Proposing and Testing an IntellectualCapital-Based View of the Firm
Kira Kristal Reed, Michael Lubatkin andNarasimhan SrinivasanSyracuse University; University of Connecticut; University of Connecticut
abstract This study examines one specific aspect of the resource-based view,intellectual capital, and its three knowledge components – human, organizational,and social capital. We hypothesize that the impact of each component on financialperformance is contingent upon the values of the other components, and that theseleveraging effects are themselves contingent upon the industry conditions in which abusiness operates. Our hypotheses are supported using line-of-business survey and FDICdata (within-industry/within-geographic region) from two non-competing resourceniches of the banking industry (personal and commercial banking).
INTRODUCTION
According to resource-based theory, a firm’s resources – particularly intangibleones – are more likely to contribute to firms attaining and sustaining superiorperformance when they are combined or integrated (Barney, 1991). This notionmight have originated with Penrose, who claims that when ‘there is an interactionbetween two kinds of resources of a firm – it affects the productive service availablefrom each (resource)’ (1959, p. 76). The importance of resource integration issimilarly evident in Teece et al. (1997), who note the difficulty that competitorswould have in duplicating a competitive advantage based on a combination ofvaluable firm-specific resources, because these combinations generally arise froman organizational process that is causally ambiguous, path dependent, and sociallycomplex. Furthermore, the importance of resource integration resonates with Dyerand Singh’s (1998) notion of complementary resources. They argued that higherrents can be generated by combining complementary resources, because the com-bined set is indivisible, and, therefore, distinctive.
Address for reprints: Kira Kristal Reed, Syracuse University, Martin J. Whitman School of Manage-ment, Department of Strategy and Human Resources, Syracuse, New York 13244-2130, USA([email protected]).
The very attributes that give resource integration its theorized advantages,however, pose thorny challenges to researchers: How do we conceptualize andthen measure a concept that is based on some firm-specific interaction of resources,which themselves are intangible, and therefore, unobservable? On these matters,critics of RBV like Foss and Knudsen (2003) and Priem and Butler (2001) expressfive primary concerns. First, RBV is not prescriptive in that it does not providemanagers with useful advice as to which specific resources they should accumulateto gain an advantage. Second, RBV lacks a clear definition of competitive advan-tage. Third, RBV suffers from a tautology problem stemming from the fact thatresources are defined in terms of the performance outcome associated with them.Fourth, RBV is ambiguous as to its relevant domain. And fifth, RBV is too general,in that many potentially advantageous resource configurations are possible, thussuggesting equifinality. These five concerns, all having to do with RBV’s lack ofspecificity have raised questions as to its status as a legitimate theory, and make itdifficult to design and test empirically.
We propose a pragmatic, though partial, resolution to these concerns based onrecent remarks by Peteraf and Barney (2003) and insights drawn from an emergingmid-range theory, an intellectual capital-based view of the firm (ICV). As a mid-range theory, ICV should lend itself better to hypotheses development and empiri-cal testing than RBV’s more generalized view. We say ‘mid-range’ because,following Pinder and Moore’s (1979) definition of mid-range theories, ICV repre-sents one specific aspect of the more general resource-based view, in that it morenarrowly considers three resources that have been theoretically linked to a firm’scompetitive advantage. Thus, ICV is well suited for dealing with the first concern.Specifically, ICV deals solely with knowledge that is created by and stored in afirm’s three capital components; i.e., in its people (human capital), social relation-ships (social capital), and information technology systems and processes (organiza-tional capital) (Edvinsson and Malone, 1997; Wright et al., 2001). Like Oster (1999)and Peteraf and Barney (2003), we deal with the second concern by definingcompetitive advantage in terms of the resource characteristics that allow a firm tooutperform rivals in the same industry. Following Peteraf and Barney (2003), weattempt to avoid the tautology problem (third concern) by defining knowledgeresources by their theoretical associations with competitive advantage and not bytheir empirical financial association.
We also say ‘mid-range’ because we examine ICV’s intra-industry associationwith the financial performance of a limited class of businesses, two non-competing resource niches of the North East US banking industry – the personaland commercial banking sectors. By attempting to hold constant the exogenousinfluences associated with industry and geographic region, we are more able tofocus our analysis of financial performance at the resource-base level, or the levelof analysis controlled by an enterprise, which Peteraf and Barney (2003) arguerepresents RBV’s relevant domain (fourth concern). Finally, and consistent with
Deephouse’s (1999) theory of strategic balance, the last concern about equifinal-ity should be reduced, given the mature and highly regulated nature of bothresource niches.
Following RBV’s more general resource interaction hypotheses, we test thethesis that the intellectual capital components represent, in Dyer and Singh’s(1998) terms, complementary resources. Accordingly, the knowledge embeddedin one component of intellectual capital (IC) can leverage the value of knowledgein the other components, such that the combination of the two results in a dis-tinctive, indivisible resource endowment that directly affects a business’ financialperformance. Also, following RBV’s assertion that resources and capabilitiesunderlie persistent performance differentials among firms, we tested our IC inter-action hypotheses twice, using two contiguous years of financial performancedata. Finally, we reason that the performance impact of the structure of thesecontingent interactions is contingent on the industry conditions that exist in anarrowly defined resource niche, specified as within-industry/within-geographicregion.
THE DIFFERENT COMPONENTS OF INTELLECTUAL CAPITAL
ICV is complementary to Leonard-Barton’s (1992) more widely understoodknowledge-base view (KBV). While both seek to explain the hidden knowledge-based dynamics that underlie a firm’s value, and both are grounded in an RBVlogic, they differ in focus. KBV is primarily interested in evaluating the effective-ness of a firm’s use of knowledge-management tools as knowledge-generatingmechanisms, such as its information technology systems and information manage-ment systems (Leonard-Barton, 1992; Nonaka et al., 2001). In contrast, ICV’sfocus is on the stocks and flows of knowledge capital embedded in an organizationand is posited to have direct associations with its financial performance ( Youndtet al., 2004).
