Clarifying the Effect of Intellectual Capital on Performance: The

British Journal of Management, Vol. *, *–* (2010) DOI: 10.1111/j.1467-8551.2010.00718.x

Clarifying the Effect of Intellectual Capital on 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, 1 Ling Tung Road, Nantun, Taichung, Taiwan 40852, Republic of China Corresponding author email: [email protected] Recent studies suggest a potential relationship between intellectual capital and dynamic capability in achieving performance. This is unsettling for managers because these studies contain little effort to develop a framework for understanding the relationship. To examine this unnerving potential, we develop and test a theoretical model that explains how dynamic capability mediates the impact of intellectual capital on performance. In this study, the scope of intellectual capital includes human capital, relational capital and structural capital. This study examines the pooled data of 242 high-technology firms from 2001 to 2008. Results from Bayesian regression analysis suggest that the effect of structural capital on performance is completely mediated by dynamic capability. Furthermore, the findings show that dynamic capability does not completely mediate the respective effects of human capital and relational capital on performance, but does so only partially. These results provide convincing support for the importance of dynamic capability through accumulating R&D and marketing capability over time, thereby enhancing firm performance. The empirical findings and the ensuing discussion will be of interest to managers and practitioners.

Introduction The business environment has already progressed from the Industrial Age to the Information Age. Traditional economic theory frequently describes the basic resources necessary for a firm in terms of the classic assets of land, labour and other economic assets (Sullivan, 2000). However, according to the resource-based view (RBV), a firm’s resources, particularly intangible ones, are more likely to contribute to the firm’s attaining The authors would like to thank Veronique Ambrosine, associate editor of the British Journal of Management, and two anonymous reviewers for their helpful suggestions. We also wish to acknowledge the National Science Council of the Republic of China, Taiwan, for financially supporting this research under Contract NSC 98-2410-H-275-001, and the substantive contributions made by Dr Shyh-Rong Fang.

and sustaining superior performance (Eisenhardt and Schoonhoven, 1996). During the past two decades, intellectual capital (IC) has been embraced by most organizations worldwide. IC plays a fundamental role within modern organizations and is part of the foundation of business in the 21st century. Studies have begun to examine the IC process by which those effects are ultimately realized (Martinez-Torres, 2006; Rudez and Mihalic, 2007). IC has thus been identified as one of the key drivers of firm-level performance (Teece, 1998; Youndt, Subramaniam and Snell, 2004). Although the importance of IC in pursuing performance is known, the specific means through which IC influences organizational performance are still underresearched. Moreover, the interaction of the external environment with organizational strategy is expected

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.

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

to be related to performance. To maximize performance, managers need to pursue competitive strategies that best match the conditions of the external environment. In other words, managers’ perceptions of the external environment are expected to affect a firm’s strategy. Therefore, a firm’s strategy must be to deploy its resources to seize opportunities in the market. Dynamic capability (DC) offers a bridge that debates in the strategy field proposing either an RBV of the firm or the emerging discourse surrounding the external dynamic business environment. While there is a wealth of literature on IC (Batjargal, 2007; Bontis, 1999; Bozbura, 2004; Bukh, Larsen and Mouritsen, 2001; Das, Sen and Sengupta, 2003; Edvinsson, 1997; Fincham and Roslender, 2003; Guthrie, 2001; Mayo, 2000; Nahapiet and Ghoshal, 1998; Nielsen, 2006), research incorporating DC into IC is scant. Existing IC studies mainly focus on ascertaining their impact and consequently their business value (Moon and Kym, 2006), but few studies utilize a theoretical focused approach to understand how DC mediates the impact of IC on firm-level performance. Drawing on previous studies related to dynamic theories (Teece, Pisano and Schuen, 1997; Winter, 2003), we posit an alternative mechanism for the IC–performance relationship whereby DC mediates the effect of IC on performance. Organization learning theory provides a conceptual framework for hypothesizing the mediating role of DC in the relationships between IC and performance (Brown and Duguid, 1991; Fiol and Lyles, 1985; Hong, Easterby-Smith and Snell, 2006). Cyert and March (1963) were the first to propose that an organization might be able to learn in ways that are distinct from the accumulated learning of individuals. They built their views on a model of decision-making within firms which emphasizes the role of rules and procedures in response to external shocks. This suggests that learning plays a significant role in the creation and development of DC. Eisenhardt and 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 can accumulate knowledge via learning processes (Nelson and Winter, 1982). Previous studies have posited that DCs exist in special operating routines and arise from learning (Argyis and

Schon, 1978; Huber, 1991). Argote (1999) identified the path of DCs as being more accurately described as a learning mechanism that guides knowledge creation. Using the perspective of organizational learning, we posit that organizational learning mechanisms are important in understanding the capability firms have and will have in meeting and addressing the challenges and changes in their environment. More specifically, DC contributes to firms’ IC to handle changing situations. From a dynamic perspective, successful performance depends on consistent and competitive behaviour that relies on a firm’s ability to learn and adapt by building and exploiting IC by DC. Over time, this can move the firm in the required direction, toward an efficient response to dynamic market conditions. This paper develops a model to explain how a firm’s performance is influenced by IC through DC, which serves as a firm’s managerial interface to the external environment. This framework is a major contribution to the literature on strategic management because it provides a theoretical basis for cumulative additions to our understanding of the concepts of IC and DC. The paper is organized as follows. First, we review the literature relating to the constructs of the theoretical model. We aim to contribute to the field of strategic decision-making by providing a directed application of resource and capability dimensions and by examining the mediating role of DC in firm-level performance. Second, we develop a series of hypotheses which constitute an integrated theoretical framework that offers a richer and more formalized account of the relationships than have been provided in the literature to date. We next take the high-tech industry as an empirical example by using pooled data. Finally, we conclude with a discussion on some implications, limitations and directions for future research derived from the findings of this paper. Figure 1 presents the theoretical model proposed to explain the underlying processes through which investments in IC lead to DC accumulation and thus to improved performance.

Explaining intellectual capital effects Intellectual capital Edvinsson and Malone (1997) divide IC into human capital and structural capital. The former

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

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Clarifying the Effect of Intellectual Capital on Performance H1a, b, c

Intellectual capital Human capital H2a, b, c Structural capital

Dynamic capabilities

H3

Performance

Control variables Firm size Leverage Firm age Export intensity

Relational capital

Figure 1. Theoretical model

is grounded on the knowledge created by and stored by an organization’s employees, while the latter is based on the embodiment, empowerment and supportive infrastructure of human capital. Structural capital is further divided into organizational capital (knowledge created by and stored in an organization’s information technology systems and processes that speeds the flow of knowledge through the organization) and customer capital (the relationships that an organization has with its customers). In recent years, although IC has captured the interest of many researchers and practitioners, it is still defined in various ways. Many definitions of IC were proposed as IC matured and are still being used and discussed in current studies (Carson et al., 2004; Reed, Lubatkin and Srinivasun, 2006). Klein, Crawford and Alchian (1998) argue that IC is knowledge, expertise and associated soft assets, rather than their hard physical capital. Sullivan (2000) posits that IC basically constitutes knowledge, lore and innovations. A list of definitions of IC proposed by researchers is shown in Table 1. Following relevant research, we define IC as the stored knowledge possessed by an organization, which is tacit knowledge, personal knowledge possessed by employees and available to network relationships through interaction (Mouritsen, Larsden and Bukh, 2001). Although IC and its defining components have been made explicit, which helps understand the notion of IC, there are a growing number of models for institutionalizing IC at the firm level (Roos and Roos, 1997). Recent research conducted by Reed, Lubatkin and Srinivasun (2006) concludes that IC can be divided into human,

organizational and social capital. Zerenler, Hasiloglu and Sezgin (2008) create a similar distinction, in which IC has three components: employee capital, customer capital and structural capital. Although earlier researchers may not agree on the precise categorization and shape of IC, there is broad consensus that it contains human capital (HC), relational capital (RC) and structural capital (SC) (Edvinsson and Sullivan, 1996). Following this consensus we integrate previously used elements of IC and assess IC in terms of HC, RC and SC. There are some significant differences between these categories. HC can leave the firm whenever it desires since the firm does not own it. SC, on the other hand, is knowledge that has been converted into something owned by the firm (e.g. a patent). The implementation of SC relies on HC and the quality of HC determines the quality of SC. From the organization’s point of view, RC is different from HC since the organization is concerned with network relationships (i.e. those relationships that are established and maintained by related partners). As external RC is formed through organizational internal HC, the RC may be the important relationship-specific assets of an organization. Organizational HC can influence the formation and maintenance of RC. Human capital HC is at the heart of IC and it is defined as the combined knowledge, skill, innovation and ability of employees (Bontis, Keow and Richardson, 2000). Similarly, Wright, McMahan and McWilliams (1994), working from an RBV, argue that in certain circumstances sustained competitive


