainentity can be easily shared with another supply chain member(Dyer and Hatch 2004), yielding performance improvements. Assuch, the collaborative use of knowledge can improve the inter-face between supply chain members resulting in enhanced supplychain integration and its associated benefits (Song and Swink2009). For example, the formal specification of the manners ofacting and operating within a supply chain, as well as the opensharing of knowledge (without much elaboration and loss ofintegrity; Dyer and Hatch 2004) resulting in enhanced visibility,allows for an increase in the overall performance of the supplychain. Theoretical substantiation offers the KBV and the notionthat explicit knowledge can be a valuable, rare, inimitable, andnonsubstitutable resource. This is especially true when suchknowledge is derived from a firm’s supply chain members,enabling managers to develop competitiveness-enhancing strate-gies. We therefore contend that the ease of communication andtransfer of explicit knowledge among supply chain members isassociated with supply chain performance (reflected in H3a).

Tacit knowledge is not only difficult to transfer among mem-bers of the supply chain, but may be unique to the specific sup-ply chain and difficult for others to replicate (Grant 1996;Dooley 2000), due to its propensity to develop in relational inter-actions (Kahn et al. 2006). Tacit knowledge focuses on cognitiveelements, or what Johnson-Laird (1983) calls “mental models,”in which individuals construct analogies, schemata, paradigms,viewpoints, beliefs, and perspectives in their minds to make senseof available information in complex realities; in this setting, newmeanings can be created (Nonaka 1994). It is because of theseaspects that Nonaka and Takeuchi (1995) label tacit knowledge asthe primary source for innovation, new product development, andthe conception of new business models. This proposed connectionbetween tacit knowledge and supply chain performance is furthergrounded in KBV’s notion of tacit knowledge derived from supplychain members serving as a valuable, rare, inimitable, and nonsub-stitutable resource. Parallels can be seen in Hall (1999) and Spek-man et al. (2002), who describe tacit knowledge as the residentfabric of the firm (or in this case a specific supply chain). We there-fore expect a positive association of tacit knowledge with supplychain performance (reflected in H3b).

We further believe there to be differential effects of theknowledge types on supply chain performance. Specifically, wesuggest the association of tacit knowledge with supply chain per-formance to be greater than the association of explicit knowledgewith supply chain performance (reflected in H3c), based on thefollowing theoretical notions derived from the KBV. Whileexplicit knowledge is likely to benefit performance benchmarks,the codified nature of this type of knowledge might allow com-petitive differentiation to only a limited degree (as explicitknowledge might transfer to competitors easily, due to its codi-fied state). While explicit knowledge is valuable, one couldargue that the inimitability property of explicit knowledge isthreatened due to the ease with which such knowledge can betransferred, and therefore copied/imitated by competitors, andthus be made redundant. Consequently, while explicit knowl-edge is certainly expected to be beneficial, what may beobserved is that this type of knowledge allows for a smallerenhancement of a supply chain’s performance. Support for thisargumentation is also found in Gorman (2001), who notes thatexplicit and documented knowledge alone does not suffice forthe complex task of technology transfer, a key element for com-petitive advantage.

In contrast, since tacit knowledge is socially complex, usuallyrequiring significant organizational learning, the tacit knowledge-base developed can be expected to serve as a source of sustainablecompetitive advantage, thus leading to enhanced supply chain per-formance (assessed relative to the competition). It is tacit knowl-edge, which is difficult to imitate by competitors, that yieldscompetitive differentiation. Parallels can be seen in the work bySwink (2006) on the development of collaborative innovationcapability grounded in knowledge, and the literature in new prod-uct development that stresses the importance of tacit knowledge(Goffin and Koners 2011). Further, the differential impact of expli-cit and tacit knowledge on supply chain performance comportswell with the KBV’s view of resources being heterogeneous andimperfectly mobile, decreasing their tendency to become redun-dant. Within this context it can be argued that tacit knowledge ischaracterized by being more imperfectly mobile, thus offering a


more valuable, rare, inimitable, and nonsubstitutable resource.These properties are enhanced by knowledge’s generation fromsupply chain members, who may offer additional intelligence forthe firm’s enhanced competitive performance. Therefore, while apositive impact of both explicit and tacit knowledge on supplychain performance is expected, we theorize that it is the hard-to-conceptualize property of tacit knowledge that can generategreater supply chain performance. This contention is due to thelearning-by-doing component of tacit knowledge, which providesa greater application foundation for supply chain performance asconceptualized in this study.

H3: Explicit (a) and tacit (b) knowledge in the supply chainare positively associated with supply chain performance,with the impact of tacit knowledge on supply chainperformance being greater than the impact of explicitknowledge on supply chain performance (c).

METHODOLOGY

Data collection and sample

The hypotheses were tested with data collected from importersoperating in the manufacturing industry. Importers were chosendue to their coordinating role in supply chains, yielding respon-dents that are familiar with both suppliers and customers. Respon-dents were asked to report on a single, specific supply chainrelationship encompassing their primary supplier and the corre-sponding key customer receiving outputs from the supplier rela-tionship. As such, the unit of analysis in our survey was the focalfirm reporting on its primary supply chain. We restricted our sam-ple to firms with 50 employees or less so as to enhance key infor-mant quality, as smaller firms usually have a general managerresponsible for a majority of the firm’s activities (key informantquality was enhanced due to these general managers possessingintimate knowledge of the firm’s primary linkages with criticalsuppliers and customers). Borrowing this approach from the mar-keting literature (e.g., Lusch and Brown 1996), we suggest itsvalue also for logistics and supply chain management research inaiding in respondent expertise. At the same time, the samplingapproach enabled us to focus on a key but often neglected sectorof the worldwide economy, SMEs, which provided a uniqueopportunity to study knowledge management dynamics.

Firm contact information was drawn from the Journal of Com-merce database. We started with a systematic random sample of3,000 U.S. addresses in 19 four-digit standard industrial classifi-cation codes in manufacturing. The sample was then restricted tofirms with fewer than 50 employees, resulting in a final samplesize of 900 firms to whom the survey package was sent follow-ing Dillman’s (2000) tailored design method; an executive sum-mary of the results was offered to motivate participation. A totalof four additional mailings were conducted to increase theresponse rate. In addition, phone calls were made to the nonre-sponding firms, resulting in a sample of 204 survey responses.Upon close examination of the data, nine records were deleteddue to missing values. The final sample thus consisted of 195records, representing a response rate of 21.7%.

Respondents were senior executives (71.7%), followed by gen-eral managers (17.3%), owners of the company (7.5%), and front-line management of the firm (3.5%). Producer goods were the mostcommonly reported on product (58.8%), followed by consumerdurables (18.2%), capital goods (13.3%), and consumer nondurables(9.7%). On average, respondents had 25 years of experience. Salesrevenue 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 thisstudy, with the late respondents serving as a proxy for nonre-spondents (Armstrong and Overton 1977). Since our researchapproach involved a total of five mailings to each addressee, aswell as phone calls to the nonresponding firms with an encour-agement to respond to our survey, we believe that the responsesreceived later in the data collection represent a valid proxy fornonrespondents. Independent sample t-tests yielded nonsignifi-cant results (p > .05), suggesting that early and late respondentsdo not differ on the key constructs under study. As such, nonre-sponse bias was considered to not be of serious concern.

Construct measures

To ensure content validity, measures were developed based onestablished scales following guidelines by Spector (1992), andadapted to our supply chain context. Refinement took place inpilot tests involving practitioners and academics, confirming thecontent domain of each construct and its corresponding measure-ments, as well as the cohesiveness, precision, and logic of ourdefinitions. Questions consisted of statements to which therespondent was asked to indicate their degree of agreement on a7-point Likert scale, anchored at strongly disagree (value = 1)and strongly agree (value = 7) (Appendix).