But, what are the different components of IC? Edvinsson and Malone (1997)posit IC is a two-level construct: human capital (the knowledge created by, andstored in, a firm’s employees) and structural capital (the embodiment, empower-ment, and supportive infrastructure of human capital). They then divide struc-tural capital into organizational capital (knowledge, created by, and stored in, afirm’s information technology systems and processes, that speeds the flow ofknowledge through the organization) and customer capital (the relationships thata firm has with its customers). Bontis (1996) also discusses customer capital as oneaspect of what he calls ‘relational capital’, or the capital that encompasses allexternal relationships. His view is similar to that referred to as external socialcapital by sociologists (Bourdieu, 1985; Burt, 1992; Coleman, 1998) and man-agement theorists (Adler and Kwon, 2002; Nahapiet and Ghoshal, 1998; Pen-nings et al., 1998; Stewart, 1997; Youndt et al., 2004). Finally, some management
An Intellectual Capital-Based View of the Firm 869
theorists identified another component, internal social capital, or the capital asso-ciated with internal relationships, for example, between employees and supervi-sors, or among employees (Leana and Van Buren, 1999; Nahapiet and Ghoshal,1998).
We reason from this literature that while the terms to label the various ICcomponents may differ, conceptually IC consists of three basic components:human, organizational, and social capital, the last containing both external andinternal dimensions. With this as background, we advance three contingenthypotheses, based on RBV’s resource interaction thesis that one component of ICcan leverage the value of knowledge in the other components, such that the relationof each component to financial performance is contingent on the knowledge valueof the other components. Consistent with Peteraf and Barney’s (2003) character-ization of RBV, our three hypotheses operate under a set of ceteris paribus assump-tions; that is, they hold constant exogenous influences of performance that mightemanate from levels of analyses other than that at the resource-level, such asPorter’s (1980) five-forces industry-level.
Using Social Capital to Leverage Human Capital
Human capital (HC), which has long been argued to be a critical resource fordifferentiating financial performance among firms (Carpenter et al., 2001; Coff,1997; Hitt et al., 2001; Pfeffer, 1994), involves both knowledge stocks (e.g. hiring ofeducated individuals) and knowledge flows (e.g. developing high levels of codifiedand tacit knowledge about a specific business and its particular market conditions)(Pennings et al., 1998).
While human resource management researchers posit that HC is directly asso-ciated with performance, recent extensions of social capital theory beyond itssocioeconomic origins (e.g. Coleman, 1998; Loury, 1987; Putnam, 1993; Schiff,1992) suggest that the inimitable value of HC can be enhanced by ‘the good willthat is engendered by the fabric of social relations and that can be mobilized tofacilitate action’ (Adler and Kwon, 2002, p. 17). In brief, rich internal and exter-nal social connections (i.e. containing information about best practices, customerneeds, competitor moves, and so on) that consist of high-status (competent andcredible) participants from a diverse set of disciplines, can reduce the amount oftime and investment required to gather information (Burt, 1992). Accordingly,these relationships can serve as valuable conduits for knowledge diffusion andtransfer (Coleman, 1998). They can also facilitate knowledge combinations,which can both support ‘knowledge creating organizations’ (Nonaka and Takeu-chi, 1995) and develop a firm’s intellectual capital (Nahapiet and Ghoshal, 1998).And, just as firms can have a human capital advantage, the complex processes thatevolve as a result of productive employee interactions can result in a humanprocess advantage (Boxall, 1996). As such, the combination of human and social
capital has process implications that can also increase financial performance.Similarly, Blyler and Coff (2003, p. 679) posit that ‘human capital (education,training, skills, etc) will not bring in critical new resources unless it is coupled withsocial networks’.
We reason, therefore, that higher levels of SC (i.e. more valuable social rela-tionships) should enhance the positive relationship between HC and performance.That is, social capital’s (SC) productive potential lies primarily in its ability toleverage the productivity of human resources, a view supported by a wide range ofHC-related phenomena such as inter-unit resource exchange and product inno-vation (Tsai and Ghoshal, 1998), entrepreneurship (Chung and Gibbons, 1997),new venture success (Florin et al., 2002), inter-firm learning (Kraatz, 1998), thecreation of intellectual capital (Hargadon and Sutton, 1997), and cross-functionalteam effectiveness (Rosenthal, 1996).
Consistent with this view, if social capital provides informational benefits (whoyou know affects what you know), it follows that the more informationally rich afirm’s internal and external ties, the more its employees will accomplish (e.g.absorb, learn, innovate). In turn, the more competent the employees (i.e. the highertheir human capital), the more they will value, assimilate, and apply knowledgefrom informationally enriched social ties (Cohen and Levinthal, 1990). This can setinto motion a virtuous and dynamic cycle: the more a firm’s human capital isenhanced by social linkages, the more attractive employees become to additionalinformationally enriched and high-status social ties, and so on. As such, we expectthat HC is positively associated with a firm’s financial performance, but its positiveassociation is enhanced, or leveraged, when combined with the firm’s internalsocial capital (ISC) and also by the firm’s external social capital (ESC). Statingformally these two contingent predictions:
Hypothesis 1a: A firm’s internal social capital will leverage the value of its humancapital such that the relationship between human capital and financial perfor-mance is contingent on the level of the firm’s internal social capital.
Hypothesis 1b: A firm’s external social capital will leverage the value of its humancapital such that the relationship between human capital and financial perfor-mance is contingent on the level of the firm’s external social capital.
Using Organizational Capital to Leverage Human Capital
Organizational capital represents a repository of knowledge that is accessiblethrough a number of sources, allowing for knowledge sharing and knowledgecreation among affiliated employees and external parties. Ulrich and Lake (1991)argue that organizational capital, more so than a firm’s business-level strategies like
An Intellectual Capital-Based View of the Firm 871
cost and differentiation, is defining the rules of the new competitive landscape.Similarly, Bartlett and Ghoshal (1998) posit that OC represents the principalsource of firm-level innovation. And Lane and Lubatkin (1998) show that theorganizational component of absorptive capacity (i.e. mechanisms used to assimi-late new knowledge) is the principal driver in explaining inter-organizationallearning. Thus, OC is comprised not only of the knowledge created by and storedin a firm’s information technology systems, as well as its structure and operatingprocedures (Edvinsson and Malone, 1997), but also of intangible elements likeculture and informal routines (Nelson and Winter, 1982; Ulrich, 1993).
