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The Effects of Knowledge Sharing on Individual Creativity in Higher Education Institutions: Socio-Technical View

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Adm. Sci. 2018, 8, 21; doi:10.3390/admsci8020021 www.mdpi.com/journal/admsci
Article
The Effects of Knowledge Sharing on Individual
Creativity in Higher Education Institutions:
Socio-Technical View
Joosung Lee
Division of Interdisciplinary Wellness Studies, Soonchunhyang University, 22 Soonchunhyang-ro,
Asan, Chungnam 31538, Korea; jsl@sch.ac.kr; Tel.: +82-41-530-4974
Received: 14 May 2018; Accepted: 7 June 2018; Published: 16 June 2018
Abstract: Knowledge sharing has multifaceted effects on organizations, such as improving work
performance, among which creativity is apparently one of the most important parts. Nevertheless,
the effects of knowledge sharing on individuals has not been paid attention sufficiently by previous
research. Furthermore, knowledge sharing research mainly concerns business organizations rather
than public organizations. This study aims to examine the effects of knowledge sharing on
individuals in a higher institution of education in Korea, for which a socio-technical view and social
capital theory is used to investigate the important antecedents of knowledge contribution, as well
as to examine social and technical facets. This study is the first research regarding the relationship
between knowledge sharing and individual creativity, and it also identifies the mediating effects of
knowledge sharing on individual creativity at an individual level in a higher education institution.
Keywords: socio-technical view; individual creativity; knowledge sharing
1. Introduction
Knowledge is considered the primary source of competitive advantage (Stewart and
Ruckdeschel 1998) and is critical to the long term sustainability and success of the organization
(Nonaka and Takeuchi 1995), thus knowledge is one of the most important resources for an
organization (Choe 2004). In the recent literature regarding knowledge management, several studies
have analyzed critical success factors and barriers, such as organizational culture, affecting
knowledge management and the adoption of knowledge management systems (KMSs) (Khan et al.
2015a, 2015b). The crucial role of alignment between enterprise knowledge and KMSs has been
suggested (Centobelli et al. 2017), and the impact of knowledge management and KMSs on individual
and corporate performance has been identified (Bhatt 2001; Dyer and Hatch 2006).
Against this backdrop, knowledge sharing, which is the central activity of knowledge
management, has multifaceted implications and potential benefits for organizations, and the effects
of knowledge sharing have been investigated by many previous researchers in multifaceted
dimensions. Knowledge sharing is known to be positively related to cost reduction, improvement of
efficiency, organization and employee performance, and organizational teamwork (Nonaka and
Takeuchi 1995; Hansen 2002; Cummings 2004; Cabrera and Cabrera 2005; and Mesmer-Magnus and
DeChurch 2009). Furthermore, effective management of knowledge sharing can promote
organizational innovation by supporting organizational members in innovating, collaborating, and
making correct decisions efficiently (Nonaka and Takeuchi 1995; Du Plessis 2005).
In today’s high risk and ruthless competitive environment, which is faced by each industry, both
academic scholars and practitioners have found that continuous innovation is a critical
competitiveness that is needed to survive, especially for knowledge base development industries
(Mumford 2000; Weiner 2000; DiPietro and Anoruo 2006). Additionally, it has been confirmed that
Adm. Sci. 2018, 8, 21 2 of 16
organizations are the most likely to succeed in a situation when they truly recognize individual
creativity and focus on nurturing and promoting creativity (Williamson 2001), as creativity is the
foundation of innovation (Dewett and Gruys 2007). Individual creativity can be used as building
blocks for organizational innovation, change, and competitiveness (Mumford 2000; Williamson 2001;
Zhou and George 2001; DiPietro and Anoruo 2006), as an individual is always regarded as the source
of a novel idea of an organization (Gilad 1984; Whiting 1988; Mumford 2000), which is the basic
element of an organization’s creative and innovative potential (Amabile 1988; Shalley 1995).
