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Understanding the knowledge
management-intellectual capital
relationship: a two-way analysis
Ahmed A.S. Seleim
Faculty of Commerce, Alexandria University, Alexandria, Egypt, and
Omar E.M. Khalil
College of Business, Kuwait University, Safat, Kuwait
Abstract
Purpose – Knowledge management (KM) and intellectual capital (IC) are believed to influence each
other, and the relationship between the two constructs is of vital importance to organizational
effectiveness. While a two-way relationship between KM and IC is conceivable, the relevant empirical
research has yet to produce satisfactory evidences on the nature of the relationship between the two
constructs. This paper aims to empirically investigate the plausible KM-IC two-way relationship in the
Egyptian software industry.
Design/methodology/approach – This research adopts a cross-sectional field survey strategy. It
adopts a research model depicting a two-way relationship between KM processes and IC dimensions.
Two sets of hypotheses describing the predicted mutual influence between KM and IC are proposed.
An instrument was adopted to collect the required data set on KM processes and IC dimensions from
38 Egyptian software firms. The partial least squares (PLS) procedure was used to assess the
measurement model and test the research hypotheses.
Findings – The analysis revealed three patterns of relationships between KM and IC: one-way
influence from KM to IC (e.g. knowledge application influences each of human capital, organizational
capital, and relational capital; one-way influence from IC to KM (e.g. human capital influences
knowledge acquisition and knowledge transfer); and two-way influence between KM and IC
(e.g. between knowledge documentation and organizational capital, between knowledge transfer and
relational capital).
Originality/value – Contrary to most of the prior KM-IC relevant research, this research has
adopted a comprehensive research model and research method to facilitate the exploration of the
mutual influences between KM processes and IC dimensions in the Egyptian software industry. To a
certain extent, the research findings confirm and support the general proposition of a mutual KM-IC
relationship. These findings should contribute to the growing research efforts aiming at developing
models that can provide a better explanation of the complex KM-IC relationship phenomenon.
Keywords Knowledge management, Intellectual capital, Software industry, Egypt
Paper type Research paper
Introduction
Knowledge management (KM) and intellectual capital (IC) movement are rooted in the
contemporary management schools of thought. The resource-based theory of the firm
(e.g. Penrose, 1959; Wernerfelt, 1984; Itami, 1987; Aker, 1989; Dierickx and Cool, 1989;
Amit and Schoemaker, 1993; Prahalad and Hamel, 1990; Barney, 1991; Hall, 1992, 1993;
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1469-1930.htm
An earlier version of this paper was presented at the International Conference and Exhibition on
Knowledge-based Business, Industry, and Education, University College of Bahrain,
January 8-10, 2011.
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Journal of Intellectual Capital
Vol. 12 No. 4, 2011
pp. 586-614
qEmerald Group Publishing Limited
1469-1930
DOI 10.1108/14691931111181742
Teece et al., 1997), the dynamic capability of the firm (e.g. Teece et al., 1997; Zahra and
Nielsen, 2002), and the knowledge-based theory of the firm (e.g. Demsetz, 1991; Kogut
and Zander, 1992, 1996; Nonaka, 1994; Spender, 1994; Nonaka and Takeuchi, 1995;
Grant, 1996; Bierly and Chakrabarti, 1996) are particularly prominent. The essence of
these schools of thought is that a firm’s ability to develop, use, and benefit from its
knowledge and intellect through learning is the only source of sustainable competitive
advantages.
KM and IC are vital sources of competitive advantage and organizational
performance (Nonaka et al., 2000; Marr et al., 2004; Curado, 2008; Shih et al., 2010). It is
imperative for organizations to use KM to accumulate IC in order to cope with their
increasingly challenging environments (Shih et al., 2010). Conceptually, KM and IC are
related, as they include the whole range of intellectual activities from knowledge
creation to knowledge leverage (Huang and Wu, 2010; Zhou and Fink, 2003; Nonaka
et al., 2000). KM encompasses the two related elements of organizational learning flows
and intellectual capital stocks (Bontis, 1999). In addition, KM and IC are believed to
influence each other, and the relationship between the two constructs is of vital
importance to organizational effectiveness (Shih et al., 2010; Rastogi, 2000; Zhou and
Fink, 2003). Serenko et al. (2010) add that although the core concepts in the KM/IC field
have been around for just over a decade, the multi-disciplinary perspectives within the
field make it an attractive and productive area of research.
While a two-way relationship between KM and IC is conceivable, there is still little
understanding of how organizations actually create and accumulate their IC by
dynamically managing their knowledge (Marr et al., 2003; Nonaka et al., 2000). There
have been relatively few discussions on the relationship between KM processes and IC
dimensions (e.g. Shih et al., 2010). In addition, the relevant empirical research has yet to
produce satisfactory evidences on the nature of the relationship between the two
constructs. Therefore, the exploration of the nature of the KM-IC relationship continues
to be an important issue that deserves further research attention.
This research is designed to empirically investigate the plausible KM-IC two-way
relationship. It utilizes a data set from 38 Egyptian software firms and adopts a PLS
(Partial Least Square) analysis in order to test two sets of hypotheses depicting the
mutual influence between KM and IC. The rest of the paper is organized accordingly. A
background on the research constructs is presented first, followed by the research model
and hypotheses, research method, research results, discussion of the research findings,
research implications, research limitations and future research, and conclusions.
Background
Knowledge and human capital in the strategic management thought
More than half century ago, Penrose (1959) argued that knowledge could be used to
explain firm’s performance and growth. Although the subsequent management
research overlooked for long the role of knowledge and KM processes in realizing
knowledge-created values (Kogut and Zander, 1992; Nonaka, 1994; Spender, 1996), the
more recent relevant research has increasingly recognized the importance of
knowledge as a resource and its relevance to competitiveness. This recognition is
clearly evident in the contemporary management schools of thought.
According to the resource-based school of thought, a firm’s competitive advantages
are derived basically from internal, firm-specific resources and capabilities that are
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valuable, rare, and inimitable (e.g. Penrose, 1959; Dierickx and Cool, 1989; Amit and
Schoemaker, 1993; Barney, 1991; Grant, 1996). An effective and efficient use of the
unique tangible and intangible assets within firms are important source of competitive
advantage (Barney, 1991; Peteraf, 1993; Doz, 1996). Among the various types of
intangible resources, knowledge and intellect are considered to be important strategic
assets (e.g. Bontis, 1998; Hall, 1992; Spender, 1996; Grant, 1996). Therefore, knowledge
and intellect is in the heart of the resource-based view of the firm.
The knowledge-based organization paradigm emphasizes that the differences in
firms’ performances are attributed to differences in the rules of creating, developing,
distributing, and using knowledge. It identifies the primary rationale for the firm as the
producer, creator and user of knowledge. Also, the existence and boundaries of the firm
could be better understood with special attention to knowledge and intellectual capital
(e.g. Demsetz, 1991; Kogut and Zander, 1992, 1996; Nonaka and Takeuchi, 1995; Conner
and Prahalad, 1996; Grant, 1996). As such, knowledge and IC are viewed as the most
important source for achieving sustainable competitive advantages (e.g. Drucker, 1993;
Bontis, 1999; Seleim et al., 2004; Seleim and Khalil, 2007; Cortini and Benevene, 2010).