Like HC and SC, we posit that HC and OC can be entwined in a virtuous anddynamic cycle. That is, OC derives its capabilities from employees – the types ofknowledge they possess and choose to store, and how they assimilate and interpretthat knowledge. In turn, OC enhances HC’s productive potential by providingemployees with a supportive, yet socially complex infrastructure (Edvinsson andMalone, 1997). Thus, HC and OC, when viewed in tandem as complementaryresources, should result in hard-to-imitate, business-specific advantages, which (aspredicted in Hypothesis 1) should positively impact financial performance. Accord-ingly, we reason that HC’s positive association with financial performance is alsoenhanced, or leveraged by, the firm’s OC. Thus:
Hypothesis 2: A firm’s organizational capital will leverage the value of its humancapital such that the relationship between human capital and financial perfor-mance is contingent on the level of the firm’s organizational capital.
Using Social Capital in Concert with Organizational Capital
SC (both internal and external) and OC represent conceptually distinct, butcomplementary, knowledge types that facilitate inter-unit exchange and innova-tion (Tsai and Ghoshal, 1998), interfirm learning (Kraatz, 1998), and cross-functional team effectiveness (Hargadon and Sutton, 1997). Moreover, they areintrinsically linked, in that their productive potential lies in their virtuous associa-tion with HC. As such, new knowledge is the ‘product of a firm’s combinativecapability to generate new applications from existing knowledge’ (Kogut andZander, 1992, p. 391). We reason, therefore, that the financial performance effectsof these two components of IC are contingent on each other.
Hypothesis 3a: The association between a firm’s organizational capital and finan-cial performance is contingent on the level of a firm’s internal social capital.
Hypothesis 3b: The association between a firm’s organizational capital and finan-cial performance is contingent on the level of a firm’s external social capital.
EXPLORING THE CONTINGENT EFFECTS OFINDUSTRY CONTEXT
Up to this point, we have assumed that the financial performance effects ofleveraging one intellectual capital component with another is not contingent oncontext-specific exogenous market influences, which may not be realistic. In thissection, we relax this simplistic assumption by adopting a contingency approach(Zajac et al., 2000). However, a contingency approach raises two additional thornychallenges – one empirical and one theoretical. The empirical challenge is how tocapture the contextual exogenous influences, while the theoretical challenge is howto predict their effects.
Regarding the empirical challenge, it is tempting to follow convention and useSIC industry-based definitions as a proxy, but even at a four-digit level of specificitythe definitions tend to be arbitrary (Barney, 2001) and suffer from serious aggre-gation bias (Lubatkin et al., 2001; Scherer and Ross, 1990). For example,McGahan and Porter (1997) note the likelihood that imprecise industry measuresmuted their study’s findings about industry determinism. Similarly, Zajac et al.note that ‘certain factors may be relevant in one industry, but not in another,relevant at one time, but not another’ (2000, p. 436), and therefore recommend awithin-industry examination. Following their lead, and that of industrial organi-zation economists (e.g. Scherer and Ross, 1990) and organization ecologists (e.g.Haveman, 1992), we will use a more fine-grained level of analysis (i.e. within-industry/within-geographic region line-of-business sampling strategy) to bettercontrol for contextual exogenous influences. By doing so, it allows us to focus ourtests on the hypothesized internal resource contingencies.
Specifically, we focus our attention on two lines-of-businesses, the personal andcommercial banking sectors within one geographical region of the USA. Definedaccordingly, these two sectors approximate what organization ecological models ofdensity-dependence refer to as ‘resource niches’; that is, within-industry bound-aries based on different customers’ preferences (Péli and Nooteboom, 1999). We doso partly because these two niches face fundamentally different contextual exog-enous influences, which will allow us to more precisely explore these contingenteffects on the IC-financial performance relationship. We do so also because, just asRBV’s relevant domain lies at the resource level (Peteraf and Barney, 2003), sodoes ICV.
For example, personal banks serve consumer markets, deriving their incomeprimarily from mortgage lending, consumer loans, and credit cards, while com-mercial banks serve corporate markets, deriving their income primarily fromcommercial loans and commercial leasing. Additionally, these two resource nichesface a different set of rivals – personal banks compete with other personal banks,thrift institutions, and credit unions, while commercial banks compete with othercommercial banks and investment banking firms – and therefore, by inference, a
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different set of competitive threats and opportunities. Lastly, these two niches facedifferent regulatory environments; e.g. personal banking customers are rigorouslyprotected against discriminatory and predatory practices with regulatory policieslike the Truth in Lending Act and Truth in Savings. Both acts are based on theassumption that that the typical personal banking customer, unlike the typicalcommercial banking customer, may not be as knowledgeable about bankingjargon. Regulators, therefore, require detailed explanations of all the terms thatapply to consumer loans, as well as calculations of the effective lending rate andsavings rate, to help customers make informed comparisons across financialinstitutions.
A side empirical benefit of exploring the contextual effects on the IC-financialperformance relationship using these two banking niches is that both resourceniches are relatively mature and heavily regulated. Maturity should discouragethese banks from pursuing what Tushman and Anderson (1986) refer to as‘competence-destroying innovations’ (i.e. radical thinking that generates newknowledge, making existing competencies obsolete and changing the nature ofcompetition). Instead, we expect that both niches will be driven more bycompetence-enhancing innovations (i.e. incremental thinking) and by isomorphicpressures to consciously imitate sector-wide best practices (DiMaggio and Powell,1983). Or, put in terms of Deephouse’s (1999) theory of strategic balance, institu-tions try to adopt behaviours similar enough to each other to ensure legitimacy,yet also different enough from each other to buffer their cash flows from the forcesof direct (purely competitive) competition. Boxall and Steeneveld’s (1999) longitu-dinal case studies of New Zealand engineering consulting firms found a similarwithin-industry effect. They observed that firm directors made a range of similarchanges, which enabled survival in the industry, while at the same time weredifferent enough to ensure performance variances.
