An Empirical Study on the Relationship between
Intellectual Capital and Knowledge Creation
Yi-Chun Huang, Associate Professor
Jih-Jen Liou, Lecturer
Dept. of Business Administration
National Kaohsiung University of Applied Sciences
peterhun@cc.kuas.edu.tw
grace@cc.kuas.edu.tw
Fax: 886-7-3961245
Tel: 886-7-3814526 ext. 7302
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Abstract
Knowledge creation has become an important topic for knowledge researchers in recent
years. However, the existing academic literatures do not provide a clear indication of how new
knowledge is created. Few empirical researches are found in this area. This study synthesizes
two different literature streams, i.e. intellectual capital and knowledge creation, in order to
understand their linkage. It examines and tests the effects of social capital, human capital and
structural capital on knowledge creation and the moderating effects on the technological
knowledge diversity between intellectual capital and knowledge creation, respectively. The
results are found as follows: First, partly proves empirically that intellectual capital is a
phenomenon of interactions. Second, partly proves empirically that technological knowledge
diversity is a phenomenon of moderations. Finally, all dimensions of intellectual capital are
positively and significantly influenced on knowledge creation.
Keywords: Knowledge Creation, Intellectual Capital, Technological Knowledge Diversity
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1. Introduction
The knowledge-based view of the firm points out that firms as receivers of knowledge and
competencies have received considerable attention in the management and strategy literatures
(Kogut and Zander, 1996; Spender, 1996). A large amount of the researches on knowledge
examine the organization as the unit of analysis and provide insight view into the importance of
knowledge transfer and acquisition between and within organizations (Ahuja, 2000; Hansen,
1999). However, the creation of new knowledge was not received much attention until
knowledge researchers have begun to explore a knowledge creation theory (Grant, 1996;
Nahapiet and Ghoshal, 1998; Nonaka, 1994). Furthermore, the existing academic literatures do
not provide a clear indication of how new knowledge is created. Few empirical researches are
found in this area.
According to the viewpoints of knowledge-based researchers, knowledge creation and
innovation arises from new combinations of knowledge and other resources (Cohen and
Levinthal, 1990; Kogut and Zander, 1992). Previous studies have recently positioned social
capital as a key factor in realizing knowledge creation (Nahapiet and Ghoshal, 1998; McFadyen
and Cannella, Jr., 2004). According to the theory, social capital and knowledge creation will have
a positive relationship because social capital directly affects the combine-and-exchange process
and provides relatively easy access to network resources (Nahapiet and Ghoshal, 1998). Wadhwa
and Kotha (2006) argued that knowledge creation is from external venturing. Sena and Shani
(1999) attempted to propose an alternative framework for intellectual capital and knowledge
creation. Nonaka, Toyama & Konno (2000) built a unified model of dynamic knowledge creation.
They argued that ba is a place of knowledge creation. Each ba has a physical, a relational, and a
spiritual dimension. Ba is here defined as a shared context in motion in which knowledge is
created, shared, and utilized (Nonaka, Toyama and Konno, 2000). A key feature of
knowledge-based companies is their heavy dependence on intellectual capital (Grandstrand,
1998). Therefore, it is worth to study further for the relationship between intellectual capital and
knowledge creation.
Despite many different terms and definitions related to the intellectual capital (Brooking,
1996; Edvinsson and Malone, 1997; Hussi and Ahonen, 2002; Marr and Schiuma, 2001; Mayo,
2001; Roos et al., 1997; Sveiby, 1997), it seems that there is a common agreement that
intellectual capital of an organization consists of three main components: human capital,
structural capital and social capital (Lonnqvist, et al., 2005; Seetharaman et al., 2004; Sveiby,
1997). Recently, there is an increasing number of researches focused on the relationship among
intellectual capital, innovation and competitiveness. In the papers of McAdam(2002), Darroch
and McNaughton(2002), Gleoet and Terziovski(2004), Liu, Chen and Tsai(2005), the interaction
between innovation and knowledge management or intellectual capital has been studied. But it is
few to explore the relationship between intellectual capital and knowledge creation. Hence, this
study will examine the impact of intellectual capital on knowledge creation.
