clarifying the effect of intellectual capital on performance

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Clarifying the Effect of Intellectual Capitalon Performance: The Mediating Role of

Dynamic Capability

Li-Chang Hsu and Chao-Hung Wang1

Department of Finance and 1Department of Marketing and Logistics Management, Ling Tung University, 1Ling Tung Road, Nantun, Taichung, Taiwan 40852, Republic of China

Corresponding author email: [email protected]

Recent studies suggest a potential relationship between intellectual capital and dynamiccapability in achieving performance. This is unsettling for managers because thesestudies contain little effort to develop a framework for understanding the relationship.To examine this unnerving potential, we develop and test a theoretical model thatexplains how dynamic capability mediates the impact of intellectual capital onperformance. In this study, the scope of intellectual capital includes human capital,relational capital and structural capital. This study examines the pooled data of 242high-technology firms from 2001 to 2008. Results from Bayesian regression analysissuggest that the effect of structural capital on performance is completely mediated bydynamic capability. Furthermore, the findings show that dynamic capability does notcompletely mediate the respective effects of human capital and relational capital onperformance, but does so only partially. These results provide convincing support for theimportance of dynamic capability through accumulating R&D and marketing capabilityover time, thereby enhancing firm performance. The empirical findings and the ensuingdiscussion will be of interest to managers and practitioners.

Introduction

The business environment has already progressedfrom the Industrial Age to the Information Age.Traditional economic theory frequently describesthe basic resources necessary for a firm in termsof the classic assets of land, labour and othereconomic assets (Sullivan, 2000). However, ac-cording to the resource-based view (RBV), afirms resources, particularly intangible ones, aremore likely to contribute to the firms attaining

and sustaining superior performance (Eisenhardtand Schoonhoven, 1996). During the past twodecades, intellectual capital (IC) has been em-braced by most organizations worldwide. ICplays a fundamental role within modern organi-zations and is part of the foundation of businessin the 21st century. Studies have begun toexamine the IC process by which those effectsare ultimately realized (Martinez-Torres, 2006;Rudez and Mihalic, 2007). IC has thus beenidentified as one of the key drivers of firm-level

performance (Teece, 1998; Youndt, Subrama-niam and Snell, 2004). Although the importanceof IC in pursuing performance is known, thespecific means through which IC influencesorganizational performance are still under-researched.

Moreover, the interaction of the external envir-onment with organizational strategy is expected

The authors would like to thank Veronique Ambrosine,associate editor of the British Journal of Management,and two anonymous reviewers for their helpful sugges-tions. We also wish to acknowledge the National ScienceCouncil of the Republic of China, Taiwan, forfinancially supporting this research under ContractNSC 98-2410-H-275-001, and the substantive contribu-tions made by Dr Shyh-Rong Fang.

British Journal of Management, Vol. *, ** (2010)

DOI: 10.1111/j.1467-8551.2010.00718.x

r 2010 The Author(s)British Journal of Management r 2010 British Academy of Management. Published by Blackwell Publishing Ltd,9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA, 02148, USA.
mailto:[email protected]:[email protected]


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to be related to performance. To maximizeperformance, managers need to pursue competi-tive strategies that best match the conditions ofthe external environment. In other words, man-agers perceptions of the external environmentare expected to affect a firms strategy. Therefore,

a firms strategy must be to deploy its resourcesto seize opportunities in the market. Dynamiccapability (DC) offers a bridge that debates inthe strategy field proposing either an RBV of thefirm or the emerging discourse surrounding theexternal dynamic business environment. Whilethere is a wealth of literature on IC (Batjargal,2007; Bontis, 1999; Bozbura, 2004; Bukh, Larsenand Mouritsen, 2001; Das, Sen and Sengupta,2003; Edvinsson, 1997; Fincham and Roslender,2003; Guthrie, 2001; Mayo, 2000; Nahapietand Ghoshal, 1998; Nielsen, 2006), research

incorporating DC into IC is scant. Existing ICstudies mainly focus on ascertaining their impactand consequently their business value (Moonand Kym, 2006), but few studies utilize atheoretical focused approach to understand howDC mediates the impact of IC on firm-levelperformance.

Drawing on previous studies related to dy-namic theories (Teece, Pisano and Schuen, 1997;Winter, 2003), we posit an alternative mechanismfor the ICperformance relationship whereby DCmediates the effect of IC on performance.

Organization learning theory provides a concep-tual framework for hypothesizing the mediatingrole of DC in the relationships between IC andperformance (Brown and Duguid, 1991; Fiol andLyles, 1985; Hong, Easterby-Smith and Snell,2006). Cyert and March (1963) were the first topropose that an organization might be able tolearn in ways that are distinct from the accumu-lated learning of individuals. They built theirviews on a model of decision-making within firmswhich emphasizes the role of rules and proce-dures in response to external shocks. This

suggests that learning plays a significant role inthe creation and development of DC. Eisenhardtand Martin (2000) and Zollo and Winter (2002)also argued that learning is at the base of DC,and guides its evolution.

DCs are organizational routines that canaccumulate knowledge via learning processes(Nelson and Winter, 1982). Previous studies haveposited that DCs exist in special operatingroutines and arise from learning (Argyis and

Schon, 1978; Huber, 1991). Argote (1999) identi-fied the path of DCs as being more accuratelydescribed as a learning mechanism that guidesknowledge creation. Using the perspective oforganizational learning, we posit that organiza-tional learning mechanisms are important in

understanding the capability firms have and willhave in meeting and addressing the challengesand changes in their environment. More specifi-cally, DC contributes to firms IC to handlechanging situations. From a dynamic perspective,successful performance depends on consistentand competitive behaviour that relies on a firmsability to learn and adapt by building andexploiting IC by DC. Over time, this can movethe firm in the required direction, toward anefficient response to dynamic market conditions.

This paper develops a model to explain how a

firms performance is influenced by IC throughDC, which serves as a firms managerial interfaceto the external environment. This framework is amajor contribution to the literature on strategicmanagement because it provides a theoreticalbasis for cumulative additions to our under-standing of the concepts of IC and DC. Thepaper is organized as follows. First, we review theliterature relating to the constructs of thetheoretical model. We aim to contribute to thefield of strategic decision-making by providing adirected application of resource and capability

dimensions and by examining the mediating roleof DC in firm-level performance. Second, wedevelop a series of hypotheses which constitutean integrated theoretical framework that offers aricher and more formalized account of therelationships than have been provided in theliterature to date. We next take the high-techindustry as an empirical example by using pooleddata. Finally, we conclude with a discussion onsome implications, limitations and directions forfuture research derived from the findings of thispaper. Figure 1 presents the theoretical model

proposed to explain the underlying processesthrough which investments in IC lead to DCaccumulation and thus to improved performance.

Explaining intellectual capital effects

Intellectual capital

Edvinsson and Malone (1997) divide IC intohuman capital and structural capital. The former

2 L.-C. Hsu and C.-H. Wang

r 2010 The Author(s)British Journal of Management r 2010 British Academy of Management.


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is grounded on the knowledge created by andstored by an organizations employees, while thelatter is based on the embodiment, empowerment

and supportive infrastructure of human capital.Structural capital is further divided into organi-zational capital (knowledge created by and storedin an organizations information technologysystems and processes that speeds the flow ofknowledge through the organization) and custo-mer capital (the relationships that an organiza-tion has with its customers).

In recent years, although IC has captured theinterest of many researchers and practitioners, itis still defined in various ways. Many definitionsof IC were proposed as IC matured and are still

being used and discussed in current studies(Carson et al ., 2004; Reed, Lubatkin andSrinivasun, 2006). Klein, Crawford and Alchian(1998) argue that IC is knowledge, expertise andassociated soft assets, rather than their hardphysical capital. Sullivan (2000) posits that ICbasically constitutes knowledge, lore and innova-tions. A list of definitions of IC proposed byresearchers is shown in Table 1. Followingrelevant research, we define IC as the storedknowledge possessed by an organization, which istacit knowledge, personal knowledge possessed

by employees and available to network relation-ships through interaction (Mouritsen, Larsdenand Bukh, 2001).

Although IC and its defining components havebeen made explicit, which helps understand thenotion of IC, there are a growing number ofmodels for institutionalizing IC at the firm level(Roos and Roos, 1997). Recent research con-ducted by Reed, Lubatkin and Srinivasun (2006)concludes that IC can be divided into human,

organizational and social capital. Zerenler, Hasi-loglu and Sezgin (2008) create a similar distinc-tion, in which IC has three components:

employee capital, customer capital and structuralcapital. Although earlier researchers may notagree on the precise categorization and shape ofIC, there is broad consensus that it containshuman capital (HC), relational capital (RC) andstructural capital (SC) (Edvinsson and Sullivan,1996). Following this consensus we integratepreviously used elements of IC and assess IC interms of HC, RC and SC.

