10270970_Reed

7/29/2019 10270970_Reed

1/27

7/29/2019 10270970_Reed

2/27

The very attributes that give resource integration its theorized advantages,

however, pose thorny challenges to researchers: How do we conceptualize and

then 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 provide

managers with useful advice as to which specific resources they should accumulate

to gain an advantage. Second, RBV lacks a clear definition of competitive advan-

tage. Third, RBV suffers from a tautology problem stemming from the fact that

resources 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, thus

suggesting equifinality. These five concerns, all having to do with RBVs lack of

specificity 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 on

recent remarks by Peteraf and Barney (2003) and insights drawn from an emerging

mid-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 RBVs more generalized view. We say mid-range because,

following Pinder and Moores (1979) definition of mid-range theories, ICV repre-

sents one specific aspect of the more general resource-based view, in that it more

narrowly considers three resources that have been theoretically linked to a firms

competitive 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 a

firms 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 defining

competitive advantage in terms of the resource characteristics that allow a firm to

outperform rivals in the same industry. Following Peteraf and Barney (2003), weattempt to avoid the tautology problem (third concern) by defining knowledge

resources by their theoretical associations with competitive advantage and not by

their empirical financial association.

We also say mid-range because we examine ICVs intra-industry association

with the financial performance of a limited class of businesses, two non-

competing resource niches of the North East US banking industry the personal

and commercial banking sectors. By attempting to hold constant the exogenous

influences associated with industry and geographic region, we are more able to

focus our analysis of financial performance at the resource-base level, or the levelof analysis controlled by an enterprise, which Peteraf and Barney (2003) argue

represents RBVs relevant domain (fourth concern). Finally, and consistent with

K. K. Reed et al.868

Blackwell Publishing Ltd 2006

7/29/2019 10270970_Reed

3/27

Deephouses (1999) theory of strategic balance, the last concern about equifinal-

ity should be reduced, given the mature and highly regulated nature of both

resource niches.

Following RBVs more general resource interaction hypotheses, we test the

thesis that the intellectual capital components represent, in Dyer and Singhs(1998) terms, complementary resources. Accordingly, the knowledge embedded

in one component of intellectual capital (IC) can leverage the value of knowledge

in the other components, such that the combination of the two results in a dis-

tinctive, indivisible resource endowment that directly affects a business financial

performance. Also, following RBVs assertion that resources and capabilities

underlie persistent performance differentials among firms, we tested our IC inter-

action hypotheses twice, using two contiguous years of financial performance

data. Finally, we reason that the performance impact of the structure of these

contingent interactions is contingent on the industry conditions that exist in anarrowly defined resource niche, specified as within-industry/within-geographic

region.

THE DIFFERENT COMPONENTS OF INTELLECTUAL CAPITAL

ICV is complementary to Leonard-Bartons (1992) more widely understood

knowledge-base view (KBV). While both seek to explain the hidden knowledge-

based dynamics that underlie a firms value, and both are grounded in an RBV

logic, they differ in focus. KBV is primarily interested in evaluating the effective-

ness of a firms use of knowledge-management tools as knowledge-generating

mechanisms, such as its information technology systems and information manage-

ment systems (Leonard-Barton, 1992; Nonaka et al., 2001). In contrast, ICVs

focus is on the stocks and flows of knowledge capital embedded in an organization

and is posited to have direct associations with its financial performance ( Youndt

et 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 firms 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, a

firms information technology systems and processes, that speeds the flow of

knowledge through the organization) and customer capital (the relationships that

a firm has with its customers). Bontis (1996) also discusses customer capital as one

aspect of what he calls relational capital, or the capital that encompasses all

external relationships. His view is similar to that referred to as external social

capital 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


7/29/2019 10270970_Reed

4/27

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 and

internal dimensions. With this as background, we advance three contingent

hypotheses, based on RBVs resource interaction thesis that one component of IC

can leverage the value of knowledge in the other components, such that the relation

of each component to financial performance is contingent on the knowledge value

of the other components. Consistent with Peteraf and Barneys (2003) character-

ization of RBV, our three hypotheses operate under a set ofceteris 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 as

Porters (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 for

differentiating financial performance among firms (Carpenter et al., 2001; Coff,

1997; Hitt et al., 2001; Pfeffer, 1994), involves both knowledge stocks (e.g. hiring of

educated individuals) and knowledge flows (e.g. developing high levels of codified

and 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 its

socioeconomic origins (e.g. Coleman, 1998; Loury, 1987; Putnam, 1993; Schiff,

1992) suggest that the inimitable value of HC can be enhanced by the good will

that is engendered by the fabric of social relations and that can be mobilized to

facilitate action (Adler and Kwon, 2002, p. 17). In brief, rich internal and exter-nal social connections (i.e. containing information about best practices, customer

needs, competitor moves, and so on) that consist of high-status (competent and

credible) participants from a diverse set of disciplines, can reduce the amount of

time and investment required to gather information (Burt, 1992). Accordingly,

these relationships can serve as valuable conduits for knowledge diffusion and

transfer (Coleman, 1998). They can also facilitate knowledge combinations,

which can both support knowledge creating organizations (Nonaka and Takeu-

chi, 1995) and develop a firms intellectual capital (Nahapiet and Ghoshal, 1998).

And, just as firms can have a human capitaladvantage, the complex processes thatevolve as a result of productive employee interactions can result in a human

process advantage (Boxall, 1996). As such, the combination of human and social



7/29/2019 10270970_Reed

5/27

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 with

social 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 capitals (SC) productive potential lies primarily in its ability to

leverage the productivity of human resources, a view supported by a wide range of

HC-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), the

creation of intellectual capital (Hargadon and Sutton, 1997), and cross-functional

team 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 a

firms 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 higher

their human capital), the more they will value, assimilate, and apply knowledge

from informationally enriched social ties (Cohen and Levinthal, 1990). This can set

into motion a virtuous and dynamic cycle: the more a firms human capital is

enhanced by social linkages, the more attractive employees become to additional

informationally enriched and high-status social ties, and so on. As such, we expect

that HC is positively associated with a firms financial performance, but its positive

association is enhanced, or leveraged, when combined with the firms internal

social capital (ISC) and also by the firms external social capital (ESC). Stating

formally these two contingent predictions:

Hypothesis 1a: A firms internal social capital will leverage the value of its human

capital such that the relationship between human capital and financial perfor-

mance is contingent on the level of the firms internal social capital.