We reason, therefore, that rivals within each banking niche may be adhering tosimilar strategic logics about IC associations. This should minimize alternativeexplanations like equifinality (i.e. an industry sector containing many possibleresource capital combinations with similar performance relationships) as wasdiscussed by Priem and Butler (2001). Said differently, to the extent that thebanks within a sector and region likely adhere to similar logics, they will intuit theimportance of the intellectual capital interactions. Following this logic, we assumethat what should distinguish their financial performance is the level of knowledgeembedded in each interacting component. This increases the probability that thedata will reveal the hypothesized pattern of associations, and thus decreases theprobability of Type II error.
The theoretical question is less easily resolved because very little is yet knownabout the contingent value of IC interactions. And, while the RBV literature isgoverned by the general belief that resource interactions should be more valuablethan the sum of its parts, it is silent as to precisely which interactions are best suited
for which exogenous contextual influences. That said, we infer from the literaturethat context should matter. For example, Nonaka (1994) argues that knowledgeresources are best understood within the context in which they are developed.Nonaka’s argument is compatible with that of Peteraf and Barney (2003), who saidRBV’s relevant domain lies at the resource level. Similarly, Batt (2000) observedthat within a single industry (telecommunications) the complexity of employee-customer interactions and the role of technology are contingent on the customerstargeted. That is, firms that competed in the low-margin resource niche haddifferent resource requirements for skill and technology than those that competedin the higher-margin services. Batt’s findings are consistent with organizationecology’s resource partitioning theory (Carroll et al., 2002), and with Boxall (2003),who concluded that the greater the variation in customer preferences, especiallyamong high-margin clientele, the greater the mix of skill levels needed.
A similar logic should apply to the two resource niches in the banking industrythat are relevant to our study. For example, we presume that the pattern of ICassociations will differ across the personal and commercial banking subgroupsbecause of dissimilarities in client needs, loan offerings, rivals, density, intensity ofcompetition, and so on. Personal banks are ‘retailers’, with tellers delivering ser-vices (e.g. checking, savings, home loans, etc) in a largely mechanized way thatrequires a minimum of personal knowledge and expertise. Or, put in Lepak andSnell’s (2002) terms, the employees working in personal banks are job-basedemployees, because they possess strategically valuable skills that are not firm-specific, and therefore, are transferable. We reason, therefore, that organizationalcapital will play a key role at personal banks in satisfying customers (i.e. to deliverservice efficiently to clientele, investments in technology that track consumer bal-ances, dispense cash, and quickly determine mortgage approvals) and regulators(e.g. to generate required reports like truth in lending and truth in savings).
However, we doubt that OC, by itself, will differentiate personal banks by theirperformance, because competitive pressures should make all personal banks awareof the importance of OC. Isomorphic pressures will cause the knowledge associatedwith OC to lose its uniqueness and become embedded in its market context (Dacin,1997), or become ‘prerequisites for participation in the industry, but do not provideany significant competitor differentiation’ (Hamel and Prahalad, 1994, p. 206).
This is not to imply that personal banking OC will have no impact on perfor-mance. Following the adage, ‘you don’t get rewarded for following the rules, butyou get punished for ignoring them’, we posit that personal banks that repeatedlyfail to maintain the prerequisite norms perform less well. Put in terms of Deep-house’s (1999) theory of strategic balance, personal banks will adopt similar levelsof OC to avoid challenges to their legitimacy by customers and regulators. Con-sistent with our first three hypotheses, however, personal banks will differentiatethemselves in hard-to-imitate ways by how they use OC in concert with HC andSC in general, and particularly with external SC.
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Commercial banks, in contrast, provide specialized services to corporate clients,who have more complex financing needs and are demanding and knowledgeableabout banking services offered by competitors. Presumably, both human and socialcapital, particularly external SC, are crucial, because the selling of these servicesrequires higher levels of employee education and training, as well as strong social tiesboth inside the firm and within the business community. Put in Lepak and Snell’s(2002) terms, commercial banking employees are knowledge workers because theirskills are strategically valuable and are not readily transferable. Unlike the job-basedemployees at personal banks, commercial banking employees generally have under-graduate degrees in business and finance, as well as extensive job training. Thesequalifications are important, for they require technical skills to analyse the credit-worthiness of their corporate clients, and to structure loan agreements to match thecredit needs of the corporation with the risk preferences of the bank. Over time andwith on-the-job experience, we can assume that these employees develop tacitknowledge about what it takes to get specific loans approved.
In sum, we have argued that context matters; that is, knowledge resources arebest understood within the specific context in which they are developed. Specifi-cally, we expect personal banks, given the nature of the resource niche in whichthey compete, will be associated with higher levels of organizational capital, whilecommercial banks, given the nature of their resource niche, will be associated withhigher levels of human and external social capital. These contingent expectationslead us to make an exploratory hypothesis:
Hypothesis 4: The structure of the contingent IC associations in the personalbanking niche will differ from that in the commercial banking niche.
METHODOLOGY
Sample
We identified 519 personal banks and 313 commercial banks operating in theNorth East area in 1999, whereby the Federal Deposit Insurance Corporation’s(FDIC) New York or Boston office served as the examiner. We chose to focus onthe banking industry because as a regulated industry, all banks (public and private)must provide objective financial information, in the form of a call report, on aquarterly basis to the FDIC. The bank call report indicates the amount of incomethat each bank derives from its personal and commercial banking units. Theavailability of data by resource niche may explain why other researchers such asDeephouse (1999), Ramaswamy (1997) and Zajac et al. (2000) used this industry toexamine within-industry effects. We chose to limit our examination to this singleregion primarily for two reasons: first, we assume that within variance of exogenousmarket and regulatory conditions in one market region would be less than it would
be across regions, thus suggesting a reasonable control for context. Second, theregion is densely populated with people and therefore with financial institutions toservice them. As such, the region increased our odds of obtaining a large enoughsample to adequately run our statistical tests. Finally, we choose to focus on eachof the two non-competing niches of the same industry, and do so within a singleregion of the USA, because this sampling strategy allows us to test our hypothesesat the level of analysis controlled by the enterprise, which Peteraf and Barney(2003) argue represents RBV’s relevant domain.
