relationship between intellectual capital and knowledge management 2012

Decision SciencesVolume 43 Number 3June 2012

C 2012 The AuthorsDecision Sciences Journal C 2012 Decision Sciences Institute

Relationship between Intellectual Capitaland Knowledge Management: An EmpiricalInvestigationI-Chieh HsuDepartment of Business Administration, National Changhua University of Education, Taiwan,e-mail: [email protected]

Rajiv SabherwalSam M. Walton College of Business, University of Arkansas, AR 72701, e-mail:[email protected]

ABSTRACTTwo important streams of the literature have examined intellectual capital (IC) andknowledge management (KM). Surprisingly, they have developed in parallel, withoutany empirical research on the relationship between them. This article empirically exam-ines how IC and KM affect each other, and also investigates their consequences, viewingthree intermediate consequences (dynamic capabilities, efficiency, and innovativeness)to mediate their effects on firm performance. In addition, this article examines the effectsof the organizations culture on IC and KM. To address these issues, a comprehensivemodel is developed and tested using a combination of survey and secondary data of 533companies in Taiwan. The results support the theoretical model. Major findings includethe following: IC affects KM and dynamic capabilities; KM facilitates innovation butnot dynamic capabilities or IC; a learning culture facilitates IC and innovation but notKM; firm performance depends on efficiency and innovation, but not directly on dy-namic capabilities; and efficiency does not depend on any of the other constructs in thestudy. The articles implications for research and practice are examined. [Submitted:November 17, 2010. Revised: June 22, 2011; October 4, 2011. Accepted: November 10,2011.]

Subject Areas: Firm Performance, Intellectual Capital, Knowledge Ac-quisition, Knowledge Creation, Knowledge Management, OrganizationalLearning, Structural Equation Modeling, and Survey Research.

INTRODUCTION

The current knowledge-based economy has led to the literature emphasizing knowl-edge management (KM) (Eisenhardt & Santos, 2002; Lee & Choi, 2003; Tan-riverdi, 2005) and intellectual capital (IC) (Youndt, Subramaniam, & Snell, 2004;Subramaniam & Youndt, 2005) as major sources of competitive advantage. KM andIC are distinct, but conceptually interrelated, concepts (cf. Nahapiet & Ghoshal,

Corresponding author.

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490 Relationship between IC and KM

1998; Easterby-Smith & Prieto, 2008). Whereas KM in firms has been defined asdoing what is needed to get the most out of knowledge resources, including bothexplicit and tacit knowledge (Sabherwal & Becerra-Fernandez, 2003), IC capturesthe sum of all knowledge firms utilized for competitive advantage (Subramaniam& Youndt, 2005, p. 451).

The literature on KM and IC share the same broad objective: understandingthe role of knowledge and its management in firm success and competitiveness(e.g., Nonaka & Takeuchi, 1995; Grant, 1996a, 1996b; Argote, McEvily, & Rea-gans, 2003). The literature on IC examines the nature of organizational knowledgeand its different types, and also how they affect firm performance (Roos, Roos, Ed-vinsson, & Dragonetti, 1998), whereas the KM literature deals with the processesand practices for managing IC (Alavi & Leidner, 2001; Sabherwal & Sabherwal,2005).

However, the literature in these two areas has developed in parallel. In theinvestigation of the effect of IC, or organizational knowledge, questions related tothe processes through which organizations manage knowledge and appropriate itsvalue receive less attention (Eisenhardt & Santos, 2002). By contrast, research onKM has given limited consideration to the pools of different types of knowledgebeing managed, including difficult-to-codify knowledge such as social capital(Nahapiet & Ghoshal, 1998; Easterby-Smith & Prieto, 2008). Thus, the priorliterature has not examined how IC and KM affect firm performance when bothaspects are simultaneously considered. Consequently, some important questionshave not yet been effectively addressed. When IC and KM are simultaneouslyexamined, how does each aspect affect firm performance? Does KM affect firmperformance through IC? Does IC affect firm performance through KM? Do bothaspects affect firm performance through a common set of mediating variables, ordo they affect firm performance through different mediators?

We believe that questions such as above are important both for practice and forfuture research on knowledge-related aspects. Moreover, we believe that answeringsuch questions requires a simultaneous investigation of both IC and KM and theireffects on firm performance through mediating variables emphasized in the priorliterature. Accordingly, we seek to contribute to a comprehensive understandingof the impact of knowledge-related aspects on firm performance by addressing thefollowing two research questions:

(i) When both KM and IC are considered, through which mediating factorsdo they affect firm performance?

(ii) How do KM and IC affect each other?

Due to the interrelated nature of IC and KM, examining one aspect while excludingthe other provides a partial view of their effects on firm performance. Addressingresearch question (i) should help us understand the effects of KM (i.e., processes formanaging knowledge) and IC (i.e., organizational knowledge) on firm performancewhen both aspects are simultaneously examined. Moreover, by including importantintermediate outcomes of IC and KM, we respond to previous calls (e.g., Lee& Choi, 2003; Subramaniam & Youndt, 2005) to open blackboxes (i.e., theintermediate outcomes needed to improve firm performance). Based on the prior

Hsu and Sabherwal 491

Figure 1: The broad theoretical model.

literature (e.g., Grant 1996a; OReilly & Tushman, 2008), we incorporate dynamiccapabilities, innovation, and efficiency as three variables mediating the effects ofKM and IC on firm performance.

Addressing research question (ii) should help in gaining an initial insightinto the potential chicken-and-egg relationship between KM and IC. Using themetaphor of stocks and flows (Dierickx & Cool, 1989), KM (which focuses onknowledge flows) can enhance the value of knowledge to the organization (De-carolis & Deeds, 1999), whereas the stock of knowledge (i.e., IC) facilitates KMby providing the inputs needed for the knowledge flows (e.g., Smith, Collins, &Clark, 2005).

A learning culture has previously been viewed as an important factor thatmight facilitate KM (e.g., Gold, Mahortra, & Segars, 2001). Although culture hasreceived less attention in the literature specific to IC (e.g., Subramaniam & Youndt,2005), the literature on knowledge (e.g., Grant, 1996a; Nahapiet & Ghoshal, 1998;Lee and Choi, 2003; Yang & Chen, 2007) implies that an organization with alearning culture would also have greater IC through the development of organiza-tional knowledge over time. Therefore, questions such as the following arise: Doesa learning culture facilitate organizational knowledge (i.e., IC)? Does a learningculture facilitate KM directly, or through IC? In order to better understand theeffect of culture on IC and KM, we incorporate learning culturewhich is charac-terized by management support for the role of knowledge, and encouragement ofemployees to interact, explore, and seek assistance (Gold et al., 2001; Nonaka &Toyama, 2005)in this study. Accordingly, in addition to the two research questionsdiscussed above, we pursue a third research question in this study:

(iii) How does organizational culture affect IC?Figure 1 summarizes the broad research model used to pursue the three researchquestions.

The rest of the article is organized as follows. The next section develops thetheoretical model for the article. The subsequent two sections present data col-lection and measures, and analysis and results, respectively. The article concludeswith a discussion of the findings and their implications, and acknowledges thestudys limitations.