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Table 1. Definitions of intellectual capital Authors Bassi (1997) Bontis (1999) Booth (1998) Bradley (1997) Brennan and Connell (2000) Brooking (1997) Choong (2008)

Edvinsson and Malone (1997)

Edvinsson and Sullivan (1996) Harrison and Sullivan (2000) Heisig, Vorbeck and Niebuhr (2001) Kim and Kumar (2009) Mouritsen, Larsden and Bukh (2005)

Pablos (2003)

Petty and Guthrie (2000) Rastogi (2003) Roos and Roos (1997)

Stewart (1997) Subramaniam and Youndt (2005) Sveiby (1998) Zerenler, Hasiloglu and Sezgin (2008)

Definition All types of organizationally relevant knowledge and its basic components are HC, SC and customer capital Encompassing HC, SC and RC The ability to translate new ideas into products or services The ability to convert invisible assets such as knowledge into resources that create wealth, not only within corporations but within a nation Can be thought of as the knowledge-based equity of a company The difference between the book value of the company and the amount of money someone is prepared to pay for it IC has been defined to include expenditures on advertising (marketing), training, startup, 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 The procession of knowledge, applied experience, organizational technology. Customer relationships and professional skills that provide Skandia with a competitive edge in the market Knowledge that can be converted into value Knowledge that can be converted into profit IC is valuable, yet invisible IC as the mixture of human, structural and relational resources of an organization 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 The difference between the company’s market value and its book value. Knowledgebased resources that contribute to the sustained competitive advantage of the firm from IC Indicative of the economic value of two categories (organization and HC) of the intellectual asset of a company 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 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 Intellectual material – knowledge, information, intellectual property, experience – that can be put to use to create wealth IC is the sum of all knowledge stacks firms utilize for competitive advantage Composed of individual competence, internal structure and external structure 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

advantage can accrue from a pool of HC. The RBV theory holds that organizations evaluate the strengths and weaknesses of their resources and then select a strategy that is achievable. HC, one of the underlying strategic resources, is both supportive and necessary for success since employees’ knowledge and skill are essential in today’s fast-paced, changing competitive climate (Subramaniam and Youndt, 2005). The knowledge and skill of individuals is an area addressed by HC theory. HC theory maintains that knowledge provides individuals with increased cogni-

tive abilities, leading to more productive and efficient activity (Davidsson and Honig, 2003). It follows that capability addresses whether or not individuals have the necessary levels and combinations of knowledge and skill to complete the tasks that they are responsible for (Hitt et al., 2001). Organizations specializing in advanced technologies need individuals who are knowledgeable, with excellent problem-solving skill and the ability to make effective decisions. Furthermore, HC theory addresses the worth of an organization’s human resources based in


Clarifying the Effect of Intellectual Capital on Performance the context of performance (Brown, Adams and Amjad, 2007). HC focuses on the value that is added to an organization’s business, ultimately in terms of profitability, solely by its stock of human 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 judgement. As long as HC continues to be developed, staff can improve their job performance and ultimately improve the firm’s performance (Hsu, 2007). Dulewicz and Herbert (1999) confirm that successful strategy must be strongly focused on the competences of human resources, which are related to the qualities that individuals possess. We can expect that the higher a firm’s stock of HC the more successful the firm will be and the greater its competitive advantage will be. HC increases as staff accumulate specialized information, skill and know-how. This allows them to communicate efficiently and effectively, which reduces decision-making errors, thereby enhancing quality and improving performance (Luthans and Youssef, 2004). Thus, for an organization, HC will be positively related to its performance. 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), RC has evolved to be described as the basis for collective action in communities (Burt, 1992; Granovetter, 1973). At its core, RC is concerned with the mobilization of resources through a social structure. RC is defined as the organization’s implicit set of available resources and ongoing relationships implemented through interactions among individuals or organizations (Kostova and Roth, 2003; Shipilov and Danis, 2006). Significantly, this definition means that the characteristics of RC will vary both with the relationships under review and the resources that can be employed through these relationships. An organization’s RC draws on the tangible links between organizational staff and members external to the organization (Burt, 1997). An organization’s RC enhances the quality of its members and the richness of information exchanged among exchange partners. RC is epito-

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mized in how it facilitates interactions and the exchange of information. An organization can gain important information or support from its suppliers, clients or other external partners. The extant RC literature has argued that, as the level of interaction between partners increases, organizational routines are established (Nelson and Winter, 1982), and thus the investment in relation-idiosyncratic assets and the level of bilateral dependence also increase (Teece, 1986). Social exchange theory (Macneil, 1980) supports the rationale of this argument. It views inter-organizational relationships in the context of a social structure whereby firms are interdependent and rely on reciprocation. Thus, relationships are embedded in a social structure (Granovetter, 1985). Social exchange concentrates on the relationship rather than the transaction so that over time a complex personal and organizational structure evolves between organizations. The key process of the relationship in social exchange is trust (Morgan and Hunt, 1994). This process moderates the impact of power and determines the perception of fairness in an exchange relationship. A prior history of cooperation between organizations has been found to reduce the exchange hazards (Deeds and Hill, 1998). RC established through prior exchanges can substitute for explicit contracts (Dyer and Singh, 1998). Through repeated interactions the parties appear to develop trust in one another such that they may no longer need to rely on formal contracts to ensure performance (Zaheer and Venkatrman, 1995). Experience with a trustworthy partner is said to raise collaborative expectations and stimulate learning as the relationship evolves (Doz, 1990). In other words, a positive effect due to relationship exchange occurs. Organizations choosing a relationship perspective as their strategic approach almost inevitably have to focus on the relationship with their customers and other stakeholders. Customer relationships are considered by many as the most important component of RC (Duffy, 2000). As the relational literature suggests, involving customers who have had close and embedded relationships with a firm showed improved firm’s performance (Bonner and Walker, 2004). Many manufacturing firms are becoming involved in closer relationships with their suppliers in order


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to utilize their skills, capabilities and information to develop new products faster and at less cost so that close relationships with suppliers have a positive influence upon firm’s performance (Walter, 2003). Reuer, Zollo and Singh (2002) argued that repeated partner-specific relationships have a stronger effect on accumulated knowledge than repeated general experience relationships. Thereby, we postulate that RC has a positive effect upon performance. Structural capital SC is conventionally used to refer to the processes and procedures that are created by, and stored in, a firm’s technology system that speeds the flow of knowledge through the organization (Carson et al., 2004; Youndt, Subramaniam and Snell, 2004). The above definition is different from the approaches previously taken in the strategic management literature (e.g. Gibson and Birkinshaw, 2004; Kang and Snell, 2009). Gibson and Birkinshaw (2004) placed organizational capital in contextual ambidexterity by building a set of systems and processes that collectively define a context that allows the meta-capabilities of alignment and adaptability to sustain firm-level performance. Kang and Snell (2009) classified organizational capital into two alternatives forms: mechanistic versus organic. These alternative forms of organization capital have different effects on acquisition and integration of knowledge within a firm. We conceptualize SC in terms of organizational processes and information systems. Organizational process refers to the manner in which people actually use the information or knowledge resources available to them in the workplace (Hobley and Kerrin, 2004). Once an organization obtains a unique routine or process for performing tasks and activities, it becomes a potential source of firm-level performance. Information systems, the second component of SC, refer to the information technology used in managing knowledge. Information exchanges made as part of these established structures and processes thus tend to follow well-established and codified guidelines. Consequently, knowledge intrinsic to SC tends to accumulate and be utilized in an established way (Brown and Duguid, 1991). It is also reflected in an organization’s customary structure and processes. Thus, SC provides an

environment that enables organizations to create and leverage knowledge. If an organization has poor procedures and systems by which to track its actions, the organization’s performance will not achieve its potential (Widener, 2006). Conversely, an organization with strong SC will have a supportive culture that encourages employees to try and learn new knowledge (Florin, Lubatkin and Schulze, 2002). Recent research suggests that organizations’ operation processes and the organizational commitment of sufficient resources have an important impact on performance (De Brentani and Kleinschmidt, 2004). Moreover, SC, such as operations, procedures and the processes of knowledge management, propels organizations’ value creation activities which have a positive effect on their performance. Since organizations are increasingly employing advanced technologies to compete in today’s economy, they should take great care to properly manage SC so that performance is achieved. We posit that investments in SC can be expected to improve 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 dynamic capability That IC improves performance is not a novel proposition, and the contribution of the present study lies not in testing the hypotheses noted above but rather in exploring whether DC provides a mechanism for explaining these effects. We are not aware of any research linking the three IC components to DC, but there are conceptual reasons to expect a relationship. This linkage is frequently seen as a response to the question of how and why some firms appear to create and sustain competitive advantage. Dynamic capability The study of DC, also known as core capability (Collis and Montgomery, 1998), organizational routine (Cohen and Levinthal, 1989), core