Hypothesis 1b: A firms external social capital will leverage the value of its human


mance is contingent on the level of the firms external social capital.

Using Organizational Capital to Leverage Human Capital

Organizational capital represents a repository of knowledge that is accessible

through 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 firms business-level strategies like



7/29/2019 10270970_Reed

6/27

cost and differentiation, is defining the rules of the new competitive landscape.

Similarly, Bartlett and Ghoshal (1998) posit that OC represents the principal

source of firm-level innovation. And Lane and Lubatkin (1998) show that the

organizational 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 stored

in a firms information technology systems, as well as its structure and operating

procedures (Edvinsson and Malone, 1997), but also of intangible elements like

culture 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 and

dynamic cycle. That is, OC derives its capabilities from employees the types of

knowledge they possess and choose to store, and how they assimilate and interpret

that knowledge. In turn, OC enhances HCs productive potential by providing

employees with a supportive, yet socially complex infrastructure (Edvinsson andMalone, 1997). Thus, HC and OC, when viewed in tandem as complementary

resources, should result in hard-to-imitate, business-specific advantages, which (as

predicted in Hypothesis 1) should positively impact financial performance. Accord-

ingly, we reason that HCs positive association with financial performance is also

enhanced, or leveraged by, the firms OC. Thus:

Hypothesis 2: A firms organizational capital will leverage the value of its human


mance is contingent on the level of the firms organizational capital.

Using Social Capital in Concert with Organizational Capital

SC (both internal and external) and OC represent conceptually distinct, but

complementary, 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 firms combinative

capability to generate new applications from existing knowledge (Kogut and

Zander, 1992, p. 391). We reason, therefore, that the financial performance effects

of these two components of IC are contingent on each other.

Hypothesis 3a: The association between a firms organizational capital and finan-

cial performance is contingent on the level of a firms internal social capital.

Hypothesis 3b: The association between a firms organizational capital and finan-

cial performance is contingent on the level of a firms external social capital.



7/29/2019 10270970_Reed

7/27

EXPLORING THE CONTINGENT EFFECTS OF

INDUSTRY CONTEXT

Up to this point, we have assumed that the financial performance effects of

leveraging one intellectual capital component with another is not contingent on

context-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 thorny

challenges one empirical and one theoretical. The empirical challenge is how to

capture the contextual exogenous influences, while the theoretical challenge is how

to predict their effects.

Regarding the empirical challenge, it is tempting to follow convention and use

SIC industry-based definitions as a proxy, but even at a four-digit level of specificity

the 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 measures

muted their studys 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 a

within-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 our

tests on the hypothesized internal resource contingencies.

Specifically, we focus our attention on two lines-of-businesses, the personal and

commercial banking sectors within one geographical region of the USA. Defined

accordingly, these two sectors approximate what organization ecological models of

density-dependence refer to as resource niches; that is, within-industry bound-

aries based on different customers preferences (Pli and Nooteboom, 1999). We do

so 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 as

RBVs relevant domain lies at the resource level (Peteraf and Barney, 2003), so

does ICV.

For example, personal banks serve consumer markets, deriving their income

primarily from mortgage lending, consumer loans, and credit cards, while com-

mercial banks serve corporate markets, deriving their income primarily from

commercial loans and commercial leasing. Additionally, these two resource niches

face a different set of rivals personal banks compete with other personal banks,

thrift institutions, and credit unions, while commercial banks compete with other

commercial banks and investment banking firms and therefore, by inference, a



7/29/2019 10270970_Reed

8/27

different set of competitive threats and opportunities. Lastly, these two niches face

different regulatory environments; e.g. personal banking customers are rigorously

protected against discriminatory and predatory practices with regulatory policies

like the Truth in Lending Act and Truth in Savings. Both acts are based on the

assumption that that the typical personal banking customer, unlike the typicalcommercial banking customer, may not be as knowledgeable about banking

jargon. Regulators, therefore, require detailed explanations of all the terms that

apply to consumer loans, as well as calculations of the effective lending rate and

savings rate, to help customers make informed comparisons across financial

institutions.

A side empirical benefit of exploring the contextual effects on the IC-financial

performance relationship using these two banking niches is that both resource

niches are relatively mature and heavily regulated. Maturity should discourage

these banks from pursuing what Tushman and Anderson (1986) refer to ascompetence-destroying innovations (i.e. radical thinking that generates new

knowledge, making existing competencies obsolete and changing the nature of

competition). Instead, we expect that both niches will be driven more by

competence-enhancing innovations (i.e. incremental thinking) and by isomorphic

pressures to consciously imitate sector-wide best practices (DiMaggio and Powell,

1983). Or, put in terms of Deephouses (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 forces

of direct (purely competitive) competition. Boxall and Steenevelds (1999) longitu-

dinal case studies of New Zealand engineering consulting firms found a similar

within-industry effect. They observed that firm directors made a range of similar

changes, which enabled survival in the industry, while at the same time were

different enough to ensure performance variances.

We reason, therefore, that rivals within each banking niche may be adhering to

similar strategic logics about IC associations. This should minimize alternative

explanations like equifinality (i.e. an industry sector containing many possible

resource capital combinations with similar performance relationships) as wasdiscussed by Priem and Butler (2001). Said differently, to the extent that the

banks within a sector and region likely adhere to similar logics, they will intuit the

importance of the intellectual capital interactions. Following this logic, we assume

that what should distinguish their financial performance is the level of knowledge

embedded in each interacting component. This increases the probability that the

data will reveal the hypothesized pattern of associations, and thus decreases the

probability of Type II error.

The theoretical question is less easily resolved because very little is yet known

about the contingent value of IC interactions. And, while the RBV literature isgoverned by the general belief that resource interactions should be more valuable

than the sum of its parts, it is silent as to precisely which interactions are best suited



7/29/2019 10270970_Reed

9/27

for which exogenous contextual influences. That said, we infer from the literature

that context should matter. For example, Nonaka (1994) argues that knowledge

resources are best understood within the context in which they are developed.