Supply chain knowledge management capability was modeledas a formative second-order construct (cf. Johnson et al. 2006;Ruiz et al. 2008) consisting of four-first-order reflective con-structs drawn from Gold et al. (2001), adapted to the supplychain context: knowledge acquisition, knowledge conversion,knowledge application, and knowledge protection. This formula-tion is based on theoretical considerations and criteria for thechoice of formative versus reflective second-order models pro-vided in Jarvis et al. (2003), Podsakoff et al. (2003b), and Gligoret al. (2013). Specifically, we consider the four knowledge man-agement aspects as forming the underlying SCKMC construct(i.e., greater knowledge acquisition, conversion, application, andprotection generate greater overall SCKMC, and not the reverse;Diamantopoulos and Siguaw 2006).

Explicit knowledge was measured by items adapted fromZander (1991) and Bresman et al. (1999), and tap into the notionof formalization of processes via manuals and documents (cf.,Smith 2001). Tacit knowledge was measured by items drawnfrom Simonin (1999), and tap into the importance of first-handexperience for tacit knowledge (cf., Lord and Ranft 2000). Bothknowledge dimensions were adapted to the supply chain context(Schoenherr et al. forthcoming). Supply chain performance mea-surement items were drawn from Zou et al. (1998) and wereadapted to the supply chain context.


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. Forthe assessment of the psychometric properties the seven con-structs were considered individually (cf., Fugate et al. 2009). Allconstructs were modeled with reflective measurement items. Torefine the measurement model, the constructs were subjected toconfirmatory factor analysis. Items were removed in an iterativeprocess, one at a time, based on cross-loadings and weak load-ings on the underlying construct (Anderson and Gerbing 1988).Final measurement items, including their mean, standard devia-tion, completely standardized loading, t-value, standard error,and R2 are summarized in Table 1.

Validity and reliability of the constructs were assessed basedon recommendations by Anderson and Gerbing (1988). Specifi-cally, content validity was provided by the structured and litera-ture-based development and design of the questionnaire and itsmeasurement items, benefiting from the involvement of practitio-ners and academics knowledgeable in the content domain. Con-vergent validity was assured by each indicator’s estimatedstandardized coefficient loading on its associated construct; ascan be seen from Table 1, each coefficient is greater than twiceits standard error. Discriminant validity was assessed by examin-ing the square roots of the average variance extracted (AVE) foreach construct, which was greater than the corresponding correla-tion coefficient, establishing discriminant validity (Fornell andLarcker 1981). Some of the four constructs associated with

SCKMC did not fulfill this criterion, suggesting a weak discrimi-nation between each other, which was expected due to their sec-ond-order conceptualization. The uni-dimensionality of theconstructs was established by confirmatory factor analysis, withall items loading well above the suggested threshold of .30(O’Leary-Kelly and Vokurka 1998). Reliability was ensured byCronbach alpha values above .70 (Table 1). Finally, constructvalidity was established by satisfactory content validity, uni-dimensionality, reliability, and convergent and discriminantvalidity (O’Leary-Kelly and Vokurka 1998). Correlations and theAVE values are provided in Table 2.

The measurement model exhibited good fit to the data (com-parative fit index = .981; incremental fit index = .982). Furthersupport was provided by the root mean square error of approxi-mation (= .049) and the v2/df ratio (332.979/209 = 1.593).Based on these evaluations, the measurement of the constructswas judged to be acceptable.

We aimed to minimize the potential for common-method biasin the questionnaire administration via several means. First, weinterspersed the dependent and independent variables, which mayhave an impact on the retrieval cues minimizing common-method bias (Podsakoff et al. 2003a). Second, we followed a rig-orous approach for informant selection, and restricted our sampleto firms with 50 employees or less. This enhanced key informantquality, as smaller firms usually have a general manager respon-sible for a majority of the firm’s activities, who therefore possessintimate knowledge of the firm’s primary linkages with criticalsuppliers and customers. Thus, our respondents were credible(Phillips 1981). And third, while common-method bias is ofgreater importance in research related to issues of social

Table 1: Final construct measurement items

Construct Variable Mean SD Loading t-value SE R2

Knowledge acquisition (a = .710) acqk1 4.815 1.402 .665 9.786 .095 .443acqk2 4.897 1.335 .635 9.239 .092 .403acqk3 4.979 1.432 .716 10.719 .096 .513

Knowledge conversion (a = .887) convk1 4.600 1.419 .845 14.276 .084 .714convk2 4.533 1.451 .871 14.966 .084 .758convk3 4.821 1.390 .840 14.129 .083 .705convk4 4.472 1.337 .703 10.933 .086 .495

Knowledge application (a = .819) appk1 5.241 1.192 .822 13.487 .073 .676appk2 5.103 1.243 .805 13.078 .077 .648appk3 4.836 1.333 .716 11.087 .086 .513

Knowledge protection (a = .892) protk1 4.600 1.581 .824 13.584 .096 .680protk2 4.713 1.482 .886 15.152 .087 .785protk3 4.821 1.426 .865 14.596 .085 .748

Tacit knowledge (a = .739) tactk1 4.908 1.301 .710 9.928 .093 .504tactk2 4.769 1.386 .626 8.578 .101 .392tactk3 4.692 1.319 .762 10.768 .093 .580

Explicit knowledge (a = .711) explk1 4.113 1.365 .576 7.534 .096 .332explk2 3.338 1.546 .877 11.017 .087 .769explk3 4.056 1.534 .576 7.530 .085 .332

Supply chain performance (a = .868) p1 4.949 1.157 .752 11.767 .074 .565p2 4.785 1.278 .877 14.739 .076 .769p3 4.651 1.277 .783 12.472 .080 .613p4 4.928 1.216 .745 11.630 .078 .556


desirability, our research focuses on specifics and does not havesocial desirability components attached to it, further minimizingthe potential for common-method bias.

In addition, following recommendations provided in Podsakoffet al. (2003a), common-method bias was empirically assessed.First, Harman’s one-factor test was conducted (McFarlin andSweeney 1992). The one-factor model exhibited significantlyworse fit than the measurement model, suggesting that common-method bias is not of serious concern (Podsakoff and Organ1986). Second, we employed the marker-variable approach(Malhotra et al. 2006), where a marker variable is included inthe model that is not theoretically expected to be related to theconstructs under study. The marker variable (gender) did nothave significant influences on the model constructs, providingfurther evidence of the minimization of common-method con-cerns. Third, to ensure that halo effects were not influential inour data (e.g., respondents anchoring their responses on perfor-mance and thus rating all independent variables highly), we usedcross tabs to examine the dispersion of the dependent perfor-mance variable across the range of independent variables in theraw data. Our analysis showed considerable dispersion amongthe 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 hasbeen commonly employed when testing second-order formativeconstructs (Diamantopoulos et al. 2008; Oh et al. 2012; Peng andLai 2012). Three control variables were included in the model:firm size, to account for different resource endowments of firmsdue to their size; years of experience with the supply chain rela-tionship, to account for greater knowledge that may have beenaccumulated over a longer period of time; and industry type, toaccount for different dynamics inherent in industries. The controlswere not found to have a significant influence (firm size:b = .013, p > .1; years of experience with the supply chain rela-tionship: b = �.025, p > .1; industry type: b = .214; p > .05).