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Clarifying the Effect of Intellectual Capital on Performance Table 2. Definitions of dynamic capability Authors Ambrosini, Bowman and Collier (2009, p. 10)

Collis (1994) Eisenhardt and Martin (2000, p. 1107) Griffith and Harvey (2001, p. 597)

Helfat (1997) Helfat et al. (2007, p. 1) Lee, Lee and Rho (2002, p. 734) Macpherson, Jones and Zhang (2004, p. 162) Nielsen (2006, p. 61) Stahle (2008, p. 165) Teece (2007) Teece, Pisano and Schuen (1997, p. 516) Wang and Ahmed (2007, p. 35)

Zahra, Sapienza and Davidsson (2006) Zollo and Winter (2002, p. 340)

Definition There are three levels of dynamic capabilities related to a manager’s 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 The capability to develop the capability that innovates faster (or better), and so on The organizational and strategic routines by which firms achieve new resource configurations as markets emerge, collide, split, evolve and die 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 The subset of competences/capabilities which allow the firm to create new products and processes and respond to changing market circumstances The capacity of an organization to purposefully create, extend or modify its resource base A source of sustainable advantage in Schumpeterian regimes of rapid change The ability of managers to create innovative responses to a changing business environment An extension of the RBV where the firm is conceived as a collection of resources, e.g. technologies, skills and knowledge-based resources A learned pattern of collective activity through which the organization systematically generates and modifies its operational routines in pursuit of improved effectiveness Difficult-to-replicate enterprise capabilities required to adopt to changing customer and technological opportunities The firm’s ability to integrate, build and reconfigure internal and external competences to address a rapidly changing environment A firm’s behavioural orientation constantly to integrate, reconfigure, renew and recreate its resources and capabilities, and upgrade and reconstruct its core capabilities in response The processes to reconfigure a firm’s resources and operational routines in the manner envisioned and deemed appropriate by its principal decision-makers A learned and stable pattern of collective activity through which the organization systematically generates and modifies its operating routines in pursuit of improved effectiveness

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 provided by various researchers. The wide array of studies across disciplines has created a broad range of definitions and emphases, as well as a wide of range of propensities that can be considered to be DC. These studies make distinct contributions; but there is a good deal of overlap in ideas. DC emerged as a complement to the RBV in an attempt to explain competitive advantage in rapidly changing environments. There is a great deal of concern with dynamism, which seeks to address how competences are renewed over time so as to provide innovative responses to market changes. Some authors have termed DC to be as

vague and tautological as absorptive capability (e.g. Dutta, Narasimhan and Rajiv, 2005). However, DC differs from the more familiar term of absorptive capability (Cohen and Levinthal, 1990). Zahra and George (2002) defined absorptive capability as a set of organizational routines and processes by which firms acquire, assimilate, transform and exploit knowledge to produce a dynamic organizational capability. Absorptive capability is an organization’s ability to understand new external knowledge, assimilate it, and apply it to commercial ends (Lane, Salk and Lyles, 2001). The term DC points to the concept of the capacity to renew competences so as to achieve congruence with the changing business environment (Teece, 1998). We here distinguish DC from absorptive capability since DC is considered to be the systematic change of efforts and the cumulative effort of capabilities over


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time. In contrast, absorptive capability can be regarded as a static theory because it addresses the fundamental issues of firms’ capability to acquire new and external knowledge and to assimilate such knowledge with existing and internal knowledge rather than accumulating it. Eisenhardt and Martin (2000) argue that, while much of the strategy literature is vague on the nature of DC, there are a number of specific examples from other research areas. These include product development routines, strategic decision-making routines and resource-allocation routines. Routines refer to stable patterns of behaviour that characterize organizational reactions to variegated internal or external stimuli. Routines seek to bring about desirable changes in the existing set of operations. For instance, a decision is made to upgrade the R&D process; many predictable and interrelated actions are initiated which will eventually conclude with the launch of the new R&D system. In this case, R&D routines are regarded as constitutive of DC and enhance future performance. The primary role of an organization, Grant (1996) argues, is to devise and establish routines that achieve knowledge integration. The concept of DC was first introduced by Teece and Pisano (1994) and Teece, Pisano and Schuen (1997) who asserted that in a dynamic environment a firm’s competitive advantage will rest on the firm’s internal routines that enable the firm to renew its stock of organizational capabilities. DC can therefore be perceived as the routines in an organization that guide and facilitate the development of the organization capabilities (Eisenhardt and Martin, 2000). Following this thinking, in this paper we adopt the definition of DC by Teece, Pisano and Schuen (1997) as the processes for reconfiguring an organization’s resources and operational routines in response to the changing environment. Examples of DC are R&D and marketing capabilities. Firms with interrupted past investments in R&D processes may have weaker knowledge endowment and consequently a more limited assimilative capability over time. In contrast, firms with a consistent increased effect in developing technological know-how over time may gain a strategic competitive advantage over their competitors who show weak commitment to R&D capability. An overall marketing capability can satisfy the current and future needs of

customers who typically require persistent and timely investments in marketing. A firm’s history of past investments in marketing can have continued economic value for the firm over time because these investments help the firm accumulate new knowledge more efficiently. Therefore, R&D activity and marketing activity are related directly to DC creation processes. This technique is 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 resources alone are not enough to create a firm-level performance; they need to be leveraged through capabilities (Szulanski, 1996). Indeed, capabilities are the transformational process by which resources are utilized and converted into an organization’s output (Dutta, Narasimhan and Rajiv, 2005). We contend that resources are the source of an organization’s capabilities, and furthermore, that capabilities are the main source of its performance (Grant, 1991). Thus, it has been recognized that the utilization and deployment of resources working in combination with capabilities can improve a firm’s performance.

HC and DC We argue that DC mediates the effect of HC on performance. The RBV focuses on organizational decision and suggests that such decisions are taken within organizational boundaries. Since these decisions are taken by organizational managers, the decisions referred to are the ones which help organizations to deal with their environment better. An organization’s difficulty in sustaining itself presents numerous choices, and the organization must then determine which of the choices will suit it best (O’Shannassy, 2008). This choice is contingent upon the environmental dynamics. In this situation, DC is a framework which suggests how an organization, especially a high-tech firm in a turbulent environment, can achieve sustained competitive advantage, and even enhance performance. The focus of DC is the development of management capability and the combination of linked HC and performance in such a way that they can function in a rapidly changing environment. This attempt