Nevertheless, the effects of knowledge sharing on individuals have not been paid enough
attention by previous research (Quigley et al. 2007). Based on the previous research regarding
creativity, to improve creativity, there are multiple approaches, amongst which the most frequently
cited one is to continually educate individuals on their capacity for generating new knowledge,
discovering applications, and maintaining the knowledge for future applications (Chen and Chen
2010; Gardner and Laskin 2011). Higher education represents the basic capacity of innovation, and
the key driver of national economic competitiveness and development quality. Thus, higher
education is currently getting much attention from practitioners and government agencies
(Fairweather 2000; Meek 2000; Chen and Chen 2010). Higher education institutions’ mission is to
create and transfer knowledge, which includes explicit and tacit knowledge. It is imperative that
students in the higher education institutions consciously or subconsciously share knowledge with
others in both formal communities (teamwork or research project) and informal communities
(Petrides and Nodine 2003). Thus, knowledge sharing is gaining much attention in higher education
institutions, as well as for its information practices and learning strategies, particularly in developed
countries, which have been receiving grants to implement knowledge management practices (Sohail
and Daud 2009).
From the absence of understanding about the current approach regarding the relationship
between knowledge sharing and individual creativity in a higher education institution case, this
research develops an integrative model to explain the effects of knowledge sharing on individual
creativity. The study draws on both a socio-technical view and social capital theory to investigate the
important antecedents of knowledge sharing, as well as examining the social and technical factors on
individual creativity through the mediating effects of knowledge sharing. Accordingly, the study
should make a theoretical fit of a socio-technical view and social capital theory in the knowledge
sharing and individual creativity context. After developing the preceding factors, related factors are
linked to perceived knowledge sharing and individual creativity extent, and then each variable and
path to examine the mediating effects of knowledge sharing on individual creativity is verified.
This paper makes four key contributions. Firstly, to the best of the authors’ knowledge, it is the
first research regarding the relationship between knowledge sharing and individual creativity, while
previous studies have only focused on the effects of knowledge sharing on organizational
performance. With this new perspective on knowledge sharing, this study is expected to establish the
first research literature of knowledge sharing’s effects on individual creativity that has not yet been
explored in previous studies. Secondly, this study set knowledge sharing as a mediator between
antecedent factors of knowledge sharing and individual creativity. With this improved approach,
this model can explain the mediating effects of knowledge sharing on individual creativity, which
contributes to both academics and practices, to facilitate individual creativity through knowledge
sharing. Thirdly, this study uses socio-technical theory and social capital theory in knowledge
sharing effects on individual creativity to propose an improved model of socio-technical view that is
suitable for knowledge sharing practices in higher education institutions. Finally, this study focuses
on the knowledge sharing practices in a higher education institution. By applying this approach, the
study provides a better rationale of understanding the role of knowledge sharing, as well as the
relationship between knowledge sharing and individual creativity in higher education institutions.
Furthermore, it also indicates abundant theoretical and practical implications for individual
creativity improvement through getting knowledge by boosting sharing among members in higher
education institutions.
Adm. Sci. 2018, 8, 21 3 of 16
2. Theoretical Background
2.1. Knowledge Management for Knowledge Sharing and Individual Creativity
As mentioned above, knowledge management is critical for successfully sharing and utilizing
individuals’ knowledge at an organization level. This impact of knowledge management and KMSs
on individual and corporate performance has been well established (Bhatt 2001; Dyer and Hatch
2006). For this purpose, the alignment between enterprise knowledge and KMSs (Centobelli et al.
2017) is important. Several researchers applied knowledge management to team creativity and to
organizational performance improvement. Dong et al. (2017) suggested ways to enhance employee
creativity via individual skill development and team knowledge sharing. Son et al. (2017) also
examined the impact of close monitoring on creativity and knowledge sharing and found the
mediating role of leader-member exchange. Men et al. (2017) investigated when and how knowledge
sharing benefitted team creativity, and pointed out the importance of cognitive team diversity.