Nonakas argument is compatible with that of Peteraf and Barney (2003), who said

RBVs 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 customers

targeted. That is, firms that competed in the low-margin resource niche had

different resource requirements for skill and technology than those that competed

in the higher-margin services. Batts findings are consistent with organization

ecologys resource partitioning theory (Carroll et al., 2002), and with Boxall (2003),

who concluded that the greater the variation in customer preferences, especially

among 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 IC

associations will differ across the personal and commercial banking subgroups

because of dissimilarities in client needs, loan offerings, rivals, density, intensity of

competition, and so on. Personal banks are retailers, with tellers delivering ser-

vices (e.g. checking, savings, home loans, etc) in a largely mechanized way that

requires a minimum of personal knowledge and expertise. Or, put in Lepak and

Snells (2002) terms, the employees working in personal banks are job-based

employees, because they possess strategically valuable skills that are not firm-

specific, and therefore, are transferable. We reason, therefore, that organizational

capital will play a key role at personal banks in satisfying customers (i.e. to deliver

service 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 their

performance, because competitive pressures should make all personal banks aware

of the importance of OC. Isomorphic pressures will cause the knowledge associated

with 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 provide

any 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 dont get rewarded for following the rules, but

you get punished for ignoring them, we posit that personal banks that repeatedly

fail to maintain the prerequisite norms perform less well. Put in terms of Deep-

houses (1999) theory of strategic balance, personal banks will adopt similar levels

of 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 and

SC in general, and particularly with external SC.



7/29/2019 10270970_Reed

10/27

Commercial banks, in contrast, provide specialized services to corporate clients,

who have more complex financing needs and are demanding and knowledgeable

about banking services offered by competitors. Presumably, both human and social

capital, particularly external SC, are crucial, because the selling of these services

requires 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 Snells

(2002) terms, commercial banking employees are knowledge workers because their

skills are strategically valuable and are not readily transferable. Unlike the job-based

employees at personal banks, commercial banking employees generally have under-

graduate degrees in business and finance, as well as extensive job training. These

qualifications are important, for they require technical skills to analyse the credit-

worthiness of their corporate clients, and to structure loan agreements to match the

credit needs of the corporation with the risk preferences of the bank. Over time and

with 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 are

best understood within the specific context in which they are developed. Specifi-

cally, we expect personal banks, given the nature of the resource niche in which

they compete, will be associated with higher levels of organizational capital, while

commercial banks, given the nature of their resource niche, will be associated with

higher levels of human and external social capital. These contingent expectations

lead us to make an exploratory hypothesis:

Hypothesis 4: The structure of the contingent IC associations in the personal

banking 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 Corporations

(FDIC) New York or Boston office served as the examiner. We chose to focus on

the 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 a

quarterly basis to the FDIC. The bank call report indicates the amount of income

that each bank derives from its personal and commercial banking units. The

availability of data by resource niche may explain why other researchers such as

Deephouse (1999), Ramaswamy (1997) and Zajac et al. (2000) used this industry to

examine within-industry effects. We chose to limit our examination to this singleregion primarily for two reasons: first, we assume that within variance of exogenous

market and regulatory conditions in one market region would be less than it would



7/29/2019 10270970_Reed

11/27

be across regions, thus suggesting a reasonable control for context. Second, the

region is densely populated with people and therefore with financial institutions to

service them. As such, the region increased our odds of obtaining a large enough

sample to adequately run our statistical tests. Finally, we choose to focus on each

of 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 hypotheses

at the level of analysis controlled by the enterprise, which Peteraf and Barney

(2003) argue represents RBVs relevant domain.

We then limited our sample list to only those banks that had been independent

for the past five years (no mergers), were US-based regional banks, and had

between $50 million and $10 billion in assets. A total of 560 banks met these

criteria, of which 273 had separate personal and commercial banking units, and

the other 287 were engaged in only a single banking sector. We restricted our

sample 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 are

extraneous to IC. We also restricted our sample to US-based banks, since non-US

banks are subject to different regulations and disclosure laws. We restricted the

sample to only regional banks, since multi-regional banks may rely on different

levels and mixes of IC components. Lastly, we excluded small banks (less that $50

million 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 manager

of personal and commercial banking business units. For banks that were engaged

in both sectors, separate mailings were sent to the senior manager of each unit. A

first mailing yielded 83 personal bank responses (16 per cent) and 61 commercial

bank responses (19.5 per cent). A second mailing, sent 18 days after the first,

yielded an additional 86 personal bank responses, and 62 commercial bank

responses. In only 18 cases where the banks are engaged in both sectors were

responses received from the senior managers of both banking units. Thus, total

responses were 169 (32.6 per cent) and 123 (39.3 per cent) for personal and

commercial 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 compare

the non-responding personal banks with the responding banks by their bank age,

total assets, total number of employees, total number of offices, business unit

interest income, and business unit loans. There was no significant difference

between respondents and non-respondents in the means of these items, and thus no

evidence of a response bias. Using the same financial information and t-tests, we

then 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 our

results should be generalizable for this population.



7/29/2019 10270970_Reed

12/27

Independent Variables

We asked each senior manager to rate their bank unit along the four components

of IC knowledge on a five-point Likert scale ranging from grossly inadequate to

excellent. In other words, the survey captures each banks senior managers

perception of his/her units collective human, social (internal and external), andorganizational capital. (The survey items used to capture these four constructs

appear in the Appendix.) Youndt et al. (2004, p. 345) used a similar approach, as

they found it necessary to use generalized metrics and wording in crafting the

specific human, social, and organizational capital items to be relevant across a large

sample of firms.