THEORETICAL DEVELOPMENTIC and KMKnowledge has been defined as the set of justified beliefs that can enhance the or-ganizations capability for effective action (Nonaka, 1994; Alavi & Leidner, 2001).An organizations IC refers to the sum of all its knowledge resources, which existin aspects within or outside the organization (Subramaniam & Youndt, 2005). IChas been viewed in terms of where the knowledge resides. For example, Edvinssonand Malone (1997) view IC as consisting of human capital, and structural capital,which includes organizational capital and customer capital. Synthesizing the priorliterature, Youndt, Subramaniam, and Snell (2004) and Subramaniam and Youndt(2005) categorized IC into three components: human capital, or the knowledge,skills, and capabilities of individual employees; organizational capital, or the in-stitutionalized knowledge and codified experience residing in databases, manuals,culture, systems, structures, and processes; and social capital, or the knowledgeembedded in networks of relationships and interactions among individuals. Whileadopting these three components of IC in conceptualizing and measuring it, wefocus on the overall IC construct when theoretically examining and empiricallytesting its relationship with KM, performance, and a learning culture.

KM has been defined as firms doing what is needed to get the most outof knowledge resources, including both explicit and tacit knowledge (Sabherwal& Becerra-Fernandez, 2003). Organizational KM includes three major processes:acquisition, conversion, and application (Gold et al., 2001). Knowledge acquisitionrefers to developing new knowledge from data, information, or knowledge (Goldet al., 2001). Knowledge conversion refers to making the acquired knowledgeuseful for the organization (Gold et al., 2001) by structuring it or transformingtacit knowledge into explicit knowledge (Nonaka, 1994). Knowledge applicationrefers to the use of knowledge to perform tasks (Sabherwal & Sabherwal, 2005),although the party that applies knowledge does not necessarily have to understandit (Conner & Prahalad, 1996). Thus, KM includes the firms processes of acquiringnew knowledge, converting its knowledge into a form that is usable and easilyaccessed, and applying knowledge (Verkasalo & Lappalainen, 1998). In this article,we focus on the overall KM construct, while recognizing its multi-dimensionalnature.

IC (e.g., Stewart, 1997; Subramaniam & Youndt, 2005) and KM (e.g.,Nonaka, 1994; Gold et al., 2001) literatures have largely evolved independently ofeach other. IC and KM are distinct, and may be viewed as referring to knowledgestocks and processes, respectively (Haas & Hansen, 2005). The prior literaturehas made conceptual connections between IC and KM literatures (Nahapiet &Ghoshal, 1998), and recognized that they are fundamentally related; organiza-tional knowledge is considered IC and the organizational processes required toacquire, convert, and apply such knowledge are considered KM (Youndt et al.,2004). Both IC and KM are important bases for organizational competitiveness,and neither can be pursued independently of the other (Wiig, 1997). However,there is little empirical research on the bi-directional relationship between IC andKM.


The Relationship between IC and KMThe relationship between IC and KM is rooted in the knowledge-based view(KBV) of the firm, which has been developed from seminal studies such as Simon(1965), Cyert and March (1963), Levitt and March (1988), and Huber (1991).KBV also adopted a logic originating from the resource-based view (RBV) of thefirm, which conveys a dual message. On the one hand, organizational resourcesare argued to lead to information-based, tangible or intangible processes (i.e.,intermediate goods), that create firm competitiveness as the final outcome (Amit& Schoemaker, 1993, p. 35). On the other hand, the same managerial processesfunction to enhance and accumulate organizational resources (Amit & Schoemaker,1993; Hamel & Prahalad, 1993). Thus, one can observe the reciprocal nature ofthe relationship between organizational resources and processes.

KBV considers knowledge to be the key source of firm competitiveness andsuggests a similar reciprocity between knowledge and KM. On the one hand,knowledge serves as the basis for KM. For example, the complementarity of dif-ferent kinds of specialized knowledge increases the scope of the knowledge beingintegrated, and thus facilitates a knowledge-based capability that is inimitablefor competitors (Grant, 1996b). On the other hand, KM improves and strengthensknowledge resources. For example, Nonaka and Takeuchis (1995) knowledge cre-ation processes that include four modes of knowledge conversion can trigger spiralsof learning at the individual, group, and organizational levels. New knowledge iscreated following the three-level learning.

Following the underlying ideology of KBV, our further literature reviewprovides considerable basis for expecting IC to affect KM. An organizations priorknowledge, or IC, affects its processes for absorbing new knowledge (Cohen &Levinthal, 1990), applying knowledge (Grant, 1996b), and converting knowledgefrom one form to another (Sabherwal & Becerra-Fernandez, 2003). This is implicitin the effect of knowledge within a KM system on the use of the system (Kulkarni,Ravindran, & Freeze, 20062007), and in the notion of organizational learning,which focuses on using prior experiences to decide future actions (Levitt & March,1988).

The prior literature provides reasons for expecting IC to affect KM by identi-fying ways in which each dimension of IC (i.e., social, human, and organizational)can affect KM. Social capital facilitates KM because interpersonal interactionsenable knowledge integration (Grant, 1996a), within-firm knowledge sharing (Na-hapiet & Ghoshal, 1998), interfirm knowledge transfer (Santoro & Bierly, 2006;Santoro & Saparito, 2006; Chen, Shih, & Yang, 2009), and knowledge creation(Nonaka, 1994). Human capital enables KM because individuals within the orga-nization can develop appropriate and needed KM processes (Argote et al., 2003),and can use their knowledge to improve KM (Nonaka & Takeuchi, 1995). Finally,organizational capital enables KM because the various forms of organizationalcapital, including transactive memory systems, organizational structure, and in-formation technologies, can be leveraged in developing KM processes (Alavi &Leidner, 2001; Olivera, Goodman, & Tan, 2008).


H1: Intellectual capital has a positive effect on knowledge management.Synthesizing KBV, our literature review also suggests that KM affects the

accumulation and development of IC. The discussion of the knowledge-creatingcompany (Nonaka & Takeuchi, 1995) recognizes the effect of KM on the creationof new knowledge. Nahapiet and Ghoshal (1998) also view the creation of new ICto depend on KM (specifically, the processes of combination and exchange).

The prior literature provides reasons for expecting KM to affect IC by iden-tifying ways in which each dimension of KM (i.e., knowledge acquisition, knowl-edge application, and knowledge conversion) facilitates IC. The effect of KM onIC can also be seen in terms of the specific dimensions of KM. One-dimensionalknowledge acquisition focuses directly on the processes for creating new knowl-edge (Gold et al., 2001). Knowledge application processes rely on direction androutines, which lead to the embedding of the knowledge within the organiza-tion (Grant, 1996a). The prior literature also suggests that repeated application ofknowledge in a given task constitutes a learning process that improves the knowl-edge being utilized (Eisenhardt & Martin, 2000). Finally, knowledge conversionleads to an increase in the overall organizations IC through processes by whichknowledge is converted into another form, and transferred to others (Nonaka &Takeuchi, 1995).