Similarly, the proponents of the dynamic capabilities approach view a firm as a set
of organizing processes and principles that are used to deploy resources in order to
achieve strategic objectives (e.g. Day, 1994; Kogut and Zander, 1992; Zahra and
Nielsen, 2002). This approach emphasizes the importance of building distinctive
competences and developing competitive capabilities, particularly knowledge-based
dynamic capabilities, for an organization (Teece et al., 1997; Zahra and Nielsen, 2002).
Therefore, the dynamic capabilities approach is closely related to the knowledge-based
theory of the firm, as the competitive success of a firm is governed by its capability to
develop new knowledge-based assets that create core competences or capabilities.
In addition, the value of human capital is inherently dependent on its potential to
contribute to the competitive advantage or core competences of the firm. The human
capital theory postulates that people possess skills, knowledge, and abilities that
provide economic value to firms (e.g. Schultz, 1961; Becker, 1964). It emphasizes the
importance of the human resources as a contributor to the acquisition and
transformation of know-how (Ducharme, 1998). In addition, IC is viewed as the
economic value of two categories of intangible assets, namely organizational and
human capital (OECD, 1999). Therefore, the true value of the knowledge-based firms
lies in their knowledge flows (i.e. KM) and knowledge bases (i.e. IC).
Knowledge management (KM)
KM is a system or a framework that integrates people, processes, and technology to
achieve sustainable results by increasing performance through learning (Gorelick and
Tantawy-Monsou, 2005; Wang, 2011). KM aids in the planning, organizing, motivating
and controlling of people, processes and systems in an organization in order to ensure
that its knowledge-related assets are continuously improved and effectively employed
(Rajesh et al., 2011).
Different views of the dimensionality of the term have emerged in the KM literature.
Johnston and Blumentritt (1998), for instance, portray KM to comprise knowledge
identification, acquisition, generation, validation, capture, diffusion, embodiment,
realization, and use. Zack (1999) views KM to include knowledge acquisition,
refinement, storage, retrieval, distribution, and presentation. Bennett and Gabriel
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(1999) define KM as a process that involves knowledge capture, storage (i.e.
documentation), dissemination and use. Gold et al. (2001) describe KM as a knowledge
process capability that consists of knowledge acquisition, conversion, application, and
protection. Marr et al. (2003) define KM as processes and practices that organizations
use in order to improve the effectiveness of the generation and application of their ICs.
Salojarvi et al. (2005), however, view KM as a process that encompasses activities in all
relevant managerial areas.
For the purpose of this research, KM is defined to include the five fundamental
processes of:
(1) knowledge acquisition (KA);
(2) knowledge creation (KC);
(3) knowledge documentation (KD);
(4) knowledge transfer (KT); and
(5) knowledge application (KAP) (Seleim and Khalil, 2007).
These five KM processes are not necessarily sequential but rather iterative and overlap
(Lee and Choi, 2003). The effective management of knowledge necessitates a thorough
understanding of the relationships not only among the KM processes themselves but
also between the KM processes and the intellectual assets of an organization.
Intellectual capital (IC)
Organizations should deploy and manage their IC resources in order to maximize value
creation (Peng, 2011). The IC term was first introduced by Galbraith (1969) as a form of
knowledge, intellect, and brainpower activity that uses knowledge to create value.
Since then, different views of IC have been emerged (Cortini and Benevene, 2010).
Edvinsson and Sullivan (1996), for instance, view IC as a knowledge that can be
converted into value. Stewart (1997) refers to IC as the aggregation of all knowledge
and competencies of employees that enable an organization to achieve competitive
advantages. In addition, IC is defined to include all non-tangible assets and resources in
an organization, including its processes, innovation capacity, and patents as well as the
tacit knowledge of its members and their network of collaborators and contact (Bontis,
1999; Cortini and Benevene, 2010).
In spite of its multidimensionality, this research conceptualizes IC as consisting of
three basic interrelated dimensions: human capital (HC), organizational (or structural)
capital (OC), and relational (or customer) capital (OR) (e.g. Dzinkowski, 2000; Ramirez
et al., 2007; Cortini and Benevene, 2010). While HC encompasses attitudes, skills, and
competences of the members of an organization, OC includes elements such as
organizational culture, routines and practices, and intellectual property (Bontis, 1996;
Marr, 2005). RC, however, includes relationships with customers, partners, and other
stakeholders (Cortini and Benevene, 2010). The investments in HC, OC, and RC are
expected to increase the value of an organization (Walsh et al., 2008).
The KM-IC relationship
IC stems from the wide recognition that knowledge is important to organizations (Dumay,
2009). IC and KM serve different purposes and include the whole range of intellectual
activities from knowledge creation to knowledge leverage (Zhou and Fink, 2003). IC
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represents the stock of knowledge at a particular time (Bontis, 2004), which has been
accumulated through knowledge flow activities (i.e. KM processes) (Shih et al., 2010).
Ramirez et al. (2007) view IC management and KM as a set of managerial activities aiming
at identifying and valuing the knowledge assets of an organization as well as leveraging
these assets through the creation and sharing of new knowledge. Schiuma and Lerro
(2008) add that improving organizational flows and management techniques for the
purpose of creating knowledge assets is the most important IC management activity.
KM and IC are believed to be closely coupled. When KM activities are used to
develop and maintain IC, it becomes a resource of sustainable competitive advantage
(Seleim and Khalil, 2007). On the other hand, when IC is properly utilized and exploited,
it increases the absorptive capacity of the organization, which, in turn, facilitates its
KM processes. In addition, Cortini and Benevene (2010) assert that knowledge can add
value to organizations through intangible assets (i.e. IC).
Nevertheless, there is little understanding of how organizations actually create and
accumulate IC by dynamically managing knowledge (Marr et al., 2003; Nonaka et al.,
2000). Issac et al. (2009) advocate the need for developing a model relating the
antecedent conditions that are necessary for the effective management of IC. Also,
Zhou and Fink (2003) argue for a theoretical relationship between IC and KM, since IC
plays an important role in the KM processes, which, in turn, facilitate the development
and accumulation of IC.
Roos et al. (1997) trace the theoretical roots of IC to two different streams of thought:
strategic stream and measurement (tactical) stream. The strategic stream focuses on
the creation and use of knowledge as well as the relationship between knowledge and
value creation. In the measurement stream, however, KM focuses on the tactical and
operational implementations of the knowledge-related activities that facilitate
knowledge capture, creation, transfer and use that, consequently, accumulate IC
(Zhou and Fink, 2003; Wiig, 1997).
Conceivably, the socialization, externalization, combination, and internalization
(SECI) model (Nonaka and Takeuchi, 1995; Nonaka and Konno, 1998) is a more fitting
theoretical foundation for understanding the KM-IC relationship. The SECI model
outlines different interactive spaces (Ba), in which tacit knowledge can be made
explicit. Huss (2004) explains that the IC components (e.g. HC, OC and RC) represent
the input for the knowledge creation process in the SECI model, and its main output
takes the form of commercially exploitable intangibles.
The four processes of the SECI model involve not only knowledge creation and
utilization but also the other KM components including knowledge transfer,
knowledge documentation, and knowledge acquisition. Despre
´s and Chauvel (2000,
p. 60) view the SECI model as a useful and rigorous model in describing the ways
knowledge is generated, transferred, and re-created in organizations.