Knowledge sharing (KS) is part of knowledge management (KM), but sometimes researchers
use the terms interchangeably (Kim and Lee 2006; Lee et al. 2010). From the literature review of
previous research, the definition of knowledge sharing has not reached an agreement by researchers.
This study defines knowledge as ideas, facts, expertise, and judgments that can influence individual,
team, and organizational performance (Bartol and Srivastava 2002), and information is considered as
the source of this knowledge. Thus, the concepts that are presented in this study are closely related
to effective KMS development as an organizational practice, as well as system infrastructure. This is
consistent with the fact that Fink and Ploder (2009) and Centobelli et al. (2017) defined KMSs as a
combination of knowledge management practices (KM-Practices), that is, a set of methods and
techniques to support the organizational processes of KM development on the one hand, and
knowledge management tools (KM-Tools), namely specific IT-based systems that support KM-
Practices on the other hand.
Knowledge sharing is means to an end, but not an end in itself. Knowledge is a critical
organizational resource and knowledge sharing can raise the sustainable competitiveness of an
organization (Davenport and Prusak 2000; Foss and Pedersen 2002). Among many means of
knowledge-based resources, knowledge sharing can help members and teams to exploit knowledge-
based resources, and capitalize on them, which will contribute to the competitiveness of an
organization (Davenport and Prusak 2000; Cabrera and Cabrera 2005; Jackson et al. 2006). Among the
multiple benefits of knowledge sharing, the most important effect of knowledge sharing should be
related to organizational creativity and innovation, because knowledge sharing does not only mean
reorganization and effective transfer of knowledge, skills, and information, but it also indicates the
creation of new knowledge and innovative ideas (Cabrera and Cabrera 2005).
Creativity needs several resources to be realized, for instance, time, materials, teamwork effort,
a great deal of hard work, knowledge resources, and strenuous mental energy. Among them,
knowledge can be viewed as an important resource that facilitates individual creativity. Knowledge
sharing among members in organization can share knowledge and information, which is an essential
source for individual creativity (Shalley et al. 2004).
Although there is some research that indicates that knowledge sharing has positive relationship
with individual creativity, there is not much actual research investigating the relationship between
knowledge sharing and individual creativity.
2.2. Socio-Technical View on Knowledge Sharing
The main idea of the socio-technical view is that an organization is composed of a social sub-
system and technical sub-system. An organization is a sophisticated system, and sub-systems are
needed in order for it to work harmoniously. Therefore, social and technical sub-systems, as two key
functions of an organization, need to be considered interactively to maintain continuous
improvement (Bostrom and Heinen 1977). Table 1 shows the definition and examples of social and
technical sub-systems.
Adm. Sci. 2018, 8, 21 4 of 16
Table 1. Social and technical subsystems.
Sub
-
Systems
Definition
s
and
Examples
Social sub-system
The social part of an organization, for example, attitude, knowledge,
values, skills, motivation, work atmosphere, and organizational
structures.
Technical sub-system The technical part of an organization that improves organizational
performance, for example, devices, tools, and techniques.
Previous research on socio-technical views is mainly about knowledge sharing, especially in
business sectors, but in public sectors, like higher education institutions, there is not much relative
research. There are many contextual factors that facilitate knowledge sharing, based on previous
research, many of them can be classified into the socio-technical factors.
In recent years, much of the knowledge sharing research has adopted an integrated socio-
technical perspective on knowledge sharing, which concentrates on the interactive role of social and
technical factors. For instance, the socio-technical perspective is utilized in conceptual research that
investigates the contextual factors of knowledge sharing in a specific company (Pan and Scarbrough
1998). Other research of the socio-technical perspective is summarized in Table 2 and 3.