H2: Knowledge management has a positive effect on intellectual capital.Together, H1 and H2 represent the expected reciprocal effects between IC

and KM, which we mentioned in the introduction. They are consistent with ar-guments in KBV (e.g., Grant, 1996a, 1996b) that organizational knowledge helpsdevelop organizational capabilities for integrating diverse knowledge bases. Thesecapabilities, in return, enhance organizational knowledge. H1 and H2 are also cap-tured by Nahapiet and Ghoshals (1998) view of the processes of exchanging andcombining knowledge to both depend on IC (especially social capital), and affectthe creation of new IC. Similarly, Hult, Ketchen, and Slater (2004) view knowl-edge acquisition and information distribution as depending on achieved memory,and as affecting shared meaning. Although H1 and H2 seem intuitively appealing,and may even appear to be obvious in nature, neither of these hypotheses hasbeen explicitly tested in prior empirical research. By testing these hypotheses, andmoreover by testing both of these reciprocal effects simultaneously, we hope thisstudy makes a valuable contribution.

Consequences of IC and KMKBV examines the effects of changes in the content and management of knowledgeon firm performance (Argote et al., 2003). Firm performance may be defined asthe extent to which the firm has achieved its business objectives (Lee & Choi,2003), and may be evaluated in terms of profitability or other financial benefits(Simonin, 1997). Although some prior literature examines the effects of IC and KMon firm performance (e.g., Youndt et al., 2004), it is difficult to attribute better firmperformance to a single factor such as KM or IC (Lee & Choi, 2003). Therefore,studies examine improvements in specific processes, such as software development(Slaughter & Kirsch, 2006), or focus on outcomes that are conceptually more


proximate to KM, such as satisfaction with KM (Sabherwal & Becerra-Fernandez,2003).

The considerable prior literature has focused on innovation and efficiency astwo consequences of KM and IC that subsequently affect firm performance. Forexample, March (1991) focused on exploration and exploitation of knowledge,and related them to innovation and efficiency, respectively. Kogut and Zander(1992) and Majchrzak, Cooper, and Neece (2004) view innovation as a directconsequence of KM, whereas Grant (1996a) and Conner and Prahalad (1996)believe that KM enables organizations to be more efficient. Others have linkeddynamic capabilities to KM (e.g., Zollo & Winter, 2002) and IC (Subramaniam& Youndt, 2005). OReilly and Tushman (2008) consider dynamic capabilitiesas representing ambidexterity between efficiency and innovation, and argued thatall three aspects influence firm performance. Although he focused on efficiencyand innovation as important consequences of KM, Grant (1996b) also highlightedthe importance of dynamic capabilities. The inclusion of dynamic capabilities,innovation, and efficiency as mediating the effects of KM and IC is thus consistentwith KBV. It is also consistent with prior empirical research (Lee & Choi, 2003;Sabherwal & Sabherwal, 2005; Subramaniam & Youndt, 2005).

Efficiency of an organization reflects the organizations ability to achieve thesame level of output with a lower level of input, or achieve a greater level of outputwith the same level of input. Efficiency (McDaniel & Kolari, 1987) and relatednotions such as productivity have been considered important determinants of firmperformance. Greater efficiency has been argued to reduce costs and make the firmmore successful (Porter, 1980).

H3a: Firm efficiency has a positive effect on firm performance.KBV posits that KM processes facilitate efficiency in two distinct ways. First,

knowledge sharing can reduce or eliminate redundancy in knowledge creation orlearning (Grant, 1996a; Sabherwal & Sabherwal, 2005). In other words, KM canenable an organizations employees to be more productive through the acquisition,conversion, and application of knowledge possessed by others (Argote & Ingram,2000), and can thereby enhance organizational efficiency. Second, KM can enhanceefficiency by reducing the need to transfer knowledge (Conner & Prahalad, 1996;Grant, 1996a). Grant (1996a, p. 114) remarks: If production requires the integra-tion of many peoples specialized knowledge, the key to efficiency is to achieveeffective integration while minimizing knowledge transfer through cross-learningby organizational members. Conner and Prahalad (1996) label such applicationof others knowledge without learning it as knowledge substitution, and considerit crucial to organizational efficiency.

H3b: Knowledge management has a positive effect on efficiency.Innovation at the organizational level has been defined as a technology,

strategy, or management practice that a firm is using for the first time, whetheror not other organizations or users have previously adopted it, or as a significantrestructuring or improvement in a process (Li & Atuahene-Gima, 2001, p. 1124).The prior literature (e.g., Porter, 1980; Brown & Eisenhardt, 1997) has emphasizedthe pursuit of innovation as a way for firms to obtain superior profit margins.


Innovation enhances firm performance through improved product/service quality,timely introduction of new products/services, and greater customer responsiveness(Bae & Lawler, 2000).

H4a: Innovation has a positive effect on firm performance.

KBV emphasizes the role of KM in firm innovation (Grant 1996a, 1996b;Nonaka & Toyama, 2005). KM contributes to firm innovation by facilitating newknowledge-based products or enabling improved products that provide a significantadditional value (Argote & Ingram, 2000; Lynn, Reilly, & Akgun, 2000; Eisenhardt& Santos, 2002). Knowledge conversion facilitates the transfer of knowledgewithin the organization, thereby enabling individuals to develop new ideas (Lee& Choi, 2003), and enables the creation of new knowledge needed for innovation(Nonaka, 1994; Nonaka & Takeuchi, 1995). Knowledge acquisition is critical tothe development of new ideas and creative solutions (Haas & Hansen, 2005), andtherefore to the development of new products and services (Gold et al., 2001;Sabherwal & Sabherwal, 2005). Knowledge application enables specializationwithin firms (Demsetz, 1991) so that individuals can focus on the development ofnew products and services while benefiting from others relevant expertise (Grant,1996b).

H4b: Knowledge management has a positive effect on innovation.

Dynamic capabilities have been defined as . . . the firms processes that useresourcesspecifically the processes to integrate, reconfigure, gain and releaseresourcesto match and even create market change (Eisenhardt & Martin, 2000,p. 1107). Dynamic capabilities include specific processes, such as product devel-opment, strategic decision-making, and forming collaborations, that contribute tovalue creation in firms (Eisenhardt & Martin, 2000). They lead to greater financialreturns and improved market performance (Brown & Eisenhardt, 1997), and arenecessary for competitive advantage (Eisenhardt & Martin, 2000).

H5a: Dynamic capabilities have a positive effect on firm performance.

Dynamic capabilities support the reconfiguration of the firms resources.They involve seeking process improvements through modifications in operatingprocesses (Zollo & Winter, 2002) and using governance modes that support change(Teece, Pisano, & Shuen, 1997). They help firms develop new products and pro-cesses in a timely fashion (Wu, 2006), and are essential for a firm engaged ininnovation-based competition (Zollo & Winter, 2002).

H5b: Dynamic capabilities have a positive effect on innovation.