Knowledge transfer (sharing) is the common factor of the four processes of the SECI
model. Socialization facilitates the conversion of new tacit knowledge through shared
experience, which allows the less communicated knowledge to be communicated
(Nonaka and Takeuchi, 1995). Therefore, the socialization processes involve knowledge
transfer. In addition, externalization is the process of articulating tacit knowledge into
explicit knowledge, which can be shared by others. In the combination and
internalization processes, knowledge is exchanged and reconfigured through
documents, meetings, or communication networks (Nonaka and Reinmoeller, 2000, p. 90).
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Effective execution of the SECI processes can generate different types of IC.
Socialization involves the accumulation of HC, OC, and RC by sharing and transferring
experiences through joint activities. Also, the conversion of tacit knowledge into
explicit knowledge through externalization creates and accumulates OC (Nonaka and
Konno, 1998). Combination creates knowledge structures in the form of systemic,
institutionalized knowledge (i.e. OC) that can be directly disseminated and distributed
((Nonaka and Takeuchi, 1995; Nonaka and Konno, 1998). Internalization, on the other
hand, accumulates HC and RC through learning by doing.
The literature provides further support to the SECI-based argument for a KM-IC
relationship. Marr et al. (2003) argue that KM is a fundamental activity for growing and
sustaining IC in organizations. Bontis (1999) posits that managing organizational
knowledge encompasses two related issues: organizational learning flows and
intellectual capital stocks. Organizational learning, as a part of KM (Rastogi, 2000),
reflects the management’s effort to managing knowledge and ensures that IC is
continually developed, accumulated, and exploited.
KM encompasses dynamic means of organizational learning, innovation,
competencies, expertise, and capability, which evolve toward the development of an
organization’s IC (Rastogi, 2000). As such, the goal of KM is to build and exploit IC
effectively. Huss (2004) adds that IC is accumulated from the daily decisions and
experiences that took place in work processes, instructions, and forms, which all
constitute different KM mechanisms. To capitalize on RC, an organization needs to
develop outstanding relationships with its coalition partners such as customer,
suppliers, competitors, and other agencies, which are considered sources of KA and
KC. Similarly, to make the most of OC, an organization has to create and transfer
knowledge. Furthermore, knowledge flows such as education and training are
expected to boost HC. KD, which takes forms such as databases, manuals, work
procedures, reports and the like, should facilitate the building and developing of OC
(Stewart, 1997).
On the other hand, HC, OC, and RC enable organizations to form, develop, and
manage knowledge (Van Buren, 1999; Wu and Tsai, 2005). Manning (2010) argues that
social capital is significant for KM purposes and can be understood as being
complementary to and parallel with the other intangible capitalizations such as HC and
OC. In addition, knowledge should be codified and institutionalized in order to be
owned and utilized (Edvinsson and Malone, 1997; Stewart, 1997; Johson, 2002;
Williams and Bukowitz, 2001; Seleim et al., 2004, 2005a). Organizations with strong
OCs will be able to acquire, create, document, apply, and transfer knowledge.
Nevertheless, KM and IC prior research can be generally classified into five groups:
(1) Research that focused on raising awareness of the importance knowledge and
IC as two strategic organizational resources (e.g. Stewart, 1997; Edvinsson and
Malone, 1997; Bontis, 1998; Nahapiet and Ghoshal, 1998; Seleim et al., 2005b).
(2) Research that addressed issues related to measuring, managing, and using IC
(e.g. Bontis, 1998, 2004; Seleim et al., 2004).
(3) Research that investigated the relationship of KM and IC to organizational
performance (e.g. Zucker et al., 1998, Dooley, 2000; Bontis, 1998; Belkaoui, 2003;
Youndt and Snell, 2004; Chen et al., 2005; Seleim and Khalil, 2007; Chong and
Lin, 2008; Chang, 2009; Ho, 2009).
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(4) Research that focused on strategy and KM (e.g. DeTore et al., 2002).
(5) Research that explored KM-IC relationships (e.g. Curado, 2008; Huang and Wu,
2010; Shih et al., 2010).
Although the theoretical foundation for the KM-IC relationship is deeply rooted in
the contemporary management schools of thought, the empirical evidence on such
a relationship is only sparse. Huang and Wu (2010) found the IC components of
HC, OC, and SC (social capital) to positively and significantly influence knowledge
productivity in the Taiwan Biotech industry. Also, Shih et al. (2010) found KC to
positively influence IC in the banking industry. In addition, KM research has
focused mainly on investigating separate KM processes in an attempt to identify
and understand their determinants (e.g. Syed-Ikhsan and Rowland, 2004).
Therefore, research aiming at investigating the mutual KM-IC relationship is
crucial to developing a better understanding of such a complex relationship as
well as the criticality of the organizational knowledge resources as idiosyncratic
assets.
Research model and hypotheses
Research model
Figure 1 presents the research model, which has been constructed based on the
reviewed KM and IC literatures. The model depicts a two-way relationship between
KM processes and IC dimensions. Double-head arrows are used to portray the
proposed positive mutual influences between the two constructs. The first-way
relationship is a directional relationship where the KM processes of KA, KC, KD, KT,
and KP are predicted to positively influence the three IC dimensions of HC, OC, and RC.
The second-way relationship is a directional relationship where the three IC
dimensions are predicted to positively influence the five KM processes.
Research hypotheses
KM influence on IC. The purpose of KM is to obtain more value from the
organization’s knowledge (Spender, 2006; Maqsood et al., 2007). In addition, KM
and organizational learning (OL) play important roles in developing organizational
capabilities (Theriou and Chatzoglou, 2008). KM may contribute to IC creation and
accumulation, since organizations use KM processes and practices in order to
improve the effectiveness of the generation and application of their ICs (Manning,
2010; Marr et al., 2003; Huss, 2004). Also, KM facilitates knowledge capture,
creation, transfer, and application with the ultimate goal of creating and
maximizing IC (Shih et al., 2010; Zhou and Fink, 2003; Liew, 2008; Wiig, 1997). KM
processes are considered to be facilitators for the accumulation of IC, and IC is
seen as a consequence of the KM processes (Rastogi, 2000). Therefore, the way
that knowledge is managed in an organization affects the creation, building, and
maximization of its IC capital.
The predicted influence of KM on IC is formalized in the following main hypothesis:
H-A. KM processes positively influence IC dimensions.
Table I presents the sub-hypotheses that formalize the influences of the five KM
processes on the three IC dimensions.
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Figure 1.
The research model
H-No
H-A:
KM positively influences IC H-No
H-B:
IC positively influences KM
A1-1 KA !HC B1-1 HC !KA
A1-2 KC !HC B1-2 HC !KC
A1-3 KD !HC B1-3 HC !KD
A1-4 KT !HC B1-4 HC !KT
A1-5 KP !HC B1-5 HC !KP
A2-1 KA !OC B2-1 OC !KA
A2-2 KC !OC B2-2 OC !KC
A2-3 KD !OC B2-3 OC !KD
A2-4 KT !OC B2-4 OC !KT
A2-5 KP !OC B2-5 OC !KP
A3-1 KA !RC B3-1 RC !KA
A3-2 KC !RC B3-2 RC !KC
A3-3 KD !RC B3-3 RC !KD
A3-4 KT !RC B3-4 RC !KT
A3-5 KP !RC B3-5 RC !KP
Table I.
