developing a model for intellectual capital …342 may 2013 ol 5, no 1 literature review edvinsson...

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INTERDISCIPLINARY JOURNAL OF CONTEMPORARY RESEARCH IN BUSINESS

COPY RIGHT © 2013 Institute of Interdisciplinary Business Research

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Developing a Model for Intellectual Capital Measurement in Knowledge-Based

Organizations

Kiarash Yazdanfar Department Of Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran

Abstract

Intellectual capital is knowledge that can be converted to value and researchers consider it to consist of

the following components: human capital, structural capital, customer capital, and social capital. These

components of intellectual capital are typically found in the minds of employees and within

organizational structures and processes. In order to give intellectual capital a high level of attention and

effectively manage an environment that helps to facilitate intellectual capital transfer, management must

understand how it is transferred throughout the organization. Although specific mechanisms may not yet

be fully understood, intellectual capital is thought to be transferred from source to receiver through an

exchange process called knowledge transfer. Knowledge transfer has been described as the transmission

and receipt of knowledge from sender to recipient. This study tries to develop a model for knowledge

based organizations in science and technology parks using structural equations modeling using 74

company manager‟s points of view. According to the SEM approach, the unknown model parameters are

chosen to make, in general, the model-reproduced covariance matrix as close as possible to the sample

covariance matrix. If the proposed model emulates the sample matrix (which can be measured by the

goodness-of-fit indices) to a sufficient extent, it can be treated as a plausible description of the

investigated phenomenon, and the theory from which the model has been derived is supported.

Keywords: Intellectual Capital, Knowledge-Based Organizations, structural equations modeling (SEM)

Introduction

Because it is important to manage knowledge development as a resource for sustainable competitive

advantage (Bassi, Cheney, & Lewis, 1998; Bontis, 2001), it is important to establish a theory, a set of

related concepts in an integrated framework that can be used to explain phenomena (Strauss & Corbin,

1998), that addresses the relationships between transfer channels and intellectual capital. Although Bontis

(2002) asserts that intellectual capital is dependent on trust and organizational culture, the literature lacks

sufficient evidence to either confirm or discredit this assertion. One of the reasons for this is that prior

research of intellectual capital does not appear to have addressed in depth how the components of

intellectual capital are transferred within an organization and how van measure in knowledge

organizations. As a matter of survival, organizations in rapidly changing environments need to possess

the ability to identify and react to changes that originate outside their boundaries. This construct, known

as dynamic capabilities, has recently emerged as a key topic for researchers interested in explaining how

firms adapt to shifting knowledge environments. This research posits that a firm‟s dynamic capabilities

enable it to integrate, build, and reconfigure internal and external competencies to address uncertain and

changing environments (Teece, Pisano, & Shuen, 1997). It has been theorized that these capabilities can

facilitate innovation and adaptation by allowing a firm the opportunity to derive economic rents from new

and innovative processes, products, and services. Recent theoretical research suggests that these

capabilities arise from an organization‟s ability to both explore for new information and exploit its

current knowledge base (O'Reilly & Tushman, 2007). Indeed, a significant amount of research has

examined the characteristics of such capabilities (Eisenhardt & Martin, 2000; Winter, 2003), their

evolution and role in firm learning (as well as the mechanisms that firms can employ to leverage their

effectiveness (Rothaermel & Hess, 2007). Despite the insights offered in the extant literature, some

researchers still question the validity and even the existence of dynamic capabilities. Such skepticism is

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warranted, as dynamic capabilities researchers have struggled to answer the fundamental question of how

these capabilities are formed. This significant gap our understanding is a result of the fact that research

has generally failed to consider the complex and multi-level nature of dynamic capability formation. We

submit that to deepen our understanding of how an organization forms dynamic capabilities, an approach

that considers a system of capability formation is vital. Such a system-level analysis allows us to analyze

the linkages between each distinct stage in the dynamic capability development process beginning with

exploration for new knowledge and culminating in its exploitation. We submit that such an analysis

requires a multi-level approach. The need for such an approach is illustrated by the fact that a dynamic

capability itself represents change at the routine or capability level of analysis. This perspective

resonates with Helfat et al.‟s (2007: 4) understanding of dynamic capabilities as “the capacity of an

organization to purposefully create, extend, or modify its resource base.” While a change in an

organization‟s resource base is a direct result of the collective actions of individuals, the outcome of

interest herein is the adaptation of the organization to an environmental knowledge shift. Therefore, we

suggest that three levels of analysis are required to more fully understand both the inputs to and outputs