KM supports organizational learning (Zollo & Winter, 2002), as well asemployees learning from each other and external sources (Conner & Prahalad,1996), and enables the firm to use prior knowledge to develop new ideas (Lee &Choi, 2003). Through these effects, KM facilitates dynamic capabilities by en-abling processes needed in dynamic markets. The prior literature on KM processesprovides further insights into reasons for expecting KM to facilitate dynamic ca-pabilities. Knowledge acquisition helps the firm to learn new operational routines


through external knowledge (Zollo & Winter, 2002), knowledge conversion en-ables dynamic capabilities through collective learning among individuals (Duncan& Weiss, 1979), and knowledge application enables dynamic capabilities throughthe replication of solutions in new contexts (Zollo & Winter, 2002). Thus, KMenables the development of dynamic capabilities (Eisenhardt & Martin, 2000).

H5c: Knowledge management has a positive effect on dynamic capabilities.IC, which has been viewed as the sum of the firms knowledge resources, is

also expected to facilitate dynamic capabilities. This is consistent with Kogut andZanders (1992, p. 384) emphasis on the need . . .to understand the knowledgebase of a firm as leading to a set of capabilities that enhance the chances forgrowth and survival. More skilled individuals (human capital) (Subramaniam& Youndt, 2005), stronger ties and relationships (social capital) (Blyler & Coff,2003), and higher levels of institutionalized knowledge (organizational capital)(Sher & Lee, 2004) contribute to processes that enable dynamic capabilities, such asproduct development and strategic decision making (Eisenhardt & Martin, 2000).Consequently, firms with greater IC have greater dynamic capabilities because theyare better able to enact their environments and respond and adapt to environmentalchange (Eisenhardt & Santos, 2002; Wu, 2007).

H5d: Intellectual capital has a positive effect on dynamic capabilities.

The Role of Learning CultureBarrett (1995, p. 36) defines learning cultures as . . . contexts in which memberscan explore, experiment in the margins, extend capabilities, and anticipate cus-tomers latent needs. Relevant cultural attributes include management support forthe role of knowledge and encouragement for employees to interact, explore, andseek assistance (Gold et al., 2001; Nonaka & Toyama, 2005).

The prior literature has revealed the importance of a learning culture inthe development of the stock of organizational knowledge (De Long & Fahey,2000). Nahapiet and Ghoshal (1998) discuss how norms and other aspects ofculture facilitate IC by motivating individuals to combine knowledge. A learningculture facilitates organizational capital, as seen in studies (Kankanhalli, Tan,& Wei, 2005) of how related aspects (e.g., enjoyment in helping others) lead togreater knowledge in electronic knowledge repositories (i.e., greater organizationalcapital). A learning culture facilitates social capital by promoting greater and moretrusting ties among individuals (ODell & Grayson, 1998). A learning culturealso facilitates human capital, because individual knowledge is improved throughlearning by doing in a culture that encourages exploration and tolerates mistakes(Vera & Crossan, 2004), and through learning by observation in a culture thatencourages collaboration (ODell & Grayson, 1998).

H6: Learning culture is positively associated with intellectual capital.

Firm Size as a Control VariableLarge companies have been argued to have economies of scale in research anddevelopment (R&D) activities, and thus can be efficient in achieving innovation(Shumpeter, 1961). Large companies are more capable of nurturing their new


Figure 2: The detailed theoretical model.

products in the market and negotiating with external environments for complimen-tary assets (Teece, 1986). Therefore, we include firm size as a control variable inexamining the effects on firm performance and interim outcomes.

Figure 2 shows the detailed research model. Consistent with earlier discus-sion, we do not include some potential hypotheses. First, we do not hypothesizeIC or KM to directly affect firm performance. Instead, we expect their effects to bemediated through efficiency, innovation, and dynamic capabilities. This dispositionhas been supported by KBV and the dynamic capabilities approach. Second, we donot hypothesize a learning culture to directly facilitate KM. Instead, we argue thata learning culture directly affects IC, but only indirectly affects KM. KBV arguesthat organizational knowledge comes from individuals knowledge (Grant, 1996a,1996b; Nonaka & Toyama, 2005). Individuals with human capital may form in-formal networks (social capital) and contribute to organizational capital such aslicenses, documents, and electronic repositories. Organizational culture has a roleto play in these behaviors (e.g., Nonaka & Takeuchi, 1995; Lee & Choi, 2003).By contrast, KM represents managerial processes and practices to manage knowl-edge. Therefore, we do not consider culture to directly affect KM. Third, we donot hypothesize IC to directly affect innovation or efficiency. This stance is consis-tent with KBV and the dynamic capabilities approach in that knowledge supportsthe development of knowledge-based capabilities, which deliver organizationaloutcomes such as innovation and efficiency.

DATA COLLECTION AND MEASURESSample and ProceduresThe studys sample is 1,466 large publicly listed Taiwanese firms, identified in 2006from the Market Observation Post System maintained by the Taiwan Stock Ex-change (http://emops.tse.com.tw/emops_all.htm). Most variables were measured


using a questionnaire survey in 2006; secondary data were used to measure effi-ciency and firm performance.

The questionnaire utilized existing measures, as discussed below. Two aca-demic domain experts with Chinese and English proficiency were invited to trans-late the questionnaire into English. Consistency of translated and original ques-tionnaires was ensured through back-translation (Bae & Lawler, 2000), using twoother academic experts with similar bilingual capability. The questionnaire waspretested through meetings, each lasting about an hour, with 12 academic domainexperts and 12 senior managers from publicly listed companies in Taiwan. Thepretest was used to evaluate the content validity of the measures, ensure the clarityof instructions and items, and make minor refinements (Tanriverdi, 2005).

We conducted an a priori power analysis to determine the required sam-ple size (Baroudi & Orlikowski, 1989) using statistical software GPower 3(http://www.psycho.uni-duesseldorf.de/abteilungen/aap/gpower3/). The researchmodel in Figure 2 includes six dependent variables, which are hypothesized to beaffected by one to four independent variables including the control variable (orga-nization size). Assuming a small effect size of .02, an alpha of .05, and a requiredstatistical power of .80, a dependent variable with one, two, three, and four inde-pendent variables requires sample sizes of 395, 485, 550, and 602, respectively.The sampling frame has a total of 1,466 companies. To obtain a sufficient numberof responses, we mailed the survey to all of these companies.

Following previous KM studies using senior managers (e.g., Gold et al.,2001; Tanriverdi, 2005) and the advice obtained in the pretest, the questionnairewas mailed to each firms general manager or vice general manager. We statedthe studys purpose and potential contribution, explained how to complete thequestionnaire, and promised confidentiality of responses. Five steps were takento increase the response rate: (i) customization of the cover letter for the specificfirm and top manager; (ii) identification of a contact person from National ScienceCouncil (NSC), Taiwan, to highlight the surveys significance; (iii) the use of phonecalls to request cooperation after mailing the survey; (iv) the provision of a giftvoucher worth US $3, as a token of appreciation; (v) the promise, and subsequentmailing, of a summary of results. These steps helped obtain usable responses from533 firms (i.e., a response rate of 36.4%). However, financial information wasavailable for 2005 and 2006 for only 510 of these firms.