The model explained 24.3% in the variance of supply chainperformance, 25.5% in the variance of tacit knowledge, and13.2% in the variance of explicit knowledge. Since the partialleast squares approach does not provide a commonly acceptedmeasure to assess the appropriateness of the model (Chin 1998;Wetzels et al. 2009), we computed a number of fit indices forrobustness. 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 recentresearch (e.g., Perols et al. 2013; Sawhney 2013). The criterioncan take on values between 0 and 1, with higher values indicat-

ing better fit. Applying the formula GoF ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffifficommunality� R2

q,

we receive a value of .576. This is greater than the suggestedcut-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 explicitknowledge in the supply chain. The results support H1

(b = .363; p < .001). H2 theorized that SCKMC is positivelyassociated with tacit knowledge in the supply chain. The resultssupport H2 (b = .505; p < .001).

H3a theorized that explicit knowledge is positively associatedwith supply chain performance. The results support H3a

(b = .281; p < .01). H3b theorized that tacit knowledge is posi-tively associated with supply chain performance. The results sup-port H3b (b = .317; p < .001). H3c suggested the impact of tacitknowledge on supply chain performance being greater than theimpact of explicit knowledge on supply chain performance. Toassess whether a significant difference exists, we conducted a Z-test. The results confirmed a stronger link between tacit knowl-edge and supply chain performance than the link between expli-cit knowledge and supply chain performance (Z = �2.025;p < .01), providing support for H3c. To supplement these find-ings, a series of exploratory post hoc analyses were conducted,which are described next.

Post hoc analyses

The differential influence of SCKMC on explicit and tacitknowledgeWe aimed to bring greater specificity and insight into the differ-ential influence of SCKMC on the two knowledge types in anexploratory post hoc test. Specifically, we assessed whether theimpact of SCKMC on tacit knowledge is greater than the impactof SCKMC on explicit knowledge. We conducted this test basedon the belief that the supply chain setting provides an environ-ment that is especially amenable for tacit knowledge to be cre-ated. Tacit knowledge has been said to provide great promise(Dooley 2000), but due to its more subtle state, has been

Table 2: Correlations and average variance extracted

KAC KCO KAP KPR EK TK SCP

Knowledge acquisition (KAC) .453Knowledge conversion (KCO) .862 .668Knowledge application (KAP) .899 .864 .612Knowledge protection (KPR) .530 .577 .673 .738Explicit knowledge (EK) .176 .353 .185 .115 .478Tacit knowledge (TK) .609 .460 .552 .552 �.021 .492Supply chain performance (SCP) .664 .516 .622 .367 .246 .435 .626

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


underutilized in many instances. While explicit knowledge is alsoan outcome of the approach underlying SCKMC, based on eachsupply chain member contributing to the specific and codifiedknowledge of the supply chain, we believe that the interactionbetween supply chain members is especially prone to generatetacit knowledge.

Our arguments rely on the properties of tacit knowledge, whichcan be described as complex, context dependent, difficult to imi-tate and codify, and not easily transferable (Kogut and Zander1992). It represents the type of knowledge that may be known,but which cannot be readily expressed verbally or in writing (Po-lanyi 1966). As such, the methodical approach inherent inSCKMC may be able to provide structure, and thus facilitate thismore elusive type of knowledge. This is consistent with argu-ments underlying the KBV, specifically when consideringSCKMC as a valuable bundle of resources offering unique advan-tages for the firm. Such differentiation may be more likely to bederived from tacit knowledge, due to its more intangible nature,its greater difficulty for transfer, and its likely property of beingless redundant. In contrast, explicit knowledge, due to its moretransferrable nature, is likely to be generated also via less complexapproaches, and may be more prone to becoming redundant. Ourreasoning builds on Nonaka’s (1991) notion of the supply chainrepresenting a complex “living organism” through which moreelevated and sophisticated knowledge can be developed. Based onthe multiple interactions among supply chain partners, the syner-gies likely to ensue are suggested to elevate tacit knowledge to agreater degree. This contention is also supported by organizationaltheorists (Granovetter 1973), who view explicit knowledge as theoutcome of arms-length transactions, whereas tacit knowledge ismore susceptible to be created in collaborative relationships. Toassess whether a significant difference exists, we conducted a Z-test. The results confirm a stronger association of SCKMC withtacit knowledge than with explicit knowledge (Z = �2.120;p < .01), providing support for our theoretical arguments.

The robustness of the SCKMC constructTo assess the robustness of our model conceptualizing SCKMCas a second-order formative construct, we constructed a compet-ing model that removes SCKMC entirely and tests the directrelationships of the individual first-order constructs on explicitand tacit knowledge. The calculation of Tenenhaus et al. (2005)“goodness of fit” criterion yielded a value of .417 for the mod-el’s global fit. This is smaller than the GoF value derived for ourproposed model (.576), suggesting the competing model to beinferior (Perols et al. 2013).

This finding bolsters our conceptualization of SCKMC as asecond-order formative construct, and by implication, the theoret-ical disposition of SCKMC as a valuable bundle of complexoperant resources offering unique advantages for the firm. Con-sidering the four aspects of knowledge acquisition, conversion,application, and protection, and the continued evolution of thesecapabilities, the result is suggestive of our arguments of SCKMCas constituting a dynamic capability able to combine, transform,or renew resources as markets evolve. In addition, the notion ofSCKMC being greater than the sum of its parts was supported.We thus substantiated, also from a statistical viewpoint, SCKMCas representing a comprehensive and integrative set of knowl-edge management competencies as part of the KBV.

The conversion of tacit into explicit knowledgeThe domain of converting tacit into explicit knowledge hasbeen gaining increasing attention, especially with heightenedemployee mobility. In an exploratory post hoc analysis, weinvestigated whether SCKMC helps firms convert tacit knowl-edge into explicit knowledge by including a path between thesetwo constructs in a competing model. The path turned out tonot be statistically significant (b = �.068, p > .1). In a furthercompeting model we assessed the moderating role of SCKMCon the relationship between explicit and tacit knowledge. Theinteraction 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 alast set of exploratory post hoc analyses, we thus tested thepotential individual moderating impact of the four constructsconstituting SCKMC on the link between tacit and explicitknowledge. To do so, we considered the prior model whichlinked all four individual knowledge management competenciesto both tacit and explicit knowledge, and added the link betweentacit and explicit knowledge. The four ensuing interaction terms,testing for the moderating role of each of the four dimensions onthe relationship between tacit and explicit knowledge, were notsignificant. This suggests that the transformation of tacit intoexplicit knowledge follows different pathways than the onesspecified herein. While the data did not support our knowledgetransformational expectations, we have brought greater clarity tothe interworkings of SCKMC in its ability to influence both tacitand explicit knowledge.

While our data did not support these relationships, the resultshave to be treated with caution. Data collection was conductedwithout the objective to test these relationships in mind, and wastherefore exploratory. Nevertheless, we thought it important toinvestigate the conversion of knowledge types in this exploratorypost hoc analysis. Future research is encouraged in this domain,to specifically study the dynamics inherent in knowledge conver-sion (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 sup-ply chain management by enhancing our understanding of theability of SCKMC to influence explicit and tacit knowledge, andby the investigation of the differential outcome effects resultingfrom explicit and tacit knowledge. Our findings offer significantinsights into knowledge within supply chains, advance academicunderstanding, and provide important implications for managers.Overall, our study is important from both a theoretical and a prac-tical perspective, enhancing the value of our research (Fawcettet al. 2011).