Research hypotheses
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IC influence on KM. IC is believed to influence KM (e.g. Roos et al., 1997; Huang and
Wu, 2010). IC plays an important role in KM in organizations, which, in turn, develops
and accumulates IC itself. If properly utilized and exploited, IC can increase the level of
absorptive capacity of the organization, which facilitates the KM processes. In
addition, IC components (e.g. HC, OC, and RC) represent the input for the knowledge
creation process in Nonaka and Takeuchi’s (1995) SECI model and its main output in
the form of commercially exploitable intangibles (Huss, 2004). Van Buren (1999) adds
that KM is influenced by the existing IC stocks in the particular organization.
Therefore, IC can lead to changes in the KM processes and practices. This prediction
is formalized in the following main hypothesis:
H-B. IC dimensions positively influence KM processes.
Table I presents the sub-hypotheses that formalizing the influences of the three IC
dimensions on the five KM processes.
Research methodology
This research adopts a cross-sectional field survey strategy in order to increase the
external validity and generalizability of the research findings beyond the research
sample.
Measurement
IC variables. The three IC variables are operationally defined as follows:
(1) Human capital (HC). HC includes the knowledge, experiences, innovation, and
skills of the employees of an organization.
(2) Organizational capital (OC). OC includes the codified and institutionalized
knowledge that resides within an organization such as organizational routines,
systems, procedure manuals, files, and organizational processes.
(3) Relational capital (RC). RC includes the capacity of employees of an organization
to develop links and connection with themselves and coalition partners such as
customers and suppliers.
Although the field lacks an agreed on conceptual framework for consistent IC
measurement (Seetharaman et al., 2004), an instrument has been adopted for measuring
the IC variables based on the available literature. For HC, a 14-item scale was adopted
based on scales previously used by Bontis (1998), Youndt (1998), and Reed (2000). For
RC, a 10-item scale was adopted based on scales used by Youndt (1998) and Bueno et al.
(2004), which reflects the capacity of the organizational members to be linked with each
other and with customers and partners. For OC, an eight-item scale was adopted based
on the work of Bontis (1998), Reed (2000), and Youndt (1998), which measures the
institutionalized knowledge. Appendix 1 exhibits the items used to measure the three
IC variables.
KM variables. KM includes the processes of acquisition, creation, documentation,
transference, and use of knowledge (Seleim and Khalil, 2007). The five KM variables
are operationally defined as follows:
(1) Knowledge acquisition (KA). Activities that select and acquire knowledge from
external sources.
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(2) Knowledge creation (KC). Activities that develop and create insights, skills, and
relationships in the organization as well as the generation of internal
knowledge.
(3) Knowledge documentation (KD). Activities that institutionalize knowledge in
the form of an organizational memory so that it can be transferred and reused in
the future.
(4) Knowledge transfer (KT). Activities that enable the exchange of knowledge
between individuals, groups, and organizational units at the different
organizational levels.
(5) Knowledge application (KP). Activities that involve the utilization of the
available knowledge in order to improve processes, products and services, and
organizational performance.
To measure the five KM variables, a modified version of the original survey of Filius
et al. (2000) was adopted. A few new items were added to the original scale in order to
improve and fit the original scale to the Egyptian context. Appendix 2 exhibits the
items used to measure the five KM variables.
Sampling
This research focused on software companies in the Egyptian private sector as the
primary population. Software industry is classified as a highly knowledge-intensive
industry. Software industry has been chosen to be the context of this investigation
because it is characterized by a high degree of product innovations and provides
markets with several diversified products that offer an appropriate setting to conduct
such a research.
The research population consisted of 107 software companies. These companies,
which are members in the Egyptian Chamber of Software Industry, are located in the
great areas of Cairo and Alexandria. On initial contact with these firms, thirty-eight
firms agreed to participate in this research.
Data collection
An instrument was prepared for data collection. In addition to the questions that were
designed to gather demographic and organizational information, the instrument
included 32 statements covering the three IC variables and 46 statements covering the
five KM practices. The instrument was translated into Arabic and translated back into
English by languages experts in order to ensure accuracy. In addition, the instrument
was tested and revised a number of times in order to fit the context of the study.
Information on the software firms located in the largest two cities of Cairo and
Alexandria that could have potentially participated in this research was obtained from
the Egyptian Chamber of Software Industry. The firms were contacted and invited to
participate in this research. Only 38 firms agreed to participate and the others refused
to participate because of data confidentiality and industry competition. Since the focus
is on analysis at the firm level, each firm’s CEO or his/her deputy was asked to respond
to the questionnaire as a representative of the whole firm.
The data collection instrument was administered to the participating software firms
by one of the two researchers. The informants were asked to respond to the IC
questions using a five-point Likert-type scale (ranging from 1 ¼strongly disagree to
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5¼strongly agree) and to respond to the KM questions using a five-point Likert-type
scale (ranging from 1 ¼very low practice to 5 ¼very high practice).
A total of 38 surveys (one per firm) were completed. The research sample (n¼38)
represents 35.5 per cent of the total population. Most of the firms in the entire sample
are relatively small, with an average of 62 total employees, of which approximately 26
were software developers. Also, the firms in the sample are relatively young with
average age of approximately eight years.
As to the demographic informants (n¼38), 82 per cent held undergraduate degrees
and 18 per cent held graduate degrees; 47 per cent had an engineering background, 34
per cent had a business background, and 19 per cent had other educational
backgrounds. The informants had an average of approximately five years tenure in the
firm and 11 years tenure in the industry.
Results
The partial least squares (PLS) procedure was used in order to test the research
hypotheses. PLS is a technique for estimating path models that involve latent variables
indirectly observed by multiple indicators (Fornell and Cha, 1994). It maximizes the
explanatory power of a conceptual model by examining the R
2
values for the
dependent (endogenous) constants. Although PLS and LISREL can model structural
relations among latent variables and relationships between latent variables and
manifest indicators, PLS has been adopted in the present study because it does not
require multivariate normal data and is considered appropriate for analyzing data
produced from small samples (Chin, 1998).
Assessment of the measurement model (reliability and validity)
To empirically validate the KM and IC measuring scales, a latent variable’s composite
scale reliability, which is a measure of internal consistency reliability analogous to
Cronbach alpha, was used. The scales were refined through the results of the PLS
factor loadings.
The original KM scale had 46 items. Of the items 12 were removed in order to
improve the scale’s reliability and validity. The final scale consists of 34 items that
measure the five KM constructs. The factor loading results exceeded the 0.60 threshold.
Each of the scale components exhibited internal consistency with KA, KC, KD, KT, and
KP, having Cronbach’s alphas of 0.831, 0.833, 0.797, 0.839, and 0.897, respectively. The
average variances explained (AVEs) were 0.499, 0.502, 0.497, 0.55, and 0.557,
respectively (Table II). The minimum standardized loadings relating an item to its
Constructs Composite scale reliability Average variance extracted
KA 0.831 0.499
KC 0.0833 0.502
KD 0.797 0.497
KT 0.839 0.515
KP 0.897 0.557
HC 0.915 0.522
OC 0.871 0.459
RC 0.829 0.455
Table II.