In order to evaluate whether responses from the 510 firms are suffi-cient for drawing conclusions of this research, we conducted post hoc poweranalysis (Baroudi & Orlikowski, 1989) using statistical software GPower 3(http://www.psycho.uni-duesseldorf.de/abteilungen/aap/gpower3/). With an alphaof .05 and the sample size of 510, the actual statistical power for the multipleregression varies from .72 to .89 for one to four independent variables assuminga small effect size of .02, and is above .80 for one to four independent variablesif the effect size is .024 or above. Therefore, we believe that the sample size isadequate.

The average age of informants was 45 years, out of which 73% were male.Seventy-six percent of the informants were top-level managers (general man-ager, vice general manager, senior vice general manager, CEO, etc.), and the restwere department or division heads. Comparing the profiles of the informants with


previous relevant research (e.g., Tanriverdi, 2005), these informants can be judgedas competent in providing the information needed for the survey. The average ageof the responding firms was 25 years, with an average of 785 employees. Ninety-one percent of the firms operated in manufacturing industries, while the rest werein service industries. On average, the firms had US $515 million in assets and US$211 million of sales in 2005.

MeasuresIn the survey, seven-point Likert-type scales ranging from one (strongly disagree)to seven (strongly agree) were used to measure learning culture, IC, KM, dynamiccapabilities, and innovation. Appendix A reports all the items for these constructs.Learning culture was measured using a six-item measure (Gold et al., 2001).The three dimensions of IChuman capital, social capital, and organizationalcapitalwere measured using five, five, and four items, respectively (Youndt et al.,2004; Subramaniam & Youndt, 2005). The three dimensions of KMknowledgeacquisition, knowledge conversion, and knowledge applicationwere measuredusing six, six, and five items, respectively (Gold et al., 2001). Dynamic capabilitieswere measured using eight items (Sher & Lee, 2004). Innovation was measuredusing a four-item measure (Bae & Lawler, 2000). Financial data, obtained fromthe Market Observation Post System, were used to measure efficiency and firmperformance. Following Sabherwal and Sabherwal (2005), efficiency was measuredas the standardized mean of sales-to-total-assets ratio in 2005 and 2006. Firmperformance was computed as the standardized mean of four measures: return onasset and earnings per share at the end of 2005 and 2006. Firm size was measuredas the logarithm of the number of employees, identified from the survey.

Thus, most constructs were measured using survey data, whereas two con-structs in the research modelfirm efficiency and firm performanceand thecontrol variable (firm size) were measured using objective, secondary data. Ourresearch that uses survey data collected from key informants is rooted in the priorrelevant literature, including research on the relationships between innovation andfirm performance (e.g., Bae & Lawler, 2000; Damanpour, Walker, & Avellaneda,2009), and between dynamic capabilities and firm performance (e.g., Song, DiBenedetto, & Nason, 2007; Pavlou & El Sawy, 2011). Subjective data are capableof providing meaningful comparisons across firms (Song, Droge, Hanvanich, &Calantone, 2005). However, our use of firm performance, computed using financialmeasures, as the final dependent variable can be unambiguous (Miller, 1988), andcan help reduce common method variance (Malik & Kotabe, 2009; Terziovski,2010). The value of combining subjective and objective measures has been shownin the prior empirical literature (e.g., Miller, 1998; Song et al., 2007; Damanpouret al., 2009; Terziovski, 2010).

Validation of MeasuresLearning culture, IC, KM, dynamic capabilities, and innovation were measuredusing a total of 49 items. In view of the causal direction being from items to con-structs, and the items being interchangeable and correlated, reflective measureswere used (Petter, Straub, & Rai, 2007). Learning culture, dynamic capabilities,


and innovation were modeled as first-order constructs. Consistent with the priorliterature, IC (Youndt et al., 2004) and KM (Tanriverdi, 2005) were modeled assecond-order constructs, each including the three first-order constructs identifiedearlier. Thus, the measurement model (Model 1) includes two second-order con-structs and nine first-order constructs (including the three first-order constructs ofIC and KM, respectively). We tested it using confirmatory factor analysis withmaximum likelihood estimates, using AMOS 4.0. Item scores were standardizedprior to confirmatory factor analysis. Correlations among pairs of item residualswere freed based on theoretical considerations. We dropped nine items, shown inAppendix A, due to loadings below .5 or due to cross-loadings. The remaining40 items loaded on their respective first-order constructs as expected. Appendix Aprovides the results.

Several indices were used to assess the fit of the measurement model(Joreskog, 1978; Medsker, Williams, & Holahan, 1994). We examined the ra-tio of chi-square to the degrees of freedom; a ratio below three indicates a good fitfor the hypothesized model. We also examined several indices (including goodnessof fit index, or GFI, and adjusted GFI, or AGFI), for which values above .90 areconsidered good, and values from .85 to .90 are considered acceptable (Medskeret al., 1994). We also examined the root mean-square residual (RMR), for whichlower values indicate better fit and a value below .05 is considered desirable (Fulk,Heino, Flanagin, Monge, & Bar, 2004), and the root mean square error of approx-imation (RMSEA), which should be below .08 for acceptable fit. As shown inAppendix A, all the fit indices are either good or acceptable, indicating that thedata have a good fit with the measurement model.

We found support for convergent validity for all nine first-order constructsand the two second-order constructs. Standardized loadings for the 40 items aresignificant (p < .001), with the lowest t-value being 17.61. Standardized loadingsfor the six first-order constructs leading to the two second-order constructs arelarge and significant (p < .001), with a minimum t-value of 12.86, indicating goodconvergent validity for all the focal constructs (Anderson & Gerbing, 1988). Dis-criminant validity was assessed in three ways (Sabherwal & Becerra-Fernandez,2003). First, 2 difference between the measurement model and a baseline model(with one latent construct) was significant (p .001). Second, the average vari-ance extracted for each first-order construct exceeded .5 and the constructs sharedvariance with every other construct. Finally, the confidence interval for each con-structs pairwise correlation estimate (i.e., + two standard errors) with every otherconstruct did not include 1 (Anderson & Gerbing, 1988).

Table 1 provides summary information about the measures, reliabilities,descriptive statistics, correlations, and square roots of average variance extracted.The standardized alphas for all the measures exceeded .80. For each of the measuresincluded in the confirmatory factor analysis, the composite reliability exceeded .80and the average variance extracted exceeded .50. These results further support themeasures. In addition, we conducted further validation tests for the use of second-order factors to represent IC and KM. These tests, and the associated results, aredescribed in Appendix B.

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.05, one-tailed). The results are consistent with the argument that a learning culture canhelp accumulate organizational knowledge (Nahapiet & Ghoshal, 1998; De Long& Fahey, 2000). The results also indicate that the effect of learning culture onKM is through IC. Thus, IC seems to be the key mediator between organizationalculture and KM and should be included in further research into the relationshipbetween organizational culture and KM (Lee & Choi, 2003).

The results indicate that the learning culture also directly affects innovation.This path was not included in the theoretical model as we expected the effectof learning culture on innovation to be mediated through IC and KM. However,the prior literature does suggest that a learning culture improves organizationalinnovativeness (e.g., Cameron & Quinn, 1999). This emergent path indicates thata learning culture leads to greater innovation, over and above its indirect effectthrough IC. Thus, a learning culture contributes to both IC and innovation.