We then limited our sample list to only those banks that had been independentfor the past five years (no mergers), were US-based regional banks, and hadbetween $50 million and $10 billion in assets. A total of 560 banks met thesecriteria, of which 273 had separate personal and commercial banking units, andthe other 287 were engaged in only a single banking sector. We restricted oursample to only those banks that have been independent for the past five years in aneffort to minimize the effects on firm performance by factors like mergers that areextraneous to IC. We also restricted our sample to US-based banks, since non-USbanks are subject to different regulations and disclosure laws. We restricted thesample to only regional banks, since multi-regional banks may rely on differentlevels and mixes of IC components. Lastly, we excluded small banks (less that $50million in assets), because their customer base may be insufficient to justify invest-ments in their human, social, and organizational capital.
At the end of the third quarter of 1999, we mailed surveys to the senior managerof personal and commercial banking business units. For banks that were engagedin both sectors, separate mailings were sent to the senior manager of each unit. Afirst mailing yielded 83 personal bank responses (16 per cent) and 61 commercialbank responses (19.5 per cent). A second mailing, sent 18 days after the first,yielded an additional 86 personal bank responses, and 62 commercial bankresponses. In only 18 cases where the banks are engaged in both sectors wereresponses received from the senior managers of both banking units. Thus, totalresponses were 169 (32.6 per cent) and 123 (39.3 per cent) for personal andcommercial banks, respectively. Average total asset size was $578 million (personalbanks) and $612 million (commercial banks).
Using financial information obtained from the FDIC, we used t-tests to comparethe non-responding personal banks with the responding banks by their bank age,total assets, total number of employees, total number of offices, business unitinterest income, and business unit loans. There was no significant differencebetween respondents and non-respondents in the means of these items, and thus noevidence of a response bias. Using the same financial information and t-tests, wethen compared the non-responding commercial banks with the responding banks,and again found no evidence of a response bias. We reason from these tests that ourtwo samples are representative of the chosen sampling frame, and therefore ourresults should be generalizable for this population.
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We asked each senior manager to rate their bank unit along the four componentsof IC knowledge on a five-point Likert scale ranging from ‘grossly inadequate’ to‘excellent’. In other words, the survey captures each bank’s senior manager’sperception of his/her unit’s collective human, social (internal and external), andorganizational capital. (The survey items used to capture these four constructsappear in the Appendix.) Youndt et al. (2004, p. 345) used a similar approach, asthey found it necessary to use ‘generalized metrics and wording’ in crafting thespecific human, social, and organizational capital items to be relevant across a largesample of firms.
We measured human capital using 12 survey items (modified for the bankingindustry) drawn in principle from Edvinsson and Malone (1997), and specificallyfrom Huselid et al. (1997) and Youndt et al. (2004). Huselid et al. (1997) devel-oped and tested items to assess the skills and abilities of employees in a humanresource department exclusively. They initially had 15 items, of which 11 loadedproperly in the factor analysis and had a Cronbach’s alpha of 0.85. We adoptedthe seven items that captured the competencies of employees in general, not justhuman resource professionals. We also adopted the five HC items used byYoundt et al. (2004), and shown to be reliable (Cronbach’s alpha of 0.81), tocapture a firm’s human resource practices. The wording of the questions fromHuselid et al. (1997) and Youndt et al. (2004) were slightly modified to make themapplicable to the banking industry and to accommodate the anchoring of ourfive-point scale.
Like the research approach of Youndt et al. (2004), Bontis (1996), and Stewart(1997), we used a questionnaire design to measure external social capital, as it lendsitself better to large cross-sectional studies than does the more in-depth measurescoming from a network analysis. Specifically, by adopting Adler and Kwon’s (2002)conceptualization of social capital as having internal and external ties, we mea-sured internal social capital (ISC) with seven items, four of which were adopted fromthe five items used by Youndt et al. (2004), and shown to be reliable (Cronbach’salpha of 0.88), to capture relationships among employees, and three items drawnfrom the marketing literature to capture a firm’s inter-functional coordination, orcollaboration across units (Han et al., 1998). Unlike ISC, no scale exists to captureexternal social capital (ESC). We therefore measured it using two items, one drawnfrom Edvinsson and Malone (1997) and the other from Han et al. (1998), as bothare intended to capture the collective relationships that may exist between a firm’semployees and its customers, and thus both closely relate to the theory of IC.
We also adopted four of Youndt et al.’s (2004) five organizational capital items, whichthey found to be marginally reliable (Cronbach’s alpha of 0.62). (The fifth item dealtwith patents and licenses, and therefore, had little relevance in banking.) We thenconstructed a fifth OC item by transforming one of Youndt and Snell’s (1998)
open-response questions about the adequacy of a firm’s budget for its informationprocessing technology, routines, and processes, into a closed item query.
Results from principal component factor analysis with oblique rotation (Table I)on the above mentioned 26 items yielded four factors with eigenvalues greater than1.0 that explained 52.5 per cent of the variance. All 12 HC items loaded together,as did the five OC items and the two ESC items. Six of the ISC items loadedtogether. However, the seventh item was dropped because it showed a cross-loading with the ESC component. Cronbach’s alpha for the twelve HC items is0.90; the six ISC items is 0.81; the two ESC items is 0.81; and the five OC itemsis 0.64. These results are consistent with Youndt et al. (2004, p. 347) who foundthat their HC, SC, and OC items loaded as expected on their appropriate dimen-sions and that each of the factors had eigenvalues greater than one. To minimizemulticollinearity, we centred all scales before entering them into the regressionanalyses (Aiken and West, 1992, pp. 32–3).
Table I. Factor analysis of the human, social (internal and external) and organizational capital surveyitems
We also did a confirmatory factor analysis using structural equation modellingby the method of maximum likelihood. The results showing the same four-factorsolution are consistent with the principal component method. Each of the surveyitems was entered as observed variables, and the underlying IC constructs wereentered as unobserved variables. The model showed a good fit with the data, basedon its c2 of 685.16 (df = 269, p = 0.000). In addition, its non-normed fit index,incremental fit index, and comparative fit index, all showed values in excess of0.90, again indicating good fit ( Jaccard and Wan, 1996). Finally, the model’sroot-mean squared index is 0.07; values less than 0.08 indicate fit for this index(Browne and Cudeck, 1993).