Internal consistency
reliability of the IC and
KM scales
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assigned construct exceeded 0.60 (except two were 0.59 and 0.59 but significant with
t-value .1.96), thereby indicating acceptable convergent validity (Table II).
The original IC scale that measures HC, OC, and RC had 32 items. The process of
improving the scale reliability and validity resulted in removing eight items from the
scale (four items from the HC component and four items from the RC component). The
refined IC scale consists of 24 items (ten items for HC, eight items for OC, and six items
for RC). Each of the three scales exhibited internal consistency with HC, OC, and RC
having Cronbach’s alphas of 0.915, 0.871, and 0.831, respectively. The average
variances explained (AVEs) were 0.522, 0.459, and 0.455, respectively (Table II). The
minimum standardized loadings relating an item to its assigned construct exceeded
0.57, which is significant at t-value .1.96.
To assess the discriminant validity of the constructs, if the correlation between two
composite constructs is not higher than their respective reliability estimate,
discriminant validity exists. Using this criterion, the results indicate that all
reliability estimates (Cronbach’s alpha) were greater than their correlations (Tables II
and III). It should also be noted that moderate correlations, ranging from .230 to .644
exist between KM processes and IC dimensions. In addition, there are significant
positive correlation coefficients among the three IC dimensions relationships, and, with
the exception of the correlations of KD to KA, KT, and KP, the correlation coefficients
among the KM variables are also significant (Table III).
Discriminant validity can also be demonstrated if the square root of the AVE in the
measurement model is larger than the correlation between the construct and all other
constructs in the table (Chin, 1998). In Table IV, the diagonal elements are the square
roots of the average variance extracted. Off-diagonal elements are correlations among
the different constructs. For adequate discriminant validity, the diagonal elements
Constructs OC HC RC KA K C KD KT
HC 0.573 **
RC 0.605 ** 0.490 **
KA 0.625 ** 0.543 ** 0.492 **
KC 0.599 ** 0.498 ** 0.412 *0.786 **
KD 0.529 ** 0.230 0.278 0.240 0.352 *
KT 0.526 ** 0.572 ** 0.621 ** 0.374 *0.487 ** 0.081
KP 0.644 ** 0.463 ** 0.525 ** 0.686 ** 0.716 ** 0.291 0.508 **
Notes: *Significant at p,0:05; ** Significant at p,0:01
Table III.
The correlation matrix of
the IC and KM constructs
Constructs OC H C R C K A K C KD KT KP
OC 0.700
HC 0.573 0.740
RC 0.605 0.490 0.704
KA 0.625 0.543 0.492 0.706
KC 0.599 0.498 0.412 0.786 0.708
KD 0.529 0.230 0.278 0.240 0.352 0.704
KT 0.526 0.572 0.621 0.374 0.487 0.081 0.710
KP 0.644 0.463 0.525 0.686 0.716 0.291 0.508 0.746
Table IV.
Discriminant validity of
the latent variables in the
substantive model
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should be greater than the off-diagonal elements in the corresponding rows and columns
(Fornell and Larcker, 1981). Using this criterion, the results in Table IV suggest adequate
convergent and discriminate validity for all constructs except for KA and KC.
Testing the research hypotheses
Two models have been fitted in order to test the two sets of the research hypotheses.
The influence of KM on IC hypotheses. Table V shows the variances in each of the three
IC dimensions that have been explained by the KM processes. KM explains 0.510 of the
variance in HC, 0.746 of the variance in OC, and 0.523 of the variance in RC. The
relatively high explained variances in the IC dimensions indicate a strong explanatory
power of the model for such new measures and phenomenon.
Table VI displays the results of testing the sub-hypotheses describing the
individual influences that KM processes may have on the IC dimensions. The results
show significant positive
b
eta coefficients for the KP – HC path (4840, T ¼3:6898),
KA-OC path (0.427, T ¼2:1827), KC-OC path (0.466, T ¼4:4272), KD-OC path (0.7020,
T¼3:3144), KP-OC path (0.6930, T ¼9:7722), KT- RC path (0.4300, T ¼2:2839), and
KP-RC path (0.5700, T ¼5:9394). These results support the acceptance of the
sub-hypotheses of A1-5,A2-1,A2-2,A2-3,A2-5,A3-4,and,A3-5. Subsequently, the
results provide a partial support to the acceptance of the main H-A hypothesis.
The influence of IC on KM Hypotheses. Table VII shows the variances in each of the
five KM processes that have been explained by the IC dimensions. IC explains 0.600 of
the variance in KA, 0.534 of the variance in KC, 0.400 of the variance in KD, 0.514 of the
variance in KT, and 0.543 of the variance in KP. The relatively high explained
From To Hypothesis No. Direction of influence Standardized path coefficient ttest
KA HC A1-1 þ0.260 1.3756
KC HC A1-2 þ0.061 0.3971
KD HC A1-3 þ0.740 0.3974
KT HC A1-4 þ0.326 1.5471
KP HC A1-5 þ0.484 3.6898
KA OC A2-1 þ0.427 2.1827
KC OC A2-2 þ0.466 4.4272
KD OC A2-3 þ0.702 3.3144
KT OC A2-4 þ0.071 0.3541
KP OC A2-5 þ0.693 9.7722
KA RC A3-1 þ0.102 0.5772
KC RC A3-2 þ0.052 0.3554
KD RC A3-3 – 20.144 0.6325
KT RC A3-4 þ0.430 2.2839
KP RC A3-5 þ0.570 5.9394
Table VI.
The standardized path
coefficients from KM
processes to IC
dimensions
The dependent variables The independent variables R
2
HC KM 0.510
OC KM 0.746
RC KM 0.523
Table V.
The IC variance
explained by KM
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variances in the KM processes indicate a strong explanatory power of the model for
such new measures and phenomenon.
Table VIII displays the results of testing the sub-hypotheses describing the
individual influences that the IC dimensions may have on the KM processes. The
results show significant positive
b
eta coefficients for the HC-KA path (0.476,
T¼2:1235), HC- KT path (0.478, T ¼2:6962), OC-KD path (0.401, T ¼2:8138), OC-KT
path (0.284, T ¼2:1614), and RC-KT path (0.508, T ¼2:3581). These results support
the acceptance of the sub-hypotheses of B1-1,B1-4,B2-3,B2-4, and B3-4.
Subsequently, the results provide a partial support to the acceptance of the main H-B
hypothesis.
Discussion
This research was designed to empirically examine the mutual influence between KM
processes and IC dimensions. Two sets of hypotheses were formulated and tested. The
first set of hypotheses predicted that the KM processes of KA, KC, KD, KT, and KP
would influence the IC dimensions of HC, OC, and RC. The second set of hypotheses
predicted that the IC dimensions of HC, OC, and RC would influence the KM processes
of KA, KC, KD, KT, and KP.