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Previous studies (Bontis, 1998; Bontis et al., 2000) have revealed that intellectual capital is
positively and significantly associated with organizational performance. In this context, the
dimensions of intellectual capital are interactive, transformable and complementary activities,
meaning that a resource’s productivity may be improved through the investments in other
resources. In addition, the diversity of technological learning can provide a major foundation for
the organizational routines that reinforce existing core competencies or can facilitate building
new ones (Lei, Hitt and Bettis, 1996; Teece, Pisano and Shuen, 1997; Zahra, Ireland and Hitt,
2000) and processes that can lead to moderate knowledge creation (Wadhwa and Kotha, 2006).
Thus, this study focuses on technological knowledge diversity as an important moderator of the
relationship between intellectual capital components and knowledge creation.
The purposes of this study are: (1) to examine relationship between intellectual capital
components and knowledge creation; (2) to study interaction effects between intellectual capital
components and knowledge creation and (3) to explore technological knowledge diversity
moderating effects between each intellectual capital component and knowledge creation.
2. Literature Review
Knowledge Creation
Knowledge can be divided into two broad types: tacit knowledge and explicit knowledge
(Polanyi, 1966). Nelson and Winter (1982) stressed that the corporate world provides special
content for the interaction and inter-permeation between tacit and explicit knowledge to produce
proprietary knowledge. Based on the perspective of resources-base, all companies comprise
knowledge series (Nelson and Winter, 1982; Huber, 1991; Kogut and Zander, 1992; Nonaka,
1994; Grant, 1996). The knowledge resources provide the capacity base for sustaining
differentiation and competitive advantages (Dierickx and Cool, 1989). Furthermore, Nonaka
(1996) identified two essential features of a knowledge productive organization: tacit which is
often highly subjective insights, and personal commitment to the enterprise and its mission.
Burt (2003) examined new ideas proposed by a sample of supply chain managers. In his
research, an idea that is a new and potentially useful to an individual’s company counts as new
knowledge. McFadyen and Cannella (2004) define new knowledge as discoveries about
phenomena that were not known previously. They used the biomedical research context, noted
new knowledge is something that does not exist anywhere before it is discovered and published
by research scientists. Both of the above works took the individual as the unit of analysis.
Wadhwa and Kotha (2006) focused on the time period from 1989 to 1999 for the
telecommunications equipment manufacturing industry. They operationalized the rate of
knowledge creation as the annual count of successful patent application for firm i in year t.
Nonaka (1994) developed a dynamic theory of organizational knowledge creation which
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provides an analytical perspective on the component of knowledge creation. The theory explains
how knowledge held by individuals, organizations, and societies. Organizations play a critical
role in mobilizing tacit knowledge held by individuals and provide the forum for a spiral of
knowledge creation. Therefore, the central theme of this study to address the extent of
knowledge creation involved at an organizational level after technological knowledge acquisition
for the firm. This study defines knowledge creation in terms of new ways of doing things, new
ideals, new ways of thinking, and improved problem solving ability to increase productivity.
Intellectual Capital and Knowledge Creation
Intellectual capital has received considerable attention from academics. Economist
Galbraith (1969) was the first to create intellectual capital concept, and described intellectual
capital as behavior that requires the exercise of the brain, namely, intellectual capital does not
mean just as intellect but demand dynamic intellect-creating activities. Steward (1991, 1997)
later adopted a systematic interpretation of intellectual capital, which classified intellectual
capital into three main types: human capital, structural capital and social capital. Despite many
different terms and definitions related to the theme, there is no widely accepted definition of
intellectual capital. However, at least three elements are common: (1) intangibility; (2)
knowledge creates value and; (3) effect of collective practice. It is assumed that competitive
advantage depends on how efficient the firm in building, sharing, leveraging and using its
knowledge.