There are some significant differences betweenthese categories. HC can leave the firm wheneverit desires since the firm does not own it. SC, on the

other hand, is knowledge that has been convertedinto something owned by the firm (e.g. a patent).The implementation of SC relies on HC and thequality of HC determines the quality of SC. Fromthe organizations point of view, RC is differentfrom HC since the organization is concerned withnetwork relationships (i.e. those relationships thatare established and maintained by related part-ners). As external RC is formed through organi-zational internal HC, the RC may be theimportant relationship-specific assets of an orga-nization. Organizational HC can influence the

formation and maintenance of RC.

Human capital

HC is at the heart of IC and it is defined as thecombined knowledge, skill, innovation and abil-ity of employees (Bontis, Keow and Richardson,2000). Similarly, Wright, McMahan and McWil-liams (1994), working from an RBV, argue thatin certain circumstances sustained competitive

Intellectual capital

Human capital

Structural capital

Relational capital

Dynamic

capabilitiesPerformanceH2a, b, c H3

H1a, b, c

Control variables

Firm size

Leverage

Firm age

Export intensity

Figure 1. Theoretical model

Clarifying the Effect of Intellectual Capital on Performance 3



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advantage can accrue from a pool of HC. TheRBV theory holds that organizations evaluate the

strengths and weaknesses of their resources andthen select a strategy that is achievable. HC, oneof the underlying strategic resources, is bothsupportive and necessary for success since em-ployees knowledge and skill are essential intodays fast-paced, changing competitive climate(Subramaniam and Youndt, 2005). The knowl-edge and skill of individuals is an area addressedby HC theory. HC theory maintains that knowl-edge provides individuals with increased cogni-

tive abilities, leading to more productive andefficient activity (Davidsson and Honig, 2003). It

follows that capability addresses whether or notindividuals have the necessary levels and combi-nations of knowledge and skill to complete thetasks that they are responsible for (Hitt et al.,2001). Organizations specializing in advancedtechnologies need individuals who are knowl-edgeable, with excellent problem-solving skill andthe ability to make effective decisions.

Furthermore, HC theory addresses the worthof an organizations human resources based in

Table1. Definitions of intellectual capital

Authors Definition

Bassi (1997) All types of organizationally relevant knowledge and its basic components are HC, SC

and customer capital

Bontis (1999) Encompassing HC, SC and RC

Booth (1998) The ability to translate new ideas into products or services

Bradley (1997) The ability to convert invisible assets such as knowledge into resources that createwealth, not only within corporations but within a nation

Brennan and Connell (2000) Can be thought of as the knowledge-based equity of a company

Brooking (1997) The difference between the book value of the company and the amount of money

someone is prepared to pay for it

Choong (2008) IC has been defined to include expenditures on advertising (marketing), training, start-

up, R&D activities, human resource expenditures, organizational structure, and values

that come from brand names, copyrights, covenants not to compete, franchises, future

interests, licences, operating rights, patents, record masters, secret processes, trademarks

and trade names

Edvinsson and Malone (1997) The procession of knowledge, applied experience, organizational technology. Customer

relationships and professional skills that provide Skandia with a competitive edge in the

market

Edvinsson and Sullivan (1996) Knowledge that can be converted into value

Harrison and Sullivan (2000) Knowledge that can be converted into profitHeisig, Vorbeck and Niebuhr (2001) IC is valuable, yet invisible

Kim and Kumar (2009) IC as the mixture of human, structural and relational resources of an organization

Mouritsen, Larsden and Bukh (2005) IC mobilizes things such as employees, customers, information technology, managerial

work and knowledge. IC cannot stand by itself as it merely provides a mechanism that

allows the various assets to be bonded together in the productive process of the firm

Pablos (2003) The difference between the companys market value and its book value. Knowledge-

based resources that contribute to the sustained competitive advantage of the firm from

IC

Petty and Guthrie (2000) Indicative of the economic value of two categories (organization and HC) of the

intellectual asset of a company

Rastogi (2003) The holistic or meta-level capability of an enterprise to coordinate, orchestrate and

deploy its knowledge resources towards creating value in pursuit of its future vision

Roos and Roos (1997) The sum of the hidden assets of the company not fully captured on the balance sheet,

and thus includes both what is in the heads of organizational members and what is left in

the company when they leave

Stewart (1997) Intellectual material knowledge, information, intellectual property, experience that

can be put to use to create wealth

Subramaniam and Youndt (2005) IC is the sum of all knowledge stacks firms utilize for competitive advantage

Sveiby (1998) Composed of individual competence, internal structure and external structure

Zerenler, Hasiloglu and Sezgin (2008) Total stocks of all kinds of intangible assets, knowledge, capabilities and relationships

etc. at employee level and organization level within a company, and can most commonly

be split into three types: HC, SC and RC




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the context of performance (Brown, Adams andAmjad, 2007). HC focuses on the value that isadded to an organizations business, ultimately interms of profitability, solely by its stock ofhuman resources (Dakhli and de Clercq, 2004).Following Colombo and Grilli (2005), companies

with greater HC (i.e. higher education or skill)are likely to have better entrepreneurial judge-ment. As long as HC continues to be developed,staff can improve their job performance andultimately improve the firms performance (Hsu,2007). Dulewicz and Herbert (1999) confirm thatsuccessful strategy must be strongly focused onthe competences of human resources, which arerelated to the qualities that individuals possess.We can expect that the higher a firms stock ofHC the more successful the firm will be and thegreater its competitive advantage will be. HC

increases as staff accumulate specialized informa-tion, skill and know-how. This allows them tocommunicate efficiently and effectively, whichreduces decision-making errors, thereby enhan-cing quality and improving performance(Luthans and Youssef, 2004). Thus, for anorganization, HC will be positively related to itsperformance.

Relational capital

Conceptualized for over a decade (Bontis, 2002;

Canibano, Garcia-Ayuso and Sanchez, 2000;Pablos, 2003; Reed, Lubatkin and Srinivasun,2006; Sanchez, Chaminade and Olea, 2000), RChas evolved to be described as the basis forcollective action in communities (Burt, 1992;Granovetter, 1973). At its core, RC is concernedwith the mobilization of resources through asocial structure. RC is defined as the organiza-tions implicit set of available resources andongoing relationships implemented through in-teractions among individuals or organizations(Kostova and Roth, 2003; Shipilov and Danis,

2006). Significantly, this definition means that thecharacteristics of RC will vary both with therelationships under review and the resources thatcan be employed through these relationships. Anorganizations RC draws on the tangible linksbetween organizational staff and members ex-ternal to the organization (Burt, 1997). Anorganizations RC enhances the quality of itsmembers and the richness of information ex-changed among exchange partners. RC is epito-

mized in how it facilitates interactions and theexchange of information. An organization cangain important information or support from itssuppliers, clients or other external partners.

The extant RC literature has argued that, asthe level of interaction between partners in-

creases, organizational routines are established(Nelson and Winter, 1982), and thus the invest-ment in relation-idiosyncratic assets and the levelof bilateral dependence also increase (Teece,1986). Social exchange theory (Macneil, 1980)supports the rationale of this argument. It viewsinter-organizational relationships in the contextof a social structure whereby firms are inter-dependent and rely on reciprocation. Thus,relationships are embedded in a social structure(Granovetter, 1985). Social exchange concen-trates on the relationship rather than the

transaction so that over time a complex personaland organizational structure evolves betweenorganizations.

The key process of the relationship in socialexchange is trust (Morgan and Hunt, 1994). Thisprocess moderates the impact of power anddetermines the perception of fairness in anexchange relationship. A prior history of coop-eration between organizations has been found toreduce the exchange hazards (Deeds and Hill,1998). RC established through prior exchangescan substitute for explicit contracts (Dyer and

Singh, 1998). Through repeated interactions theparties appear to develop trust in one anothersuch that they may no longer need to rely onformal contracts to ensure performance (Zaheerand Venkatrman, 1995). Experience with atrustworthy partner is said to raise collaborativeexpectations and stimulate learning as the rela-tionship evolves (Doz, 1990). In other words, apositive effect due to relationship exchangeoccurs.

Organizations choosing a relationship perspec-tive as their strategic approach almost inevitably

have to focus on the relationship with theircustomers and other stakeholders. Customerrelationships are considered by many as the mostimportant component of RC (Duffy, 2000). Asthe relational literature suggests, involving cus-tomers who have had close and embeddedrelationships with a firm showed improved firmsperformance (Bonner and Walker, 2004). Manymanufacturing firms are becoming involved incloser relationships with their suppliers in order




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to utilize their skills, capabilities and informationto develop new products faster and at less cost sothat close relationships with suppliers have apositive influence upon firms performance (Wal-ter, 2003). Reuer, Zollo and Singh (2002) arguedthat repeated partner-specific relationships have a

stronger effect on accumulated knowledge thanrepeated general experience relationships. There-by, we postulate that RC has a positive effectupon performance.