We measured human capital using 12 survey items (modified for the banking

industry) drawn in principle from Edvinsson and Malone (1997), and specifically

from 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 human

resource department exclusively. They initially had 15 items, of which 11 loaded

properly in the factor analysis and had a Cronbachs alpha of 0.85. We adopted

the seven items that captured the competencies of employees in general, not just

human resource professionals. We also adopted the five HC items used by

Youndt et al. (2004), and shown to be reliable (Cronbachs alpha of 0.81), to

capture a firms human resource practices. The wording of the questions from

Huselid et al. (1997) and Youndt et al. (2004) were slightly modified to make them

applicable 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 lends

itself better to large cross-sectional studies than does the more in-depth measures

coming from a network analysis. Specifically, by adopting Adler and Kwons (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 from

the five items used by Youndt et al. (2004), and shown to be reliable (Cronbachs

alpha of 0.88), to capture relationships among employees, and three items drawnfrom the marketing literature to capture a firms inter-functional coordination, or

collaboration across units (Han et al., 1998). Unlike ISC, no scale exists to capture

external social capital (ESC). We therefore measured it using two items, one drawn

from Edvinsson and Malone (1997) and the other from Han et al. (1998), as both

are intended to capture the collective relationships that may exist between a firms

employees 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 capitalitems, which

they found to be marginally reliable (Cronbachs alpha of 0.62). (The fifth item dealt

with patents and licenses, and therefore, had little relevance in banking.) We then

constructed a fifth OC item by transforming one of Youndt and Snells (1998)



7/29/2019 10270970_Reed

13/27

open-response questions about the adequacy of a firms budget for its information

processing 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 than

1.0 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 loaded

together. However, the seventh item was dropped because it showed a cross-

loading with the ESC component. Cronbachs alpha for the twelve HC items is

0.90; the six ISC items is 0.81; the two ESC items is 0.81; and the five OC items

is 0.64. These results are consistent with Youndt et al. (2004, p. 347) who found

that 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 regression

analyses (Aiken and West, 1992, pp. 323).

Table I. Factor analysis of the human, social (internal and external) and organizational capital survey

items

Item1 Component 1 Component 2 Component 3 Component 4

HC1 0.730 0.046 0.093 0.217HC2 0.749 0.094 0.207 0.113

HC3 0.767 0.184 0.115 0.032

HC4 0.716 0.035 0.281 0.072

HC5 0.674 0.308 0.120 0.089

HC6 0.543 0.106 0.161 0.314

HC7 0.669 0.212 0.312 0.292

HC8 0.364 0.325 0.323 0.141

HC9 0.520 0.137 0.240 0.222

HC10 0.596 0.216 0.312 0.292

HC11 0.407 0.226 0.467 0.134

HC12 0.493 0.385 0.267 0.252ISC1 0.305 0.682 -0.53 0.031

ISC2 0.110 0.838 0.060 0.193

ISC3 0.165 0.771 0.125 0.127

ISC4 0.310 0.411 0.226 0.165

ISC5 0.100 0.614 0.404 0.098

ISC6 0.074 0.509 0.492 -0.010

ISC7 0.143 0.298 0.466 0.303

ESC1 0.304 0.118 0.752 0.038

ESC2 0.290 0.012 0.802 0.116

OC1 0.237 0.169 -0.052 0.590

OC2 0.307 0.148 0.106 0.484OC3 0.093 -0.117 0.157 0.672

OC4 0.055 0.158 0.034 0.669

OC5 0.114 0.114 0.106 0.583

1 See the Appendix for the full articulation of each item referred to with an abbreviated symbol.



7/29/2019 10270970_Reed

14/27

We also did a confirmatory factor analysis using structural equation modelling

by the method of maximum likelihood. The results showing the same four-factor

solution are consistent with the principal component method. Each of the survey

items was entered as observed variables, and the underlying IC constructs were

entered 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 of

0.90, again indicating good fit (Jaccard and Wan, 1996). Finally, the models

root-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-year

interest income by sector, but not their cost and profit data. We thus obtained this

line-of-business financial performance data for the full years ending 31 December

1999 (available in March 2000) and 2000 (available in March 2001) from

the FDICs bank call-reports and used the log of them (to correct for skewness) as

our dependent variables. We use the 2000 data to assess the sustainability of

any IC-based advantages, following Peteraf and Barneys (2003) assertion that

resources and capabilities underlie persistent performance differences among

firms.

As for our use of interest income as our dependent variable, there is widespread

precedence for using revenue-based performance measures when cost and profit

measures 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 that

testing the relationship between our independent variables and an objective

measure of performance was a more conservative and valid test than using asurvey-based approach to assess managers perceptionsof financial performance.

Control Variables

We controlled for organizational size and age, two variables that organization

ecologists 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 predicted

that knowledge creation and diffusion was inherently evolutionary in nature and

would thus be influenced by an organizations 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



7/29/2019 10270970_Reed

15/27

showed that the three variables loaded on one component (Cronbachs

alpha = 0.94). We measured age of the bank by taking the log transformation to

correct for skewness.

Contingent Industry Effects

Hypothesis 4 predicts that intellectual capital is contingent on the industry context

in which it is developed; that industry context is best understood in a narrowly

defined 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 see

if senior personal and commercial managers from the two banking sectors perceive

various key industry conditions differently. As part of our survey instrument, we

asked 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 banking

industry in the past few years as indicated in recent Federal Reserve Bank reports

and include: (1) interest rate changes; (2) rise in housing demand; (3) rise in the

number of personal bankruptcy filings; (4) rise in individuals investment in the

stock 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 of

credit scoring models to process credit card and mortgage applications; (8) deregu-

lation allowing banks to offer investment products and insurance; and (9) increase

in the intensity of competition in the industry.

A factor analysis (not shown) indicated that items 15 could be combined into

one scale, the individual factor, and items 69 could be combined into another

scale, the organizational factor (there were no significant cross loadings). Scale

reliabilities indicated that the combined items 15 had a Cronbachs alpha of 0.75,

while the combined items 69 had a Cronbachs alpha of 0.62. We then used these

two 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 commercial

banks. Thus, preliminary t-tests results suggest differences in the operating contexts

of 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 between

personal 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).



7/29/2019 10270970_Reed

16/27

Levels of ISC were statistically indistinguishable. Thus, again we found partial

support for Hypothesis 4, in that the profile of the IC components differs across the

two banking sectors.