LimitationsThe above results need to be considered in view of the studys limitations. First,this study is cross-sectional in nature, which may have reduced the importanceof relationships that might take time to occur (perhaps, for example, the effect ofKM on IC). Although there is precedence for using cross-sectional data to testreciprocal effects (e.g., Sabherwal, 1999), a longitudinal study would have beenmore effective for investigating the reciprocal relationship between KM and IC.

Second, the study was conducted using a sample of firms in Taiwan, whichhave distinct cultural traits that have been identified to inhibit knowledge sharing(Hsu, 2006). Further research is needed to examine whether our findings generalizeto other cultural contexts with less salient impediments to knowledge sharing.

Third, theoretical considerations, concerns for parsimony, and limitations ofsecondary data in Taiwan led us to construct a theoretical model that excludes


moderating effects (e.g., firm size on the relationship between KM and innovation)and other potentially important variables, such as organizational structure. Inclu-sion of moderating effects and additional antecedent variables may have providedsomewhat different results. We also acknowledge that firm performance can beaffected by multiple factors that could not all be included in this study. This isnot unusual in field studies where some of the variables affecting a dependentvariable have to be excluded to focus on the constructs of greatest relevance to thestudy. Consistent with prior field studies using survey data, we develop and testour research model from a theoretical perspective in order to test the focal theory.

Finally, the limitations of secondary data further prevented us from devisingindicator-type measures for innovation (e.g., Hess & Rothaermel, 2011), or evendynamic capabilities (e.g., Sirmon & Hitt, 2009) that will offer a complementaryapproach in testing the two constructs as interim outcomes of IC and KM, ashas been attempted for efficiency here in the research. Inclusion of the measuresdeveloped from secondary data may improve the robustness of our findings.

Implications for PracticeDespite the above limitations, the results of this study offer some potentially valu-able implications for practice. First, efficiency does not depend either directly orindirectly on any of the other constructs, including IC, KM, and learning cul-ture. Therefore, managers of firms emphasizing efficiency should be cautious ininvesting in these three knowledge-related aspects, and should specifically seekopportunities (such as systems and routines that enable knowledge reuse) that canenhance efficiency.

Second, our study highlights the importance of IC and a learning culture,especially for firms that are not competing based on efficiency. IC benefits theorganization through improvement in dynamic capabilities and KM, both of whichbenefit the organization by enabling innovation. The emergent results also highlightthe importance of learning culture in attaining innovation. Therefore, the resultsshould encourage managers of firms that are seeking to innovate through investingin IC and a learning culture.

Third, our study indicates that the development of IC and a learning culturemay be more important than investments in KM processes. IC affects KM and dy-namic capabilities, but KM affects neither IC nor dynamic capabilities. A learningculture affects IC and innovation. Therefore, managers should use mechanisms(e.g., manuals, systems) to store individual knowledge (organizational capital),hire and develop high-quality employees (human capital), who are willing to col-laborate with each other (social capital), and promote risk taking and trust (alearning culture). These actions would also indirectly facilitate innovation, KM,and dynamic capabilities.

Finally, even though KM depends on IC, the importance of KM is worthadvising here. KM facilitates innovation, which improves firm performance. Anorganization seeking to establish competitive advantage based on innovation shouldrealize that KM can help achieve this goal. This research has proposed that KMshould include the following processes for it to result in outcomes of innovation:knowledge acquisition, conversion, and application.


Implications for Future ResearchFuture research on knowledge, or IC, and its management (i.e., KM) could benefitfrom this study in at least two important ways. First, our study shows the benefitsof simultaneously examining both IC and KM, and future studies in this areawould benefit from similarly incorporating both aspects. Doing so not only helpsin examining the relationships among these two dimensions but also helps inreducing the likelihood of potentially misleading results that may be obtained ifeither IC or KM is excluded. Simultaneous incorporation of IC and KM in thisstudy enabled us to conclude that IC facilitates KM (support for H1) but KM doesnot facilitate IC (nonsupport for H2). The results suggest that the current stateof IC affects current KM processes, but current KM processes might not affectcurrent IC, possibly because current KM processes might affect future IC overtime. However, these findings may be due to the cross-sectional nature of thisstudy and how IC and KM might evolve; a longitudinal study, incorporating thepotential lagged effects of KM, would help in further examining the relationshipbetween IC and KM. Simultaneously examining IC and KM also enabled us toconclude that a learning culture facilitates IC (support for H6) but, as expected,not KM.

Second, future studies on knowledge and its management would benefit fromadopting a more comprehensive view of the various aspects of performance con-sequences. Instead of examining either the effects on overall firm performance orthe effects on intermediate outcomes such as innovation, efficiency, and dynamiccapabilities, this study incorporated them both. This produced results that questionsome conclusions reached in prior studies where firm performance and intermedi-ate effects were not studied simultaneously. More specifically, the results of thisstudy do not support the expected effect of KM on efficiency (H3b) or the two rela-tionships involving dynamic capabilities: its expected effect on firm performance(H5a), and its expected dependence on KM (H5c). Moreover, the simulatenousincorporation of firm performance and intermediate effects revealed the intuitivelyappealing but unexpected effect of learning culture on innovation. Further researchis needed to better understand whether these results can be generalized, or are dueto the research methods, measures, and samples. Nevertheless, this study indicatesthat future research would benefit from similar simultaneous consideration of firmperformance and intermediate effects.

CONCLUSION

We have proposed and empirically tested a comprehensive model of the mutualrelationship between IC and KM; their effects on firm performance, mediated byefficiency, innovation, and dynamic capabilities; and their dependence on the orga-nizations culture. We have provided some results that are consistent with the priorliterature: IC facilitates KM and dynamic capabilities; KM, a learning culture, anddynamic capabilities facilitate innovation; and innovation and efficiency facilitatefirm performance. However, our study also provides some surprises: neither ICnor KM affects efficiency; KM also does not affect IC or dynamic capabilities;and dynamic capabilities do not directly affect firm performance. Thus, this studycontributes to the literature by providing insights into the relationship between IC


and KM, as well as their consequences. It also raises some interesting questions forfuture research on how organizations benefit from the management of knowledgeand IC.

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APPENDIX A: RESULTS FOR THE MEASUREMENT MODELa

Itemsb,c Factor Loadingsd

Scale 1: Learning Culture (Standardized alpha = .88, Compositereliabilitye = .88)

My company encourages employees to explore and experiment. .696My company encourages employees to ask others for assistance when

needed..736

My company encourages employees to interact with other groups. .813My company clearly states its overall organizational vision and

objectives..764



My company thinks the benefits of sharing knowledge outweigh thecosts.

.710

Senior management in my company clearly supports the role ofknowledge in my companys success.

.718

Scale 2: Intellectual Capital (Standardized alpha = .83, Compositereliability = .92)

Subscale 1: Human Capital (Standardized alpha = .84, Compositereliability = .83)

.831

My companys employees are widely considered competent in theindustry.