of dynamic capabilities: the individual, routine, and organization level. Thus, a contribution of our

research is to extend the current focus of dynamic capabilities researchers beyond the traditionally used

capability level of analysis. While this focus is not surprising given that dynamic capabilities are often

defined as higher-order capabilities or heuristics (Teece et al., 1997), viewing the construct through a

broader lens will shed light on the system through which these capabilities emerge. While this focus is

not novel, the prior research that has considered the role of individuals has focused primarily on the role

of middle and top managers (O'Reilly & Tushman, 2007). While an analysis of different management

layers clearly improves our understanding of the organizational decision making process, it does not

directly allow for consideration of how organizations explore for new information or exploit current

knowledge bases, especially in high-tech industries. A deeper understanding of these processes is vital, as

both exploratory and exploitive activities are critical to an organization‟s innovative efforts (Tushman,

Smith, Wood, Westerman, & O‟Reilly, 2004). We suggest that to understand the process through which

organizations sense and react to environmental knowledge shifts requires a deeper analysis of the

organization‟s intellectual human capital. By considering the roles of both star and „non-star‟ or average

employees in the innovative activities of an organization we advance a framework that illustrates a

system through which dynamic capabilities can be developed. To illuminate the roles that individuals

play in facilitating these activities, we incorporate the construct of boundary spanning (Aldrich & Herker,

2006) into multi-level framework of organizational learning. Aldrich & Herker. (2006) offer a general

theory of organizational learning that links the individual, group, and organizational levels of analysis. Of

key interest is the authors‟ position that two key aspects of organizational learning, the ability to intuit

and interpret new knowledge, occur at the individual level. As part of our theoretical framework, we

develop a typology of individuals, which organizations employ to overcome stage-specific knowledge

gaps. This synthesis allows us to provide an analysis of how incumbent firms in knowledge-intensive

industries utilize different individuals to effectuate their continuous adaptation and thus innovation

efforts. The boundary condition imposed by this setting is appropriate given that the purpose of dynamic

capabilities is to allow existing firms to address rapidly changing or high velocity environments

(Eisenhardt & Martin, 2000; Teece et al., 1997) through a continuous change in a firm‟s resource base

(Helfat et al. 2007). In the spirit of Helfat, our theoretical analysis is buttressed by data and anecdotal

evidence that detail the experiences of incumbent firms in knowledge-intensive industries that are

attempting to build dynamic capabilities. We begin our framework development at the organizational

level of analysis by investigating the different knowledge gaps an innovating firm faces. We then turn to

a more micro-level of analysis to illustrate an important heterogeneity in an organization‟s intellectual

human capital. It is through this analysis that we develop a typology of individuals based on the nature

and level of connectivity. Finally, we explicate how effective dynamic capabilities, in terms of technical

and evolutionary fitness result when firms use different individuals to span different knowledge gaps.

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Literature review

Edvinsson and Sullivan (1998a) define intellectual capital as “the knowledge that can be converted into

value” (p.358). The scholars Nahapiet and Ghoshal (1998) use the term to refer to “the knowledge and

knowing capability of a social collectivity, such as an organization, intellectual community, or

professional practice” (p. 245). In spite of the elusiveness of its definition, scholars and practitioners

concur that intellectual capital is a valuable organizational resource that can be used to create differential

advantage (Nahapiet & Ghoshal, 1998; Stewart, 1997). Part of the elusiveness of its definition resides on

the fact that much of the knowledge in an organization is tacit rather than explicit and is therefore

difficult to explain or even to see.

Much of it exists in the intangible and un-codified talents of experts and the experience of employees. For

Sullivan (1998), an organization‟s intellectual capital is composed of human capital, structural capital,

and customer capital. Human capital is the tacit knowledge or know-how that resides in the minds of the

organization‟s employees and cannot be owned by the company. Structural capital relates to the

knowledge from which an organization can derive value as well as the knowledge that “describes or

defines the ways in which the firm operates” (p. 179). The latter includes the definition of the structure

and organization of the company, operational or technical methods and procedures, administrative and

managerial methods and analysis, and the “collective ethos or way of doing business” (p. 179). Customer

capital includes the knowledge embedded in the relationships with the company‟s customers. According

to Sullivan (1998), structural and customer capital encompass both tacit knowledge and knowledge

codified and captured in a format that can be described and used by others. Codified, explicit knowledge

is considered the intellectual assets owned by the organization. Some of these intellectual assets can be

commercialized and thus legally protected. Sullivan emphasizes that organizations seek to develop

intellectual assets and leverage them. Based on the “organizational knowledge” literature, Bontis (1996,

1999) conceptualizes intellectual capital as a second order multi-dimensional construct which includes

three sub-domains: (a) the human capital, the tacit knowledge in the minds of the employees; (b) the

structural capital, the organizational routines of the business; and (c) the relational capital, the knowledge

embedded in the relationships established with customers and the outside environment.