We then ran a Chow test (Chow, 1960) to examine whether the structure of the

IC 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

Table II. Descriptive statistics and correlations

Part I: Personal banking (n = 169)

Variable Mean S.D. 1 2 3 4 5 6 7 8

1. Size -0.11 0.92

2. Age 97.75 43.25 0.15

3. HC 3.31 0.56 0.03 0.08

4. OC 3.38 0.56 0.03 0.12 0.61**

5. ISC 3.32 0.58 -0.01 -0.03 0.61** 0.42**

6. ESC 3.12 0.98 0.05 0.53** 0.35** 0.41**

7. Bus-level

interest

income

($ million)

21,051 38,236 0.75** 0.28** 0.08 0.05 -0.04 0.08

8. Bus-levelinterest

income

($ million)

26,783 49,615 0.75** 0.25** 0.08 0.07 -0.04 0.11 0.98**

Part II: Commercial banking (n = 123)

Variable Mean S.D. 1 2 3 4 5 6 7 8

1. Size -0.07 0.85

2. Age 85.86 52.94 0.22*

3. HC 3.51 0.48 -0.02 -0.07

4. OC 3.20 0.47 -0.07 0.01 0.41**

5. ISC 3.37 0.60 -0.13 -0.05 0.51** 0.28**

6. ESC 3.53 0.83 0.06 0.54** 0.29** 0.39**

7. Bus-level

interest

income

($ million)

5,030 9,650 0.59** 0.07 0.15 0.06 -0.09 0.22*

8. Bus-level

interest

income

($ million)

6,292 11,492 0.59** 0.08 0.23* 0.06 -0.01 0.26** 0.98**

Note: * p 0.05, ** p 0.01, *** p 0.001.



7/29/2019 10270970_Reed

17/27

47.39 (279), respectively, with 12 parameters. According to the equation set forth

in 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 and

industry sector conditions, suggest that the data should not be pooled across

sectors and that the regressions to test Hypotheses 13 should, therefore, be run

separately.

Personal Banks

We tested our first three hypotheses twice, using 1999 and 2000 financial perfor-

mance data and OLS regression analysis. Consistent with RBVs 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 1999

dependent variables 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-level

interest income dependent variables

Variable 1999 log of business-level

interest income

2000 log of business-level

interest income

Beta S.E. Beta S.E.

Intercept 3.65*** 0.15 3.78*** 0.13

Controls

Size 0.32*** 0.02 0.34*** 0.02

Log age 0.21*** 0.06 0.09** 0.07

Main effects

HC 0.06 0.06 0.03 0.06

OC 0.05 0.05 0.08 0.06

ISC -0.08 0.05 -0.09 0.05

ESC 0.01 0.03 0.03 0.03Interaction terms

HC OC 0.23* 0.09 0.20* 0.11

HC ISC 0.20* 0.09 0.21* 0.10

HC ESC -0.13* 0.06 -0.12* 0.06

OC ISC -0.20* 0.09 -0.21* 0.10

OC ESC 0.03 0.07 0.04 0.07

ISC ESC -0.02 0.05 -0.01 0.05

Model

F 22.72*** 20.68***

R2 0.65 0.63

Notes: p 0.10, * p 0.05, ** p 0.01, *** p0.001.



7/29/2019 10270970_Reed

18/27

(p 0.001). In both models, size and age, when entered by themselves, explain a

high percentage (R2 = 0.59 and 0.58, respectively) of that variance, and none of

the four main effect IC components are significant. However, the relationship

between 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 effect

these IC associations have on financial performance remains in the same direction.

Thus, these multi-year findings lend some support to Peteraf and Barneys (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 and

external) are significant in 1999 and 2000, lending partial support for Hypotheses

1a and 1b, although the coefficient associated with the HC-ESC interaction in both

models is in the opposite direction to that predicted by Hypothesis 1b. Hypothesis2 (interaction of HC and OC) is also supported, and Hypothesis 3a is partially

supported (OC and ISC), although its coefficient in both models is also in the

opposite 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 coefficients

can be misleading (Aiken and West, 1992), Figure 1 plots the significant ones using

the 1999 data. To generate the graphs, each IC component was split into a

dichotomous 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 have

low levels (at or below the mean) of either ISC or OC, their performance is more

sensitive to the level of HC than when levels of ISC or OC are high (above the

mean). Accordingly, human capital seems to play an important role at personal

banks, but only when ISC is low, meaning that the internal processes through

which 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 consider

to be important. Specifically, if personal banks do not have sufficient OC to analyse

the growing consumer requests for mortgages and credit cards, the process must be

done by competent individuals (HC). These individuals are legally required to

apply consistent standards across customers, while at the same time maintaining

the banks loan quality. Thus, in this particular market, OC and HC become

substitutive. Additionally, if ISC is less than optimal, then individuals (HC) must

learn the various regulations regarding fair lending practices and the techniques ofloan qualification without the information efficiencies that can be gained by groups

of individuals (ISC) working to learn these procedures together.



7/29/2019 10270970_Reed

19/27

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 strong

relationships with their customers, they stand to gain little by investing further in

HC. However, when ESC is low, there is a good return on HC investments.

Finally, the OC-ISC interaction with its associated negative coefficient sign

appears to be meaningful (i.e. the combination of high levels of both may engender

a 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 reduce

the flow of new knowledge into the group, resulting in parochialism and inertia

Personal Banking Interaction Graph

Interaction between HC and ESC

High and Low Values of HC

HighLowLogofBusinessL

evelInterestIncome

4.30

4.20

4.10

4.00

3.90

High and Low ESC

Low

High


LogofBusinessLevelInterest

Income

Interaction between ISC and OC

High and Low Values of ISC

HighLow

4.30

4.20

4.10

4.00

3.90

High and Low OC

Low

High


Interaction between HC and OC



evelInterestIncome

4.3

4.2

4.1

4.0

3.9

High and Low OC

Low

High


Interaction between HC and ISC


HighLowLogofBusinessLevelInterest

Income

4.3

4.2

4.1

4.0

3.9

High and Low ISC

Low

High

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.



7/29/2019 10270970_Reed

20/27

(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 to

high levels can result in negative returns.

Commercial Banks

As with personal banks, we again tested our first three hypotheses twice, using 1999

and 2000 financial performance data and OLS regression analysis. The overall

model (Table IV) explains 44 per cent of the 1999 dependent variables variance

(p 0.001) and 46 per cent of the 2000 variance (p 0.001). In both models, size

and age, when entered by themselves, explain a high percentage (R2 = 0.36 and

0.34, respectively) of that variance, although unlike personal banks, age does notimpart a significant influence. Inconsistent with RBVs 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-level

interest income dependent variables (n = 118)

Variable 1999 log of business-level

interest income

2000 log of business-level

interest income

Beta S.E. Beta S.E.