.702

My companys employees are creative and bright. .854My companys employees are experts in their particular jobs and

function..788

My companys employees are highly skilled.My companys employees develop new ideas and knowledge.Subscale 2: Social Capital (Standardized alpha = .92, Composite

reliability = .92).863

My companys employees are skilled at collaborating with each otherto diagnose and solve problems.

.877

My companys employees share information and learn from oneanother.

.876

My companys employees interact and exchange ideas with peoplefrom different areas of the company.

.886

My companys employees apply knowledge from one area of thecompany to problems and opportunities that arise in another.

.810

My companys employees partner with customers, suppliers, alliancepartners, etc., to develop solutions.

Subscale 3: Organizational Capital (Standardized alpha = .88,Composite reliability = .88)

.834

Much of my companys knowledge is contained in manuals,databases, etc.

.760

My companys culture (stories, rituals) contains valuable ideas, waysof doing business, etc.

.853

My company embeds much of its knowledge and information instructures, systems, and processes.

.895

My company uses patents and licenses as a way to store knowledge.Scale 3: Knowledge Management (Standardized alpha = .88,

Composite reliability = .93)Subscale 1: Knowledge Acquisition (Standardized alpha = .84,

Composite reliability = .85).852

My company has processes for exchanging knowledge with ourbusiness partners.

.804

My company has processes for acquiring knowledge about newproducts/services within our industry.

.864

My company has processes for acquiring knowledge aboutcompetitors in the industry.

.743

My company has processes for generating new knowledge fromexisting knowledge.

My company uses feedback from projects to improve subsequentprojects.



My company has teams devoted to identifying best practices.Subscale 2: Knowledge Conversion (Standardized alpha = .93,


My company has processes for transferring organizational knowledgeto individuals.

.843

My company has processes for absorbing knowledge fromindividuals into the organization.

.903

My company has processes for absorbing knowledge from businesspartners into the organization.

.827

My company has processes for integrating different sources and typesof knowledge.

.837

My company has processes for replacing outdated knowledge. .818My company has processes for converting competitive intelligence

into plans of action.Subscale 3: Knowledge Application (Standardized alpha = .94,


My company has processes for applying knowledge learned frommistakes.

.881

My company matches sources of knowledge to problems andchallenges.

.916

My company uses knowledge to improve efficiency. .861My company is able to locate and apply knowledge to changing

competitive conditions..923

My company quickly links sources of knowledge in solvingproblems.

.889

Scale 4: Dynamic Capabilities (Standardized alpha = .94, Compositereliability = .94)

My companys employees have developed unique ways ofcollaboration to improve innovative capabilities of the company.

.796

My companys employees are sensitized to environmental changesand respond to them.

.784

My companys employees devote to improving the competitiveposition of the company in the industry.

.822

My companys employees proactively participate in organizationalchange to help the company respond to environmental changes.

.848

My companys employees continually innovate to make knowledgeand capabilities of the company inimitable.

.820

My companys employees continually innovate to rapidly accumulateknowledge assets of the company.

.842

My companys employees integrate different areas of knowledge toimprove innovations in products/services.

.860

My companys employees devote to improving recognition ofcompany name and reputation.

Scale 5: Innovation (Standardized alpha = .85, Composite reliability= .86)

My company develops and produces new products or servicescontinually.

.819

My company gives priority on making efforts to increase the qualityof products or services.

.715


Model 2 d.f.

My company provides variousproducts or services to satisfycustomers various tastes.

.807

My company switches quicklybetween different types ofproducts or services to respondto fluctuations in marketdemand.

.751

Proposed model (three first-orderfactors and two second-orderfactors, each with threefirst-order factors)

1544.76 686 2/d.f. = 2.25; GFI = .88;AGFI = .85; RFI = .91;NFI = .92; CFI = .95;IFI = .95; RMR = .042;RMSEA = .049

Base-line model (one factor) 3772.18 701 Comparison with theproposed model:Difference in 2 =2227.42; Difference ind.f. = 15; p < .001

aN = 533.bThe informants were asked to indicate the degree of their agreement on the items. Thescale for their answers ranged from 1 = strongly disagree to 7 = strongly agree.cDue to cross-loadings or low loadings, nine items were dropped. These items are markedwith .dThe first-order factor loadings reported were from the standardized solution. All t-valuesare significant at the .001 level, with the lowest t-value being 17.61. The second-orderfactor loadings were also from the standardized solution. All t-values are significant at the.001 level, with the lowest t-value being 12.86.eComposite reliabilities of the second-order constructs were derived using the error vari-ances and loadings of the first-order constructs on the second-order construct.

APPENDIX B: FURTHER VALIDATION TESTS FOR IC AND KM

The theoretical model contains two second-order constructs, IC and KM. In ad-dition to establishing convergent validity and discriminant validity of the fulltheoretical model, we verified the dimensionality, convergent validity, and dis-criminant validity of IC and KM constructs by following an established procedure(Tanriverdi, 2005).

We started with the IC construct, for which four models were constructedand compared. Model 1 hypothesizes that a unidimensional first-order factor ex-plains the variances among all items of the scale of IC. Model 2 hypothesizesthree correlated first-order factors: human capital, social capital, and organiza-tional capital. Comparing Model 1 (2 = 569.96, d.f. = 31, 2/d.f. = 18.39,GFI = .796. AGFI = .64, RMSEA = .19) against Model 2 (2 = 86.91, d.f. =28, 2/d.f. = 3.10, GFI = .97, AGFI = .94, RMSEA = .06) suggests the su-periority of Model 2 (2 = 483.05, d.f. = 3, p < .001). This supports mul-tidimensionality of IC, which should contain three dimensions: human capital,social capital, and organizational capital. Further, in Model 2, standardized fac-tor loadings of measurement items for all three first-order factors are significant


(p < .001), supporting convergent validity of IC. In Model 2, pairs of correlationsamong the first-order factors differ significantly from zero, and are below the .90cut-off value (Bagozzi, Yi, & Phillips, 1991). This suggests that each individualfirst-order factor captures unique theoretical content, and supports discriminantvalidity of IC (Anderson, 1987; Bagozzi et al., 1991).

The subsequent comparison examines whether a second-order factor explainsthe patterns of covariance (complementarities) among the first-order factors. Toachieve this, an external criterion variable is used (Venkatraman, 1990; Tanriverdi,2005). Two models including dynamic capabilities as the criterion variable arecompared: Model 3, which examines the direct effects of the three first-orderfactors on dynamic capabilities, and Model 4, which includes a second-orderfactor (IC) representing the first-order factors, as affecting dynamic capabilities.Three criteria were used to compare the two models: (i) fit measures of the twomodels (Venkatraman, 1990); (ii) the target coefficient (T) (Marsh & Hocevar,1985); and (iii) significance of the second-order factor loadings (Venkatraman,1990).