Dependent Variable
Bank performance can be closely approximated by the interest banks earn fromtheir lending activities, and regulation requires that banks report their end-of-yearinterest income by sector, but not their cost and profit data. We thus obtained thisline-of-business financial performance data for the full years ending 31 December1999 (available in March 2000) and 2000 (available in March 2001) fromthe FDIC’s bank call-reports and used the log of them (to correct for skewness) asour dependent variables. We use the 2000 data to assess the sustainability ofany IC-based advantages, following Peteraf and Barney’s (2003) assertion thatresources and capabilities underlie persistent performance differences amongfirms.
As for our use of interest income as our dependent variable, there is widespreadprecedence for using revenue-based performance measures when cost and profitmeasures are not available, as is the case for those studies that rely on line-of-business data (Lubatkin et al., 2001), or that examine privately held organizations,the common sample frame used by organizational ecologists (e.g. Haveman, 1992)and family firm scholars (e.g. Schulze et al., 2001). We additionally reasoned thattesting the relationship between our independent variables and an objectivemeasure of performance was a more conservative and valid test than using asurvey-based approach to assess managers’ perceptions of financial performance.
Control Variables
We controlled for organizational size and age, two variables that organizationecologists generally view as affecting survival (e.g. Hannan and Freeman, 1984).Youndt et al. (2004, p. 347) also controlled for age and size because they predictedthat knowledge creation and diffusion was ‘inherently evolutionary in nature’ andwould thus be influenced by an organization’s age and access to resources (asproxied by size). We measured size by constructing an index of three measures:total assets, number of employees, and number of branch locations. Factor analysis
showed that the three variables loaded on one component (Cronbach’salpha = 0.94). We measured age of the bank by taking the log transformation tocorrect for skewness.
Contingent Industry Effects
Hypothesis 4 predicts that intellectual capital is contingent on the industry contextin which it is developed; that industry context is best understood in a narrowlydefined resource niche and not at the industry level; and line-of business data(within-industry/within geographic region) can adequately proxy for these niches.As a preliminary attempt to verify these assumptions, we conducted a t-test to seeif senior personal and commercial managers from the two banking sectors perceivevarious key industry conditions differently. As part of our survey instrument, weasked these managers to indicate, using a five-point scale, how important each ofnine conditions was for their unit (1 = completely unimportant to 5 = very impor-tant). The conditions represent changes that have been relevant to the bankingindustry in the past few years as indicated in recent Federal Reserve Bank reportsand include: (1) interest rate changes; (2) rise in housing demand; (3) rise in thenumber of personal bankruptcy filings; (4) rise in individuals’ investment in thestock market; (5) rise in the use of electronic payment mechanisms (i.e. debit cards,ACH); (6) introduction of PC banking, and more recently Web banking; (7) use ofcredit scoring models to process credit card and mortgage applications; (8) deregu-lation allowing banks to offer investment products and insurance; and (9) increasein the intensity of competition in the industry.
A factor analysis (not shown) indicated that items 1–5 could be combined intoone scale, the individual factor, and items 6–9 could be combined into anotherscale, the organizational factor (there were no significant cross loadings). Scalereliabilities indicated that the combined items 1–5 had a Cronbach’s alpha of 0.75,while the combined items 6–9 had a Cronbach’s alpha of 0.62. We then used thesetwo scales and t-tests, and found significant differences in the means for both scales(p � 0.01). Specifically, the senior personal bank managers rated both sets ofindustry factors as being more important to their business unit than commercialbanks. Thus, preliminary t-tests results suggest differences in the operating contextsof these two sectors.
RESULTS AND DISCUSSION
Table II presents descriptive statistics and Pearson correlations for personal banks(Part I) and commercial banks (Part II). Using t-tests to test for differences betweenpersonal banks and commercial banks on each of the IC components, we found, asexpected, that personal banks have higher means on the OC dimension (p � 0.01),while commercial banks are higher on HC (p � 0.001) and ESC (p � 0.001).
An Intellectual Capital-Based View of the Firm 881
Levels of ISC were statistically indistinguishable. Thus, again we found partialsupport for Hypothesis 4, in that the profile of the IC components differs across thetwo banking sectors.
We then ran a Chow test (Chow, 1960) to examine whether the structure of theIC relationships are homogenous across sectors. Formally, the residual sum ofsquares (and the corresponding sample sizes) for the personal banking data, com-mercial banking data, and the combined samples are 9.21 (160), 13.70 (118), and
47.39 (279), respectively, with 12 parameters. According to the equation set forthin Maddala (1992), this led to an F of ((47.39 - (9.21 + 13.7))/12)/((9.18 + 13.7)/(160 + 118 - 24)), or 22.67 (df = 160 + 118 - 12). Consistent with Hypothesis 4,structural differences existed (F = 22.67; p � 0.001). As such, the Chow test,along with findings from the t-tests about the profile of IC components andindustry sector conditions, suggest that the data should not be pooled acrosssectors and that the regressions to test Hypotheses 1–3 should, therefore, be runseparately.
Personal Banks
We tested our first three hypotheses twice, using 1999 and 2000 financial perfor-mance data and OLS regression analysis. Consistent with RBV’s resource inter-action thesis, the results (Table III) show that IC interactions are more valuablethan the sum of their parts. The overall model explains 65 per cent of the 1999dependent variable’s variance (p � 0.001) and 63 per cent of the 2000 variance
Table III. Regression analysis for personal banking: includes 1999 and 2000 log of business-levelinterest income dependent variables
(p � 0.001). In both models, size and age, when entered by themselves, explain ahigh percentage (R2 = 0.59 and 0.58, respectively) of that variance, and none ofthe four main effect IC components are significant. However, the relationshipbetween four of the six possible IC interactions remain stable for both models.Specifically, four of these IC interactions are significant in both models (p � 0.05).The interaction terms are almost identical in terms of Beta weights, and the effectthese IC associations have on financial performance remains in the same direction.Thus, these multi-year findings lend some support to Peteraf and Barney’s (2003)assertion that resources and capabilities underlie persistent performance differ-ences among firms.