Structural capital

SC is conventionally used to refer to the processesand procedures that are created by, and stored in,a firms technology system that speeds the flow ofknowledge through the organization (Carsonet al., 2004; Youndt, Subramaniam and Snell,

2004). The above definition is different from theapproaches previously taken in the strategicmanagement literature (e.g. Gibson and Birkin-shaw, 2004; Kang and Snell, 2009). Gibson andBirkinshaw (2004) placed organizational capitalin contextual ambidexterity by building a set ofsystems and processes that collectively define acontext that allows the meta-capabilities ofalignment and adaptability to sustain firm-levelperformance. Kang and Snell (2009) classifiedorganizational capital into two alternativesforms: mechanistic versus organic. These alter-

native forms of organization capital have differ-ent effects on acquisition and integration ofknowledge within a firm.

We conceptualize SC in terms of organiza-tional processes and information systems. Orga-nizational process refers to the manner in whichpeople actually use the information or knowledgeresources available to them in the workplace(Hobley and Kerrin, 2004). Once an organizationobtains a unique routine or process for perform-ing tasks and activities, it becomes a potentialsource of firm-level performance. Information

systems, the second component of SC, refer to theinformation technology used in managing knowl-edge. Information exchanges made as part ofthese established structures and processes thustend to follow well-established and codifiedguidelines. Consequently, knowledge intrinsic toSC tends to accumulate and be utilized in anestablished way (Brown and Duguid, 1991). It isalso reflected in an organizations customarystructure and processes. Thus, SC provides an

environment that enables organizations to createand leverage knowledge.

If an organization has poor procedures andsystems by which to track its actions, theorganizations performance will not achieve itspotential (Widener, 2006). Conversely, an orga-

nization with strong SC will have a supportiveculture that encourages employees to try andlearn new knowledge (Florin, Lubatkin andSchulze, 2002). Recent research suggests thatorganizations operation processes and the orga-nizational commitment of sufficient resourceshave an important impact on performance (DeBrentani and Kleinschmidt, 2004). Moreover,SC, such as operations, procedures and theprocesses of knowledge management, propelsorganizations value creation activities whichhave a positive effect on their performance. Since

organizations are increasingly employing ad-vanced technologies to compete in todayseconomy, they should take great care to properlymanage SC so that performance is achieved. Weposit that investments in SC can be expected toimprove performance. Hence, we hypothesize:

H1a: HC is positively related to performance.

H1b: RC is positively related to performance.

H1c: SC is positively related to performance.

Intellectual capital and dynamiccapability

That IC improves performance is not a novelproposition, and the contribution of the presentstudy lies not in testing the hypotheses notedabove but rather in exploring whether DCprovides a mechanism for explaining theseeffects. We are not aware of any research linkingthe three IC components to DC, but there are

conceptual reasons to expect a relationship. Thislinkage is frequently seen as a response to thequestion of how and why some firms appear tocreate and sustain competitive advantage.

Dynamic capability

The study of DC, also known as core capability(Collis and Montgomery, 1998), organizationalroutine (Cohen and Levinthal, 1989), core




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competence (Collis, 1994; Hamel and Prahalad,1989), architectural competence (Crowston, 1997)and absorptive capability (Cyert and March,1963), has been ongoing since the early 1990s.Table 2 lists the definitions of DC providedby various researchers. The wide array ofstudies across disciplines has created a broadrange of definitions and emphases, as well as awide of range of propensities that can be

considered to be DC. These studies make distinctcontributions; but there is a good deal of overlapin ideas.

DC emerged as a complement to the RBV in anattempt to explain competitive advantage inrapidly changing environments. There is a greatdeal of concern with dynamism, which seeks toaddress how competences are renewed over timeso as to provide innovative responses to marketchanges. Some authors have termed DC to be as

vague and tautological as absorptive capability(e.g. Dutta, Narasimhan and Rajiv, 2005). How-ever, DC differs from the more familiar term ofabsorptive capability (Cohen and Levinthal,1990). Zahra and George (2002) defined absorp-tive capability as a set of organizational routinesand processes by which firms acquire, assimilate,transform and exploit knowledge to produce adynamic organizational capability. Absorptive

capability is an organizations ability to under-stand new external knowledge, assimilate it, andapply it to commercial ends (Lane, Salk andLyles, 2001). The term DC points to the conceptof the capacity to renew competences so as toachieve congruence with the changing businessenvironment (Teece, 1998). We here distinguishDC from absorptive capability since DC isconsidered to be the systematic change of effortsand the cumulative effort of capabilities over

Table2. Definitions of dynamic capability

Authors Definition

Ambrosini, Bowman and Collier

(2009, p. 10)

There are three levels of dynamic capabilities related to a managers perceptions of

environmental dynamism. At the first level we find incremental dynamic capabilities . . . ,

at the second level are renewing dynamic capabilities . . . , at the third level are

regenerative dynamic capabilities

Collis (1994) The capability to develop the capability that innovates faster (or better), and so onEisenhardt and Martin (2000, p. 1107) The organizational and strategic routines by which firms achieve new resource

configurations as markets emerge, collide, split, evolve and die

Griffith and Harvey (2001, p. 597) The creation of a difficult-to-imitate combination of resources, including effective

coordination of inter-organizational relationships, on a global basis that provides a firm

with a competitive advantage

Helfat (1997) The subset of competences/capabilities which allow the firm to create new products and

processes and respond to changing market circumstances

Helfat et al. (2007, p. 1) The capacity of an organization to purposefully create, extend or modify its resource

base

Lee, Lee and Rho (2002, p. 734) A source of sustainable advantage in Schumpeterian regimes of rapid change

Macpherson, Jones and Zhang

(2004, p. 162)

The ability of managers to create innovative responses to a changing business

environment

Nielsen (2006, p. 61) An extension of the RBV where the firm is conceived as a collection of resources, e.g.

technologies, skills and knowledge-based resourcesStahle (2008, p. 165) A learned pattern of collective activity through which the organization systematically

generates and modifies its operational routines in pursuit of improved effectiveness

Teece (2007) Difficult-to-replicate enterprise capabilities required to adopt to changing customer and

technological opportunities

Teece, Pisano and Schuen

(1997, p. 516)

The firms ability to integrate, build and reconfigure internal and external competences

to address a rapidly changing environment

Wang and Ahmed (2007, p. 35) A firms behavioural orientation constantly to integrate, reconfigure, renew and recreate

its resources and capabilities, and upgrade and reconstruct its core capabilities in

response

Zahra, Sapienza and Davidsson

(2006)

The processes to reconfigure a firms resources and operational routines in the manner

envisioned and deemed appropriate by its principal decision-makers

Zollo and Winter (2002, p. 340) A learned and stable pattern of collective activity through which the organization

systematically generates and modifies its operating routines in pursuit of improved

effectiveness




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time. In contrast, absorptive capability can beregarded as a static theory because it addressesthe fundamental issues of firms capability toacquire new and external knowledge and toassimilate such knowledge with existing andinternal knowledge rather than accumulating it.

Eisenhardt and Martin (2000) argue that, whilemuch of the strategy literature is vague on thenature of DC, there are a number of specificexamples from other research areas. Theseinclude product development routines, strategicdecision-making routines and resource-allocationroutines. Routines refer to stable patterns ofbehaviour that characterize organizational reac-tions to variegated internal or external stimuli.Routines seek to bring about desirable changes inthe existing set of operations. For instance, adecision is made to upgrade the R&D process;

many predictable and interrelated actions areinitiated which will eventually conclude with thelaunch of the new R&D system. In this case,R&D routines are regarded as constitutive of DCand enhance future performance. The primaryrole of an organization, Grant (1996) argues, is todevise and establish routines that achieve knowl-edge integration.

The concept of DC was first introduced byTeece and Pisano (1994) and Teece, Pisano andSchuen (1997) who asserted that in a dynamicenvironment a firms competitive advantage will

rest on the firms internal routines that enable thefirm to renew its stock of organizational cap-abilities. DC can therefore be perceived as theroutines in an organization that guide andfacilitate the development of the organizationcapabilities (Eisenhardt and Martin, 2000). Fol-lowing this thinking, in this paper we adopt thedefinition of DC by Teece, Pisano and Schuen(1997) as the processes for reconfiguring anorganizations resources and operational routinesin response to the changing environment. Exam-ples of DC are R&D and marketing capabilities.

Firms with interrupted past investments inR&D processes may have weaker knowledgeendowment and consequently a more limitedassimilative capability over time. In contrast,firms with a consistent increased effect in devel-oping technological know-how over time maygain a strategic competitive advantage over theircompetitors who show weak commitment toR&D capability. An overall marketing capabilitycan satisfy the current and future needs of

customers who typically require persistent andtimely investments in marketing. A firms historyof past investments in marketing can havecontinued economic value for the firm over timebecause these investments help the firm accumu-late new knowledge more efficiently. Therefore,

R&D activity and marketing activity are relateddirectly to DC creation processes. This techniqueis also applied in the managerial literature (e.g.Kor and Mahoney, 2005; Thornhill, 2006).