Theoretical implications

The findings offer important insight for logistics and supplychain management scholars interested in the investigation ofknowledge and its potential within a supply chain context. Spe-cifically, we demonstrate that SCKMC is positively associated


with knowledge present within a supply chain. We introducedthe concept of SCKMC, and conceptualized it as a comprehen-sive approach consisting of knowledge acquisition, knowledgeconversion, knowledge application, and knowledge protection,representing a set of interconnected, operant resources. Our workdemonstrates the value of this framework, borrowed from theinformation systems literature, in the domain of supply chainmanagement. By operationalizing knowledge management capa-bility within a supply chain setting, we work to address the callsby Freeze and Kulkarni (2007) and Wong and Wong (2011),who encouraged a greater understanding of knowledge. Our find-ings show that SCKMC manifests itself in explicit and tacitknowledge within the supply chain. This is an important exten-sion to literature as it demonstrates not only the importance ofknowledge management capability as a bundle of valuableresources, but its ability to generate both explicit and tacitknowledge among supply chain partners. We have thus effec-tively tied the SCKMC construct to the KBV and its valuepropositions inherent in valuable, rare, inimitable, and nonsubsti-tutable resources. Since the generation of explicit and tacitknowledge ensues from SCKMC, it can be viewed as valuable,and since a structured and comprehensive approach for SCKMCoccurs within a unique supply chain context, it can be consid-ered as rare. The inimitability property derives from a specialclimate that may be needed to successfully implement theapproach, and the nonsubstitutability property rests in the dis-tinctive result of SCKMC that may not be replicated by alternatemechanisms.

In our dichotomization of the two knowledge types, we reliedon the most prominent classifications in the knowledge manage-ment literature. The importance of this distinction and the poten-tial ensuing differential effects were recently noted andencouraged in the logistics and supply chain management litera-ture (e.g., Anand et al. 2010; Grawe et al. 2011). In addition, theresults provide evidence for the value of a formal approach infacilitating cognitive processing, and demonstrate the value ofinterconnected, operant resources. SCKMC can thus be viewedas an interorganizational routine and a dynamic capability able toyield beneficial outputs.

We further found in an exploratory post hoc analysis thatSCKMC was more effective in influencing tacit knowledge thanit was in influencing explicit knowledge within a supply chain.This result substantiates our theorization of the differential influ-ence, specifically our view of SCKMC as a valuable bundle ofresources offering unique advantages for the firm. Support wasfound for tacit knowledge’s more intangible nature and greaterdifficulty for transfer, as was for explicit knowledge’s moretransferrable nature, and its likelihood to be more redundant andto be generated also via less complex approaches. We demon-strated the conduciveness and potential of a collaborative supplychain to generate the more elusive type of tacit knowledge. Ourrationale, which argued for the unique pairing of partners andtheir respective skills, yielding a higher and more holistic type ofknowledge, was confirmed, as was Nonaka’s (1991) notion ofviewing supply chains as living organisms.

Our results further advance the literature by not only concep-tualizing explicit and tacit knowledge as outcomes of SCKMCseparately but more importantly by demonstrating the effects ofeach knowledge type on supply chain performance. Specifically,

the data supported our KBV-based arguments for the impact ofexplicit and tacit knowledge on supply chain performance, recog-nizing the two knowledge types as valuable, rare, inimitable, andnonsubstitutable resources. The collaborative development ofexplicit and tacit knowledge via interactions with supply chainpartners is an asset that can yield competitive differentiation forthe firm. More importantly, while prior research has establishedthe value of knowledge for performance (e.g., Craighead et al.2009; Fugate et al. 2009; Grawe et al. 2011), we demonstratethat it is only when explicit and tacit knowledge are separatelyconsidered that the intricacies of their effects are understood.This contention was derived based on the KBV’s interpretationof resources as being heterogeneous and imperfectly mobile, andthe notion that different types of resources exist that possess dif-fering degrees of ability in effectuating an outcome. At this pointit is worth noting that a mere copying and internalizing ofknowledge from supply chain partners already present withintheir organizations may yield redundant resources, reducing theiroverall effectiveness in influencing competitive performance(Hamel 1991; Levin and Cross 2004). Our conceptualization ofSCKMC as a dynamic capability elevates external resources tohigher levels, demonstrating their ability to generate explicit andtacit knowledge for the firm, which in turn enables competitiveperformance.

In this research we considered different types of knowledgeresources. Specifically, our results indicate that it is tacit knowl-edge, characterized by applied skills and learning-by-doing thatexhibits a greater impact on a supply chain’s competitive perfor-mance. The findings provide substantiation for our contentionthat due to its more elusive and complex nature, tacit knowledgerepresents a resource that is more imperfectly mobile, thus offer-ing a more valuable, rare, inimitable, and nonsubstitutableresource for the firm under the KBV. Tacit knowledge wasshown to also provide a greater application foundation for supplychain performance as conceptualized in this study, and as suchwas confirmed to be more effective.

In contrast, explicit knowledge, due to its ease of communica-tion and transfer, exhibits a lesser ability to impact supply chainperformance (although it is important to note that we did findsupport for the codified nature of this type of knowledge toallow for competitive differentiation to a degree). We note herethat our performance measure was designed to test for this spe-cific outcome, in that it assesses competitive performance (Kahnet al. 2006). As such, we do not postulate that explicit knowl-edge is less valuable per se, but that it is less valuable to differ-entiate the firm from competition. Explicit knowledge is less ableto retain the characteristics needed to meet the requirements forbeing a resource under the KBV, since, due to its codified nat-ure, this knowledge may be easily transferred to competitors.This observation is indicative of the more obvious disposition ofexplicit knowledge, being less context dependent and more easilyto transfer among competitors.

No support was found in our exploratory post hoc analysesfor the link between tacit and explicit knowledge, as was for themoderating role of SCKMC and its individual elements. Knowl-edge conversion (i.e., the conversion of tacit into explicit knowl-edge) follows different pathways than specified in this research,and future studies are encouraged to delve deeper into thisdomain.


Overall, through the specification of differential knowledgeeffects on supply chain performance, this work contributes to ourunderstanding of the complex influence of explicit and tacitknowledge within supply chain relationships. As such, it answerscalls in the literature for a greater understanding of knowledgemanagement dynamics (Craighead et al. 2009). For example,Hult et al. (2004) stress the lack of attention that has been paidto the link between knowledge and supply chain outcomes,Ferdows (2006) encourages supply chain management to not takea passive role in knowledge management research, and Craig-head et al. (2009) argue that there is still much to be learnedabout performance improvement possible via supply chainknowledge. Most recently, Grawe et al. (2011) encourage thestudy of knowledge synthesis mechanisms, as well as the consid-eration of different knowledge types. This research answers thesecalls while extending one of the most prominent classificationsin the knowledge management literature, that is, the differentia-tion between explicit and tacit knowledge, a distinction also fun-damental to the theory of knowledge creation (Polanyi 1966).

This research further contributes to the field via its contextualfocus on SMEs. Limited research exists within the domains oflogistics and supply chain management research that taps intothis sector (Tokman et al. 2007; Bode et al. 2011). We focusedon SMEs since SCKMC may be especially valuable for them(Narula 2004), due to their often limited resources in developingspecialized expertise in-house (Lu and Beamish 2001). By inves-tigating knowledge management in an SME setting, we alsoanswered the call by Durst and Edvardsson (2012) for more spe-cific insight in this context.