Specifically, the interactions of human capital and both types of SC (internal andexternal) are significant in 1999 and 2000, lending partial support for Hypotheses1a and 1b, although the coefficient associated with the HC-ESC interaction in bothmodels is in the opposite direction to that predicted by Hypothesis 1b. Hypothesis2 (interaction of HC and OC) is also supported, and Hypothesis 3a is partiallysupported (OC and ISC), although its coefficient in both models is also in theopposite direction. The interaction of OC and ESC (Hypothesis 3b) is not signifi-cant. VIF diagnostics (all below 4.1 for both models) suggest there are no multi-collinearity problems.
Because interpretations of interactions terms solely from regression coefficientscan be misleading (Aiken and West, 1992), Figure 1 plots the significant ones usingthe 1999 data. To generate the graphs, each IC component was split into adichotomous variable (0, 1) whereby values above the mean were considered high(equal to 1), and values at or below the mean were considered low (equal to 0).Thus, interactions with positive coefficients indicate that when personal banks havelow levels (at or below the mean) of either ISC or OC, their performance is moresensitive to the level of HC than when levels of ISC or OC are high (above themean). Accordingly, human capital seems to play an important role at personalbanks, but only when ISC is low, meaning that the internal processes throughwhich employees share information and interact are either ineffective or, at least,less than optimal; or when OC is low, meaning that their information-processinginfrastructure is perceived to be less than adequate.
This result is not surprising given the market conditions personal banks considerto be important. Specifically, if personal banks do not have sufficient OC to analysethe growing consumer requests for mortgages and credit cards, the process must bedone by competent individuals (HC). These individuals are legally required toapply consistent standards across customers, while at the same time maintainingthe bank’s loan quality. Thus, in this particular market, OC and HC becomesubstitutive. Additionally, if ISC is less than optimal, then individuals (HC) mustlearn the various regulations regarding fair lending practices and the techniques ofloan qualification without the information efficiencies that can be gained by groupsof individuals (ISC) working to learn these procedures together.
The associated negative coefficient of the HC-ESC interaction may be mislead-ing when interpreted without recognizing the context. When ESC is high, perfor-mance appears insensitive to HC levels. That is, when these banks have strongrelationships with their customers, they stand to gain little by investing further inHC. However, when ESC is low, there is a good return on HC investments.
Finally, the OC-ISC interaction with its associated negative coefficient signappears to be meaningful (i.e. the combination of high levels of both may engendera dark side). While this result seems counter-intuitive, we offer an explanation.First, we infer from Adler and Borys (1996) that firms with high levels of organi-zation capital (i.e. excellent documentation of knowledge, routinized processes, etc)may take on some of the inertial and coercive attributes of dysfunctional bureau-cratic organizations. Second, strong solidarity with in-group members may reducethe flow of new knowledge into the group, resulting in parochialism and inertia
Personal Banking Interaction Graph
Interaction between HC and ESC
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Interaction between HC and OC
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Figure 1.Note: The intellectual capital components depicted in these figures represent bifurcated variables.High values for each of the components were calculated by equating all values above the mean to 1.Low values for each of the components were calculated by equating all values at the mean and belowto 0.
An Intellectual Capital-Based View of the Firm 885
(Adler and Kwon, 2002; Gargiulo and Bernassi, 1999). Or, as Powell and Smith-Doerr (1994, p. 393) put it, ‘The ties that bind may also turn into ties that blind.’While we have to be cautious in generalizing this finding to other industry sectors,it seems that at least in personal banking, OC and ISC as investment priorities tohigh levels can result in negative returns.
Commercial Banks
As with personal banks, we again tested our first three hypotheses twice, using 1999and 2000 financial performance data and OLS regression analysis. The overallmodel (Table IV) explains 44 per cent of the 1999 dependent variable’s variance(p � 0.001) and 46 per cent of the 2000 variance (p � 0.001). In both models, sizeand age, when entered by themselves, explain a high percentage (R2 = 0.36 and0.34, respectively) of that variance, although unlike personal banks, age does notimpart a significant influence. Inconsistent with RBV’s resource interaction thesis,but consistent with the results from the Chow test, none of the six IC interactions
Table IV. Regression analysis for commercial banking: includes 1999 and 2000 log of business-levelinterest income dependent variables (n = 118)
are significant, but two of the main effect IC components, ISC and ESC, aresignificant in both models, although ISC has a significance level just below 0.10 in2000. This finding concurs with our previously stated assumption that socialcapital, particularly external SC, is crucial in the commercial banking sector,because the selling of these services requires strong social ties both inside the firmand within the business community. In contrast to personal banks, however, itseems that the whole is not more valuable than the sum of its parts. (VIF diagnosticsare all below 2.9 for each year.) Exploring possible three-way interactions (seeFigure 2) reveals that one three-way (HC, ISC, and ESC) is significant associatedwith the 1999 financial performance data (p � 0.01), but not with the 2000 data.
CONCLUSION
Petty and Guthrie (2000) state that IC research has achieved its mission of defin-ing IC and its constructs, and communicating the importance of IC in the
Commercial Banking Interaction Graph
Interaction between ISC and ESC
HC Held Constant at Low
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HC Held Constant at High
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Figure 2.Note: The intellectual capital components depicted in these figures represent bifurcated variables.High values for each of the components were calculated by equating all values above the mean to 1.Low values for each of the components were calculated by equating all values at the mean and belowto 0.
An Intellectual Capital-Based View of the Firm 887
creation of competitive advantage. Therefore, they suggest that a ‘second stage’of development in IC research is needed whereby empirical tests legitimize thestudy of this construct and provide more robust evidence on which to build. Ourtwo-sample test provided some evidence, but did so with a new theoretical twist.Specifically, we drew insight from RBV’s more general resource interactionthesis and mid-range theory development, to hypothesize that the relation ofeach IC component to firm performance is contingent on the value of the othercomponents. We then explored the contextual boundaries of ICV by usingwithin-industry/within-geographic region, or distinct resource niches or ‘line-of-business’ data. By doing so, we offered a pragmatic, though partial resolution tofive concerns of RBV, as expressed by Foss and Knudsen (2003) and Priem andButler (2001).