Figure 2 depicts a revised research model summarizing the findings of the two-way
analysis of the KM-IC relationship. It reveals three patterns of detected relationships
between KM and IC:
From To Hypothesis No Direction of influence Standardized path coefficient ttest
HC KA B1-1 þ0.476 2.1235
HC KC B1-2 220.126 0.5852
HC KD B1-3 þ0.111 0.5506
HC KT B1-4 þ0.478 2.6962
HC KP B1-5 220.016 0.0695
OC KA B2-1 þ0.250 1.6729
OC KC B2-2 þ0.138 0.9833
OC KD B2-3 þ0.401 2.8138
OC KT B2-4 þ0.284 2.1614
OC KP B2-5 þ0.123 0.6958
RC KA B3-1 þ0.261 1.1688
RC KC B3-2 220.108 0.4187
RC KD B3-3 þ0.165 1.0472
RC KT B3-4 þ0.508 2.3581
RC KP B3-5 þ0.131 0.4400
Table VIII.
The standardized path
coefficients from IC
dimensions to KM
processes
Dependent variable Independent variables R
2
KA IC 0.600
KC IC 0.534
KD IC 0.400
KT IC 0.514
KP IC 0.543
Table VII.
The KM variance
explained by IC
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(1) One-way influence from KM to IC (e.g. KA-OC, KC-OC, KP-HC, KP-OC, and
KP-RC) as the KM processes influence the IC dimensions but the IC dimensions
don’t influence the KM processes.
(2) One-way influence from IC to KM (e.g. HC-KA, HC-KT, and OC-KT) as the IC
dimensions influence the KM processes, but the KM processes don’t influence
the IC dimensions.
(3) Two-way influence between KM and IC (e.g. KD-OC and KT-RC) as the
influence between the KM processes and IC dimensions is reciprocal.
These findings suggest that the original research model describing the KM-IC
relationship may be too simplistic to capture the rather complex and dynamic KM-IC
relationships.
Nevertheless, the findings of testing the research hypotheses of the influence of the
KM processes on the IC dimensions provide a partial support for the acceptance of the
first main hypothesis (H-A). The four KM processes of KA, KC, KD, and KP have
significant positive influences on OC. In addition, KP has significant positive influence
on HC and RC; and KT has a significant positive influence on RC. KM processes
collectively explain 51 per cent of the variance in HC, 75 per cent of the variance in OC,
and 52 per cent of the variance in RC. Although not all the paths from KM to IC are
Figure 2.
The revised research
model
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significant, the findings suggest a relatively strong explanatory power of the
sub-model depicting directional relationships from KM to IC.
Furthermore, the findings of testing the research hypotheses on the influence of IC
dimensions on KM processes provide a partial support for the acceptance of the second
main hypothesis (H-B). HC has a significant and positive influence on KA and KT; OC
has a significant and positive influence on KD and KT; and RC has a significant and
positive influence on KT. Collectively, the three dimensions of IC explain 60 per cent of
the variance in KA, 53 per cent of the variance in KC, 40 per cent of the variance in KD,
51 per cent of the variance in KT, and 54 per cent of the variance in KP. Although not
all the paths from IC to KM are significant, the findings suggest a relatively strong
explanatory power of the sub-model depicting directional relationships from IC to KM.
The findings described in the consequential two-way relationship model (Figure 2)
confirm, to a certain extent, the existence of a two-way relationship between KM and IC
in the investigated software firms. As to the influence of the KM processes on the
accumulation of IC dimensions, the research findings lend support to the socialization,
externalization, combination, and internalization (SECI) model of Nonaka and Takeuchi
(1995) as a theoretical foundation for the KM-IC relationship. They also confirm Wu and
Tsai’s (2005) claim that the levels of IC tend to significantly influence KM effectiveness.
Moreover, the findings are consistent with Huang and Wu’s (2010) finding of having HC,
OC, and social capital positively and significantly influencing knowledge productivity.
Among the KM processes, KP seems to be the most influential process on the three
IC dimensions. Effective KP activities will likely accumulate and enhance the three IC
components of HC, OC, and RC in the investigated software firms. The involvement of
the software firms’ employees in applying their individual and organizational
knowledge in developing, marketing, and supporting software products and services
should augment the different types of IC in these firms.
In addition, with the exception of KT, the KM processes were found to influence OC.
This finding is consistent with the premises of the SECI model as well as with those of
Shih et al. (2010), who found KC to positively influence OC. It suggests that the KM
processes and activities aiming at codifying, documenting, storing, and applying the
newly acquired and created knowledge should contribute to the institutionalization of
such knowledge and the enrichment of the organizational capitals of the investigated
software firms. Also, the utilization of advanced information technology systems and
tools – , e.g. portals, data and knowledge bases, intranets, and extranets – in the
software firms should further improve the effectiveness of the KA, KC, KD, and KP
processes, and, consequently, have a greater impact on OC.
KT was also found to have a significant positive effect on RC. This finding suggests
that more effective knowledge sharing activities among the employees in the software
firms and between the employees and their constituencies (e.g. customers, partners,
vendors, government institutions) should result in achieving better collaborations and
relationships and, consequently, enhancing their social networks and relationship
capitals. This finding is consistent with the principles of the SECI model (Nonaka and
Takeuchi, 1995; Nonaka and Konno, 1998), and confirms the assertion that KT is the
common factor of the four processes (i.e. knowledge flows) of the SECI activities, which
facilitate the accumulation of HC, OC, and RC in organizations.
As to the influence of the IC dimensions on the KM processes, HC positively
influences KA and KT; OC positively affects KT and KD; and RC positively affects KP.
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These findings provide a partial support to Huss’ (2004) assertion that the IC
components of HC, OC, and RC are considered to be inputs for the knowledge creation
process in the SECI model. They also confirm Rezgui’s (2007) argument that the
accumulations of IC and knowledge creation (KC) are two closely related organizational
activities.
In addition, the finding that HC influences KA and KT supports the belief that HC is
the origin of all types of knowledge (Argyris and Schon, 1996; Senge, 1990), and a main
source of intellect, knowledge, innovation, and invention, especially in
knowledge-intensive industries such as software. It also supports Chareonsuk and
Chansa-ngavej’s (2008) argument that organizations with abundant HC should be able
not only to boost their operational efficiency but also to accumulate solid OC.
HC appears to be a particularly crucial input for the knowledge flow activities
within the investigated software firms. The higher the level of HC the more successful
are the KA and KT activities. The availability of appropriate human capitals in the
form of tacit knowledge (e.g. experience, values, beliefs, attitudes) and explicit
knowledge (e.g. awareness of best practice and industry standards) is essential to
developing and maintaining effective absorptive capacities in the software firms.
These absorptive capacities, in turn, should enable the software firms of not only
acquiring knowledge from external sources (KA) but also effectively absorbing and
sharing such knowledge (KT) internally and externally.
Furthermore, OC was found to positively influence KT and KD. The software
development methods and techniques are highly standardized and are likely to be
institutionalized and accumulated in the form of OC. In addition, the codified and
institutionalized knowledge (e.g. organizational routines, systems, procedure manuals,
files, and organizational processes) that resides in the knowledge repositories within
the software firms should facilitate the exchange of knowledge among the employees
in these firms as well as between the employees and the firms’ stakeholders
(e.g. partners, customers, suppliers, government institutions, and competitors). In
addition, when organizations, such as the investigated software firms, develop
experience in using advanced information technology techniques (e.g. databases,
knowledge bases, knowledge management systems) for organizing and storing
knowledge, they should be able to use such experience to effectively codify and
document any newly acquired and created knowledge.