Furthermore, there is a common agreement on that intellectual capital of an organization
consists of three main components: human capital, structural capital and social capital
(Lonnqvist, et al., 2005; Nahapiet and Ghoshal, 1998; Seetharaman et al., 2004; Subramaniam
and Youndt, 2005; Sveiby, 1997). Reviewing previous studies, intellectual capital has been
identified as a set of intangible (resources, capability and competences) that drives the
organizational performance and value creation (Roos and Roos, 1997; Bontis, 1998; Bontis et al.,
2000; Marr and Roos, 2005). Therefore, this study adopts three dimensions: human capital,
structural capital and social capital to explore the relationship between each intellectual capital
dimension and knowledge creation in further.
Human Capital
Human capital is the primary component of intellectual capital (Edvinsson and Malone, 1997;
Stewart, 1997; Bontis, 1998; Choo and Bontis, 2002), because human interaction is the critical
source of intangible value in the intellectual age (O’Donnell et al., 2003). On individual level,
knowledge generation and transfer depends on the individuals’ willingness. On organizational
level, the human capital is the source of innovation and strategic renewal (Bontis, 1998). The
characteristics of human capital are creative, bright, skilled employees, with expertise in their
roles and functions, who constitute the predominant sources for new ideas and knowledge in an
organization (Snell and Dean, 1992). Thus, Nonaka, Toyama and Konno (2000) argued that
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knowledge creation is a human process, we cannot really answer the question of how to create
high-quality knowledge without understanding human factors. Therefore, this study proposes the
following hypothesis:
H1: The greater human capital in organizations, the higher extent of knowledge creation.
Structural Capital
Structural capital represents the organization’s capabilities to meet its internal and external
challenges. The characteristics of structural capital include infrastructures, information systems,
routines, procedures and organizational culture for retaining, package and move knowledge
(Cabrita and Vaz, 2006). Organizational knowledge creation represents a process whereby the
knowledge held by individuals is amplified and internalized as part of an organization’s
knowledge base (Nonaka, 1994). According to Nonaka and Takeuchi (1995), knowledge
management requires a commitment to “create new, task-related knowledge, disseminate it
throughout the organization and embody it in products, services and systems.” At the
organizational level knowledge is generated from internal operations or from outside sources
communicating with the corporate structure.
Hibbard and Carrillo (1998) claim that organizations adopted information technologies,
which supports management of intellectual assets to improve its employees’ value creation.
Nonaka, Toyama, and Konno (2000) point out tools can be useful in managing information and
supporting knowledge creation. Moreover, when organizations harness their preserved
knowledge through structured recurrent activities, they deepen their knowledge and legitimize its
perceived value (Katila and Ahuja, 2002).
In addition, knowledge cannot be understood without context, and hence knowledge creation
requires shared context. Nonaka, Toyama, and Konno (2000) argued that managers should built
up the ba (circumstances) by providing time, space, attention, and opportunities for
relationship-building of knowledge creation. They can provide physical space such as meeting
rooms, cyberspace such as a computer network, or mental space such as common goals to foster
interactions. Creating mental space that fosters “love, care, trust, and commitment” among
organizational members is important because it forms the foundation of knowledge creation
(Nonaka, Toyama, and Konno, 2000). Therefore, this study proposes the following hypothesis:
H2: The greater structural capital in organizations, the higher extent of knowledge creation.
Social Capital
The concept of social capital was originally used in community studies to describe
relational resources embedded in personal ties in the community (Jacobs, 1965). The concept has
been applied to a wide range of intra- and inter-organization studies (Burt, 1992; Nahapiet and
Ghoshal, 1998; Yli-Renko, Autio and Spaienza, 2001). Researchers have positioned social
capital as a key factor in understanding knowledge creation (Nahapiet and Ghoshal, 1998). This
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study examined the relational dimension of social capital. Key facts of the relational dimension
of social capital include share and leverage knowledge among and between partners. An
organization’s social capital enhances the quality of group work and richness of information
exchange among team members (Subramaniam and Youndt, 2005). Social capital and knowledge
creation will have a positive relationship because social capital directly affects the
combine-and-exchange process and provides relatively easy access to network resources
(Nahapiet and Ghoshal, 1998). Therefore, this study proposes the following hypothesis:
H3: The greater social capital in organizations, the higher extent of knowledge creation.