Human capital represents the aggregate knowledge, skills, abilities and other competencies (KSAOs) of

an organization‟s employees (Ployhart, Weekley, Baughman, 2006). The RBV allows any resource that is

valuable, rare, inimitable, and non-substitutable to be a source of sustained competitive advantage

(Barney, 1991). Perhaps “the most strategically important of the firm‟s resources” (Grant, 1991, p. 110)

is knowledge. The focus of the current study is on resources in the form of human capital. While human

capital is not as tangible as other forms of capital controlled by organizations, it has been referred to as

“the ultimate determinant of organizational performance” (Youndt, Snell, Dean, & Lepak, 1996, p. 836).

Human capital has been referred to as the “productive capabilities of human beings” (Snell & Dean,

1992, p. 469), it is in fact more accurately the net productive capabilities of human beings within an

organization (Ployhart et al., 2006). The economic view of human capital conceptualizes it as knowledge

nested within individuals and views it as existing at the individual level, not the firm (Becker, 1964).

Knowledge investments may either reflect general or specific “human capital”. According to Becker,

general investments benefit not only the investing firm but all firms, while specific investments have a

disproportionate benefit to the investing firm. Because Becker cites hiring costs as a source of specific

human capital investment (i.e., it only increases productivity for the investing firm and its value is lost

when the employee leaves), it should be noted that most employees within an organization represent at

least some portion of firm-specific human capital investment. The value of human capital has been touted

by numerous researchers (Dencker, Gruber, & Shah, 2009) and it has often been cited as a source of

competitive advantage. Dierickx and Cool (1989) and provides a useful framework for tying together

these different conceptualizations of human capital with the RBV to understand how organizations

maintain their optimum human capital levels. In many organizations human capital resource levels exist

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in a state of constant fluctuation. Staffing levels describe the current state of human capital levels. When

the number of employees in the organization is below the number desired the organization is said to be

understaffed while too many employees would be found in an overstaffed organization. When an

organization is understaffed, qualified applicants will be sought out, high performance work systems are

used to recruit and select qualified new hires from potential employees. When these individuals join the

organization, human capital accumulation takes place. Once these individuals are hired, they are then

trained and deployed within the organization so that their KSAs may add to the organization‟s human

capital asset stocks, which increases the organization‟s staffing level. Eventually turnover occurs and

these employees leave the organization (erosion) which results in a reduction of staffing levels. If

turnover leads to a reduction in staffing levels which falls below the organization‟s targeted level, this

cycle is triggered and begins again. Each link in the cycle is important to organizational performance and

most have been examined in prior research. Despite the attention given to these interconnected processes,

little attention has been paid to the importance of staffing levels. This is especially surprising given the

goal of this cycle is to maintain staffing levels so that acceptable levels of human capital exist to

guarantee sustainable operations within the organization. Considering the vast number of articles

examining the importance of human capital quality, it is surprising that none have examined the

importance of quantity. It should be noted that some researchers have examined the effects of human

capital loss as a result of turnover, however this only provides us with an incomplete picture of human

capital levels within an organization. It is impossible to fully comprehend asset stocks when we focus

solely on assets flowing from the organization but ignore the flow of assets entering the organization

(Dierickx & Cool, 1989). As long as new employees are hired and trained at a rate that matches

organizational turnover then human capital levels remain relatively unchanged. Organizations would

benefit from understanding the relationship that exists between human capital and staffing levels. Simply

having human capital within an organization is not enough to guarantee success. As organizations

struggle to remain competitive, they often downsize which results in remaining employees assuming

higher workloads often leading to adverse organizational outcomes (see De Meuse, Marks, & Dai, 2010,

for a recent review).