Intercept 3.52*** 0.15 3.60*** 0.16

Controls

Size 0.31*** 0.04 0.311*** 0.04

Log age -0.05 0.08 -0.04 0.09

Main effects

HC 0.18 0.11 0.15 0.11

OC 0.10 0.03 0.10

ISC -0.17* 0.08 -0.13 0.08

ESC 0.12* 0.05 0.12* 0.05Interaction terms

HC OC 0.19 -0.07 0.19

HC ISC 0.04 0.15 0.19 0.16

HC ESC -0.17 0.14 -0.05 0.14

OC ISC -0.10 0.18 -0.28 0.19

OC ESC 0.09 0.11 0.06 0.11

ISC ESC 0.11 0.10 0.07 0.10

Model

F 6.92*** 7.03***

R2 0.44 0.46

Notes: p 0.10, * p 0.05, ** p 0.01, *** p 0.001.



7/29/2019 10270970_Reed

21/27

are significant, but two of the main effect IC components, ISC and ESC, are

significant in both models, although ISC has a significance level just below 0.10 in

2000. This finding concurs with our previously stated assumption that social

capital, particularly external SC, is crucial in the commercial banking sector,because the selling of these services requires strong social ties both inside the firm

and within the business community. In contrast to personal banks, however, it

seems that the whole is not more valuable than the sum of its parts. (VIF diagnostics

are all below 2.9 for each year.) Exploring possible three-way interactions (see

Figure 2) reveals that one three-way (HC, ISC, and ESC) is significant associated

with 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



evelInterestIncome

3.9

3.8

3.73.6

3.5

3.4

3.3

3.23.1

High and Low ESC

Low

High

Commercial Banking Interaction Graph

Interaction between ISC and ESC

HC Held Constant at High


HighLowLogofBusinessLevelInterest

Income

3.9

3.8

3.7

3.6

3.5

3.4

3.3

3.23.1

High and Low ESC

Low

High

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.



7/29/2019 10270970_Reed

22/27

creation of competitive advantage. Therefore, they suggest that a second stage

of development in IC research is needed whereby empirical tests legitimize the

study of this construct and provide more robust evidence on which to build. Our

two-sample test provided some evidence, but did so with a new theoretical twist.

Specifically, we drew insight from RBVs more general resource interactionthesis and mid-range theory development, to hypothesize that the relation of

each IC component to firm performance is contingent on the value of the other

components. We then explored the contextual boundaries of ICV by using

within-industry/within-geographic region, or distinct resource niches or line-of-

business data. By doing so, we offered a pragmatic, though partial resolution to

five concerns of RBV, as expressed by Foss and Knudsen (2003) and Priem and

Butler (2001).

The results from the Chow test, t-tests, and regression analyses all suggest that

IC interactions are best understood within the very specific industry conditions inwhich they are developed, a finding consistent with Peteraf and Barneys (2003)

recent argument. Indeed, and consistent with the industrial organization economic

and organization ecological literatures, we found evidence of an aggregation bias

when pooling data across banking sectors, even within the same general industry

and geographical region.

We also found evidence that, contrary to intuition, IC-interactions in some

markets may experience diminished returns (i.e. too much of a good thing is not

always 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 might

lead to insular or bureaucratic behaviour that will negatively impact perfor-

mance in the long term. This has implications for managers in terms of how it

structures group decision making processes, such as commercial banks using loan

approval committees as the primary means of making decisions. Particularly for

firms operating in mature industries, managers may want to consider if certain

routines and procedures that once led to efficiencies, have become outdated or

have evolved into an extra layer of bureaucracy which will negatively impactprofitability. Lastly, as advancements continue to be made in technology, the

ways in which clients are serviced will change. This may result in HC being

substituted by technology especially in personal banking regarding such services

as loan approval, and account statements. Thus, instead of investing heavily in

both people and information systems, managers may look at these as substitutive

resources.

The empirical testing of our hypotheses was confined to measuring perfor-

mance in terms of profitability. However, more research should be done to

determine 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



7/29/2019 10270970_Reed

23/27

effect. Guest (1997, p. 266) suggested that as it relates to testing the relationship

between HRM and performance, in particular, a balanced stakeholder approach

would 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 be

taken as well as behavioural measures such as customer satisfaction. This bal-

anced stakeholder approach would also require responses to be gathered from

multiple 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 one

geographical region, thereby raising for some, questions about the generalizability

of our findings. We think that this concern is mitigated by our findings from

Hypothesis 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 kind

of theory that is stable across different business contexts, and therefore is not well

suited 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 may

not have fully controlled for all contextual dimensions. For example, Beard and

Dess (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 thus

not 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, given

multiple-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 artificially

cause a relationship between the interaction term and secondary measures ofperformance, as was hypothesized and found twice by our study, using two years

of performance data. However, our sample choice leaves unanswered questions

like what the structure of the IC interaction-performance relationship might be

in more growth-oriented, technologically advanced industries. Extending Petty

and Guthrie (2000), we therefore call for a third stage of development in IC

research, whereby empirical tests can further explore IC interactions in other

competitive contexts, with the objective of expanding the scope of the mid-range

contingency expectations presented herein.



7/29/2019 10270970_Reed

24/27

APPENDIX

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 banks 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 banks 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, whether

it was personal banking or commercial banking, as opposed to requesting that he/she answer with regards to

his/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).

REFERENCES

Adler, P. S. and Borys, B. (1996). Two types of bureaucracy: enabling and coercive. AdministrativeScience Quarterly, 41, 1, 6189.

Adler, P. S. and Kwon, S-K. (2002). Social capital: prospects for a new concept. Academy of Management Review, 27, 1, 1740.



7/29/2019 10270970_Reed

25/27

Aiken, L. S. and West, S. G. (1992). Multiple Regression: Testing and Interpreting Interactions. Newbury Park,CA: Sage.

Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17, 1,99120.

Barney, J. (2001). Is the resource-based view a useful perspective for strategic managementresearch? Yes. Academy of Management Review, 26, 1, 4157.