Moderating Effect of Social Capital
These three forms of intellectual capital are not independent; conversely, the effect of
intellectual capital can be optimized only when these three forms of capital interact and
complement one another. Therefore, their interrelationships also play an important role in
shaping these influences (Subramaniam and Youndt, 2005). Accordingly, this study will explore
the interaction effects among intellectual capital component and knowledge creation.
Individuals and their associated human capital may encourage the questioning of popular
norms and originate new ways of thinking, but their unique ideas often need to connect to one
another for radical breakthroughs to occur. Dosi (1982) pointed out knowledge creation is a
path-dependent process. Knowledge creation requires network members jointly experience
problem-solving process and spend time together discussing, reflecting, observing, and
interacting (Seufert, vonKrogh and Bach, 1999). While human capital provides organizations
with a platform for diverse ideas and thoughts, social capital encourages collaboration both
within and across organization (Subramaniam and Youndt, 2005). Thus, social capital augments
human capital’s role in reinforcing knowledge creation is expected. Therefore, this study
proposes the following hypotheses:
H4: The greater social capital in organizations, the stronger the influence of human capital on
knowledge creation.
Groups and team play a substantial role in deploying knowledge within organizations
(Nonaka, 1994). Preserved knowledge tends to be used in structured, recurrent activities and is
generally perceived to be more reliable and robust than other knowledge. Consequently,
structural capital not only improves how an organization’s codified knowledge in patents,
databases and licenses is leveraged, but also improves how these knowledge sources are updated
and reinforced. An organization’s social capital enhances the quality of group work and richness
of information exchange among team members. Thus, social capital augments structural capital’s
role in reinforcing knowledge creation. Therefore, this study proposes the following hypotheses:
H5: The greater social capital in organizations, the stronger the influence of structural capital
on knowledge creation.
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Moderating Effect of Technological Knowledge Diversity
Knowledge creation results from new combinations of knowledge and other resources
(Cohen and Levinteal, 1990). Previous research has emphasized that firms seeking to reconfigure
their knowledge must move beyond local search and span technological as well as orgnizational
boundaries (Rosenkopf and Nerkar, 2001). To accumulate the necessary knowledge, many
organizations turn to external activities such as alliances, joint ventures, mergers and acquistions,
and corporate venture capital investment (Schildt, Maula, and Keil, 2005, Wadhwa and Kotha,
2006). Access and exposure to diverse knowledge domains enlightens organizations about new
ways by which existing problems can be solved (Rosenkopf and Nerkar, 2001).
Access to new external knowledge can influence knowledge creation within the firms. The
firms may use new information to support, complement or agument their internal R&D
capabilities, exploit it to enter new markets or introduce new products earlier than competitors
who lack access to new external knowledge (Chesbrough and Tucci, 2003; Maula et al., 2003),
and improve existing products by adding new features and functions (Keil et al., 2004). In
addition, access to new information improves the absorptive capacity of the firms (Cohen and
Levinthal, 1990) and creates a basis for their future assimilation of additional external knowledge
(Ahuja and Katila, 2001).
If an organization’s knowledge base is narrow, its core capabilities are likely to evolve into
core rigidities(Lenonard-Barton, 1992). A broader knowledge base provides the firm with
increased flexibility and adaptability to environmental changes(Volberda, 1996). In other words,
knowledge diversity engender a fluid mind-set conducive to new idea genertion. Thus,
technological knowledge diversity plays an important role in whether a firm is able to maximize
learning from internal and external sources to create new knowledge (Wadhwa and Kotha, 2006).
Thus, this study expects technological knowledge diversity augments intellectual capital’s role in
enhancing knowledge creation. The following hypothesis is proposed:
H6: The greater technological knowledge diversity in organizations, the stronger the
influence of human capital on knowledge creation.
H7: The greater technological knowledge diversity in organizations, the stronger the
influence of structural capital on knowledge creation.
H8: The greater technological knowledge diversity in organizations, the stronger the
influence of social capital on knowledge creation.