Research Methodology & Approach

Research can be classified as quantitative and qualitative in nature. Quantitative research is the focus of the

current study. According to Ary, et al. (2002), quantitative research explains phenomenon by using

objective measurement and statistical analysis of numeric data. Quantitative research can be classified as

either experimental or non-experimental. Experimental research involves the manipulation of one variable

on another variable. Non-experimental research looks for relationships among variables, but does not

manipulate them. Three types of non-experimental research include: a) causal-comparative research; b)

correlational research; and c) survey research (pp. 24-25). The methodology chosen for the present study is

survey research. Survey research provides a broad picture of the subject being studied and provides an easy

way to generalize to a population (Salkind, 2000). Many advantages and disadvantages have been cited in

regards to survey research. He cites the following advantages: a) the ability to collect a wide scope of

information from a large population; b) it deals with a real situation in the sense that a researcher collects

data in the actual situation; and c) it provides a first step in developing hypotheses or in identifying more

specific problems for research. The disadvantages suggest that survey research can be: a) more extensive

than intensive in the sense that it does not dig down to discover deeper issues below the surface; b)

demanding of time and money; and c) lacking in external validity (p. 61). The current study will employ a

cross-sectional method. Salkind (2000) highlights some of the advantages and disadvantages for using this

type of approach. The advantages for using cross-sectional survey research include: a) it is relatively

inexpensive; b) the study can be conducted in a short-time span; c) there is a low-rate of subjects who drop

out the study; and d) it requires no long-term administration or cooperation between staff and participants.

The disadvantages are related to the following: a) it limits the comparability of groups; b) it does not reveal

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the continuity of development on a person-by person case; c) it examines people of the same chronological

age who may be of different maturational ages; and d) it does not reveal the direction of changes that may

take place in a group (p. 202). For purposes of the current study, the advantages far outweigh the

disadvantages for using cross-sectional survey research. The setting of this study, in order to examine

reliability and validity of measure in knowledge based organizations, is a survey based case study of 74

managers of Pardis science and technology park in Tehran. Based on literature review we found six

construction of our model namely: Knowledge/skill, Customer/relations, Organizational structure,

Technology, Human capital, Brand capital.

Structural equation models require a fairly large sample size for effective analysis (Tabachnick & Fidell,

1996). General structural equation models (SEM) are comprised of two interrelated components, a

measurement model and a structural model. The measurement model specifies relationships between

observed variables (manifest variables) and latent variables (Medsker, Williams, & Holahan, 1994), while

the structural model explains the relationships among latent variables. Anderson and Gerbing (1988)

advocated a two-step approach starting with the measurement model. The measurement model builds on a

priori theoretical foundation to describe or explain the relationship between the underlying latent factors

and the empirical measures. Confirmatory factor analysis (CFA) is used to evaluate the measurement

model with respect to the degree to which the data are consistent with the proposed model. Thus, testing

whether the observed variables represent the latent variables well and the overall fit of the measurement

model needs to be done prior to testing the proposed structural model (Anderson & Gerbing, 1988). It is

very common to consider the re-specification of a baseline measurement model when the initial model fails

to fit the data adequately. Re-specification of the model, however, is controversial in that a re-specified

model with an improved fit to the data may not be the best-fitting model in the sense that it capitalizes on

chance co variation in the sample data and thus, compromises the generalize ability of the model

(MacCallum, Roznowski, & Necowitz, 1992).

Data Analysis & Findings

The likelihood that the model capitalized on chance is reduced considerably with cross-validation. A

number of fit statistics are applied to assess the goodness-of-fit of the model. Measures of fit include the

goodness-of-fit index (GFI), the adjusted goodness-of-fit (AGFI), the comparative fit index (CFI), the

normed fit index (NFI), the Tucker-Lewis coefficient (TLI) which is also called the Bentler-Bonett non-

normed fit index (NNFI), and the root mean square of approximation (RMSEA). Values over 0.9 on the

four indexes GFI, AGFI, TLI, and NFI indicate reasonable fit (Jöreskog & Sörbom, 1996). The CFI is the

least affected by sample size and values of CFI over 0.9 indicate a reasonable fit and values over 0.95

represent a good fit (Holmes-Smith, 2001). RMSEA represents the discrepancy per degree of freedom,

which is measured in terms of the population, not only in the sample used for estimation. RMSEA is

relatively robust to sample size and values between .00 and .05 indicate a close fit, values between .05

and .08 indicate reasonable fit, and RMSEA greater than 0.08 reflects a poor fit (Browne, 1993).