Mediating role of DC

It is important to realize that intangible resourcesalone are not enough to create a firm-levelperformance; they need to be leveraged throughcapabilities (Szulanski, 1996). Indeed, capabilitiesare the transformational process by which

resources are utilized and converted into anorganizations output (Dutta, Narasimhan andRajiv, 2005). We contend that resources are thesource of an organizations capabilities, andfurthermore, that capabilities are the main sourceof its performance (Grant, 1991). Thus, it hasbeen recognized that the utilization and deploy-ment of resources working in combination withcapabilities can improve a firms performance.

HC and DC

We argue that DC mediates the effect of HC onperformance. The RBV focuses on organizationaldecision and suggests that such decisions aretaken within organizational boundaries. Sincethese decisions are taken by organizationalmanagers, the decisions referred to are the oneswhich help organizations to deal with theirenvironment better. An organizations difficultyin sustaining itself presents numerous choices,and the organization must then determine whichof the choices will suit it best (OShannassy,

2008). This choice is contingent upon theenvironmental dynamics. In this situation, DCis a framework which suggests how an organiza-tion, especially a high-tech firm in a turbulentenvironment, can achieve sustained competitiveadvantage, and even enhance performance. Thefocus of DC is the development of managementcapability and the combination of linked HC andperformance in such a way that they can functionin a rapidly changing environment. This attempt




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to address such a development is to address themediating role of DC.

The extant literature, however, is not clearabout how DC mediates the impact of HC onperformance. HC theory posits that people are avaluable organizational resource, and organiza-

tions should have strategies to recognize thatthese people possess valuable skills which con-tribute to the long-term deployment of competi-tive capability (Collis and Montgomery, 1998;OReilly and Pfeffer, 2000). Employees know-how, as an essential part of HC, is perceived asone of the most valuable resources associatedwith firms competitiveness. Substantial work bymany researchers (Lepak and Snell, 1999; Muel-ler, 1996) has discriminated between valuable andunique HC on the one hand and organizationaleffectiveness on the other. In other words, based

on the DC view, an organization needs to ensureHC that leads to competitive advantage isconstantly updated and altered in a way thatother competitors are unable to imitate it; suchHC is hence dynamic in nature (Skaggs andYoundt, 2004). HC is not independent ofenvironmental context and needs to be seen inlight of the organizations continuous attempt toadapt itself to the ever-changing environment(Chadwick and Dabu, 2009). More specifically,capability may fail to be accumulated and re-created by poorly performing companies because

of their lack of endowment in HC. Hence, wepropose that DC mediates the effect of HC onperformance.

RC and DC

To our knowledge, RC is a jointly generated assetin an exchange relationship that cannot begenerated by either firm in isolation and can onlybe created through joint relation-specific assets(Rocks, Gilmore and Carson, 2005). Otherwise, itwould be easy for firms to switch partners with

little penalty when other partners offer virtuallyidentical products (Ulaga, 2003). Asanuma (1989)was among the first to point out how the relation-specific skills developed between suppliers andautomakers generate competitive capabilities.Similarly, Dyer (1996) pointed to a positiverelationship between relation-specific investmentand innovating performance in automakers andtheir suppliers. Additionally, Saxenian (1994)found that Hewlett Packard and other Silicon

Valley firms effectively redeploy and reconfiguretheir resources by developing long-term partner-ships with suppliers. These studies indicate thatDC generated through RC investments is realizedby greater relation-specific assets.

However, RC also has a number of costs and

risks, Establishing RC requires investments oftime and other resources, and without mainte-nance relationships may decay over time (Burt,2002). While strong relationships provide soli-darity benefits that facilitate the pursuit ofcommon goals, they may also result in group-think (Janis, 1982). These negative effects of long-term RC are called their dark side. Previousstudies have identified the role of the dark side inlong-term relationships (Grayson and Ambler,1999). This dark side consists of a loss ofobjectivity and opportunism. A loss of objectivity

occurs when the partners perception of anorganization becomes stale or they are too similarin their thinking (Moorman, Zaltman andDeshpande, 1992). This similarity in thinkingcan result in overly similar behaviour within anorganization and thus a lower innovativenessthat further decreases performance. Opportunismis generally defined as taking advantage ofopportunities with little regard for principles.Opportunism appears to have a negative impacton performance, as shown by Rindfleisch andHeides literature review (1997).

With knowledge of the dark side of RC, DCcan facilitate the unpacking of those problemswith existing organizational resources. We believethat the potential to create organizational com-petitive advantage is dependent not only on itsrelationship with other external partners but alsoon its development of DC (Dyer and Singh,1998). This is especially true in a dynamicenvironment where an organization must dosomething unique to deal with the dark side ofRC. By developing long-term capability deploy-ments in the value chain, organizations are more

willing to face the problem of the dark side of RCin a fiercely dynamic environment (Westerlundand Svahn, 2008). In line with this reasoning, thekey features establishing RC are the necessity oforganization resources and operational routinesreconfiguring, in return for the benefits ofimproved performance and joint value creation(Zajac and Olsen, 1993). Thus, DC tends to bethe relationship spanners that collectively shapeand are inevitably influenced by the RC in




10/27

network relationships. In other words, DC is alsocreated by an inter-organizational network thatcontains greater resources, knowledge and rela-tional idiosyncratic assets (Mowery, Oxley andSilverman, 1996).

SC and DC

SC is the knowledge that has been captured by thefirm and embedded in the organization throughorganizational routines, practices and processes(Jansen et al., 2009). Furthermore, Schroeder,Bates and Junttila (2002) provide empiricalevidence that SC is a critical strategic resourcefor organizations. SC is codified, and its creation,preservation and enhancement occur throughrepetitive activities (Nelson and Winter, 1982).Such codification is manifested in organizations

that use it to retain and accumulate knowledge.Information exchange made as part of theseestablished structures and resources thus tendsto follow well-established and codified guidelines.Consequently, SC tends to be accumulated andutilized in established ways (Moran, 2005).

However, an organizations exposure to avariety of new and overlapping environmentalchallenges influences its performance. Ratherthan relying on established ways for problem-solving, organization requires questioning pre-vailing resources and looking for fundamentally

different solutions to existing problems. Exposureto diverse resource domains enlightens organiza-tions about new ways by which existing problemscan be solved (Rosenkopf and Nerkar, 2001).Organizations consequently begin to question thepromises behind prevailing organizational pro-cesses and systems and broaden their repertoiresof problem-solving approaches, thereby increas-ing their likelihood of deploying DC (Zahra,Sapienza and Davidsson, 2006). If an organiza-tion is viewed as a bundle of resources, DCunderlies the functions of transforming organiza-

tional resources into performance in such formsas organization processes and systems thatdeliver value superior to that of competitors;such transformation is implemented in a swift,precise and creative manner in line with environ-mental changes.

Barney, Wright and Ketchen (2001) arguedthat the capabilities to change quickly in thedynamic market are costly for other competitorsto imitate and thus can be a source of sustained

competitive advantage, further improving perfor-mance. Accordingly, we posit that SC per se maynot be the source of a firms performance; and itis only possible if it is applied sooner and moreastutely than competitors to create capabilityconfigurations. Therefore, we argue that the

ability to apply resources sooner and moreastutely is, indeed, the major function of DC.Thus, we contend that DC is the mediatorbetween SC and performance. Accordingly, weposit the following hypotheses:

H2a: DC mediates the relation between HCand performance.

H2b: DC mediates the relation between RCand performance.

H2c: DC mediates the relation between SC andperformance.

Effects of dynamic capability

Although the exact relation between a firmsinternal capability and its external environmentremains unclear (Zajac, Kraatz and Bresser, 2000),a firms capability is usually influenced by theexternal dynamic environment. As firms analysetheir capabilities, they are usually eager to under-

stand their advantages over competitors. Thecommonly employed framework, which seeks toexplain the competitive capabilities of a firm by theapplication of resources developed in a specifictime period, tends to regard a firms capabilities aspassive responses to the external environment.However, a firm confronted with a competitivecrisis will need to utilize the firms accumulatedcapabilities to deal with it. In explaining such aphenomenon, in place of a static strategy, what isneeded is an analysis that examines the dynamicstrategy, which may be applied to exploit a firms

DC. Thus, DC enhances the firms capability toface a fierce external environment.