Finally, RC was found to affect KT in the investigated software firms. This finding
supports the argument that organizations need to recognize the different knowledge
needs of their stakeholders and the ways that knowledge is created in order to
successfully manage their ICs (Marr et al., 2003). Extensive and effective interactions
between the relatively small Egyptian software firms and their stakeholders in the
Egyptian and Arab markets are crucial to competition and survival. Such interactions
require establishing social networks and involve the sharing of diverse knowledge,
which, in turn, facilitates the accumulation of the relational capitals in these firms
Swart and Kinnie, 2003).
Implications
The findings of this research should be of interest to both researchers and
practitioners. For researchers in the KM, IC, and organizational learning fields,
contrary to most of the prior KM and IC relevant research, this research has adopted a
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research model and research method that facilitated the formulation and empirical
testing of a comprehensive set of hypotheses in order to explore the entire gamut of
mutual influences between KM processes and IC dimensions.
To a certain extent, the detected empirical evidence in this research confirms and
supports the general proposition of a mutual KM-IC relationship. KM and IC are two
interdependent constructs. These findings should contribute to the growing research
efforts aiming at developing models that can provide a better explanation of the complex
KM-IC relationship phenomenon and its determinants. Further research efforts are
needed in order to build and test more complex KM-IC models that may lead to a new
management paradigm that view KM and IC as two interdependent constructs.
For practitioners, the findings of this research are reached based on applying a
dynamic approach that views IC and KMP as two strategic performance areas of a
firm. KM and IC should be treated as systematic processes of creating and
continuously enhancing a firm’s strategic objectives. KM is not a goal by itself, and
businesses do not exist with the purpose of spreading and advancing knowledge. They
exist to create value by selling competitive products and high quality service
(Muntean, 2009). Strategies, organizational designs, institutional systems, and human
development should be adopted in order to facilitate synergistic interactions between
the KM processes and IC dimensions in order to create value.
The research findings may guide the managerial efforts aiming at enhancing the
intellectual capital and knowledge flow in the Egyptian software firms and the other
similar organizations. The Egyptian software firms are young and small and are
competing with much larger and well-established international software firms.
Knowledge can add value to these firms through their intangible assets (Cortini and
Benevene, 2010).
KM and IC should not be divorced from business strategies of the software firms.
KM and IC are strategic only to the extent that they are linked to the firm’s core
capabilities. Therefore, CEOs need to look more closely at how IC and KM fit into the
firm’s specific business strategy.
Since the KM processes of knowledge acquisition, creation, documentation, transfer,
and application were found to influence the human, organizational, and relational
forms of the intellectual capital, software firms should adopt innovative approaches to
effectively practice these knowledge management processes in order to create value
and sustain competitiveness. Social capital is viewed as an important catalyst for
effective implementation of KM, and as a significant moderator of the relationship
between knowledge creating activities and IC (Wu and Tsai, 2005; Manning, 2010).
Since the Egyptian culture is rich in its social capital, the software firms should adopt
policies and managerial practices (e.g. use high levels of authority delegation, develop
and support communities of practices, and provide forums for convenient and informal
communication channels) in order to leverage their social capitals in enhancing their
KM practices.
The human, organizational, and relational capitals were found to influence knowledge
acquisition, documentation, transfer, and application. Therefore, the Egyptian software
firms should adopt human resources and knowledge management policies and practices
that focus on the creation, management, measurement, and evaluation of their core
intellectual capitals, especially the organizational capital. They should also establish and
integrate a variety of IT systems (e.g. databases, knowledge bases, enterprise systems,
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e-manuals and instructions, intranet, extranet) to facilitate the exchange and sharing of
knowledge and the accumulation of their organizational capitals.
The relational capital enables the software firms to learn from their environment so
that they can better serve their customers’ needs in the form of affordable high quality
software and service. Effective contact between employees and their customers and
partners facilitate the conversion of human capital into relational capital and
organizational capital (Reed, 2000). Since the organizational and operational capitals
interact through the organizational culture (Cortini and Benevene, 2010), the Egyptian
software firms should embark on developing organizational cultures that encourage
and reward the accumulation of these two forms of intellectual capital.
In addition, since the Egyptian software firms tend to be young and small, they need
to retain and make the best of their existing human resources rather than attracting
new and superiorly paid information technology professionals. They also need to
establish and maintain clear connections between their missions and their
organizational practices (Brown and Yoshika, 2003), and align their intellectual
capital and knowledge management strategies.
Research limitations and future research
For the purpose of this research, an instrument measuring the KM and IC variables has
been adopted based on the available literature. However, the literatures on KM and IC
lack generally agreed on models for consistent measurement of the research constructs
(e.g. Seetharaman et al., 2004). In addition, the measurement of the KM and IC variables
has been determined by the subjective perceptions of the informants (Browen and
Wiersema, 1999). Although there is evidence of the predictive and discriminant validity
of the variables measurements, it is not clear whether the statements included in the
measuring instrument reflect the entire gamut of KM and IC aspects in the investigated
firms. Future similar research should attempt to further refine the KM and IC measures
used in the present study and/or adopt more objective ones.
In addition, the findings of the present study were drawn based on a cross-sectional
data set representing a sample of 38 young and small Egyptian software firms. This
method, however, signifies an implicit assumption that the produced model parameters
are stable across the software firms and over time. However, the KM and IC constructs
used in the present study may have firm and time-specific components, which vary
across firms and over time. In order to take such a possibility into consideration and
verify the internal and external validity of the finding of the present study, future
research should adopt designs such as longitudinal and case-based research methods
in order to decrease the possible confounding effect of firm and time variability.
The findings of this investigation suggest that the adopted research model may be
too simplistic to capture the rather complex and dynamic KM-IC relationship and its
determinants. Although organizational culture may have an effect on the management
of KM processes and IC components, it was not investigated in the present study.
Cortini and Benevene (2010), for instance, found HC to interact with OC through
organizational culture. Nonaka and Takeuchi (1995) add that when the members of an
organization possess certain tacit knowledge, it becomes part of the organization’s
culture, which is intangible part of the OC. Future research designs should attempt to
investigate the interaction effect of KM and organizational culture on IC as well as the
interaction effect of IC and organizational culture on KM.
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Finally, the research model of this study overlooked the possible interactions among
the KM processes themselves (e.g. Seleim and Khalil, 2007), the interactions among the
IC dimensions themselves (e.g. Shih et al., 2010), and the other paths that could have
been drawn and tested between the KM and IC constructs. For example, KC and KP
may indirectly influence IC dimensions through KT. Also, HC may indirectly influence
the KM processes through the accumulation of OC and/or RC. Future research designs
should build and test models that explore different patterns of paths between the KM
processes and IC dimensions.
Conclusions
KM and IC should be integrated to maximize organizational effectiveness (Wiig,
1997). However, the relationship between KM and IC is complex and so is its
management. In order to effectively manage such a relationship, it is imperative to
understand where and how the accumulated IC is reflected in managing KM
activities in organizations.
This research was designed to use a two-way analysis in order to investigate the
KM-IC relationship in the Egyptian software industry. It provides some empirical
evidence on the mutual relationship between the two constructs as a number of
significant positive influences between KM processes and IC dimensions have been
detected. In particular, KP emerged as the most influential KM process on the
accumulation of HC, OC, and RC. Similarly, HC and OC appear to be the most
influential IC dimensions on the KM processes of KA, KD, and KT. Given the variances
explained in each of the KM processes and IC dimensions, the resulting model
describing the mutual relationship between KM and IC has a relatively strong
explanatory power for such a complex social phenomenon.