Clarifying the Effect of Intellectual Capital on Performance to address such a development is to address the mediating role of DC. The extant literature, however, is not clear about how DC mediates the impact of HC on performance. HC theory posits that people are a valuable organizational resource, and organizations should have strategies to recognize that these people possess valuable skills which contribute to the long-term deployment of competitive capability (Collis and Montgomery, 1998; O’Reilly and Pfeffer, 2000). Employees’ knowhow, as an essential part of HC, is perceived as one of the most valuable resources associated with firms’ competitiveness. Substantial work by many researchers (Lepak and Snell, 1999; Mueller, 1996) has discriminated between valuable and unique HC on the one hand and organizational effectiveness on the other. In other words, based on the DC view, an organization needs to ensure HC that leads to competitive advantage is constantly updated and altered in a way that other competitors are unable to imitate it; such HC is hence dynamic in nature (Skaggs and Youndt, 2004). HC is not independent of environmental context and needs to be seen in light of the organization’s continuous attempt to adapt itself to the ever-changing environment (Chadwick and Dabu, 2009). More specifically, capability may fail to be accumulated and recreated by poorly performing companies because of their lack of endowment in HC. Hence, we propose that DC mediates the effect of HC on performance. RC and DC To our knowledge, RC is a jointly generated asset in an exchange relationship that cannot be generated by either firm in isolation and can only be created through joint relation-specific assets (Rocks, Gilmore and Carson, 2005). Otherwise, it would be easy for firms to switch partners with little penalty when other partners offer virtually identical products (Ulaga, 2003). Asanuma (1989) was among the first to point out how the relationspecific skills developed between suppliers and automakers generate competitive capabilities. Similarly, Dyer (1996) pointed to a positive relationship between relation-specific investment and innovating performance in automakers and their suppliers. Additionally, Saxenian (1994) found that Hewlett Packard and other Silicon

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Valley firms effectively redeploy and reconfigure their resources by developing long-term partnerships with suppliers. These studies indicate that DC generated through RC investments is realized by greater relation-specific assets. However, RC also has a number of costs and risks, Establishing RC requires investments of time and other resources, and without maintenance relationships may decay over time (Burt, 2002). While strong relationships provide solidarity benefits that facilitate the pursuit of common goals, they may also result in groupthink (Janis, 1982). These negative effects of longterm RC are called their dark side. Previous studies have identified the role of the dark side in long-term relationships (Grayson and Ambler, 1999). This dark side consists of a loss of objectivity and opportunism. A loss of objectivity occurs when the partners’ perception of an organization becomes stale or they are too similar in their thinking (Moorman, Zaltman and Deshpande, 1992). This similarity in thinking can result in overly similar behaviour within an organization and thus a lower innovativeness that further decreases performance. Opportunism is generally defined as taking advantage of opportunities with little regard for principles. Opportunism appears to have a negative impact on performance, as shown by Rindfleisch and Heide’s literature review (1997). With knowledge of the dark side of RC, DC can facilitate the unpacking of those problems with existing organizational resources. We believe that the potential to create organizational competitive advantage is dependent not only on its relationship with other external partners but also on its development of DC (Dyer and Singh, 1998). This is especially true in a dynamic environment where an organization must do something unique to deal with the dark side of RC. By developing long-term capability deployments in the value chain, organizations are more willing to face the problem of the dark side of RC in a fiercely dynamic environment (Westerlund and Svahn, 2008). In line with this reasoning, the key features establishing RC are the necessity of organization resources and operational routines reconfiguring, in return for the benefits of improved performance and joint value creation (Zajac and Olsen, 1993). Thus, DC tends to be the relationship spanners that collectively shape and are inevitably influenced by the RC in


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network relationships. In other words, DC is also created by an inter-organizational network that contains greater resources, knowledge and relational idiosyncratic assets (Mowery, Oxley and Silverman, 1996). SC and DC SC is the knowledge that has been captured by the firm and embedded in the organization through organizational routines, practices and processes (Jansen et al., 2009). Furthermore, Schroeder, Bates and Junttila (2002) provide empirical evidence that SC is a critical strategic resource for organizations. SC is codified, and its creation, preservation and enhancement occur through repetitive 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 these established structures and resources thus tends to follow well-established and codified guidelines. Consequently, SC tends to be accumulated and utilized in established ways (Moran, 2005). However, an organization’s exposure to a variety of new and overlapping environmental challenges influences its performance. Rather than relying on established ways for problemsolving, organization requires questioning prevailing resources and looking for fundamentally different solutions to existing problems. Exposure to diverse resource domains enlightens organizations about new ways by which existing problems can be solved (Rosenkopf and Nerkar, 2001). Organizations consequently begin to question the promises behind prevailing organizational processes and systems and broaden their repertoires of problem-solving approaches, thereby increasing their likelihood of deploying DC (Zahra, Sapienza and Davidsson, 2006). If an organization is viewed as a bundle of resources, DC underlies the functions of transforming organizational resources into performance in such forms as organization processes and systems that deliver value superior to that of competitors; such transformation is implemented in a swift, precise and creative manner in line with environmental changes. Barney, Wright and Ketchen (2001) argued that the capabilities to change quickly in the dynamic market are costly for other competitors to imitate and thus can be a source of sustained

competitive advantage, further improving performance. Accordingly, we posit that SC per se may not be the source of a firm’s performance; and it is only possible if it is applied sooner and more astutely than competitors to create capability configurations. Therefore, we argue that the ability to apply resources sooner and more astutely is, indeed, the major function of DC. Thus, we contend that DC is the mediator between SC and performance. Accordingly, we posit the following hypotheses: H2a: DC mediates the relation between HC and performance. H2b: DC mediates the relation between RC and performance. H2c: DC mediates the relation between SC and performance.

Effects of dynamic capability Although the exact relation between a firm’s internal capability and its external environment remains unclear (Zajac, Kraatz and Bresser, 2000), a firm’s capability is usually influenced by the external dynamic environment. As firms analyse their capabilities, they are usually eager to understand their advantages over competitors. The commonly employed framework, which seeks to explain the competitive capabilities of a firm by the application of resources developed in a specific time period, tends to regard a firm’s capabilities as passive responses to the external environment. However, a firm confronted with a competitive crisis will need to utilize the firm’s accumulated capabilities to deal with it. In explaining such a phenomenon, in place of a static strategy, what is needed is an analysis that examines the dynamic strategy, which may be applied to exploit a firm’s DC. Thus, DC enhances the firm’s capability to face a fierce external environment. Although firms may be confronted with many threats emerging from a dynamic environment, at the same time, many opportunities are created for growth and profitability (Utterback, 1994). Consequently, firms in a dynamic environment need to develop many new products to secure performance. However, exploiting these opportunities requires strong and patient DC for R&D invest-


Clarifying the Effect of Intellectual Capital on Performance ments as well as continuous innovation (Blonigen and Taylor, 2000). Large amounts of R&D investments accumulated by DC to maintain excellent research capabilities and state-of-theart facilities are especially important for firms to build their technological competences (Pike, Roos and Marr, 2005). Continuous R&D activities accumulated by DC also ensure the control of key knowledge and allow the firms to build proprietary research platforms that lead to future success. Thus, when firms are heavy and longterm investors in R&D activities, it is expected that this will also result in long-term improvements in performance (Lantz and Sahut, 2005). Similarity, once a firm’s marketing strategy has been established, its managers must decide how the available resources should be allocated. Of course, the major objective of this strategy is to attract their target markets, establishing the competitive position of their product within these markets, and to generate cash flow from each product entry. In order to achieve these goals, a firm typically requires persistent investment in marketing activity over a long period. Especially from a relationshipbased marketing viewpoint (Hunt and Morgan, 1995), a firm’s history of past marketing investment can have continued economic value for the firm both in the present and the future because this investment helps the firm retain stronger customer relationships, which are important for profitability and paramount for the future direction of business (Rauyruen and Miller, 2007). Accordingly, we infer that firms may be differentiated in DC regarding R&D capability (Helfat, 1997) and marketing capability (Deeds, DeCarolis and Coombs, 1999; Griffith and Harvey, 2001), which are associated with firm-level performance. This leads to the following hypothesis: H3: The possession of dynamic capabilities for R&D and marketing is positively related to the achievement of performance.