Although firms may be confronted with manythreats emerging from a dynamic environment, atthe same time, many opportunities are created forgrowth and profitability (Utterback, 1994). Con-sequently, firms in a dynamic environment needto develop many new products to secure perfor-mance. However, exploiting these opportunitiesrequires strong and patient DC for R&D invest-




11/27

ments as well as continuous innovation (Blonigenand Taylor, 2000). Large amounts of R&Dinvestments accumulated by DC to maintainexcellent research capabilities and state-of-the-art facilities are especially important for firms tobuild their technological competences (Pike,

Roos and Marr, 2005). Continuous R&D activ-ities accumulated by DC also ensure the controlof key knowledge and allow the firms to buildproprietary research platforms that lead to futuresuccess. Thus, when firms are heavy and long-term investors in R&D activities, it is expectedthat this will also result in long-term improve-ments in performance (Lantz and Sahut, 2005).

Similarity, once a firms marketing strategy hasbeen established, its managers must decide how theavailable resources should be allocated. Of course,the major objective of this strategy is to attract

their target markets, establishing the competitiveposition of their product within these markets, andto generate cash flow from each product entry. Inorder to achieve these goals, a firm typicallyrequires persistent investment in marketing activityover a long period. Especially from a relationship-based marketing viewpoint (Hunt and Morgan,1995), a firms history of past marketing invest-ment can have continued economic value for thefirm both in the present and the future because thisinvestment helps the firm retain stronger customerrelationships, which are important for profitability

and paramount for the future direction of business(Rauyruen and Miller, 2007). Accordingly, we inferthat firms may be differentiated in DC regardingR&D capability (Helfat, 1997) and marketingcapability (Deeds, DeCarolis and Coombs, 1999;Griffith and Harvey, 2001), which are associatedwith firm-level performance. This leads to thefollowing hypothesis:

H3: The possession of dynamic capabilities forR&D and marketing is positively related to theachievement of performance.

Method

Data and sample

High-tech industries are identified according tothe classification suggested by Hall (1994) andChandler (1994) according to the researchintensity of the industries and an informalassessment of those industries that are likely to

grow faster. Therefore, this study examines thehigh-tech industries in Taiwan. According to areport in Spring 2006 by the World Semiconduc-tor Trade Statistics, Taiwanese integrated circuitfoundries and integrated circuit packagingachieved the highest sale growth rates for that

industry worldwide. Taiwanese high-tech indus-tries have overall consistently achieved significantaccomplishments, making Taiwan the thirdlargest producer of semiconductors. Taiwanesehigh-tech firms, however, face fiercely competi-tive rivals in other Asian countries such as Koreaand Japan, and so the dynamic strategy of itshigh-tech industry has emerged as an imperativeissue for Taiwan. The important role of Taiwa-nese high-tech industry in the world economydemonstrates that this sample is suitable for theissue of this study.

The top high-tech firms, ranked by companyassets, were extracted from 2001 to 2008. Theobservation of this time period could reflect theinteractions of intangible assets and firmsperformance. Although the period 20012008may be perceived as not enough long to measureDC, it may still account for the relationshipbetween IC and performance.

The original observations of 300 firms werematched with firm-level data from the TaiwanEconomic Journal, which annually compiles a listof firms financial reports. These lists are well

received in the professional economic and finan-cial communities and various indexes of thesesurveys have been used to support numerousresearch projects (Chen and Huang, 2006; Chuet al., 2006; Hsieh, Kim and Yang, 2009; Ke,Chiang and Liao, 2007; Liu, Tseng and Yen,2009; Peng and Fang, 2010). However, manyfirms did not report the type of information werequire in this study, and those with firm-levelinformation missing from the database wereeliminated from the sample. The final data setconsisted of 242 valid firms from the following

industries: communication technology firms (SICcode 3663) (23%), electronic equipment firms(SIC code 3641) (18%), semiconductor andrelated device firms (SIC code 3674) (31%),computer firms (SIC code 3571) (12%), othermachinery equipment firms (SIC code 3541)(16%). The industry classification system is basedon the US-based Standard Industrial Classifica-tion (SIC), which was created by the USgovernment (1941).




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Variables and measure

Dynamic capability. In line with the definitionsof DC in this study, the following properties ofDC will receive attention to operationalize thenotion of DC: (1) DC needs investment in specificresources; (2) the investment in DC needs to be

continuous over a long time period, rather thanin intervals; and (3) DC is asset-specific to acertain extent. As argued earlier we operationa-lize the notion of DC in terms of R&D andmarketing capabilities. In doing so, we use thepercentage increase in R&D and marketingdeployment to capture the magnitude of changein a firms resource deployment over a 3-yearperiod. In a high-tech industry, the DC for R&Dand marketing requires at least 3 years forconversion into a successful product (Kor andMahoney, 2005). Accordingly, to capture the

historical dynamics in investment levels, weestablish the functions to calculate the averagepercentage increases in two proposal indexes forDC during period t, t 1 and t 2. We created acomposite measure of DC based on the twospecific capabilities (R&D capability and market-ing capability). The overall score is an average ofthe two items.

Intellectual capital. Both theoretical and empiri-cal research has been undertaken on IC in recent

years. Measuring and managing IC has beenfound to be important for a companys long-termsuccess, and thus numerous IC indicators havealso been identified. With regard to the empiricalresearch on the indicators that have been pro-posed and are used to measure IC, researchershave developed various measurements for eachcategory of IC that enable the intangibles reportedto be compared with other firms. Published ICmeasurement models can be divided into threegroups, including scorecards (Edvinsson andMalone, 1997; Sveiby, 1997), monetary value

(Brooking, 1997; Sullivan, 2000) and market value(Mouritsen, Larsden and Bukh, 2001; Stewart,1997). Choong (2008) regards each measure as areflection of the different facets of IC.

We measured HC using three ratios, covering(1) educational level, (2) work ability and (3) thevalue and uniqueness of the organizational work-force. Our method of measurement was drawn inprincipal from Edvinsson and Malone (1997),Huselid, Jackson and Schuler (1997), Youndt,

Subramaniam and Snell (2004) and Wang (2008).We use average educational level to measureemployee profession. Employees have a highereducational level that enables them to do their jobsuccessfully. We use employee productivity tooperationalize work ability. A higher employee

productivity reflects a stronger work ability ofemployees. The value and uniqueness of theorganizational workforce was measured by theratio of employee added value, indicating theadded value created by employees in relation tothe value of the organization.

As in the research of Canibano, Garcia-Ayusoand Sanchez (2000), Liu, Tseng and Yen (2009),Lynn and Dallimore (2002), we used two indexesto measure SC. Information system process ismeasured by the ratio of information technologyexpense to total administrative expense (i.e. the

information technology expense ratio). In thisprocess we see more inter-unit exchange; further-more, innovation of information technologyexpense can facilitate inter-firm learning andcross-functional team effectiveness. Product devel-opment process is operationalized as the ratio ofadministrative expense to total revenue. A productdevelopment process refers to the sequence ofactivities which an enterprise employs to conceive,design and commercialize the product (Ulrich andEppinger, 2003). Many of these activities needhigh-level R&D employees and precision equip-

ment. Administrative expenses include items suchas salaries (e.g. information technology employeesalaries) and rent (e.g. facilities rent) (Bernsteinand Wild, 2000). There is a tendency for theseexpenses to increase, especially in prosperoustimes (i.e. the period of successful product devel-opment). Therefore, higher administrative expenserepresents faster growth of product development(Moon and Kym, 2006). Although the use of theratio of R&D expense to total operating expensehas been adopted in many studies (e.g. Chan,Lakonishok and Sougiannis, 2001), we were

concerned about the occurrence of the multi-collinearity of DC for R&D. Therefore, we deletedthis ratio.

We also adopted two ratios from Lynn andDallimore (2002), Van Buren (1999) and Pablos(2003) to measure RC. The ratio of 5% of keyaccount sales divided by the total sales represents aproxy of the customer relationship measure.According to the 80/20 principle, 20% of keyaccount sales account for 80% of company sales.




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As stated by Piercy and Lane (2006), the clients ofmost companies are diverse, with some represent-ing an extremely high share of their sales. As aconsequence, firms dedicate most of their resourcesto their key accounts (Homburg, Workman andJensen, 2002; Workman, Homburg and Jensen,

2003). A core assumption in the key accountliterature is that firms are willing to increase theirinput to important customers because they hope toenhance the relationships (e.g. Ivens and Pardo,2007). We use the percentage of total expenses paidto major suppliers over total revenue to measurethe relationships between firms and their majorsupplier partners. Table 3 shows the operationali-zation, indicators and sources of all the constructsin the proposed model.