3. Research Method
Research Framework
Based on the above discussion, this study proposed a conceptual model. Fig. 1 illustrates the
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research framework. The three components of intellectual capital influence on knowledge
creation directly. However, these influences are not always isolated, given that human capital,
structural capital, and social capital are often intertwined in organizations. Therefore, their
interrelationships also play an important role in shaping these influences. In addition,
technological knowledge diversity plays an important role in whether a firm is able to maximize
learning from external sources to create new knowledge. Thus, this study examines that
technological knowledge diversity augments intellectual capital’s role in enhancing knowledge
creation.
Moderating Effects
 Technology Knowledge Diversity
Intellectual Capital
 Human Capital
 Structural Capital
 Social Capital
Knowledge Creation
Interactive Effects
 Human Capital x Social Capital
 Structural Capital x Social Capital
Fig. 1 Conceptual Structure for this Research
Variable Definition and Measurement
Dependent variable.
The knowledge creation, our dependent variable, was
operationalized by the research of Nonaka (1991, 1994), as well as Nonaka and Takeuchi (1995).
Thus, this study defines knowledge creation in terms of new ways of doing things, new ideals,
new ways of thinking, and improved problem solving ability to increase productivity. The four
items assessing the extent of knowledge creation were based on the original discussions of
knowledge creation (Nonaka, 1991, 1994). A five-point scale was adopted to measure the extent
of the questionnaire items, with one indicating "strongly disagree" and five indicating "strongly
agree."
Independent variable.
The intellectual capital, our independent variable, was
operationalized by previous studies (Roos and Roos, 1997; Bontis, 1998; Bontis et al., 2000;
Marr and Roos, 2005). This study defines intellectual capital as a set of intangible resources,
capability and competences that drives the organizational knowledge and value creation and it
consists of three main components: human capital, structural capital and social capital
(Lonnqvist, et al., 2005; Nahapiet and Ghoshal, 1998; Seetharaman et al., 2004; Subramaniam
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and Youndt, 2005; Sveiby, 1997).
The measurement of human capital was based on the research of Huang (2002) and Van
Buren (1999). Human capital includes attracting and maintaining human resources, training, and
employee evaluation. Nine items are used to assess human capital. The measurement of
structural capital was based on the research of Davenport and Prusak (1998) and Walsh and
Ungson(1991). Eight items are used to assess an organization’s ability to appropriate and store
knowledge in physical organization-level condition such as the hardware and software
management, system building, operational evaluation, structure, processes and culture. And lastly,
social capital drew the core ideas of the social structure literature (Burt, 1992) as well as on the
more specific knowledge management literature (Gupta and Govindarajan, 2000). Six items are
used to assess an organization’s overall ability to share and leverage knowledge among and
between networks of employees, government, customers, alliance partners, suppliers and
technical collaboration. A five-point scale was adopted to measure the extent of the questionnaire
items, with one indicating "strongly disagree" and five indicating "strongly agree."
Moderating variable. The technological knowledge diversity, our moderating variable,
was operationalized by previous studies (Wadhwa and Kotha, 2006). This study defines
technological knowledge diversity as the numbers of method adopted by a business to acquire
key technology by internal development or external transfer. To measure the degree of
technological knowledge diversity by calculating the number of a business to acquire key
technology by internal development or external transfer, we identified five types of technology
acquisition: Independent R&D, technical collaboration, technology purchase, know-how
purchase, and patent rights through licensing.
Research Subjects and Data Collection
Data were collected from the samples of the Taiwanese Biotechnology Industry. Intellectual
capital is an organizational construct that requires “strategic awareness” from informants to
answer the questionnaire. Therefore, this study selected the managers of R&D department to
reply the questionnaire. The questionnaires were mailed to (1) members of the Taiwan
Pharmaceutical Manufacturers Association and the Pharmaceutical Manufacturers Association of
Chinese Medicine. (2) Biotechnology firms listed in the 2005 survey of the Taiwan Institute of
Economic Research. A total of 100 questionnaires were mailed to pharmaceutical companies,
210 to Chinese medicine pharmaceuticals and 370 to biotechnology companies. To increase
response rates, two follow-ups (personal visits and telephone calls) were carried out. 18, 25 and
60 valid respondents were obtained, respectively, from the TPMA, PMACM and biotechnology
firms. A total of 103 valid responses were obtained, after 6 weeks, represent a valid response rate
of 15%. An analysis of respondents and non respondents showed no differences in industry
members, number of employees, and revenues.