Managerial implications

As competition increases, the importance of knowledge manage-ment capability for logistics and supply chain management as acompetitive foundation is likely to increase as well. Against thisreality, managerially, the results provide important insights, espe-cially for SMEs. First, our findings clearly demonstrate theimportance of SCKMC. We confirmed that it is the establishmentof SCKMC that leads to knowledge within a specific supplychain, and the ultimate enhancement of supply chain perfor-mance. As such, our results provide a stimulus for managers toinvest in SCKMC incorporating specific key supply chain part-ners. Our concept of SCKMC drew on its encapsulation in thefour supply chain knowledge management aspects identified, towhich managerial attention should be devoted. This findingshould be especially valuable for SMEs, which have beendescribed as lagging behind in knowledge management endeav-ors (McAdam and Reid 2001). This observation may be attrib-uted to the short distance between executive and functionallevels in SMEs, and the ensuing perception that a formal knowl-edge management system may not be necessary. As such, knowl-edge sharing in SMEs primarily occurs informally (Durst andEdvardsson 2012). The more formal approach, as presentedherein, was demonstrated to be effective in SMEs, and can thusprovide a template for SMEs to enhance their knowledge man-agement capability.

Second, pursuing SCKMC promises the generation of internalknowledge, which has been said to be limited in SMEs (Lu andBeamish 2001). The structured approach can thus provide guid-

ance for SMEs on how to harness knowledge distributed acrossthe supply chain, strengthening internal expertise. While SCKMCis certainly able to influence both explicit and tacit knowledge, itis the finding that the approach is especially amenable to createtacit knowledge that has implications for practicing managers. Assuch, this result provides further impetus for firms to focus onwhat they can do best, that is, their core competencies, and torely on outside partners for the remaining tasks. The findingspoint to the fact that besides the physical product that the firm isreceiving from suppliers, the potential for knowledge transferand generation cannot be neglected, and can be, in someinstances, even more valuable than the physical product. Similarvalue can be placed on knowledge obtained through customers,which may provide the firm unique insight into market develop-ments, trends, and changing preferences. Managers are thus pro-vided with the advice to nurture the supply chain knowledgecapability among suppliers and customers, as it is through thisprocess that higher levels of knowledge within the supply chaincan be achieved, which will benefit the firm. These findingsillustrate that all knowledge does not have to be generated inter-nal to the firm (which may consume significant resources), butcan be harnessed from interactions with suppliers and customersin the supply chain. This offers SMEs a unique opportunity toenhance their knowledge repositories by tapping into theseentities.

Third, the findings of this study suggest that managers mustcarefully consider the type of knowledge fostered within a spe-cific supply chain. While both explicit and tacit knowledge areimportant, it is tacit knowledge that can provide greater competi-tive differentiation. If the improvement of such performance met-ric is the objective, the generation of tacit knowledge should beemphasized, due to its greater impact in influencing competitiveperformance. The distinctive intelligence derived from supplychain members, especially the insights that are imperfectlymobile, as encapsulated in tacit knowledge, represent a morevaluable asset in generating competitive disparity. This providesvaluable guidance for SMEs, which are often constrained in theirresources devoted to knowledge management.

LIMITATIONS AND FUTURE DIRECTIONS

Although this study made several advances to the literature, limi-tations must be noted. While our approach to focus on firms with50 employees or less is consistent with prior research (e.g.,Lusch and Brown 1996), provides advantages for the identifica-tion of the key informant, and offers unique insight into the con-text of SMEs, our study is limited as it may not be generalizableto larger firms and the supply chains in which they may operate.Future research should thus seek the replication of the presentstudy among a sample of larger companies. We also restrictedour survey to firms in the manufacturing industry. Although thisis a frequent practice in empirical logistics and supply chainmanagement research, it also limits the generalizability of ourresults, suggesting a need for future research to expand this workto other industries. Our research is further limited by a singlerespondent completing the survey. An alternative approachwould have involved multiple respondents in the supply chainrelationship, including the focal firm as well as their most impor-


tant supplier and customer (a dyadic or even triadic surveydesign). In addition, our questions asked for the respondent tofocus on their primary supply chain, averaging the potentiallydifferent knowledge processes across the two partners. An alter-nate approach would have involved the measuring of the knowl-edge processes with both customers and suppliers separately.

Further, although our model is theoretically and empiricallysupported, the employment of a cross-sectional design does notallow for fully discerning causality. It could be argued thatknowledge is both an antecedent and a consequence of knowl-edge management capability. In fact, in an ongoing supply chainrelationship, SCKMC generates knowledge which can then serveas an input to SCKMC to generate additional knowledge. Whilewe took the theoretical approach of SCKMC generating knowl-edge, the specificity of this relationship in terms of causationwould provide further insights into this important area. Futureresearch employing longitudinal data collection is needed toovercome these shortcomings. We also note that while we pilot-tested our questionnaire with knowledgeable practitioners andacademics for the refinement of our scales, we did not conduct aformal pretest with a smaller subsample of our population.Future research is encouraged to not omit this important step, tobe able to afford a more rigorous survey methodology. We alsodid not address nonresponse bias directly, but rather used laterespondents as a proxy for nonrespondents. While commonlydone, a more rigorous approach would have involved the com-parison of respondents to actual nonrespondents. Data from thesecould have been obtained by approaching them after the end ofthe survey administration, with the request to answer a shortersurvey. Last, while our operationalization and ensuing measure-ment of our two knowledge dimensions as separate constructs isan extension to current literature, future research is encouragedto refine and improve upon our measurement. Specifically, recenttheoretical and conceptual advances, published after the data col-lection for this work (Venkitachalam and Busch 2012), could betaken into consideration in further advancing the measurement ofexplicit and tacit knowledge. We provide a first step toward thisundertaking. The same applies to our SCKMC construct, whichwe based on the seminal work of Gold et al. (2001). Besides theaspects of knowledge acquisition, conversion, application andprotection, additional aspects, such as knowledge exchange anddissemination across the supply chain, could be considered.

While this study has limitations, the findings suggest severalexciting avenues to extend this research. First, future workshould conceptualize explicit and tacit knowledge as two sepa-rate constructs, rather than a uni-dimensional measure. In thisvein, one could investigate how the length of the supply chainrelationship influences the resource stocks of both explicit andtacit knowledge. Our expectation would be that the more maturethe relationship becomes, the less explicit and the more tacitknowledge exists within the supply chain due to the nature ofSCKMC building on prior knowledge to generate new knowl-edge and the development of implicit routines.

Second, the creation of explicit and tacit knowledge could alsobe examined from the theory of knowledge creation (Nonaka1994). Specific measures for each of the four mechanisms ofcombination, internalization, socialization, and externalizationcould be developed, similar to Kahn et al. (2006) and Anandet al. (2010), and their influence on types of knowledge could be

examined. In addition, research is needed that provides greaterspecificity into what may constitute explicit and tacit knowledgein a supply chain context, such as the knowledge about suppliercapabilities and capacities.

Third, we found that SCKMC influences tacit knowledge to agreater degree than it influences explicit knowledge. We sug-gested that this result may stem from our setting of a collabora-tive supply chain, in which the unique interplay between thevarious supply chain members as part of SCKMC, can providesuch potential. In-depth, longitudinal case studies tracking thedevelopment of both explicit and tacit knowledge generated fromSCKMC in a specific supply chain could help to better under-stand this issue.

Fourth, while we contributed to the literature by differentiatingexplicit and tacit knowledge and their unique influences on per-formance, we did not consider how the different knowledge typesinfluence performance. Future research is therefore encouraged toidentify and assess intermediate processes that are created by thetwo knowledge types, through which knowledge impacts perfor-mance. In addition, while we focused on one of the most promi-nent knowledge classifications in the literature and differentiatedbetween explicit and tacit knowledge, other aspects of knowledgecould be investigated, such as market or technological knowl-edge, and their differential impact on performance.