Performance. Performance is operationalized interms of the monetary terms that a firm receivesin exchange for the price it pays for products orservices. Transaction cost theory (Williamson,1985), which supports this rationale, has domi-nated theoretical and empirical research in this

area. Traditional performance measurement em-ploys a financial-based index (Usoff, Thibodeauand Burnaby, 2002) such as return on assets(ROA), return on sales and return on equity.Return on equity is ruled out because it is seen tobe more sensitive to capital structure difference.Both ROA and return on sales generate similarfindings and are highly correlated (r5 0.76). Theaverage pre-tax earnings of a company for aperiod of time are divided by the average tangible

Table3. Definitions of independent variables

Constructs Variables Adapted from

Intellectual capital (IC)

Human capital (HC) AEL: average educational level of employee Edvinsson and Malone (1997); Huselid,

Jackson and Schuler (1997); Youndt,

Subramaniam and Snell (2004); Wang (2008)

EP: employee productivityEAV: employee added value

Structural capital (SC) ITR: information technology expense ratio Lynn and Dallimore (2002); Edvinsson and

Malone (1997); Canibano, Garcia-Ayuso and

Sanchez (2000); Liu, Tseng and Yen (2009)

ARR: ratio of administrative expense to total

revenue

Relational capital (RC) KA: key account Lynn and Dallimore (2002); Van Buren

(1999); Pablos (2003)

ESR: percentage of total expenses paid to

main suppliers

Dynamic capability (DC) RD: % increase in R&D development5

(1/2)f[(RDEt1 RDEt2)/RDEt2]1[(RDEt2 RDEt3)/RDEt3]g

Kor and Mahoney (2005); DEste (2002);

Thornhill (2006)

MK: % increase in marketingdevelopment5 (1/2)f[(MKEt1 MKEt2)/MKEt2]1[(MKEt2 MKEt3)/

MKEt3]gPerformance ROA: return on assets Firer and Williams (2003); Hitt, Hoskisson

and Kim (1997); Lant, Milliken and Batra

(1992)

Control variables FS: firm size Heimeriks and Duysters (2007); Hsu and

Pereira (2008)

FA: firm age Huergo and Jaumandreu (2004); Thornhill

(2006)

LE: leverage Smith and Warner (1979); Delios and

Beamish (1999); Geringer, Tallman and Olsen

(2000)

EI: export intensity5 export sales/total sales Aulakh, Kotabe and Teegen (2000); Chiao,

Yang and Yu (2006)

Key account: customers who exchange with a firm more than 10% of total sales are included. RDE, R&D expenditure; MKE,

marketing expenditure.




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assets of the company, i.e. ROA. The result is acompany ROA that is then compared with theindustry average. While some studies have madeuse of ROA and return on sales simultaneously(Grant, 1987), others have used ROA only (Hitt,Hoskisson and Kim, 1997). Nevertheless, some

researchers (e.g. Stewart, 1997) argue that ROAis more appropriate in IC studies because ROA isuseful in high-tech industry for stock marketvaluations. It can be used to illustrate thefinancial value of intangible assets. This featuretends to get the attention of CEOs. For thesereasons, this study uses ROA, which is collectedfrom the Taiwan Economic Journal Database for242 high-tech firms over the period 20012008, asthe measure of firm-level performance.

Control variables. To support the theoreticalmodel in this study, we also include four controlvariables because of their potential impact onperformance, as suggested by the extant literature.Firm size is proxied by the natural logarithm oftotal capital (Segars and Grover, 1995). Huergoand Jaumandreu (2004) also controlled for firmsage because they predicted that IC creation wasinherently revolutionary in nature and would thusbe influenced by a firms age (Rosen, 1991; Zahra,1999). Leverage, which is another control variable,is employed as a proxy of a firms capital structure.

It reflects a firms financial risk, which might limitthe firms available economic resources to supportlong-term intangible investment (Smith and War-ner, 1979). It is calculated as the reaction of long-term debt over total assets. Following Shoham(1996) and Geringer, Tallman and Olsen (2000), wealso used an environmental variable, which mea-sures internationalization using the index of exportintensity. International markets, in general, canaffect the success of the firms innovation activities.One reason is that companies must address diverseand inconsistent laws, national cultures and in-

dustry forces (Rosenzweig and Singh, 1991; Zahraand Garvis, 2000). This seems to be a good relativeindicator and has been widely used. The opera-tional definition of internationalization is exportintensity (Aulakh, Kotabe and Teegen, 2000).

Bayesian regression model

Dynamic environments are often complex, andthe relationship between predictor and the

resulting risks must be explored over the longterm rather than at a specific point in time. Onedifference between Bayesian regression and theclassical approach is that Bayesian methods canincorporate information external to the study foranalysis (Gelman et al., 2004). Such information

is specified in a prior distribution and iscombined with the study data in the form of theprobability of producing a posterior distributionon which inferences are based (Zhao et al., 2006).Thus, the basic motivations for adopting aBayesian approach to analyse a dynamic modelis that prior knowledge or pilot information caneasily be incorporated into such a model. Theincorporation of prior information often resultsin inferences that are more precise than thoseobtained with traditional methods (De la Cruz-Mesia and Marshall, 2003).

Among methods based on probability meth-ods, Bayesian inference offers several advantagesover traditional statistical estimates. The first isthat prior information can be incorporated intothe analysis. In analysing DC, the inclusion ofprior information leads to an important prag-matic advantage. Many statistical models oftenrequire information about the underlying un-known parameters, and some parameter valuesare just not applicable to the underlying manage-ment theory (e.g. RC and HC in our model). ABayesian analysis makes it very easy to incorpo-

rate such information directly. A second advan-tage is the comparative ease with which varioussources of uncertainty can be incorporatedaccurately into the analysis. Bayesian methodsestimate the probability distribution of para-meters in complex models without relying onlarge sample approximation to normality (Con-gdon, 2003). Any conclusion derived from atraditional statistical analysis should include anindication of the uncertainty of the conclusion.For example, the point estimate of an unknownparameter is more or less worthless without an

indication of the uncertainty underlying theestimate.

Bayesian regression is not the main topic ofthis paper; we give the brief concept of theBayesian method in the Appendix. Bayesiantreatment of the regression model in this paperis a simple case of hierarchical regression analysis(Shively, Sager and Walker, 2009). A recentexample for application of Bayesian regressionis taken from an optimal policies of inventory




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model study carried out by Azoury and Miyaoka(2009). In order to investigate the mediatingeffect of DC, the following three Bayesianregressions are constructed:

ROA i1 cIC d1LE e1FS f1FA

g1EI 1

DC i2 aIC d2LE e2FS f2FA

g2EI 2

ROA i3 c0IC bDC d3LE e3FS

f3FA g3EI 3

where LE is leverage, FS is firm size, FA is firmage, EI is export intensity, i is the intercept, anda, b, c, d, e, f, g and c0 are parameters.

In order to estimate parameters of regression,most previous studies investigating a dynamicsystem are based on traditional statistical meth-ods such as original least squares, partial leastsquares and maximum likelihood. However,estimating methods that need to capture thedynamic characteristic and consider uncertaintyare necessary. Here we use the Markov chainMonte Carlo (MCMC) method (Cowles andCarlin, 1996; Gimenez et al., 2009), which hasbeen proved to be appropriate for handling suchissues and can be used in dynamic prediction

(Spiegelhalter et al., 1996), although it has notbeen used in the management literature as muchas it could be. Thus, parameter estimates wereproduced by an MCMC algorithm, Gibbssampling, using Version 1.4 of the programBUGs (Bayesian inference using Gibbs sampling)(Spiegelhalter et al., 2003).

A variable that accounts for the relationbetween the predictor and the criterion is referredto as a mediator (Baron and Kenny, 1986).According to Mackinnon (2000), regression is themost common method for testing mediation.

Figure 2(a) represents the effect of IC on firm-level performance, which is often referred to asthe direct effect. We use equation (1) to estimateparameter c. Figure 2(b) represents the simplestform of mediation, in which DC mediates theeffect of IC on performance. We refer to it as thetotal effect (Frazier, Barron and Tix, 2004;Preacher and Hayes, 2004). We use equation (2)to estimate parameter a and equation (3) toestimate parameters b and c0 (i.e. total effect).

Result

Table 4 presents variable descriptive statistics anda correlation matrix. The most common method

for testing mediation was developed by Kennyand his colleagues (Baron and Kenny, 1986;Kenny, Kashy and Bolger, 1998). Following thismethod, there are four steps in establishingwhether DC mediates the relation between ICand performance by performing the above-mentioned three Bayesian regressions. Table 5presents the estimates obtained from the Baye-sian analysis.

First, equation (1) shows that there is asignificant relation between HC (b15 0.9371,po0.001), RC (b25 0.1806, po0.001) and SC

(b35 0.1321, po0.01) and performance (see pathc in Figure 2(a)). Thus, Hypotheses 1a, 1b are 1care supported. This result agrees with Carpenter,Sanders and Gregersen (2001) and Youndt,Subramaniam and Snell (2004). Second, equation(2) shows that there is a significant effect ofHC (b15 0.6641, po0.01), RC (b25 0.8670, po0.001) and SC (b35 0.6753, po0.01) on DC(see path a in Figure 2(b)). Thus, Hypotheses 2a,2b and 2c are supported. Third, the mediatingrole of DC related to performance is estimatedcontrolling for the effects of DC on performance

(b25 0.3344, po0.01). Thus, Hypothesis 3 is alsosupported. These results are in agreement withprevious studies (Zott, 2003). The final step is toshow whether or not the strength of the relationbetween IC and performance is reduced when themediator (i.e. DC) is added to the model. Morespecifically, we need to compare path c in 2(a)and path c0 in 2(b). When the effect of IC onperformance decreases to zero (i.e. not statisti-cally significant) with the inclusion of DC, perfect

A

B

ROA

DC

a

c

b

ROA

c

IC

IC

Figure2. (a) Direct effect: IC affects ROA. (b) Mediation

design: IC affects ROA indirectly through DC.