Method Data and sample High-tech industries are identified according to the classification suggested by Hall (1994) and Chandler (1994) according to the research intensity of the industries and an informal assessment of those industries that are likely to

11

grow faster. Therefore, this study examines the high-tech industries in Taiwan. According to a report in Spring 2006 by the World Semiconductor Trade Statistics, Taiwanese integrated circuit foundries and integrated circuit packaging achieved the highest sale growth rates for that industry worldwide. Taiwanese high-tech industries have overall consistently achieved significant accomplishments, making Taiwan the third largest producer of semiconductors. Taiwanese high-tech firms, however, face fiercely competitive rivals in other Asian countries such as Korea and Japan, and so the dynamic strategy of its high-tech industry has emerged as an imperative issue for Taiwan. The important role of Taiwanese high-tech industry in the world economy demonstrates that this sample is suitable for the issue of this study. The top high-tech firms, ranked by company assets, were extracted from 2001 to 2008. The observation of this time period could reflect the interactions of intangible assets and firm’s performance. Although the period 2001–2008 may be perceived as not enough long to measure DC, it may still account for the relationship between IC and performance. The original observations of 300 firms were matched with firm-level data from the Taiwan Economic Journal, which annually compiles a list of firms’ financial reports. These lists are well received in the professional economic and financial communities and various indexes of these surveys have been used to support numerous research projects (Chen and Huang, 2006; Chu et al., 2006; Hsieh, Kim and Yang, 2009; Ke, Chiang and Liao, 2007; Liu, Tseng and Yen, 2009; Peng and Fang, 2010). However, many firms did not report the type of information we require in this study, and those with firm-level information missing from the database were eliminated from the sample. The final data set consisted of 242 valid firms from the following industries: communication technology firms (SIC code 3663) (23%), electronic equipment firms (SIC code 3641) (18%), semiconductor and related device firms (SIC code 3674) (31%), computer firms (SIC code 3571) (12%), other machinery equipment firms (SIC code 3541) (16%). The industry classification system is based on the US-based Standard Industrial Classification (SIC), which was created by the US government (1941).


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Variables and measure Dynamic capability. In line with the definitions of DC in this study, the following properties of DC will receive attention to operationalize the notion of DC: (1) DC needs investment in specific resources; (2) the investment in DC needs to be continuous over a long time period, rather than in intervals; and (3) DC is asset-specific to a certain extent. As argued earlier we operationalize the notion of DC in terms of R&D and marketing capabilities. In doing so, we use the percentage increase in R&D and marketing deployment to capture the magnitude of change in a firm’s resource deployment over a 3-year period. In a high-tech industry, the DC for R&D and marketing requires at least 3 years for conversion into a successful product (Kor and Mahoney, 2005). Accordingly, to capture the historical dynamics in investment levels, we establish the functions to calculate the average percentage increases in two proposal indexes for DC during period t, t 1 and t 2. We created a composite measure of DC based on the two specific capabilities (R&D capability and marketing capability). The overall score is an average of the two items. Intellectual capital. Both theoretical and empirical research has been undertaken on IC in recent years. Measuring and managing IC has been found to be important for a company’s long-term success, and thus numerous IC indicators have also been identified. With regard to the empirical research on the indicators that have been proposed and are used to measure IC, researchers have developed various measurements for each category of IC that enable the intangibles reported to be compared with other firms. Published IC measurement models can be divided into three groups, including scorecards (Edvinsson and Malone, 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 a reflection of the different facets of IC. We measured HC using three ratios, covering (1) educational level, (2) work ability and (3) the value and uniqueness of the organizational workforce. Our method of measurement was drawn in principal from Edvinsson and Malone (1997), Huselid, Jackson and Schuler (1997), Youndt,

Subramaniam and Snell (2004) and Wang (2008). We use average educational level to measure employee profession. Employees have a higher educational level that enables them to do their job successfully. We use employee productivity to operationalize work ability. A higher employee productivity reflects a stronger work ability of employees. The value and uniqueness of the organizational workforce was measured by the ratio of employee added value, indicating the added value created by employees in relation to the value of the organization. As in the research of Canibano, Garcia-Ayuso and Sanchez (2000), Liu, Tseng and Yen (2009), Lynn and Dallimore (2002), we used two indexes to measure SC. Information system process is measured by the ratio of information technology expense to total administrative expense (i.e. the information technology expense ratio). In this process we see more inter-unit exchange; furthermore, innovation of information technology expense can facilitate inter-firm learning and cross-functional team effectiveness. Product development process is operationalized as the ratio of administrative expense to total revenue. A product development process refers to the sequence of activities which an enterprise employs to conceive, design and commercialize the product (Ulrich and Eppinger, 2003). Many of these activities need high-level R&D employees and precision equipment. Administrative expenses include items such as salaries (e.g. information technology employee salaries) and rent (e.g. facilities rent) (Bernstein and Wild, 2000). There is a tendency for these expenses to increase, especially in prosperous times (i.e. the period of successful product development). Therefore, higher administrative expense represents faster growth of product development (Moon and Kym, 2006). Although the use of the ratio of R&D expense to total operating expense has been adopted in many studies (e.g. Chan, Lakonishok and Sougiannis, 2001), we were concerned about the occurrence of the multicollinearity of DC for R&D. Therefore, we deleted this ratio. We also adopted two ratios from Lynn and Dallimore (2002), Van Buren (1999) and Pablos (2003) to measure RC. The ratio of 5% of key account sales divided by the total sales represents a proxy of the customer relationship measure. According to the 80/20 principle, 20% of key account sales account for 80% of company sales.


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Clarifying the Effect of Intellectual Capital on Performance Table 3. Definitions of independent variables Constructs Intellectual capital (IC) Human capital (HC)

Variables

Adapted from

AEL: average educational level of employee

Edvinsson and Malone (1997); Huselid, Jackson and Schuler (1997); Youndt, Subramaniam and Snell (2004); Wang (2008)

Structural capital (SC)

EP: employee productivity EAV: employee added value ITR: information technology expense ratio

Relational capital (RC)

ARR: ratio of administrative expense to total revenue KA: key account

Performance

ESR: percentage of total expenses paid to main suppliers RD: % increase in R&D development 5 (1/2)f[(RDEt1 – RDEt2)/ RDEt2]1[(RDEt2 – RDEt3)/RDEt3]g MK: % increase in marketing development 5 (1/2)f[(MKEt1 – MKEt2)/ MKEt2]1[(MKEt2 – MKEt3)/ MKEt3]g ROA: return on assets

Control variables

FS: firm size

Dynamic capability (DC)

FA: firm age LE: leverage

EI: export intensity 5 export sales/total sales

Lynn and Dallimore (2002); Edvinsson and Malone (1997); Canibano, Garcia-Ayuso and Sanchez (2000); Liu, Tseng and Yen (2009)

Lynn and Dallimore (2002); Van Buren (1999); Pablos (2003)

Kor and Mahoney (2005); D’Este (2002); Thornhill (2006)

Firer and Williams (2003); Hitt, Hoskisson and Kim (1997); Lant, Milliken and Batra (1992) Heimeriks and Duysters (2007); Hsu and Pereira (2008) Huergo and Jaumandreu (2004); Thornhill (2006) Smith and Warner (1979); Delios and Beamish (1999); Geringer, Tallman and Olsen (2000) 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.

As stated by Piercy and Lane (2006), the clients of most companies are diverse, with some representing an extremely high share of their sales. As a consequence, firms dedicate most of their resources to their key accounts (Homburg, Workman and Jensen, 2002; Workman, Homburg and Jensen, 2003). A core assumption in the key account literature is that firms are willing to increase their input to important customers because they hope to enhance the relationships (e.g. Ivens and Pardo, 2007). We use the percentage of total expenses paid to major suppliers over total revenue to measure the relationships between firms and their major supplier partners. Table 3 shows the operationalization, indicators and sources of all the constructs in the proposed model.

Performance. Performance is operationalized in terms of the monetary terms that a firm receives in exchange for the price it pays for products or services. Transaction cost theory (Williamson, 1985), which supports this rationale, has dominated theoretical and empirical research in this area. Traditional performance measurement employs a financial-based index (Usoff, Thibodeau and 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 to be more sensitive to capital structure difference. Both ROA and return on sales generate similar findings and are highly correlated (r 5 0.76). The average pre-tax earnings of a company for a period of time are divided by the average tangible