Bartlett, C. A. and Ghoshal, S. (1998). Managing Across Borders: The Transnational Solution. Boston, MA:Harvard Business School Press.

Batt, R. (2000). Strategic segmentation in front-line services: matching customers, employees andhuman resource systems. International Journal of Human Resource Management, 11, 3, 54061.

Beard, D. W. and Dess, G. G. (1981). Corporate-level strategy, business-level strategy, and firmperformance. Academy of Management Journal, 24, 4, 66388.

Blyler, M. and Coff, R. W. (2003). Dynamic capabilities, social capital, and rent appropriation: tiesthat split pies. Strategic Management Journal, 24, 7, 67786.

Bontis, N. (1996). Intellectual capital: an exploratory study that develops measures and models.Management Decision, 36, 2, 6376.

Bourdieu, P. (1985). The forms of capital. In Richardson, J. (Ed.), Handbook of Theory and Research forthe Sociology of Education. New York: Greenwood, 24158.

Boxall, P. (1996). The strategic HRM debate and the resource-based view of the firm. HumanResource Management Journal, 6, 3, 5975.

Boxall, P. (2003). HR strategy and competitive advantage in the service sector. Human ResourceManagement Journal, 13, 3, 520.

Boxall, P. and Steeneveld, M. (1999). Human resource strategy and competitive advantage: alongitudinal study of engineering consultancies. Journal of Management Studies, 36, 4, 44363.

Browne, M. W. and Cudeck, R. (1993). Alternative ways of assessing model fit. In Bollen, K. andLong, J. S. (Eds.), Testing Structural Equation Models. Newbury Park, CA: Sage, 13662.

Burt, R. S. (1992). Structural Holes: The Social Structure of Competition. Cambridge, MA: Harvard Uni-versity Press.

Carpenter, M. A., Sanders, W. G. and Gregersen, H. B. (2001). Bundling human capital withorganizational context: the impact of international assignment experience on multinational firm

performance and CEO pay. Academy of Management Journal, 44, 3, 493512.Carroll, G. R., Dobrev, S. D. and Swaminathan, A. (2002). Organizational processes of resourcepartitioning. Research in Organizational Behavior, 24, 140.

Chow, G. C. (1960). Tests of equality between sets of coefficients in two linear regressions.Econometrika, 28, 591605.

Chung, L. H. and Gibbons, P. T. (1997). Corporate entrepreneurship: the roles of ideology andsocial capital. Group and Organization Management, 22, 1, 1030.

Coff, R. (1997). Human assets and management dilemmas: coping with hazards on the road toresource-based theory. Academy of Management Review, 22, 2, 374402.

Cohen, W. M. and Levinthal, D. A. (1990). Absorptive capacity: a new perspective on learning andinnovation. Administrative Science Quarterly, 35, 1, 12852.

Coleman, J. (1998). Social capital in the creation of human capital. American Journal of Sociology, 94,

S95120.Dacin, T. (1997). Isomorphism in context: the power and prescription of institutional norms.Academy of Management Journal, 40, 1, 4681.

Deephouse, D. L. (1999). To be different, or to be the same? Its a question (and theory) of strategicbalance. Strategic Management Journal, 20, 2, 14766.

DiMaggio, P. J. and Powell, W. W. (1983). The iron cage revisited: institutional isomorphism andcollective rationality in organizational fields. American Sociological Review, 48, 14760.

Dyer, J. H. and Singh, H. (1998). The relational view: cooperative strategy and sources of inte-organizational competitive advantage. Academy of Management Review, 23, 4, 66079.

Edvinsson, L. and Malone, M. S. (1997). Intellectual Capital: Realizing your Companys True Value byFinding its Hidden Brain Power. New York: Harper Business (a division of Harper CollinsPublishers).

Florin, J., Lubatkin, M. and Schulze, W. (2002). A social capital model of high growth ventures.Academy of Management Journal, 46, 3, 37484.

Foss, N. and Knudsen, T. (2003). The resource-based tangle: towards a sustainable explanation ofcompetitive advantage. Managerial and Decision Economics, 24, 291308.



7/29/2019 10270970_Reed

26/27

Gargiulo, M. and Bernassi, M. (1999). Trapped in your own net? Network cohesion, structural holes,and the adaptation of social capital. Organization Science, 11, 2, 18396.

Guest, D. E. (1997). Human resource management and performance: a review and research agenda.The International Journal of Human Resource Management, 8, 3, 26376.

Hamel, G. and Prahalad, C. K. (1994). Competing for the Future. Boston, MA: Harvard Business SchoolPress.

Han, J. K., Kim, N. and Srivastiva, R. (1998). Market orientation and organizational performance:is innovation a missing link?. Journal of Marketing, 62, 4, 3045.

Hannan, M. T. and Freeman, J. H. (1977). The population ecology of organizations. American Journalof Sociology, 83, 92984.

Hannan, M. T. and Freeman, J. H. (1984). Structural inertia and organizational change. AmericanSociological Review, 49, 14964.

Hargadon, A. and Sutton, R. I. (1997). Technology brokering and innovation in a product devel-opment firm. Administrative Science Quarterly, 42, 4, 71649.

Haveman, H. A. (1992). Between a rock and a hard place: organizational change and performanceunder conditions of fundamental environmental transformation. Administrative Science Quarterly,37, 4875.

Hitt, M. A., Bierman, L., Shimizu, K. and Kochhar, R. (2001). Direct and moderating effects of

human capital on strategy and performance in professional service firms: a resource-basedperspective. Academy of Management Journal, 44, 1, 1328.

Huselid, M. A., Jackson, S. E. and Schuler, R. S. (1997). Technical and strategic human resourcemanagement effectiveness as determinants of firm performance. Academy of Management Journal,40, 1, 17188.

Jaccard, J. and Wan, C. K. (1996). LISREL approaches to interaction effects in multiple regression.In Lewis-Beck, M. S. (Ed.), Quantitative Applications in the Social Sciences, Vol. 114. Thousand Oaks,CA: Sage.

Kogut, B. and Zander, U. (1992). Knowledge of the firm, combinative capabilities, and the repli-cation of technology. Organization Science, 3, 3, 38397.