The results from the Chow test, t-tests, and regression analyses all suggest thatIC interactions are best understood within the very specific industry conditions inwhich they are developed, a finding consistent with Peteraf and Barney’s (2003)recent argument. Indeed, and consistent with the industrial organization economicand organization ecological literatures, we found evidence of an aggregation biaswhen pooling data across banking sectors, even within the same general industryand geographical region.
We also found evidence that, contrary to intuition, IC-interactions in somemarkets may experience diminished returns (i.e. too much of a good thing is notalways good). This was evidenced by the negative coefficients in two of the two-way interaction terms in the personal banking sample. For example, we specu-lated that having high levels of organizational and internal social capital mightlead to insular or bureaucratic behaviour that will negatively impact perfor-mance in the long term. This has implications for managers in terms of how itstructures group decision making processes, such as commercial banks using loanapproval committees as the primary means of making decisions. Particularly forfirms operating in mature industries, managers may want to consider if certainroutines and procedures that once led to efficiencies, have become outdated orhave evolved into an extra layer of bureaucracy which will negatively impactprofitability. Lastly, as advancements continue to be made in technology, theways in which clients are serviced will change. This may result in HC beingsubstituted by technology especially in personal banking regarding such servicesas loan approval, and account statements. Thus, instead of investing heavily inboth people and information systems, managers may look at these as substitutiveresources.
The empirical testing of our hypotheses was confined to measuring perfor-mance in terms of profitability. However, more research should be done todetermine whether IC-interactions in different contexts lead to other diminishedreturns in such performance indicators as efficiency and employee productivity.If this is the case, then perhaps HRM practices can be tailored to moderate this
effect. Guest (1997, p. 266) suggested that as it relates to testing the relationshipbetween HRM and performance, in particular, a balanced stakeholder approachwould require that financial, customer, and employee constituencies be consid-ered. This would require that performance be measured in financial and behav-ioural terms. The same may be true when considering IC interactions. Tounderstand the full set of relationships, other types of financial measures can betaken as well as behavioural measures such as customer satisfaction. This bal-anced stakeholder approach would also require responses to be gathered frommultiple people within an organization to gain different stakeholders’ perspec-tives of resource allocation patterns.
Of course, our sample was limited to two sectors of a single industry in onegeographical region, thereby raising for some, questions about the generalizabilityof our findings. We think that this concern is mitigated by our findings fromHypothesis 4 and the two-sector research design, which highlights the very context-specific nature of ICV. We reason from this finding that ICV may not be the kindof theory that is stable across different business contexts, and therefore is not wellsuited to tests using pooled cross-industry data. That said, while we used a fine-grain analysis (i.e. within-industry/within-geographic region line-of-business sam-pling strategy) for an industry which itself is regulated and conservative, we maynot have fully controlled for all contextual dimensions. For example, Beard andDess (1981) noted that even firms within an industry may differ on relative size,capital intensity, and debt structure.
That said, the focal industry in our study is regulated and mature, and thusnot the kind of dynamic market conditions to which stocks and flows of knowl-edge are paramount for attaining and sustaining superior performance. As such,banking industry conditions promoted a conservative test of our hypotheses,because in this mature, regulated industry, IC was still found to matter.Common methods bias should not have posed a serious problem; that is, givenmultiple-items of each IC component and the higher-order nature of our inter-active constructs, we find it hard to imagine that the respondent would artificiallycause a relationship between the interaction term and secondary measures ofperformance, as was hypothesized and found twice by our study, using two yearsof performance data. However, our sample choice leaves unanswered questionslike what the structure of the IC interaction-performance relationship might bein more growth-oriented, technologically advanced industries. Extending Pettyand Guthrie (2000), we therefore call for a ‘third stage’ of development in ICresearch, whereby empirical tests can further explore IC interactions in othercompetitive contexts, with the objective of expanding the scope of the mid-rangecontingency expectations presented herein.
An Intellectual Capital-Based View of the Firm 889
Human capital items (To what extent are your employees . . .)1
HC1* . . . highly skilled?HC2* . . . widely considered the best in your industry?HC3* . . . creative and bright?HC4* . . . experts in their particular jobs and functions?HC5* . . . able to develop new ideas and knowledge?HC6** . . . able to anticipate the effect external changes in your industry will have on your
bank and its clients?HC7** . . . able to take appropriate risks to accomplish objectives?HC8** . . . able to retain broad knowledge of many of your bank’s divisions’ functions?HC9** . . . able to influence their peers in other companies?HC10** . . . able to exhibit leadership in your area and in the corporation?HC11** . . . able to focus on the quality of service provided?HC12** . . . able to educate and influence managers on relevant issues?Internal social capital items (How adequately do your employees . . .)ISC1* . . . share information and learn from one another?ISC2* . . . interact and exchange ideas with people from different areas of the bank?ISC3* . . . apply knowledge from one area of the bank to problems and opportunities that
arise in another?ISC4* . . . have the capacity to partner with customers, suppliers, alliance partners, etc, to
develop business solutions?ISC5*** . . . share information about competitors to other departments?ISC6*** . . . share information about customers to other departments?ISC7*** . . . share resources with other business units?External social capital items (How adequately do your employees . . .)ESC1 . . . regularly visit with customers?ESC2*** . . . visit customers accompanied by the bank’s top managers?Organizational capital items (How adequately has your area . . .)OC1* . . . documented knowledge in manuals, databases, etc?OC2* . . . routinized processes (e.g. processing new clients, handling client complaints, etc)?OC3* . . . tailored your software and mainframe computer systems to your organization (i.e.
proprietary)?OC4* . . . protected vital knowledge and information to prevent loss in the event key people
leave the organization?OC5* . . . received an annual information technology budget (for personnel, hardware,
software, etc) that allows you to provide quality service?
Notes: 1 Each respondent was requested to answer these questions considering only his/her business-unit, whetherit was personal banking or commercial banking, as opposed to requesting that he/she answer with regards tohis/her entire bank.* Denotes items adopted from Youndt et al. (2004).** Denotes items adopted from Huselid et al. (1997).*** Denotes items adopted from Han et al. (1998).
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