14


assets of the company, i.e. ROA. The result is a company ROA that is then compared with the industry average. While some studies have made use 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 ROA is more appropriate in IC studies because ROA is useful in high-tech industry for stock market valuations. It can be used to illustrate the financial value of intangible assets. This feature tends to get the attention of CEOs. For these reasons, this study uses ROA, which is collected from the Taiwan Economic Journal Database for 242 high-tech firms over the period 2001–2008, as the measure of firm-level performance. Control variables. To support the theoretical model in this study, we also include four control variables because of their potential impact on performance, as suggested by the extant literature. Firm size is proxied by the natural logarithm of total capital (Segars and Grover, 1995). Huergo and Jaumandreu (2004) also controlled for firm’s age because they predicted that IC creation was inherently revolutionary in nature and would thus be influenced by a firm’s age (Rosen, 1991; Zahra, 1999). Leverage, which is another control variable, is employed as a proxy of a firm’s capital structure. It reflects a firm’s financial risk, which might limit the firm’s available economic resources to support long-term intangible investment (Smith and Warner, 1979). It is calculated as the reaction of longterm debt over total assets. Following Shoham (1996) and Geringer, Tallman and Olsen (2000), we also used an environmental variable, which measures internationalization using the index of export intensity. International markets, in general, can affect the success of the firm’s innovation activities. One reason is that companies must address diverse and inconsistent laws, national cultures and industry forces (Rosenzweig and Singh, 1991; Zahra and Garvis, 2000). This seems to be a good relative indicator and has been widely used. The operational definition of internationalization is export intensity (Aulakh, Kotabe and Teegen, 2000). Bayesian regression model Dynamic environments are often complex, and the relationship between predictor and the

resulting risks must be explored over the long term rather than at a specific point in time. One difference between Bayesian regression and the classical approach is that Bayesian methods can incorporate information external to the study for analysis (Gelman et al., 2004). Such information is specified in a prior distribution and is combined with the study data in the form of the probability of producing a posterior distribution on which inferences are based (Zhao et al., 2006). Thus, the basic motivations for adopting a Bayesian approach to analyse a dynamic model is that prior knowledge or pilot information can easily be incorporated into such a model. The incorporation of prior information often results in inferences that are more precise than those obtained with traditional methods (De la CruzMesia and Marshall, 2003). Among methods based on probability methods, Bayesian inference offers several advantages over traditional statistical estimates. The first is that prior information can be incorporated into the analysis. In analysing DC, the inclusion of prior information leads to an important pragmatic advantage. Many statistical models often require information about the underlying unknown parameters, and some parameter values are just not applicable to the underlying management theory (e.g. RC and HC in our model). A Bayesian analysis makes it very easy to incorporate such information directly. A second advantage is the comparative ease with which various sources of uncertainty can be incorporated accurately into the analysis. Bayesian methods estimate the probability distribution of parameters in complex models without relying on large sample approximation to normality (Congdon, 2003). Any conclusion derived from a traditional statistical analysis should include an indication of the uncertainty of the conclusion. For example, the point estimate of an unknown parameter is more or less worthless without an indication of the uncertainty underlying the estimate. Bayesian regression is not the main topic of this paper; we give the brief concept of the Bayesian method in the Appendix. Bayesian treatment of the regression model in this paper is a simple case of hierarchical regression analysis (Shively, Sager and Walker, 2009). A recent example for application of Bayesian regression is taken from an optimal policies of inventory


15

Clarifying the Effect of Intellectual Capital on Performance model study carried out by Azoury and Miyaoka (2009). In order to investigate the mediating effect of DC, the following three Bayesian regressions are constructed: ^ ¼ i1 þ cÎC þ d1 LE ^ þ e1 FS ^ þ f1 FA ^ ROA ^ þ g1 EI

ð1Þ

A c ROA

IC B

DC a

ROA

IC c′

^ ¼ i2 þ aÎC þ d2 LE ^ þ e2 FS ^ þ f2 FA ^ DC ^ þ g2 EI

b

ð2Þ

Figure 2. (a) Direct effect: IC affects ROA. (b) Mediation design: IC affects ROA indirectly through DC.

^ ¼ i3 þ c0ÎC þ bDC ^ þ d3 LE ^ þ e3 FS ^ ROA ^ þ g3 EI ^ þ f3 FA

ð3Þ

where LE is leverage, FS is firm size, FA is firm age, EI is export intensity, i is the intercept, and a, b, c, d, e, f, g and c 0 are parameters. In order to estimate parameters of regression, most previous studies investigating a dynamic system are based on traditional statistical methods such as original least squares, partial least squares and maximum likelihood. However, estimating methods that need to capture the dynamic characteristic and consider uncertainty are necessary. Here we use the Markov chain Monte Carlo (MCMC) method (Cowles and Carlin, 1996; Gimenez et al., 2009), which has been proved to be appropriate for handling such issues and can be used in dynamic prediction (Spiegelhalter et al., 1996), although it has not been used in the management literature as much as it could be. Thus, parameter estimates were produced by an MCMC algorithm, Gibbs sampling, using Version 1.4 of the program BUGs (Bayesian inference using Gibbs sampling) (Spiegelhalter et al., 2003). A variable that accounts for the relation between the predictor and the criterion is referred to as a mediator (Baron and Kenny, 1986). According to Mackinnon (2000), regression is the most common method for testing mediation. Figure 2(a) represents the effect of IC on firmlevel performance, which is often referred to as the direct effect. We use equation (1) to estimate parameter c. Figure 2(b) represents the simplest form of mediation, in which DC mediates the effect of IC on performance. We refer to it as the total effect (Frazier, Barron and Tix, 2004; Preacher and Hayes, 2004). We use equation (2) to estimate parameter a and equation (3) to estimate parameters b and c 0 (i.e. total effect).

Result Table 4 presents variable descriptive statistics and a correlation matrix. The most common method for testing mediation was developed by Kenny and his colleagues (Baron and Kenny, 1986; Kenny, Kashy and Bolger, 1998). Following this method, there are four steps in establishing whether DC mediates the relation between IC and performance by performing the abovementioned three Bayesian regressions. Table 5 presents the estimates obtained from the Bayesian analysis. First, equation (1) shows that there is a significant relation between HC (b1 5 0.9371, po0.001), RC (b2 5 0.1806, po0.001) and SC (b3 5 0.1321, po0.01) and performance (see path c in Figure 2(a)). Thus, Hypotheses 1a, 1b are 1c are 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 of HC (b1 5 0.6641, po0.01), RC (b2 5 0.8670, po 0.001) and SC (b3 5 0.6753, po0.01) on DC (see path a in Figure 2(b)). Thus, Hypotheses 2a, 2b and 2c are supported. Third, the mediating role of DC related to performance is estimated controlling for the effects of DC on performance (b2 5 0.3344, po0.01). Thus, Hypothesis 3 is also supported. These results are in agreement with previous studies (Zott, 2003). The final step is to show whether or not the strength of the relation between IC and performance is reduced when the mediator (i.e. DC) is added to the model. More specifically, we need to compare path c in 2(a) and path c 0 in 2(b). When the effect of IC on performance decreases to zero (i.e. not statistically significant) with the inclusion of DC, perfect


16 AEL, average educational level of employee; EAV, employee added value; EP, employee productivity; ITR, information technology expense ratio; ARR, ratio of administrative expense to total revenue; KA, key account; ESR, percentage of total expenses paid to main suppliers; RDE, R&D expenditure; MKE, marketing expenditure. *po0.05; **po0.01.

1 0.095 1 0.054 0.198** 1 0.009 0.159* 0.046 1 0.003 0.186** 0.095 0.049 1 0.040 0.207** 0.209** 0.113 0.040 1 0.092 0.057 0.025 0.035 0.099 0.147* 1 0.076 0.009 0.032 0.127 0.036 0.362** 0.024 1 0.093 0.313** 0.140* 0.039 0.208** 0.344** 0.115 0.042 1 0.094 0.001 0.012 0.019 0.111 0.057 0.301** 0.019 0.386** 1 0.236** 0.357** 0.124 0.203** 0.072 0.024 0.147* 0.145* 0.300** 0.339** 1 0.527** 0.089 0.327** 0.018 0.200** 0.270** 0.029 0.264** 0.152* 0.247** 0.202** 1 0.463** 0.289** 0.276** 0.090 0.023 0.113 0.062 0.028 0.396** 0.150* 0.210** 0.431** 0.089 0.135 0.506 0.44 1 0.097 0.231 0.678 1.034 0.188** 1.32 3.290 25.816 9.571 0.623** 12.032 12.595 71.570 1.942 0.254** 0.376 0.214 0.963 0.000 0.076 0.042 0.038 0.275 0.003 0.174* 0.720 0.249 0.997 0.013 0.097 0.173 0.317 1.500 0.677 0.322** 0.162 0.382 3.083 0.659 0.188** 0.486 2.804 28.319 0.618 0.006 0.494 0.248 0.983 0.002 0.394** 13.940 7.689 39.0 2.0 0.059 0.504 0.244 0.912 0.002 0.138 14.940 7.586 39 4 0.127 1. ROA 2. AEL 3. EAV 4. EP 5. ITR 6. ARR 7. KA 8. ESR 9. RDE 10. MKE 11. LE 12. FS 13. FA 14. EI

1 Min Max SD Mean

Table 4. Correlations matrix and descriptive statistics

2

3

4

5

6

7

8

9

10

11

12

13

L.-C. Hsu and C.-H. Wang mediation is said to have occurred, which is referred to as complete mediation (James and Brett, 1984). When the effect of IC on performance is a non-trivial amount, but not zero, partial mediation is said to have occurred. In equation (3), SC (b4 5 0.0805, p40.1) is not statistically significant. This indicates that the effect of SC is completely mediated by DC. Here, HC (b2 5 0.5524, po0.001) and RC (b3 5 0.1068, po0.001), which are smaller than those of equation (1), are statistically significant. Thus, the results provide evidence for the partial mediating role of DC in the HC–performance link and the RC–performance link.