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Measurement Properties
This study assessed construct reliability by calculating Cronbach’s alpha coefficients for
each of the intellectual capital and knowledge creation. According to the test result, human
capitalαhas coefficient 0.83, structural capital 0.89, social capital 0.84 and knowledge creation
α has coefficient 0.91. All of the scales were above the suggested value of 0.70 (Nunnally,
1978). Thus, we concluded the measures utilized in the study were valid and internally
consistent.
Using AMOS5.0, we conducted confirmatory factor analysis (CFA) of three aspects of
intellectual capital and knowledge creation. Overall, the CFA results suggested that intellectual
capital model provided a moderate fit for data and that the knowledge creation model provided a
good fit for the data. Table 1 summarizes the results of confirmatory factor analyses on the
measurement model. As the factor loading indicate, the measurement model performed very well.
The standardized factor loading are all above 0.53, while recommended minimum in the social
sciences is usually 0.40 (Ford, McCallum and Tait, 1986). The average variances extracted range
from 0.72 to 0.86, while recommended minimum 0.50 (Fornell and Larcker, 1981).
The measurement model can be used to evaluate discriminate validity. Constructs
demonstrate discriminate validity if the variance extracted for each is higher than the squared
correlation between the constructs (Fornell and Larcker, 1981). We examined each pair of
constructs in our measurement model and found that all demonstrate discriminate validity.
Convergent validity is also evident: positive correlations exist among the three intellectual
capital constructs, as is expected for constructs representing different dimensions of the same
underlying. Table 2 reports means, standard deviations, and correlations for the variables of the
study.
4. Results
Descriptive Statistics
Table 2 provides the means, standard deviations, and correlation for the variables used in
the models. We found knowledge creation to be positively and significantly corrected with all of
the dimensions for intellectual capital. Structural capital, in particular, was strongly correlated
with the dependent variable (r = 0.694). Technological knowledge diversity was positively and
significantly corrected with dependent variable.
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Table 1 Measurement Model
Factor name
Items
Std.
Loading
St. Error
t
Value
Human
Capital
HC1
HC2
HC3
HC5
HC6
HC7
SC2
SC4
SC5
SC6
SC7
SC8
SC9
SC10
SC1
SC2
SC3
SC4
SC5
SC8
KC1
KC2
KC3
KC4
0.57
0.75
0.54
0.68
0.76
0.76
0.62
0.57
0.59
0.76
0.85
0.87
0.74
0.70
0.65
0.55
0.73
0.93
0.72
0.53
0.80
0.85
0.90
0.84
0.15
0.13
0.12
0.12
0.13
5.29
7.17
4.97
6.39
7.24
0.21
0.23
0.21
0.19
0.18
0.21
0.19
5.68
5.22
5.43
6.95
7.71
7.88
6.74
0.27
0.23
0.25
0.32
0.26
4.59
4.12
4.90
5.39
4.83
0.12
0.12
0.13
9.22
9.88
9.11
Structural
Capital
Social
Capital
Knowledge
creation
Table 2
Cronbach’s
Alpha
Composite
Reliability
Average
Variance
Extracted
0.83
0.94
0.78
0.89
0.95
0.72
0.84
0.95
0.81
0.91
0.95
0.86
The Correlations and Descriptive Statistics
Variable
KC
Knowledge Creation (KC)
1.00
HC
StC
SoC
TKD
Human Capital (HC)
.654**
1.00
Structural Capital (StC)
.694**
.675**
1.00
Social Capital(SoC)
.593**
.625**
.541**
1.00
Technological Knowledge Diversity (TKD)
. 476**
.273**
.294**
.428**
1.00
Mean
3.81
3.39
3.89
3.49
3.22
S.D.