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mediation is said to have occurred, which isreferred to as complete mediation (James andBrett, 1984). When the effect of IC on perfor-mance is a non-trivial amount, but not zero,partial mediation is said to have occurred.

In equation (3), SC (b45 0.0805, p40.1) is not

statistically significant. This indicates that theeffect of SC is completely mediated by DC. Here,HC (b25 0.5524, po0.001) and RC (b35 0.1068,po0.001), which are smaller than those ofequation (1), are statistically significant. Thus,the results provide evidence for the partialmediating role of DC in the HCperformancelink and the RCperformance link.

Discussion and managerial implications

Our interest in investigating the relationshipbetween IC and performance was triggered bytwo observations, the first of which was theinherent uncertainty of the fast changing dynamicenvironment widely noted in organizationalstudies as theorized by Peteraf (1993). Althoughthe RBV shifts the focus of strategy to firmsinternal characteristics by identifying its re-sources in a specific time period and how thesemay have been created, we argue that under staticcircumstances the RBV is equally, if not more,

relevant for understanding IC. The RBV can beregarded as a static theory (Priem and Butler,2001) because it fails to address the fundamentalissue of how future resources can be created andthen accumulated for firms in a dynamic envir-onment. Most studies argue that IC is groundedin RBV logic (Reed, Lubatkin and Srinivasun,2006), but our findings suggest that IC needs tobe focused on dynamic strategic considerations.Since the association between IC and DC doesindeed represent a causal connection between thetwo concepts, our results have important man-

agerial implications. To provide further coher-ence to our conceptual model, we identified DCas the primary strategy that mediates the effectsof IC on firm-level performance. DC underscoresthe accumulation of capabilities embedded in anorganization and is posited to be directlyassociated with its financial performance. Therehas to be a conscious strategy of seeking anadvantageous position in IC and implementing it.An appreciation of the impact of IC on DC willT

able4.

Correlationsmatrixanddescriptivestatistics

Mean

SD

Max

Min

1

2

3

4

5

6

7

8

9

10

11

12

13

1.

ROA

0.0

89

0.1

35

0.5

06

0.4

4

1

2.

AEL

0.0

97

0.2

31

0.6

78

1.0

34

0.1

88**

1

3.

EAV

1.3

2

3.2

9025.8

16

9.5

71

0.6

23**

0.4

63**

1

4.

EP

12.0

32

12.5

9571.5

70

1.9

42

0.2

54**

0.2

89**

0.5

27**

1

5.

ITR

0.3

76

0.2

14

0.9

63

0.0

00

0.0

76

0.2

76**

0.0

89

0.2

36**

1

6.

ARR

0.0

42

0.0

38

0.2

75

0.0

03

0.1

74*

0.0

90

0.3

27**

0.3

57**

0.0

94

1

7.

KA

0.7

20

0.2

49

0.9

97

0.0

13

0.0

97

0.0

23

0.0

18

0.1

24

0.0

01

0.0

93

1

8.

ESR

0.1

73

0.3

17

1.5

00

0.6

77

0.3

22**

0.1

13

0.2

00**

0.2

03**

0.0

12

0.3

13**

0.0

95

1

9.

RDE

0.1

62

0.3

82

3.0

83

0.6

59

0.1

88**

0.0

62

0.2

70**

0.0

72

0.0

19

0.1

40*

0.0

54

0.1

98**

1

10.

MKE

0.4

86

2.8

0428.3

19

0.6

18

0.0

06

0.0

28

0.0

29

0.0

24

0.1

11

0.0

39

0.0

09

0.1

59*

0.0

46

1

11.

LE

0.4

94

0.2

48

0.9

83

0.0

02

0.3

94**

0.3

96**

0.2

64**

0.1

47*

0.0

57

0.2

08**

0.0

03

0.1

86**

0.0

95

0.0

49

1

12.

FS

13.9

40

7.6

8939.0

2.0

0.0

59

0.1

50*

0.1

52*

0.1

45*

0.3

01**

0.3

44**

0.0

40

0.2

07**

0.2

09**

0.1

13

0.0

40

1

13.

FA

0.5

04

0.2

44

0.9

12

0.0

02

0.1

38

0.2

10**

0.2

47**

0.3

00**

0.0

19

0.1

15

0.0

92

0.0

57

0.0

25

0.0

35

0.0

99

0.1

47*

1

14.

EI

14.9

40

7.5

86

39

4

0.1

27

0.4

31**

0.2

02**

0.3

39**

0.3

86**

0.0

42

0.0

76

0.0

09

0.0

32

0.1

27

0.0

36

0.3

62**

0.0

24

AEL,averageeducationallevelof

employee;EAV,employeeaddedvalue;E

P,employeeproductivity;ITR,

informationtechnologyexpenseratio;ARR,ratioo

fadministrativeexpense

tototalrevenue;KA,

keyaccoun

t;ESR,percentageoftotalexpensespaid

tomainsuppliers;RDE,

R&Dexpendit

ure;MKE,marketingexpenditure.

*po0.0

5;**po0.0

1.




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enhance the level of a firms performance and its

sustainability.Second, by elaborating our theoretical model

in terms of the three distinct sub-constructs of ICand two sub-constructs of DC, we offer a rich setof results. Our results show that DC completelymediated the SCperformance link, but onlypartially mediated the HCperformance linkand the RCperformance link. This supportsour conceptualization of the mediating role ofDC between IC and performance.

We find that the effect of HC and RC onperformance is partially mediated by DC. This

result suggests that HC and RC might directlyaffect performance or indirectly affect perfor-mance through DC. We deduce this finding fromtwo effects. First, we examine the sources ofperformance, especially HC. The direct effect ofHC on performance is significant, and thisfinding is in agreement with Carmeli (2004) andother authors, e.g. Barczak and Wilemon (2003),Bontis (1998) and Bosma et al. (2004). Second,we re-examine the source of performance with

particular attention to accumulated long-term

resources. Our study is among the first to explorethe mediating role of DC between the HCperformance link, finding that there is a plausibleexplanation to the partial mediation. This findingstresses that HC derived through DC accumula-tion strongly contributes to performance, whichhas important managerial implications. HC playsa central role not only in technology innovation,but also in new knowledge absorption. Today thesuccess of any firm is measured in terms ofcontinuous innovation, relying on retainingemployees with skills and knowledge rather than

high employee turnover. Organizational learningtheory supports the rationale for this finding(Nelson and Winter, 1982). Organizational learn-ing is an intrinsically social and collectivephenomenon, involving joint problem-solvingand coordinated search. It may require the skillsand knowledge of individuals. Organizationallearning is also cumulative and path-dependent;what is learned and practised is stored andexposed in the firms economic performance.

Table5. Testing mediator effects using Bayesian regression

Testing steps in mediation model Equation (1) Equation (2) Equation (3)

Coefficients t values Coefficients t values Coefficients t values

Testing step 1 (path c)

Outcome: ROA

Predictor: HC (Hypothesis 1a) 0.9371***

8.8420RC (Hypothesis 1b) 0.1806*** 4.8976

SC (Hypothesis 1c) 0.1321** 2.4396

Testing step 2 (path a)

Outcome: DC

Predictor: HC (Hypothesis 2a) 0.6641** 3.0232

RC (Hypothesis 2b) 0.8670** 3.7923

SC (Hypothesis 2c) 0.6753*** 4.2007

Testing step 3 (paths b and c 0)

Outcome: ROA

Mediator: DC (path b) (Hypothesis 3) 0.3344** 2.832

Predictor: HC 0.5524*** 7.4210

RC 0.1068*** 3.5270

SC 0.0805 0.5444

Control variables

LE 0.3066*** 9.8876 0.3961*** 1.7890 0.4398*** 10.837FS 0.0573*** 4.0125 0.0294* 2.7012 0.0271* 2.5608

EI 0.0242 1.4525 0.0103 1.5808 0.0299 1.1287FA 0.0400*** 3.7073 0.1011*** 4.8250 0.0170 0.1868Constant 0.1707*** 3.6482 0.9861*** 5.1046 0.037 0.6914Adj R2 0.4670 0.4731 0.5816

F 10.2931*** 16.2356*** 20.8360***

*po0.05; **po0.01; ***po0.001.