Discussion and managerial implications Our interest in investigating the relationship between IC and performance was triggered by two observations, the first of which was the inherent uncertainty of the fast changing dynamic environment widely noted in organizational studies as theorized by Peteraf (1993). Although the RBV shifts the focus of strategy to firms’ internal characteristics by identifying its resources in a specific time period and how these may have been created, we argue that under static circumstances the RBV is equally, if not more, relevant for understanding IC. The RBV can be regarded as a static theory (Priem and Butler, 2001) because it fails to address the fundamental issue of how future resources can be created and then accumulated for firms in a dynamic environment. Most studies argue that IC is grounded in RBV logic (Reed, Lubatkin and Srinivasun, 2006), but our findings suggest that IC needs to be focused on dynamic strategic considerations. Since the association between IC and DC does indeed represent a causal connection between the two concepts, our results have important managerial implications. To provide further coherence to our conceptual model, we identified DC as the primary strategy that mediates the effects of IC on firm-level performance. DC underscores the accumulation of capabilities embedded in an organization and is posited to be directly associated with its financial performance. There has to be a conscious strategy of seeking an advantageous position in IC and implementing it. An appreciation of the impact of IC on DC will


17

Clarifying the Effect of Intellectual Capital on Performance Table 5. Testing mediator effects using Bayesian regression Testing steps in mediation model

Testing step 1 (path c) Outcome: ROA Predictor: HC (Hypothesis 1a) RC (Hypothesis 1b) SC (Hypothesis 1c) Testing step 2 (path a) Outcome: DC Predictor: HC (Hypothesis 2a) RC (Hypothesis 2b) SC (Hypothesis 2c) Testing step 3 (paths b and c 0 ) Outcome: ROA Mediator: DC (path b) (Hypothesis 3) Predictor: HC RC SC Control variables LE FS EI FA Constant Adj R2 F

Equation (1)

Equation (2)

Coefficients

t values

0.9371*** 0.1806*** 0.1321**

8.8420 4.8976 2.4396

Equation (3)

Coefficients

t values

0.6641** 0.8670** 0.6753***

3.0232 3.7923 4.2007

Coefficients

0.3344** 0.5524*** 0.1068*** 0.0805 0.3066*** 9.8876 4.0125 0.0573*** 0.0242 1.4525 0.0400*** 3.7073 3.6482 0.1707*** 0.4670 10.2931***

0.3961*** 1.7890 0.0294* 2.7012 0.0103 1.5808 0.1011*** 4.8250 0.9861*** 5.1046 0.4731 16.2356***

t values

2.832 7.4210 3.5270 0.5444

0.4398*** 10.837 0.0271* 2.5608 0.0299 1.1287 0.0170 0.1868 0.037 0.6914 0.5816 20.8360***

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

enhance the level of a firm’s performance and its sustainability. Second, by elaborating our theoretical model in terms of the three distinct sub-constructs of IC and two sub-constructs of DC, we offer a rich set of results. Our results show that DC completely mediated the SC–performance link, but only partially mediated the HC–performance link and the RC–performance link. This supports our conceptualization of the mediating role of DC between IC and performance. We find that the effect of HC and RC on performance is partially mediated by DC. This result suggests that HC and RC might directly affect performance or indirectly affect performance through DC. We deduce this finding from two effects. First, we examine the sources of performance, especially HC. The direct effect of HC on performance is significant, and this finding is in agreement with Carmeli (2004) and other 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 explore the mediating role of DC between the HC– performance link, finding that there is a plausible explanation to the partial mediation. This finding stresses that HC derived through DC accumulation strongly contributes to performance, which has important managerial implications. HC plays a central role not only in technology innovation, but also in new knowledge absorption. Today the success of any firm is measured in terms of continuous innovation, relying on retaining employees with skills and knowledge rather than high employee turnover. Organizational learning theory supports the rationale for this finding (Nelson and Winter, 1982). Organizational learning is an intrinsically social and collective phenomenon, involving joint problem-solving and coordinated search. It may require the skills and knowledge of individuals. Organizational learning is also cumulative and path-dependent; what is learned and practised is stored and exposed in the firm’s economic performance.


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Thus, long-term HC accumulation can be seen as idiosyncratic problem-solving knowledge capital. As predicted, the results show that, although DC does not account for all the effects of RC on performance, it does act as an important mediator. The direct effect of RC on performance is consistent with our discussion about the main source of performance using RC in the context of this study. In the prevalent literature, RC and DC have rarely been studied together, with the exception of Blyler and Coff (2003). The relationship between RC and performance is important, as DC significantly mediates this link. This is a significant finding due to its strategy implications that RC must be involved in DC for R&D activities and marketing activities, and this will affect performance. Our finding offers a relational view of competitive advantage that focuses on network routines and processes. This framework is valuable because it provides a theoretical basis for cumulative firm capability in our understanding 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 relationships both on the supply side (e.g. with R&D institutions or research laboratories) (Lee, Lee and Pennigs, 2001) and on the demand side (e.g. with customers) (Ulaga, 2003). From the supply-side relationship, a firm’s relationships are with actors involved in research and technology development. In a dynamic environment, organizations develop close cooperation with specific partners and then nurture social ties with those partners. These social ties should support the firm. Partnerspecific experience facilitates dyadic relationship adjustments, which suggests that prior ties facilitate adjustment as a consequence of familiarity and the development of inter-organizational routines. In this study, we show that at least some of the RC manifests its influence on a firm’s performance through establishing long-term relationships with supply-side partners. From the viewpoint of demand-side relationships, RC investments are made by firms for the development of customer relationships in order to acquire tangible benefits such as lower cost, higher quality and more reliable delivery. RC investment in long-term customer relationship accumulation enables access to loyal customers

and new customers, as well as providing new market information and service (Rocks, Gilmore and Carson, 2005). In these situations, the customers may arguably be prepared to help the firm through information sharing, technical assistance and loyalty programmes, in return for the benefits of improved performance. The prior literature has stressed the positive link between SC and performance (Bru¨derl and Preisendorfer, 1998). Interestingly, our findings show that DC plays a fully mediative role in this relationship. This finding supports the important implication that DC should be employed as a significant means of resource renewal and restoring capability diversity, as well as avoiding the inertia and simplicity that result from the scarcity of long-term efficient resource deployment within an organizational structure. Meanwhile, organizational processes, one component of SC, depend on employees actually using the information or knowledge resources available to them. Processes directly affect the efficiency of the employees’ actions. Once firms obtain a unique routine process and stock this know-how, it ultimately becomes an important resource for performance. Another component of SC is the information system used in managing knowledge. An information system with an innovation component may not have a great effect on the performance of an organization, but when the information system comprises not only the knowledge created by but also stored in a firm’s technology system, SC would represent the principal source of firmlevel innovation. This interpretation is consistent with the finding of Dierickx and Cool (1989). We show that such a decomposition helps enrich the understanding of SC phenomena we discovered when SC is viewed as a dynamic resource; this should result in long-term competitive advantage and business-specific advantage, which should positively impact financial performance, as predicted in our model. Given the cross-section and time series nature of our data set, we pool together observations across firms (242 high-tech firms) and years (2001–2008). Pooling data estimation methods have the advantage that they allow us to account for long-term performance of IC over time. The model may not represent firms that are driven by short-term orientation only. However, prior studies are mostly based on a cross-sectional design and qualitative research (e.g. Keil, 2004;


Clarifying the Effect of Intellectual Capital on Performance Lazonick and Prencipe, 2005; Meyer and LiebDoćzy, 2003; Noda and Collis, 2001) and do not fully capture the IC process. For example, RC investments may be reflected in the future, something that is impossible to identify with cross-sectional data. Thus, the model developed and tested here could benefit from being tested in a longitudinal design based on secondary data. The secondary data set, as well as the indexes, of methodology have been widely employed in the managerial literature (Firer and Williams, 2003; Krause, Handfirld and Tyler, 2007; Le, Walters and Kroll, 2006; Rothaermel and Deeds, 2006; Subramaniam and Youndt, 2005).