.70
.57
.60
.67
.65
Note: **p＜0.01, *p＜0.05
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Relationship between Intellectual Capital and Knowledge Creation
Table 3 shows that, using model 1, all dimensions of intellectual capital were positively and
significantly influence on knowledge creation, and explain 55.3% of total variance. Therefore,
these results support Hypothesis 1, 2, 3. This research examined the multicollinearity. Hair,
Anderson, Tatham and Black (1998) suggest that tolerance and VIF threshold values are 0.1 and
10, respectively. From model 1 in Table 3, the VIF values of all predictive variables are far away
from threshold values. In other words, there is less multicollinearity among predictive variables.
Table 3 depicts the results for overall model, comprising main and interaction effects. The
Ra2 for model 2 increased from 0.553 to 0.613, attributable to the moderating effects. As
expected, social capital significantly positively moderates the relationship between structural
capital and knowledge creation, thereby providing support for hypothesis 5. However, the social
capital by human capital interaction was not significantly related to knowledge creation, thus was
not providing support for hypothesis 4.
Table 3 Relationship between Intellectual Capital and Knowledge Creation
Knowledge Creation
Dep. Var.
Model 1
Ind. Var.
Model 2
Beta
St. Error
p.
VIF
Beta
St. Error
p.
Human Capital (HC)
0.236
0.128
0.025
2.22
0.248
0.129
0.019
Structural Capital (StC)
0.415
0.113
0.000
1.92
0.281
0.150
0.030
Social Capital (SoC)
0.220
0.095
0.017
1.72
0.295
0.024
0.035
Human Capital x Relational Capital
Structural Capital x Relational Capital
F
38.89
49.85
Ra2
0.553
0.613
△Ra2
0.06
Moderating Effects of Technological Knowledge Diversity on Knowledge Creation
To test the moderating effects on the technological knowledge diversity between
intellectual capital and knowledge creation, we used moderated regression analysis. Table 4
depicts the results for overall model, comprising main and interaction effects. The Ra2 for model
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4 increased from 0.590 to 0.620, attributable to the moderating effects. Hypothesis 7 argues that
technology knowledge diversity moderates the relationship between structural capital and
knowledge creation. From model 4 in Table 4, technological knowledge diversity by structural
capital was significantly related to knowledge creation, thereby providing support for hypothesis
7. But, technological knowledge diversity by human capital and social capital were statistically
insignificant (Table 4, model 4). Thus, Hypothesis 6 and 8 were not supported.
Table 4 Moderating Effects of Technological Knowledge Diversity on Knowledge Creation
Knowledge Creation
Dep. Var.
Model 3
Ind. Var.
Model 4
Beta
St. Error
p.
VIF
Beta
St. Error
p.
Human Capital (HC)
0.249
0.121
0.013
2.23
0.298
0.113
0.002
Structural Capital (StC)
0.388
0.107
0.000
1.93
0.227
0.132
0.046
Social Capital (SoC)
0.124
0.096
0.177
1.91
Technological Knowledge Diversity
0.241
0.079
0.001
1.23
0.361
0.020
0.001
Human Capital x Technological
Knowledge Diversity
Structural Capital x Technological
Knowledge Diversity
Social capital x Technological
Knowledge Diversity
F
45.07
55.91
Ra2
0.590
0.620
△Ra2
0.03
5. Discussion
Results of Research
The purpose of this study was to theoretically and empirically examine the link between
intellectual capital and creation. This study provides evidence that all dimensions of intellectual
capital were positively and significantly influence on knowledge creation. This finding proves
that social capital as a key factor in understanding knowledge creation (McFadyen and Canella,
2004; Nahapiet and Ghoshal, 1998). Furthermore, this finding meets the argument of knowledge
creation is a human process (human capital) raised by Nonaka, Toyama and Konno (2000) as
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well as the argument proposed by Katila and Ahuja (2002) that when organizations harness their
preserved knowledge through structured recurrent activities (structural capital), they deepen their
knowledge and legitimize its perceived value.
Additionally, this research examines that social capital augments human capital’s and
structural capital’s role in reinforcing knowledge creation. The research finds that social capital
by structural capital interaction was significantly related to knowledge creation. This finding
meets the argument of managers should built up the ba (structural capital) by providing time,
space, attention, and opportunities for relationship-building (social capital) of knowledge
creation proposed by Nonaka, Toyama, and Konno (2000). But the social capital by human
capital interaction was not significantly related to knowledge creation. Therefore, this study
partly proves empirically that intellectual capital is a phenomenon of interactions, the findings of
this study is different from previous related studies (Cabrita and Vaz, 2006; Subramaniam and
Youndt, 2005).