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Figure 1: Scale Model for Measurement of Intellectual Capital

Figure 2: T-Student Statistics and Relationships Significance

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Figure 3: Modification on Model for RMSEA decline

Figure 4: Standardized Model after Modification

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Table 1: Standardized Model after Modification

Goodness-

of-fit

Indicator

df

2 SRMR RMSEA GFI AGFI NFI NNFI CFI

Acceptable

level 5 05.0 1.0 9.0 9.0 9.0 9.0 9.0

Results 1.065 0.039 0.047 0.91 0.91 0.94 0.99

To gain the reliability of model, a panel of Experts were informed on the study and provided directions in

order to critique the proposed instrument for improvement. The experts were asked to categorize and rate

the entire instrument and each item within the questionnaire for content, clarity, wording, format, and

thoroughness, ease of use, focus, and appropriateness. The panel of experts allowed for the examination of

face and content validity to ensure the items developed obtain the proper results. This procedure identified

if any questions needed updating due to new developments in the field of service quality and safety quality.

Moreover, a confirmatory factor analyses (CFA) was carried out to assess the validity of the questionnaire.

In other words, this analysis assessed the fit between the data and the specified CFA Measurement Model.

The second set of analyses tested the structural model.

Discussion & Conclusion

How do organizations identify and react to changes that originate outside their boundaries? This question is

fundamental to both organization theory and strategic management. Organization theory scholars that draw

on the organizational learning literature suggest that the ability to simultaneously explore new knowledge

and to exploit existing knowledge allows an organization to continuously adapt to changing environments

and this is named intellectual capital (Levinthal and March, 1993; March, 1991). The ability of an

organization to concurrently pursue exploration and exploitation has been described as ambidexterity

(O‟Reilly and Tushman, 2007), because engaging in exploration requires fundamentally different routines,

processes, and skills than those necessary for exploitation. To answer the question of how organizations

identify and react to changes that originate outside their boundaries, strategy scholars have recently begun

to advance a dynamic capabilities perspective. They suggest that a firm‟s “ability to integrate, build, and

reconfigure internal and external competences to address rapidly changing environments” lies at the center

of its capability to not only adapt to changing environments (Teece, Pisano, and Shuen, 1997: 516), but also

to introduce favorable market change (Eisenhardt and Martin, 2000). Thus, a dynamic capability has been

defined as “the capacity of an organization to purposefully create, extend, or modify its resources base”

(Helfat et. al, 2007: 4). Key to understanding dynamic capabilities, therefore, is the organization‟s ability to

alter its resource base in a repeatable and reliable fashion, as guided by the organization‟s strategic intent. In

their recent theoretical treatise, O‟Reilly and Tushman (2007) synthesized these two theoretical

perspectives to suggest that ambidexterity is an important dynamic capability. The creation and

maintenance of this dynamic capability, therefore, requires that an organization not only engages in

exploration to create new capabilities, but also that the organization builds on and exploits current

capabilities as well. While it is readily apparent that managers have at their disposal multiple mechanisms

to build dynamic capabilities (for an overview see Helfat et al., 2007), we have virtually no understanding

of the nuanced contingency effects that arise when different antecedents to dynamic capabilities within and

across the dimensions of the exploration-exploitation framework are employed simultaneously. The critical

theoretical dimension that we highlight, therefore, is whether the different dynamic capability mechanisms

that firms use to build and change their resources are exploratory or exploitative in nature. We focus here in

on one mechanism that firms can employ to build dynamic capabilities: recruiting and retaining of

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intellectual human capital while explicitly controlling for acquisitions. Our choice in focusing on this

mechanism stems from the fact that expertise in these activities are representative of dynamic capabilities

that allow firms to access and build new capabilities in order to change their existing resource base (Gulati,

1998; Cockburn and Henderson, 2001), and thus to develop new processes, products, or services. While

some of these mechanisms have been studied in isolation in prior research (Zucker and Darby, 1997;

Gardner, 2005), we know very little about the simultaneous effects of these mechanisms on innovative

performance in general (Rothaermel and Hess, 2007). We know practically nothing about the simultaneous

effects of leveraging different types of intellectual human capital and different types of strategic alliances in

an effort to modify a firm‟s existing resources or to create new resources. In high velocity industries

antecedents to innovation often come from outside of the organizational boundaries (Powell et al., 1996).

Therefore, in such industries, an organization‟s innovative performance is inextricably linked to its ability

to create and manage connections with other organizations. Prior research investigating the importance of

this connectivity has primarily focused on the important role strategic alliances play in developing an

organization‟s ability to access sources of external knowledge (Hagedoorn, 1993; Gulati, 1999; Rothaermel

and Deeds, 2004). It is important to note however, that this capability is also related to the firm‟s scope of

collaborations; both formal (strategic alliances) and informal (interpersonal) relationships (Powell et al.,

1996).

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