19

manifests a meditative role on the relationships between IC and performance. Third, the Bayesian regression model is considered here as an alternative to the traditional approach. In response to the necessity of dynamic analysis, Bayesian statistics provides a formal model for uncertain environments. Bayesian regression, which is also called dynamic regression, can capture the nature of dynamic models because the parameter estimation is updated over time through iterative simulation (Gelman et al., 2004), although it has not been used in the study of management as much as it could be.

Limitations and future research directions Theoretical contribution By empirically validating a theoretically derived model, this study offers three major contributions to the study of strategic management. First, the role of IC in affecting performance is well discussed in previous management studies. What is less understood is how IC affects performance in dynamic environments over time. A static perspective suggests that IC is strongly and positively related to firms’ performance, but for different reasons. We extend prior IC research on the static concept of short-term discrete investment to the dynamic concept of long-term capability accumulation. We add to the conceptual richness of the construct by considering the implications of DC. Our findings are particularly noteworthy. The results suggest that while a firm may have good performance in the short term, it may struggle with rivals in a turbulent environment, because accumulated capability may be scarce in the long term. Second, we investigate to what extent DC mediates the effect of IC on performance. The combination of DC and IC theory opens up a new domain of dynamic strategy. Our focus on DC complements the previous research that has begun to explore the process whereby IC is associated with performance. More specifically, we integrate IC and the influence of DC on performance into one model and reconcile what had previously been presumed to be independent. In the prevalent research, DC and the three subconstructs of IC are rarely studied together. In this study, we show that at least some DC

While we believe we have developed a sound and rich theoretical model and tested it with reliable secondary data, there are some limitations. As discussed earlier, more dynamic capabilities exist beyond the realm of R&D and marketing. Capturing a more comprehensive picture of DC may allow researchers to capture a full rather than a partial mediating role of DC. Moreover, the partial mediating role of DC on HC and RC needs further investigation. Future research can explore in greater detail the real causes of the partial mediating role of DC on the HC– performance link and RC–performance link. It is vital that its causes are explored and then used for the strategic space in the competitive horizon of the company. Moreover, it is also highly desirable to replicate this study with other types of firms and industries to generalize the empirical findings and determine whether the same relationships hold. In the growing number of IC studies, no generally accepted conceptualization of performance has emerged (Krause, Handfirld and Tyler, 2007). Some authors posit that performance does not necessarily employ financial technique (Easton and Araujo, 1992). However, our proposed model indicates that performance is measured in terms of ROA. More research is necessary to show that firms with relatively high IC are more likely to employ non-financial measures such as the balanced scorecard (Sveiby, 1997), the intangible assets monitor, or the Skandia navigator (Skandia, 1994). Such a measurement might also help companies identify


20


determinants which have matched with their performance. The inability of the measurement system to cope with the intangible nature of IC is often seen as a problem. An important area for further research involves identifying and operationalizing HC, RC and SC. Researchers could use other variables to operationalize the three sub-constructs of IC such as relationship benefit and sacrifice (Lapierre, 2000) used by RC, leadership index and employee retention used by HC (Skandia, 1994), and the outsourcing index used in SC (Skandia, 1994). If it is found that these variables can precisely measure the three subconstructs of IC, this will promote a fuller understanding of the concept of IC for the strategic manager. It is imperative that strategic managers fully understand the complexity of IC and the relative effectiveness of each context to help maximize firm-level performance. Finally, a limitation of using Bayesian regression is that the results are not based completely on observed data. Future research could extend the empirical analysis of our paper to observed data to examine how IC affects firm-level performance through DC. We hope that our study serves to support such an understanding.

Appendix This appendix is not a thorough review of Bayesian methods. But Bayesian methods are less familiar than some other statistical approaches in strategic management, and so it is appropriate to provide some methodological background. We provide a brief summary of the Bayesian regression methods employed for the analysis in this study.

Bayesian estimation Bayesian inference combines (1) a likelihood function with (2) prior probability distributions for the parameters of each model to produce (3) posterior probability distributions for quantities of interest. In essence, Bayesian analysis uses data to move from a state of great uncertainty about parameter values, described by prior probability distributions, to a state of greater certainty, described by posterior probability distributions.

(1) The likelihood function quantifies the probability of observing the data given particular parameter values relative to the probability of the data under other parameter values. (2) Prior probability distributions, or priors, describe beliefs or information about parameter values prior to the observations that form the data set. Newcomers to Bayesian methods are often uncomfortable about specifying priors since this requires subjective decisions when little hard information is available for guidance. (3) Posterior probability distributions describe the degree of belief that can be assigned to possible values of the model parameters, summarizing information contained in the data and the prior distributions. As the sample size increases, the influence of the priors on the posteriors diminishes.

Implementation of Bayesian models For this study, Bayesian models were implemented in WinBUGs 1.4 (Spiegelhalter et al., 2003). WinBUGs allows estimation of a wide variety of Bayesian models using MCMC simulation. MCMC methods approximate posterior distributions by drawing large numbers of samples from them. By increasing the lengths of the Markov chains, summaries of the distribution, such as means, can be estimated to an arbitrary degree of precision. The user’s manual demonstrates how to specify a model, how to use the software to estimate parameters and how to interpret the output. The following are the WinBUGs model specifications for the examples in this paper. # The model 1 model; # Priors for regression parameters f a0 dnorm(0.0,1.0E-6) a1 dnorm(0.0,1.0E6) a2 dnorm(0.0,1.0E-6) a3 dnorm(0.0,1.0E6) a4 dnorm(0.0,1.0E-6) a5 dnorm(0.0,1.0E6) a6 dnorm(0.0,1.0E-6) a7 dnorm(0.0,1.0E6) # Define the regression for(i in 1:N) f mu[i]o a01a1*HC[i]1a2*RC[i]1a3*SC[i]1a4*LE[i 1]1a5*FS[i]1a6*EI[i]1a7*FA[i] g


21

Clarifying the Effect of Intellectual Capital on Performance

for(i in 1:N) f ROA[i] dnorm(mu[i],tau) g # Priors for precision parameters tau dgamma(1.0E-3,1.0E-3) # Monitor the standard deviation sdo- 1/sqrt(tau) g # The data list(N 5 1452, HC 5 c(0.1915, 0.1688, 0.1737, . . .), RC 5 c(0.489, 0.266,0.2774, . . .), SC 5 c(0.1333, 0.129, 0.1385, . . .), LE 5 c(0.2034,0.1561,0.1276, . . .), FS 5 c(2.2648, 2.29, 2.301, . . .), EI 5 c(0.3164, 0.361, 0.31, . . .), FA 5 c(7, 8, 9, . . .), ROA 5 c(0.116, 0.039, 0.0112, . . .)) # The initial value list(tau 5 1)

i

HC

…

FA

sd mu[i]

X[i]

ROA

tau

for (i IN 1: N)

Figure 3. Graphical model for Bayesian regression

A graphical representation of the model appears in Figure 3.

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Li-Chang Hsu is an associate professor of economics at Ling Tung University, Taiwan. His current research interests are in the area of technology management, strategy management and forecasting theory. His papers have been published in the African Journal of Business Management, Technological Forecasting and Social Change, Expert Systems with Applications, Journal of Electronic Commerce Research, Service Business and others. Chao-Hung Wang is an associate professor of management at Ling Tung University, Taiwan. His current research interests are in the area of strategy management, relationship marketing and dynamic modelling. His papers have been published in the African Journal of Business Management, Asia Pacific Management Review, Technological Forecasting and Social Change, Tourism Management, Expert Systems with Applications, Journal of Electronic Commerce Research, Service Business and others.