Although produced using a cross-sectional research setting and subjective
measurements of the research variables, the findings of this research, to a certain
extent, confirm and support the general proposition of a mutual KM-IC relationship.
Consequently, this research contributes to the growing research efforts aiming at
developing models that are capable of providing a better explanation of the KM-IC
relationship phenomenon and its determinants.
Managers operating in the knowledge economy are required to be “knowledge
leaders,” who must be aware of the relationship between knowledge and those who
possess it in order to successfully fulfill their leadership responsibilities (McFarlane,
2008). Based on the findings of this research, managers in the Egyptian software
firms and in other similar organizations are expected to develop strategies, adopt
structures, and construct systems that effectively coordinate and integrate the
efforts aiming at managing knowledge, human resource, and customer relationship
in order to enhance knowledge flows, accumulate IC, and create and sustain
business values.
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Appendix 1
Items used in measuring IC
(1) Relational capital (RC):
.Our employees are skilled at collaborating with each other to diagnose and solve
problems.
.Our employees share information and learn from one another.
.Our employees interact and exchange ideas with people from different areas of the
company.
.Our employees partner with customers, suppliers, alliance partners, etc., to develop
business solutions.
.Our employees apply knowledge from one area of the company to problems and
opportunities that arise in another.
.Customer information is disseminated in our company.
.Our company collects product specification data from the customer.
.Our company evaluates the new product buy customers before being marketed.
.Our company understands customer needs.
.Our company embeds customer’s feedback to the final product before final release.
(2) Organizational capital (OC):
.Our company documents knowledge in manuals and database.
.Organizational processes in our company is contained in structures, systems,
mechanisms, and manuals.
.Our company uses patents/register software and copyrights as a way to store
knowledge.
.Our company protects knowledge and key information to avoid loss if key people left
the company.
.Our company documents software projects in order to be used in other projects.
.Our company is characterized by efficiency.
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.Our company possesses work methods and procedures in support of innovations and
new products.
.Our company possesses techniques, mechanisms for knowledge sharing and
exchange.
(3) Human capital (HC):
.Our employees are highly skilled.
.Our employees are widely considered the best in our industry.
.Our employees are creative and bright.
.Our employees are considered experts in their particular jobs and functions.
.Our employees develop new ideas and knowledge.
.Our employees are able to expect the influence of external change in the industry on
the company and customers.
.Our employees are risk taker in order to achieve organizational goals.
.Our employees possess leadership abilities in their work.
.Our employees are able to focus on the quality of service provided.
.Our employees are educated and able to influence their managers.
.Our employees are able to work in integrated teams.
.Our employees are able to find simple solutions for more complex problems.
.Our employees possess a full understanding of the company and are able to integrate
organizational knowledge across different areas in the company.
.Our employees are able to develop and maintain strong relationships with others.
Appendix 2
Items used in measuring KM practice
(1) Knowledge acquisition (KA):
.The members in your firm actively participate in professional networks or
associations.
.Firm regularly collects information about the needs of its customers.
.Firm regularly conducts knowledge gab analysis.
.Your firm hires consultants when important skills/information are not available
in-house.
.Your firm hires new staff members who possess missing knowledge.
.Your firm conduct research (i.e. with universities and/or research centers) to explore
future possibilities or to gain technical knowledge.
.The employees in your firm regularly attend courses, seminars, or other training
programs to remain informed.
.Your firm considers competitors as a source of inspiration for developing new
methods and/or products.
(2) Knowledge documentation (KD):
.Your firm uses brainstorming sessions for problem solving.
.Your firm evaluates failures and successes and “lesson learned” are set down.
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.Your firm has available up-to-date handbooks, manuals, CDs, and so forth, which are
frequently used.
.Your firm informs its members systematically of changes in procedures, handbook,
and so forth.
.Your firm has documented the specific knowledge and skills of its individual members.
.Your firm encourages its experts to make explicit the methods they use in developing
software products.
.Your firm keeps and maintains knowledge maps, knowledge networks, and data
warehouses.
(3) Knowledge transfer (KT):
.Your firm assigns mentors to the new hires to help them find their way in the
organization.
.Your firm extracts the experiences of its experts and shares them with others in the
organization.
.The employees in your firm share with colleagues and others their knowledge/know
how.
.The knowledge in your firm is distributed in informal ways.
.The knowledge in your firm is distributed in formal ways.
.Your firm holds regular business update meetings to discuss software development
issues.
.The members in your firm regularly inform each other about positive experiences
and successful work methods.
.Your firm conducts intercollegial review in which members discuss their methods of
working.
.The members in your firm change jobs regularly in order to distribute their
know-how.
.Your firm uses mechanisms and means for knowledge exchange across individuals,
groups, and organizational levels.
(4) Knowledge creation (KC):
.Individual performances are assessed regularly and discussed in individual
evaluative conferences.
.Problems, failures, and doubts are discussed openly in your firm.
.New ideas lead to re-design of work methods and processes in your firm.
.Members are assigned to new projects depending on know-how and availability.
.Your firm endeavors to find knowledge combination that contributes to its identity.
.The members in your firm are rewarded for developing new knowledge and testing
new ideas.
.Your firm promotes and stimulates a learning climate among employees.
.Your firm contributes to the development of the important ideas and knowledge in
the industry.
.The important issues in your firm are explored using scenario- or simulation
techniques.
.Your firm analyzes benchmark at the industry level.
.Your firm conducts data mining to discover new knowledge and insights.
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relationship
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(5) Knowledge application (KAP):
.Selling knowledge, products, or services gets explicit attention in your firm.
.Customer feedback is used to improve products/services in your firm.
.Your firm uses existing know-how in a creative manner for new applications.
.Your firm does marketing research among potential clients before developing new
products or services.
.Your firm tries to conquer dysfunctional beliefs within the organization.
.Your firm utilizes multi-disciplinary teams to perform tasks and/or make decisions.
.Your firm has capabilities to integrate its knowledge across different areas.
.Your firm maximizes knowledge use through its organizational structure,
management systems, and practices.
.Your firm attempts to discover the problems that cause gabs between targets and
achievements.
.Your firm attempts to use its stocks of knowledge across different software projects.
About the authors
Ahmed A.S. Seleim is an Associate Professor at Alexandria University, Egypt. He holds B com.,
MBA, and PhD degrees from Alexandria University, Egypt. His research has been published in
journals such as Journal of Global Information Management, International Journal of Knowledge
Management, Journal of Information and Knowledge Management, The Learning Organization,
and Arab Journal of Administration Sciences. His research interest includes management
information systems, knowledge management, and intellectual capital.
Omar E.M. Khalil is a Professor of Information Systems at Kuwait University. He has a PhD in
Information Systems from the University of North Texas, USA. He has been professionally active
as officer, member, and participant in a number of international and regional professional
associations. His numerous publications have appeared in many journals, scholarly books, and
refereed professional proceedings. His research interest includes information systems effectiveness,
global information systems, information quality, e-government, and knowledge management.
Omar E. M. Khalil is the corresponding author and can be contacted at: okhalil@cba.edu.kw
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