Fifth, while our choice of supply chain performance as adependent variable was theoretically substantiated and is manage-rially meaningful, the influence of the two knowledge types onother performance measures is needed, which will lead to a moreholistic view of our framework; for example, cycle time could beexamined.

Last, researchers could also look at the diversity of the sup-ply chain in terms of the unique knowledge assets and resourcesthat individual members bring to the table. Our expectationwould be that the more diverse and unique individual supplychain entities are within a specific supply chain, the greater thepotential for knowledge generation, especially in terms of tacitknowledge.

APPENDIXQuestionnaire items

In answering the questionnaire, respondents were asked to focuson one of their primary supply chains, involving the firm’s pri-mary supplier and the corresponding key customer in that supplychain. Respondents were asked to keep this supply chain in mindwhen completing the survey.

Knowledge acquisition

Working with my supply chain partners, we have developedprocesses for. . .

. . . acquiring knowledge about new products/serviceswithin our industry (acqk1).. . . generating new knowledge from existing knowledge(acqk2).. . . collaborating (acqk3).


Knowledge conversion

Working with my supply chain partners, we have developed pro-cesses for. . .

. . . integrating different sources and types of knowledge(convk1).. . . organizing knowledge (convk2).. . . replacing outdated knowledge (convk3).. . . filtering knowledge (convk4).

Knowledge application


. . . using knowledge to solve new problems (appk1).

. . . taking advantage of new knowledge (appk2).

. . . locating and apply knowledge to changing competitiveconditions (appk3).

Knowledge protection


. . . protecting knowledge from inappropriate use outsidethe 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 belearned through first-hand experience (tactk1).New employees in our supply chain could only learn theirjob by first-hand experience (tactk2).The knowledge used in our supply chain is highly complexand 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 docu-mented (explk1).New employees in our supply chain could easily learntheir entire job from work manuals (explk2).A competitor would have relatively little difficulty copyingthe routines and processes that we use in our supply chain

because they are straightforward and easily documented(explk3).

Supply chain performance

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

. . . been very profitable (p1).

. . . generated a high volume of business (p2).

. . . helped us achieve rapid growth (p3).

. . . improved our competitiveness (p4).

REFERENCES

Alavi, M., and Leidner, D.E. 2001. “Knowledge Management andKnowledge Management Systems: Conceptual Foundations andResearch Issues.” MIS Quarterly 25(1):107–36.

Amit, R., and Schoemaker, P.J.H. 1993. “Strategic Assets andOrganizational Rent.” Strategic Management Journal 14(1):33–46.

Anand, G., Ward, P.T., and Tatikonda, M.V. 2010. “Role ofExplicit and Tacit Knowledge in Six Sigma Projects: AnEmpirical Examination of Differential Project Success.”Journal of Operations Management 28(4):303–15.

Anderson, J.C., and Gerbing, D.W. 1988. “Structural EquationModeling in Practice: A Review and Recommended Two-Step Approach.” Psychological Bulletin 103(3):411–23.

Armstrong, J.S., and Overton, T.S. 1977. “EstimatingNonresponse Bias in Mail Surveys.” Journal of MarketingResearch 14(3):396–402.

Badaracco, J.L. 1991. The Knowledge Link: How Firms CompeteThrough Strategic Alliances. Boston, MA: Harvard UniversityPress.

Bode, C., Lindemann, E., and Wagner, S.M. 2011. “DrivingTrucks and Driving Sales? The Impact of Delivery Personnelon Customer Purchase Behavior.” Journal of BusinessLogistics 32(1):99–114.

Bresman, H., Birkinshaw, J., and Nobel, R. 1999. “KnowledgeTransfer in International Acquisitions.” Journal ofInternational Business Studies 30(3):439–62.

Brown, J.S., and Duguid, P. 2001. “Knowledge andOrganization: A Social-Practice Perspective.” OrganizationScience 12(2):198–213.

Chin, W.W. 1998. “The Partial Least Squares Approach toStructural Equation Modeling.” In Modern Methods forBusiness Research, edited by G.A. Marcoulides, 295–336.Mahwah, NJ: Lawrence Erlbaum Associates Inc.

Cohen, W.M., and Levinthal, D.A. 1990. “Absorptive Capacity:A New Perspective on Learning and Innovation.”Administrative Science Quarterly 35(1):128–52.

Craighead, C.W., Hult, G.T.M., and Ketchen, D.J., Jr. 2009.“The Effects of Innovation-Cost Strategy, Knowledge, andAction in the Supply Chain on Firm Performance.” Journal ofOperations Management 27(5):405–21.

Cui, A.S., Griffith, D.A., and Cavusgil, S.T. 2005. “TheInfluence of Competitive Intensity and Market Dynamism on


Knowledge Management Capabilities of MultinationalCorporation Subsidiaries.” Journal of International Marketing13(3):32–53.

Defee, C.C., Williams, B., Randall, W.S., and Thomas, R. 2010.“An Inventory of Theory in Logistics and SCM Research.”International Journal of Logistics Management 21(3):404–89.

Diamantopoulos, A., Riefler, P., and Roth, K.P. 2008.“Advancing Formative Measurement Models.” Journal ofBusiness Research 61(12):1203–18.

Diamantopoulos, A., and Siguaw, J.A. 2006. “Formative VersusReflective Indicators in Organizational Measure Development:A Comparison and Empirical Illustration.” British Journal ofManagement 17(4):263–82.

Dillman, D.A. 2000. Mail and Internet Surveys: The TailoredDesign Method. 2nd ed. New York: Wiley & Sons.

Dooley, K.J. 2000. “The Paradigms of Quality: Evolution andRevolution in the History of the Discipline.” Advances in theManagement of Organizational Quality 5:1–28.

Durst, S., and Edvardsson, I.R. 2012. “Knowledge Managementin SMEs: A Literature Review.” Journal of KnowledgeManagement 16(6):879–903.

Dyer, J.H. 1996. “Specialized Supplier Networks as a Source ofCompetitive Advantage: Evidence From the Auto Industry.”Strategic Management Journal 17(4):271–91.

Dyer, J.H., and Hatch, N.W. 2004. “Relation-SpecificCapabilities and Barriers to Knowledge Transfers: CreatingAdvantage Through Network Relationships.” StrategicManagement Journal 27(8):701–19.

Dyer, J.H., and Nobeoka, K. 2000. “Creating and Managing aHigh-Performance Knowledge-Sharing Network: The ToyotaCase.” Strategic Management Journal 21(3):345–67.

Ericsson, K.A., and Hastie, R. 1994. “Contemporary Approachesto the Study of Thinking and Problem Solving.” In Thinkingand Problem Solving, edited by R.J. Sternberg, 37–79. SanDiego, CA: Academic Press Inc.

Fawcett, S.E., Waller, M.A., and Bowersox, D.J. 2011.“Cinderella in the C-Suite: Conducting Influential Research toAdvance the Logistics and Supply Chain Disciplines.”Journal of Business Logistics 32(2):115–21.

Ferdows, K. 2006. “Transfer of Changing Product Know-How.”Production and Operations Management 15(1):1–9.

Fornell, C., and Larcker, D.F. 1981. “Evaluating StructuralEquation Models With Unobservable Variables andMeasurement Error.” Journal of Marketing Research 18(1):39–50.

Freeze, R.D., and Kulkarni, U. 2007. “Knowledge ManagementCapability: Defining Knowledge Assets.” Journal ofKnowledge Management 11(6):94–109.

Fugate, B.S., Stank, T.P., and Mentzer, J.T. 2009. “LinkingImproved Knowledge Management to Operational andOrganizational Performance.” Journal of OperationsManagement 27(4):247–64.