Technological knowledge diversity, this research’s second potential moderator, moderates
the main relationship partly. In this work’s augment for moderation, the study assumed that
technological knowledge diversity augments intellectual capital’s role in enhancing knowledge
creation. The research finds that technology knowledge diversity moderates the relationship
between structural capital and knowledge creation. But, technological knowledge diversity by
human capital and social capital were statistically insignificant. Therefore, this study partly
proves empirically that technological knowledge diversity is a phenomenon of moderations. The
findings of this study are similar to previous related studies (Wadhwa and Kotha, 2006).
Implication
The findings of this study have several implications. First, previous studies have examined
the relationships between social capital (McFadyen and Canella, 2004) as well as external
venturing (Wadhwa and Kotha, 2006) and knowledge creation. This research is one of the few
empirical efforts to examine the relationship between intellectual capital and knowledge creation.
Second, previous works of the relationship between intellectual capital and innovation (Darroch
and McNaughton, 2002; Gleoet and Terziovski, 2004; Liu, Chen and Tsai, 2005; McAdam, 2002;
Subramaniam and Youndt, 2005) as well as organizational performance and value creation
(Bontis, 1998; Bontis et al. 2000; Cabrita and Vaz, 2006; Marr and Roos, 2005; Roos and Roos,
1997) have been studied. But it is few to explore the relationship between intellectual capital and
knowledge creation. The findings of this study provide evidence of the critical role that
intellectual capital plays in explaining knowledge creation. Third, previous intellectual capital
studies recommend generalization of their results to other countries. This study proves that
intellectual capital is substantively and significantly related to knowledge creation in Taiwan’s
biotechnology industry. Fourth, this study partly proves empirically that intellectual capital is a
15
phenomenon of interactions, i.e., the social capital by human capital interaction was not
significantly related to knowledge creation. A possible explanation for the lack of moderation, as
Lynn (1999) stressed that human capital resembles a root, absorbing all nutrition, structural
capital is like a trunk, transferring and transmitting nutrition, and relational (social) capital is like
leaves, conveying environmental produce elements. Fifth, this study partly proves empirically
that technological knowledge diversity is a phenomenon of moderations. A possible explanation
for the lack of moderation is that the sample firms may lack the assumed diversity. In other
words, the firms may have been overly focused on certain acquiring type of technological
knowledge they may have exhausted the reorganizing pool from new knowledge acquisition.
Limitations and Future Research
This study is not without limitations. This research’s valid sample size was relatively small,
given the number of variables in this study’s models. Another limitation was that this research
depended on perceptual measures because it was difficult to obtain relevant objective measures
capturing the variations in intellectual capital and knowledge creation. This study also recognizes
that the link between intellectual capital and knowledge creation is complex and contingent on
several multidimensional organizational actions, for example, organizational learning as well as
specific strategic activities. However, this study did not take the possible influences of such
action into consideration. Nonetheless, by synthesizing two different literature streams, i.e.
intellectual capital and knowledge creation, this study has initialized efforts to understand the
multidimensional intellectual capital- knowledge creation linkage.
The results of this study suggest several avenues for future research on the knowledge
creation. One such approach would be to focus more closely on the link between social capital
and knowledge creation. For example, Adler and Kwon (2002) proposed various contingencies
governing the value of social capital could be applied to further knowing of the influences of
social capital on knowledge creation. A fertile area for future research is the moderating effects.
For example, the development and testing of a framework for technological learning (Lei, Hitt
and Bettis; Teece, Pisano and Shuen, 1997; Zahra, Ireland and Hitt, 2000) moderates the
relationship between intellectual capital and knowledge creation. Additionally, there is a need for
research into the moderating effects of knowledge integration (Nonaka and Takeuchi, 1995;
Zahra, Ireland and Hitt, 2000) on the relationship between intellectual capital and knowledge
creation.
16
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