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Thus, long-term HC accumulation can be seenas idiosyncratic problem-solving knowledgecapital.

As predicted, the results show that, althoughDC does not account for all the effects of RC onperformance, it does act as an important med-

iator. The direct effect of RC on performance isconsistent with our discussion about the mainsource of performance using RC in the context ofthis study. In the prevalent literature, RC andDC have rarely been studied together, with theexception of Blyler and Coff (2003). The relation-ship between RC and performance is important,as DC significantly mediates this link. This is asignificant finding due to its strategy implicationsthat RC must be involved in DC for R&Dactivities and marketing activities, and this willaffect performance. Our finding offers a relational

view of competitive advantage that focuses onnetwork routines and processes. This frameworkis valuable because it provides a theoretical basisfor cumulative firm capability in our under-standing of the resources of RC.

This finding expands upon the work of Oliver(1990). Marketing channel theory (Frazier, 1983)provides a possible explanation for the finding.Entrepreneurial networks include relationshipsboth on the supply side (e.g. with R&D institu-tions or research laboratories) (Lee, Lee andPennigs, 2001) and on the demand side (e.g. with

customers) (Ulaga, 2003). From the supply-siderelationship, a firms relationships are with actorsinvolved in research and technology develop-ment. In a dynamic environment, organizationsdevelop close cooperation with specific partnersand then nurture social ties with those partners.These social ties should support the firm. Partner-specific experience facilitates dyadic relationshipadjustments, which suggests that prior ties facil-itate adjustment as a consequence of familiarityand the development of inter-organizationalroutines. In this study, we show that at least

some of the RC manifests its influence on a firmsperformance through establishing long-term re-lationships with supply-side partners. From theviewpoint of demand-side relationships, RCinvestments are made by firms for the develop-ment of customer relationships in order toacquire tangible benefits such as lower cost,higher quality and more reliable delivery. RCinvestment in long-term customer relationshipaccumulation enables access to loyal customers

and new customers, as well as providing newmarket information and service (Rocks, Gilmoreand Carson, 2005). In these situations, thecustomers may arguably be prepared to help thefirm through information sharing, technicalassistance and loyalty programmes, in return

for the benefits of improved performance.The prior literature has stressed the positivelink between SC and performance (Bru derl andPreisendorfer, 1998). Interestingly, our findingsshow that DC plays a fully mediative role in thisrelationship. This finding supports the importantimplication that DC should be employed as asignificant means of resource renewal and restor-ing capability diversity, as well as avoiding theinertia and simplicity that result from the scarcityof long-term efficient resource deployment withinan organizational structure. Meanwhile, organi-

zational processes, one component of SC, dependon employees actually using the information orknowledge resources available to them. Processesdirectly affect the efficiency of the employeesactions. Once firms obtain a unique routineprocess and stock this know-how, it ultimatelybecomes an important resource for performance.Another component of SC is the informationsystem used in managing knowledge. An infor-mation system with an innovation componentmay not have a great effect on the performance ofan organization, but when the information

system comprises not only the knowledge createdby but also stored in a firms technology system,SC would represent the principal source of firm-level innovation. This interpretation is consistentwith the finding of Dierickx and Cool (1989). Weshow that such a decomposition helps enrich theunderstanding of SC phenomena we discoveredwhen SC is viewed as a dynamic resource; thisshould result in long-term competitive advantageand business-specific advantage, which shouldpositively impact financial performance, as pre-dicted in our model.

Given the cross-section and time series natureof our data set, we pool together observationsacross firms (242 high-tech firms) and years(20012008). Pooling data estimation methodshave the advantage that they allow us to accountfor long-term performance of IC over time. Themodel may not represent firms that are driven byshort-term orientation only. However, priorstudies are mostly based on a cross-sectionaldesign and qualitative research (e.g. Keil, 2004;




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Lazonick and Prencipe, 2005; Meyer and Lieb-Do czy, 2003; Noda and Collis, 2001) and do notfully capture the IC process. For example, RCinvestments may be reflected in the future,something that is impossible to identify withcross-sectional data. Thus, the model developed

and tested here could benefit from being tested ina longitudinal design based on secondary data.The secondary data set, as well as the indexes, ofmethodology have been widely employed in themanagerial literature (Firer and Williams, 2003;Krause, Handfirld and Tyler, 2007; Le, Waltersand Kroll, 2006; Rothaermel and Deeds, 2006;Subramaniam and Youndt, 2005).

Theoretical contribution

By empirically validating a theoretically derivedmodel, this study offers three major contributionsto the study of strategic management. First, therole of IC in affecting performance is welldiscussed in previous management studies. Whatis less understood is how IC affects performancein dynamic environments over time. A staticperspective suggests that IC is strongly andpositively related to firms performance, but fordifferent reasons. We extend prior IC research onthe static concept of short-term discrete invest-

ment to the dynamic concept of long-termcapability accumulation. We add to the concep-tual richness of the construct by considering theimplications of DC. Our findings are particularlynoteworthy. The results suggest that while a firmmay have good performance in the short term, itmay struggle with rivals in a turbulent environ-ment, because accumulated capability may bescarce in the long term.

Second, we investigate to what extent DCmediates the effect of IC on performance. Thecombination of DC and IC theory opens up a

new domain of dynamic strategy. Our focus onDC complements the previous research that hasbegun to explore the process whereby IC isassociated with performance. More specifically,we integrate IC and the influence of DC onperformance into one model and reconcile whathad previously been presumed to be independent.In the prevalent research, DC and the three sub-constructs of IC are rarely studied together. Inthis study, we show that at least some DC

manifests a meditative role on the relationshipsbetween IC and performance.

Third, the Bayesian regression model is con-sidered here as an alternative to the traditionalapproach. In response to the necessity of dynamicanalysis, Bayesian statistics provides a formal

model for uncertain environments. Bayesianregression, which is also called dynamic regres-sion, can capture the nature of dynamic modelsbecause the parameter estimation is updated overtime through iterative simulation (Gelman et al.,2004), although it has not been used in the studyof management as much as it could be.

Limitations and future researchdirections

While we believe we have developed a sound andrich theoretical model and tested it with reliablesecondary data, there are some limitations. Asdiscussed earlier, more dynamic capabilities existbeyond the realm of R&D and marketing.Capturing a more comprehensive picture of DCmay allow researchers to capture a full ratherthan a partial mediating role of DC. Moreover,the partial mediating role of DC on HC and RCneeds further investigation. Future research canexplore in greater detail the real causes of thepartial mediating role of DC on the HC

performance link and RCperformance link. Itis vital that its causes are explored and then usedfor the strategic space in the competitive horizonof the company. Moreover, it is also highlydesirable to replicate this study with other typesof firms and industries to generalize the empiricalfindings and determine whether the same rela-tionships hold.

In the growing number of IC studies, nogenerally accepted conceptualization of perfor-mance has emerged (Krause, Handfirld andTyler, 2007). Some authors posit that perfor-

mance does not necessarily employ financialtechnique (Easton and Araujo, 1992). However,our proposed model indicates that performance ismeasured in terms of ROA. More research isnecessary to show that firms with relatively highIC are more likely to employ non-financialmeasures such as the balanced scorecard (Sveiby,1997), the intangible assets monitor, or theSkandia navigator (Skandia, 1994). Such ameasurement might also help companies identify




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determinants which have matched with theirperformance.

The inability of the measurement system tocope with the intangible nature of IC is often seenas a problem. An important area for furtherresearch involves identifying and operationalizing

HC, RC and SC. Researchers could use othervariables to operationalize the three sub-con-structs of IC such as relationship benefit andsacrifice (Lapierre, 2000) used by RC, leadershipindex and employee retention used by HC(Skandia, 1994), and the outsourcing index usedin SC (Skandia, 1994). If it is found that thesevariables can precisely measure the three sub-constructs of IC, this will promote a fullerunderstanding of the concept of IC for thestrategic manager. It is imperative that strategicmanagers fully understand the complexity of IC

and the relative effectiveness of each context tohelp maximize firm-level performance. Finally, alimitation of using Bayesian regression is that theresults are not based completely on observeddata. Future research could extend the empiricalanalysis of our paper to observed data to examinehow IC affects firm-level performance throughDC. We hope that our study serves to supportsuch an understanding.

Appendix

This appendix is not a thorough review ofBayesian methods. But Bayesian methods areless familiar than some other statistical ap-proaches in strategic management, and so it isappropriate to provide some methodologicalbackground. We provide a brief summary ofthe Bayesian regression methods employed forthe analysis in this study.

Bayesian estimationBayesian inference combines (1) a likelihoodfunction with (2) prior probability distributionsfor the parameters of each model to produce (3)posterior probability distributions for quantitiesof interest. In essence, Bayesian analysis uses datato move from a state of great uncertainty aboutparameter valu

clarifying the effect of intellectual capital on performance

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