Gligor, D.M., Holcomb, M.C., and Stank, T.P. 2013. “AMultidisciplinary Approach to Supply Chain Agility:Conceptualization and Scale Development.” Journal ofBusiness Logistics 34(2):94–108.

Goffin, K., and Koners, U. 2011. “Tacit Knowledge, LessonsLearned, and New Product Development.” Journal of ProductInnovation Management 28(2):300–18.

Gold, A., Malhotra, A., and Segars, A.H. 2001. “KnowledgeManagement: An Organizational Capabilities Perspective.”Journal of Management Information Systems 18(1):185–214.

Gorman, M.E. 2001. “Types of Knowledge and Their Roles inTechnology Transfer.” Journal of Technology Transfer 27(3):219–31.

Granovetter, M. 1973. “The Strength of Weak Ties.” AmericanJournal of Sociology 78(6):1360–80.

Grant, R.M. 1996. “Prospering in Dynamically-CompetitiveEnvironments: Organizational Capability as KnowledgeIntegration.” Organization Science 7(4):375–88.

Grawe, S.J., Daugherty, P.J., and Roath, A.S. 2011. “KnowledgeSynthesis and Innovative Logistics Processes: EnhancingOperational Flexibility and Performance.” Journal of BusinessLogistics 32(1):69–80.

Griffis, S.E., Goldsby, T.J., Cooper, M., and Closs, D.J. 2007.“Aligning Logistics Performance Measures to the InformationNeeds of the Firm.” Journal of Business Logistics 28(2):35–56.

Gunasekaran, A., Patel, C., and McGaughey, E. 2004. “AFramework for Supply Chain Performance Measurement.”International Journal of Production Economics 87(3):333–47.

Hair, J.F., Hult, G.T.M., Ringle, C.M., and Sarstedt, M. 2013. APrimer on Partial Least Squares Structural EquationModeling (PLS-SEM). Thousand Oaks, CA: Sage.

Hall, R. 1999. “Rearranging Risks and Rewards in Supply ChainManagement.” Journal of General Management 24(3):22–32.

Hamel, G. 1991. “Competition for Competence and InterpartnerLearning Within International Strategic Alliances.” StrategicManagement Journal 12(Special Issue):83–103.

Hult, G.T.M., Ketchen, D.J., Jr., and Slater, S.F. 2004.“Information Processing, Knowledge Development, andStrategic Supply Chain Performance.” Academy ofManagement Journal 47(2):241–53.

Jarvis, C.B., MacKenzie, S.B., and Podsakoff, P.M. 2003. “ACritical Review of Construct Indicators and MeasurementModel Misspecification in Marketing and ConsumerResearch.” Journal of Consumer Research 30(2):199–218.

Johnson, G.J., Bruner, G.C., II, and Kumar, A. 2006.“Interactivity and Its Facets Revisited: Theory and EmpiricalTest.” Journal of Advertising 35(4):35–52.

Johnson-Laird, P.N. 1983. Mental Models. Cambridge, CA:Cambridge University Press.

Kahn, K.B., Maltz, E.N., and Mentzer, J.T. 2006. “DemandCollaboration: Effects on Knowledge Creation, Relationships,and Supply Chain Performance.” Journal of BusinessLogistics 27(2):191–221.

Kogut, B., and Zander, U. 1992. “Knowledge of the Firm,Combinative Capabilities, and the Replication ofTechnology.” Organization Science 3(3):383–97.

Lanier, D., Jr., Wempe, W.F., and Zacharia, Z.G. 2010.“Concentrated Supply Chain Membership and FinancialPerformance: Chain- and Firm-Level Perspectives.” Journalof Operations Management 28(1):1–16.

Lee, D., and Van den Steen, E. 2010. “Managing Know-How.”Management Science 56(2):270–85.

Leonard, D. 1995. Wellsprings of Knowledge: Building andSustaining the Source of Innovation. Boston, MA: HarvardBusiness School Press.


Levin, D.Z., and Cross, R. 2004. “The Strength of WeakTies You Can Trust: The Mediating Role of Trust inEffective Knowledge Transfer.” Management Science 50(11):1477–90.

Li, S.-T., and Tsai, F.-C. 2009. “Concept-Guided QueryExpansion for Knowledge Management With Semi-AutomatedKnowledge Capturing.” Journal of Computer InformationSystems 49(4):53–65.

Linderman, K., Schroeder, R.G., and Sanders, J. 2010. “AKnowledge Framework Underlying KnowledgeManagement.” Decision Sciences 41(4):689–719.

Lord, M.D., and Ranft, A.L. 2000. “Organizational LearningAbout New International Markets: Exploring the InternalTransfer of Local Market Knowledge Studies.” Journal ofInternational Business Studies 31(4):573–89.

Lu, J.W., and Beamish, P.W. 2001. “The Internationalization andPerformance of SMEs.” Strategic Management Journal 22(6–7):565–86.

Lusch, R.F., and Brown, J.R. 1996. “Interdependency,Contracting, and Relational Behavior in Marketing Channels.”Journal of Marketing 60(4):19–38.

Lyles, M.A., and Salk, J.E. 1996. “Knowledge Acquisition FromForeign Parents in International Joint Ventures: An EmpiricalExamination in the Hungarian Context.” Journal ofInternational Business Studies 27(5):877–903.

Madhavaram, S., and Hunt, S.D. 2008. “The Service-DominantLogic and a Hierarchy of Operant Resources: DevelopingMasterful Operant Resources and Implications for MarketingStrategy.” Journal of the Academy of Marketing Science 36(1):67–82.

Malhotra, N.K., Kim, S.S., and Patil, A. 2006. “CommonMethod Variance in IS Research: A Comparison ofAlternative Approaches and a Reanalysis of Past Research.”Management Science 52(12):1865–83.

McAdam, R., and Reid, R. 2001. “SME and Large OrganisationPerceptions of Knowledge Management: Comparisons andContrasts.” Journal of Knowledge Management 5(3):231–41.

McFarlin, D.B., and Sweeney, P.D. 1992. “Distributive andProcedural Justice as Predictors of Satisfaction With Personaland Organizational Outcomes.” Academy of ManagementJournal 35(3):626–37.

Molina, L.M., Llor�ens-Montes, J., and Ruiz-Moreno, A. 2007.“Relationship Between Quality Management Practices andKnowledge Transfer.” Journal of Operations Management 25(3):682–701.

Mooradian, N. 2005. “Tacit Knowledge: Philosophic Roots andRole in KM.” Journal of Knowledge Management 9(6):104–13.

Narula, R. 2004. “R&D Collaboration by SMEs: NewOpportunities and Limitations in the Face of Globalisation.”Technovation 24(2):153–61.

Neisser, U. 1967. Cognitive Psychology. Upper Saddle River,NJ: Prentice Hall.

Nonaka, I. 1991. “The Knowledge-Creating Company.” HarvardBusiness Review 69(November-December):96–104.

Nonaka, I. 1994. “A Dynamic Theory of OrganizationalKnowledge Creation.” Organization Science 5(1):14–37.

Nonaka, I., and Takeuchi, H. 1995. The Knowledge CreatingCompany. New York: Oxford University Press.

Norman, P.M. 2004. “Knowledge Acquisition, Knowledge Loss,and Satisfaction in High Technology Alliances.” Journal ofBusiness Research 57(6):610–19.

Oh, L.B., Teo, H.H., and Sambamurthy, V. 2012. “The Effectsof Retail Channel Integration Through the Use of InformationTechnologies on Firm Performance.” Journal of OperationsManagement 30(5):368–81.