Comparing Model 3 (the first-order model: 2 = 325.44, d.f. = 104, 2/d.f.= 3.13, GFI = .94, AGFI = .91, RMSEA = .06) against Model 4 (the second-order model: 2 = 328.60, d.f. = 106, 2/d.f. = 3.10, GFI = .94, AGFI = .91,RMSEA = .06) suggests the superiority of Model 4 (2 = 3.16, d.f. = 2, p >.1) due to its parsimonious nature (fewer parameters and more degrees of freedom)(Venkatraman, 1990). The literature supports the use of either 2/d.f. < 3.0 (e.g.,Carmines & McIver, 1981) or 2/d.f. < 5.0 (e.g., Kline, 2005) as a cut-off formodel parsimony. All the other fit indices are satisfactory for Model 4. The targetcoefficient (T = 0.99) approximates 1, the theoretical upper limit. This supportsthe second-order factor model as the second-order factor explains 99% of therelationships among the first-order factors (Marsh & Hocevar, 1985). Finally, thestructural path from IC to dynamic capabilities in the second-order factor model ispositive and significant as hypothesized (standardized path coefficient = .91, p .05) due to its parsimonious nature (fewerparameters to be estimated and more degrees of freedom) (Venkatraman, 1990).The target coefficient (T = .98) supports the second-order factor model as thesecond-order factor explains 98% of the relationships among the first-order factors(Marsh & Hocevar, 1985). Finally, the structural path from KM to innovation inModel 4 is positive and significant as hypothesized (standardized path coefficient= .66, p < .001). All second-order factor loadings are significant (p < .001),further supporting the second-order factor model. Together, these results confirmthe reliability, multidimensionality, and convergent and discriminant validity of theKM construct. A second-order construct explains the complementarities amongthe three first-order KM dimensions, as predicted by the theory of KM.

APPENDIX C: MEDIATION TESTS

To further validate the results (e.g., that IC and KM do not directly affect firmperformance), we conducted the mediation analyses described in this appendix.We created a base model that includes all the direct and indirect paths. Thus,the base model included all the hypothesized paths and the following additionalpaths: from learning culture to KM, efficiency, innovation, dynamic capabilities(DC), and organizational performance (OP); from IC to efficiency, innovation,and OP; and from KM to OP. To test each mediated effect, we created a modelconstraining the path from the independent to dependent construct to zero, andthen compared this constrained model with the base model. A significant increasein 2 indicates partial mediation whereas a nonsignificant increase in 2 indicatesfull mediation. We also computed the asymmetrical confidence interval for eachindirect effect with a biased-corrected bootstrap, which is considered more reliablethan the normal distribution assumed by the Sobel test (Mackinnon, Lockwood, &Williams, 2004; Qureshi, Fang, Ramesy, McCole, Ibboston, & Compeau, 2009).If the asymmetrical confidence interval includes zero, it implies that the indirecteffect is not significant, and does not support the presence of mediation, whereasif it does not include zero, it implies that the indirect effect is significant, andsupports the presence of mediation (Shrout & Bolger, 2002; Mackinnon et al.,2004). Overall, this is a stringent test of mediation, is considered an improvementover Baron and Kenny (1986), and is suitable for structural models (e.g., Shrout& Bolger, 2002; Qureshi et al., 2009). The results of these mediation tests areprovided in Table C1 at the end of this appendix.


As can be seen in Table C1, we fixed three paths, one at a time, to constructthree models in order for comparison with the base model. To test the mediatingeffect of innovation on KM OP, the path is set to zero. To test the mediatingeffect of innovation and dynamic capabilities on IC OP, we set the path to zero.To be comprehensive, we also test the mediating effect of innovation on DC OP as IC OP is mediated by dynamic capabilities and then innovation. Whenany of the three paths is set to zero, the pertinent model does not deteriorate as 2does not increase. Thus, in each of the three models, full mediation is generallysupported. However, the more stringent test requires that the indirect effects arealso significant. The fact that some asymmetrical confidence intervals include zeroonly suggests that indirect effects are not necessarily significant. Full mediationcould have not been supported due to the nonsignificant indirect effect of KM onOP, and that of IC on OP. However, with these results, it is safe to conclude thatIC and KM do not directly affect OP, respectively.

Table C1: Test of mediationNested model comparison.

FixedFollowingPaths toZero 2 d.f. GFI AGFI NFI RMSEA 2

Asymmetricalconfidenceintervals

Base (no pathwas fixed)

1715.36 801 .87 .84 .91 .047

Model 1: KM OP

1715.66 802 .87 .84 .91 .047 .3 90%L = .317,90%U = .277

Model 2: IC OP

1715.95 802 .87 .84 .91 .047 .59 90%L = .282,90%U = .216

Model 3: DC OP

1716.37 802 .87 .84 .91 .047 1.01 90%L = .163,90%U = .739

Notes: IC: intellectual capital; KM: knowledge management; DC: dynamic capabilities; OP:organizational performance. The lower (L) and upper (U) levels of the 90% asymmetricalconfidence intervals are reported in the last column.

I-Chieh Hsu received a Bachelors degree in business administration from Na-tional Chengchi University, Taiwan, and a PhD from the University of Manchester,Manchester, UK. He is currently a professor in the Department of Business Admin-istration, National Changhua University of Education, Taiwan. He is also involvedwith a research team that is summoned by National Science Council, Taiwan, inorder to advance research on intellectual capital. He was a visiting scholar at theSchool of Labor and Employment Relations, University of Illinois at Urbana Cham-paign, and at the College of Business Administration, University of Missouri-St.Louis. His research appears in IEEE Transactions on Engineering Management,Journal of Business Research, Journal of Global Information Management, In-ternational Journal of Information Management, International Journal of HumanResource Management, and other journals. He has also coauthored an article inAdvances in International Management, and a management textbook with a groupof international scholars. His current research interests include intellectual capitalmanagement, knowledge management, and diversity management.


Rajiv Sabherwal received his Bachelors degree in engineering (electronics)from Regional Engineering College, Bhopal, India, his post-graduate degree inmanagement from Indian Institute of Management, Calcutta, and his PhD fromthe University of Pittsburgh. He is Walton Professor and Department Chairof Information Systems in the Sam M. Walton College of Business at theUniversity of Arkansas, Fayetteville. He has taught at the University of MissouriSt. Louis, Florida State University, and Florida International University. He wasthe 2009 Fulbright-Queens School of Business Research Chair of knowledgemanagement at Queens School of Business in Kingston, Ontario, and was earliervisiting professor at the National University of Singapore. He has published numer-ous articles in leading journals including Management Science, Decision Sciences,Organization Science, Information Systems Research, MIS Quarterly, and Califor-nia Management Review. He has coauthored textbooks on knowledge managementand business intelligence. His research interest includes strategic alignment, in-formation systems planning, knowledge management, business intelligence, andsocial aspects of systems development. He has served as senior editor at MIS Quar-terly, guest senior editor at Information Systems Research, a departmental editorat IEEE Transactions on Engineering Management, and on the editorial boards forthese and several other journals, including Management Science, Journal of As-sociation for Information Systems (AIS), and Journal of Management InformationSystems (MIS). He was the conference co-chair for the International Conferenceon Information Systems (ICIS) 2010. He is the editor-in-chief of Transactions onEngineering Management, and a fellow of the AIS.

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