Kraatz, M. S. (1998). Learning by association? Interorganizational networks and adaptation toenvironmental change. Academy of Management Journal, 41, 6, 62143.

Lane, P. J. and Lubatkin, M. (1998). Relative absorptive capacity and interorganizational learning.Strategic Management Journal, 19, 5, 46177.Leana, C. R. and Van Buren, H. J. III (1999). Organizational social capital and employment

practices. Academy of Management Review, 24, 3, 53855.Leonard-Barton, D. (1992). Core capabilities and core rigidities: a paradox in managing new

product development. Strategic Management Journal, 13, S11125.Lepak, D. P. and Snell, S. A. (2002). Examining the human resource architecture: the relationships

among human capital, employment, and human resource configurations. Journal of Management,28, 4, 51743.

Loury, G. C. (1987). Why should we care about group inequality. Social Philosophy and Policy, 5,24971.

Lubatkin, M., Schulze, W. S., Mainkar, A. and Cotterill, R. W. (2001). Ecological investigation of

firm effects in horizontal mergers.Strategic Management Journal

, 22, 4, 33557.Maddala, G. S. (1992). Introduction to Econometrics, 2nd edition. Englewood Cliffs, NJ: Prentice Hall.McGahan, A. M. and Porter, M. E. (1997). How much does industry matter, really?. Strategic

Management Journal, 18, S1530.Nahapiet, J. and Ghoshal, S. (1998). Social capital, intellectual capital, and the organizational

advantage. Academy of Management Review, 23, 2, 24266.Nelson, R. R. and Winter, S. G. (1982). An Evolutionary Theory of Economic Change. Cambridge, MA:

Belknap Press.Nonaka, I. (1994). A dynamic theory of organizational knowledge creation. Organization Science, 5, 1,

1437.Nonaka, I. and Takeuchi, H. (1995). The Knowledge-Creating Company. New York: Oxford University

Press.Nonaka, I., Reinmoller, P. and Toyama, R. (2001). Integrated information technology systems for

knowledge creation. In Dierkes, M., Berthoin-Antal, A., Child, J. and Nonaka, I. (Eds), Hand-book of Organizational Learning and Knowledge. Oxford: Oxford University Press.

Oster, S. (1999). Modern Competitive Advantage, 3rd edition. New York: Oxford University Press.



7/29/2019 10270970_Reed

27/27

Pli, G. and Nooteboom, B. (1999). Market partitioning and the geometry of the resource space.American Journal of Sociology, 104, 113253.

Pennings, J. M., Lee, K. and van Witteloostuijn, A. (1998). Human capital, social capital, and firmdissolution. Academy of Management Journal, 41, 4, 42440.

Penrose, E. (1959). The Theory of the Growth of the Firm. Oxford: Basil Blackwell.Peteraf, M. A. and Barney, J. B. (2003). Unraveling the resource-based tangle. Managerial and Decision

Economics, 24, 4, 30923.Petty, R. and Guthrie, J. (2000). Intellectual capital literature review: measurement, reporting and

management. Journal of Intellectual Capital, 1, 2, 15576.Pfeffer, J. (1994). Competitive advantage through people. California Management Review, 36, 2, 928.Pinder, C. C. and Moore, L. F. (1979). The resurrection of taxonomy to aid the development of

middle range theories of organizational behavior. Administrative Science Quarterly, 24, 99118.Porter, M. E. (1980). Competitive Strategy. New York: Free Press.Powell, W. and Smith-Doerr, L. (1994). Networks and economic life. In Smelser, N. J. and

Swedberg, R. (Eds), The Handbook of Economic Sociology. Princeton, NJ: Princeton University Press,368402.

Priem, R. L. and Butler, J. E. (2001). Is the resource-based view a useful perspective for strategicmanagement research?. Academy of Management Review, 26, 1, 2240.

Putnam, R. D. (1993). The prosperous community: social capital and public life. American Prospect,13, 3542.Ramaswamy, K. (1997). The performance impact of strategic similarity in horizontal mergers:

evidence from the U.S. banking industry. Academy of Management Journal, 40, 3, 697715.Rosenthal. E. (1996). Social Networks and Team Performance. Unpublished dissertation, Univer-

sity of Chicago.Scherer, F. M. and Ross, D. (1990). Industrial Market Structure and Economic Performance. Boston, MA:

Houghton Mifflin Company.Schiff, M. (1992). Social capital, labor mobility, and welfare. Rational Society, 4, 15775.Schulze, W., Lubatkin, M., Dino, R. and Buchholtz, A. (2001). Agency relationships in family firms:

theory and evidence. Organization Science, 12, 2, 99116.Stewart, T. A. (1997). Brain power: who owns it . . . how they profit from it. Fortune, 135, 5, 10410.

Teece, D., Pisano, G. and Shuen, A. (1997). Dynamic capabilities and strategic management.Strategic Management Journal, 18, 7, 50933.Tsai, W. and Ghoshal, S. (1998). Social capital and value creation: the role of intrafirm networks.

Academy of Management Journal, 41, 4, 46478.Tushman, M. L. and Anderson, P. (1986). Technological discontinuities and organizational envi-

ronments. Administrative Science Quarterly, 31, 3, 43965.Ulrich, D. (1993). Profiling organizational competitiveness: cultivating capabilities. Human Resource

Planning, 16, 3, 117.Ulrich, D. and Lake, D. (1991). Organizational capability: creating competitive advantage. Academy

of Management Executive, 5, 1, 7792.Wright, P. M., Dunford, B. B. and Snell, S. A. (2001). Human resources and the resource based view

of the firm. Journal of Management, 27, 70121.

Youndt, M. A. and Snell, S. A. (1998). Human Resource Management, Intellectual Capital andOrganizational Performance. Paper presented at the Academy of Management Meetings.Youndt, M. A., Subramaniam, M. and Snell, S. A. (2004). Intellectual capital profiles: an examina-

tion of investments and returns. Journal of Management Studies, 41, 2, 33561.Zajac, E. J., Kraatz, M. S. and Bresser, R. K. F. (2000). Modeling the dynamics of strategic fit: a

normative approach to strategic change. Strategic Management Journal, 21, 4, 42953.


Documents

10270970_Reed