O’Leary-Kelly, S.W., and Vokurka, R.J. 1998. “The EmpiricalAssessment of Construct Validity.” Journal of OperationsManagement 16(4):387–405.

Peng, D.X., and Lai, F. 2012. “Using Partial Least Squares inOperations Management Research: A Practical Guideline andSummary of Past Research.” Journal of OperationsManagement 30(6):467–80.

Perols, J., Zimmermann, C., and Kortmann, S. 2013. “On theRelationship Between Supplier Integration and Time-to-Market.” Journal of Operations Management 31(3):153–67.

Phillips, L.W. 1981. “Assessing Measurement Error in KeyInformant Reports: A Methodological Note on OrganizationalAnalysis in Marketing.” Journal of Marketing Research 18(4):395–415.

Podsakoff, P.M., MacKenzie, S.B., Lee, J.-Y., and Podsakoff,N.P. 2003a. “Common Method Biases in BehavioralResearch: A Critical Review of the Literature andRecommended Remedies.” Journal of Applied Psychology 88(5):879–903.

Podsakoff, P.M., MacKenzie, S.B., Podsakoff, N.P., and Lee,J.Y. 2003b. “The Mismeasure of Man(agement) and ItsImplication for Leadership Research.” Leadership Quarterly14(6):615–56.

Podsakoff, P.M., and Organ, D.W. 1986. “Self-Reports inOrganizational Research: Problems and Prospects.” Journal ofManagement 12(4):531–44.

Polanyi, M. 1966. The Tacit Dimension. Garden City, NY:Doubleday.

Roy, S., Sivakumar, K., and Wilkinson, I.F. 2004. “InnovationGeneration in Supply Chain Relationships: A ConceptualModel and Research Propositions.” Journal of the Academyof Marketing Science 32(1):61–79.

Ruiz, D.M., Gremler, D.D., Washburn, J.H., and Carri�on, G.C.2008. “Service Value Revisited Specifying a Higher-Order,Formative Measure.” Journal of Business Research 61(12):1278–91.

Samuel, K.E., Goury, M.-L., Gunasekaran, A., and Spalanzani,A. 2011. “Knowledge Management in Supply Chain: AnEmpirical Study From France.” Journal of StrategicInformation Systems 20(3):283–306.

Sawhney, R. 2013. “Implementing Labor Flexibility: A MissingLink Between Acquired Labor Flexibility and PlantPerformance.” Journal of Operations Management 31(1–2):98–118.

Schoenherr, T., Griffith, D.A., and Chandra, A. Forthcoming,“Intangible Capital, Knowledge and New Product DevelopmentCompetence in Supply Chains: Process, Interaction andContingency Effects Among SMEs.” International Journal ofProduction Research. doi:10.1080/00207543.2014.894258.

Simonin, B.L. 1999. “Ambiguity and the Process of KnowledgeTransfer in Strategic Alliances.” Strategic ManagementJournal 20(7):595–623.


Smith, E.A. 2001. “The Role of Tacit and Explicit Knowledge inthe Workplace.” Journal of Knowledge Management 5(4):311–21.

Smith, K.G., Collins, C.J., and Clark, K.D. 2005. “ExistingKnowledge, Knowledge Creation Capability, and the Rate ofNew Product Introduction in High-Technology Firms.”Academy of Management Journal 48(2):346–57.

Song, M., and Swink, M. 2009. “Marketing-ManufacturingIntegration Across Stages of New Product Development: Effectson the Success of High- and Low-Innovativeness Products.”IEEE Transactions on Innovation Management 56(1):31–44.

Spector, P.E. 1992. Summated Rating Scale Construction: AnIntroduction. Newbury Park, CA: Sage Publications.

Spekman, R.E., Spear, J., and Kamauff, J. 2002. “Supply ChainCompetency: Learning as a Key Concept.” Supply ChainManagement: An International Journal 7(1):41–55.

Swink, M. 2006. “Building Collaborative Innovation Capability.”Research Technology Management 49(2):37–47.

Teece, D.J., Pisano, G.P., and Shuen, A. 1997. “DynamicCapabilities and Strategic Management.” StrategicManagement Journal 18(7):509–33.

Tenenhaus, M., Vinzi, V.E., Chatelin, Y.-M., and Lauro, C.2005. “PLS Path Modeling.” Computational Statistics & DataAnalysis 48(1):159–205.

Tokman, M., Richey, R.G., Marino, L.D., and Weaver, K.M.2007. “Exploration, Exploitation and Satisfaction in SupplyChain Portfolio Strategy.” Journal of Business Logistics 28(1):25–56.

Venkitachalam, K., and Busch, P. 2012. “Tacit Knowledge:Review and Possible Research Directions.” Journal ofKnowledge Management 16(2):356–71.

Voelpel, S., Dous, M., and Davenport, T. 2005. “Five Steps toCreating a Global Knowledge-Sharing System: SiemensShare-Net.” Academy of Management Executive 19(2):9–23.

Wetzels, M., Odekerken-Schr€oder, G., and van Oppen, C. 2009.“Using PLS Path Modeling for Assessing HierarchicalConstruct Models: Guidelines and Empirical Illustration.”Management Information Systems Quarterly 33(1):177–95.

Wong, W.P., and Wong, K.Y. 2011. “Supply ChainManagement, Knowledge Management Capability, and TheirLinkages Toward Firm Performance.” Business ProcessManagement 17(6):940–64.

Zack, M.H. 1999. “Managing Codified Knowledge.” SloanManagement Review 40(4):45–58.

Zander, U. 1991. Exploiting a Technological Edge: Voluntaryand Involuntary Dissemination of Technology. Stockholm:Institute of International Business.

Zou, S., Taylor, C.R., and Osland, G.E. 1998. “The EXPERFScale: A Cross-National Generalized Export PerformanceMeasure.” Journal of International Marketing 6(3):37–58.

SHORT BIOGRAPHIES

Tobias Schoenherr (PhD Indiana University) is AssociateProfessor in the Broad College of Business at Michigan StateUniversity. His research focuses on strategic supply management,including strategic sourcing and leveraging the supply base. Hehas more than 40 journal publications, which include publica-tions in Management Science, Journal of Operations Manage-ment, Journal of Business Logistics, Production and OperationsManagement, Decision Sciences, and Journal of Supply ChainManagement. He is an Associate Editor for the Journal of Oper-ations Management and Decision Sciences, and serves on severalEditorial Review Boards, including the Journal of BusinessLogistics and IEEE Transactions on Engineering Management.

David A. Griffith (PhD Kent State University) is Chairpersonand Professor of Marketing at Lehigh University. His researchfocuses on inter-firm governance and global marketing strategyand has appeared in the Journal of Marketing Research, Journalof Marketing, Journal of International Business Studies, StrategicManagement Journal, Journal of Operations Management, andthe Journal of Business Logistics. He has served as the JohnWilliam Byington Endowed Chair in Global Marketing and Pro-fessor of Marketing at Michigan State University, and on facultyat the University of Hawai’i, the Japan-America Institute of Man-agement Science, Wirtschaftsuniversit€at Wien, and the Universityof Oklahoma.

Aruna Chandra (PhD Kent State University) is a Professorof Management at the Scott College of Business, Indiana StateUniversity. She holds doctoral degrees in Strategy/InternationalBusiness and in English / Linguistics from Kent State University.Her research interests include small business / new venture strat-egies, innovation ecosystems and business framework conditionsin emerging market contexts.


Documents

Knowledge Management in Supply Chains: The Role of Explicit and Tacit Knowledge