2.3. Social Capital Theory
Nahapiet and Ghoshal (1998) defined social capital as “the sum of the actual and potential
resources embedded within, available through, and derived from the network of relationships
possessed by an individual or social unit”. Social capital theory is mainly about a perspective that
regards social relationships as productive resources (Chiu et al. 2006).
Based on the empirical research (Nahapiet and Ghoshal 1998; Tsai and Ghoshal 1998), social
capital has proved to facilitate organizational innovation and resource exchange. In terms of
knowledge sharing, Bergami and Bagozzi (2000) found that social capital had positive influence on
knowledge attainment and exploitation in technology-based firms. Besides the traditional
organization, the research of social capital in networks has been investigated by many researchers.
Wasko and Faraj (2005) examined the effects of social capital and motivation on knowledge
management in a virtual electronic practice.
However, higher education institutions are different from other organizations in terms of
organizational structure and culture, as well as interactions among students, which are more
dynamic. Consequently, it is still unclear what the effects of social capital on knowledge sharing
involving knowledge management and resource exchange in higher education institution are (Ellison
et al. 2007).
3. Hypotheses
Based on the theoretical support regarding the effect that social and technical factors affect
knowledge sharing and that knowledge sharing affects individual creativity, a research model has
been developed and the following hypotheses have been proposed. See Figure 1.
Adm. Sci. 2018, 8, 21 5 of 16
Figure 1. Research model.
3.1. Social Factors and Knowledge Sharing
3.1.1. Social Interaction Ties
Social interaction ties, as information channels and resource flows (Tsai and Ghoshal 1998), can
provide more channels for knowledge sharing among members in an organization (Nahapiet and
Ghoshal 1998). Social networks in the organization must facilitate communication among members,
in order to improve the capabilities of knowledge sharing (Kim and Lee 2006).
From the point of social capital theory, social interaction ties provide access to knowledge
resource by giving opportunities for an exchange of knowledge (Nahapiet and Ghoshal 1998).
Furthermore, recent studies proved that social interaction ties had a positive influence on the unit
resource combination and exchange (Cabrera and Cabrera 2005), thus, knowledge sharing could be
promoted, so members could acquire more knowledge. Chiu et al. (2006) examined the positive social
interaction ties’ effects on knowledge sharing, which indicated that people who had more social
interaction ties tended to participate more actively in knowledge sharing. Participating in knowledge
sharing activities not only expanded the access to knowledge resources, but it also gave more
opportunities to interact with other members. Accordingly, Hypotheses 1a and 1b are given, as
follows:
Hypothesis 1a (H1a). An individual’s perceived social interaction ties are positively associated with their
intensity of knowledge sharing.
Hypothesis 1b (H1b). An individual’s perceived social interaction ties are positively associated with their
quality of knowledge sharing.
3.1.2. Social Trust
Many previous researchers have argued that social trust is an important enabler for knowledge
sharing, because it helps members in an organization to overcome barriers and intentions, so as to
start knowledge sharing activities more easily (Butler and Murphy 2007). Von Krogh (1998) indicated
that trust as a kind of organizational culture could enhance communication speed, because members
with high trust toward others could share knowledge and information without hesitation, thus
activating knowledge sharing. Moreover, there were also several empirical studies that directly
Adm. Sci. 2018, 8, 21 6 of 16
proved that trust could lead to better knowledge sharing (Nonaka and Takeuchi 1995; Chiu et al.
2006; Kim and Lee 2006). Without trust, individuals were reluctant to share knowledge with others,
both in formal and informal knowledge sharing practices (Andrews and Delahaye 2000).
Therefore, trust was a particularly significant variable for facilitating knowledge sharing. Blau
(1964) believed that trust was essential for creating and maintaining relationships for knowledge
sharing, and led to a good quality of knowledge sharing. Accordingly, Hypotheses 2a and 2b are
given, as follows:
Hypothesis 2a (H2a). An individual’s perceived social trust is positively associated with their intensity of
knowledge sharing.
Hypothesis 2b (H2b). An individual’s perceived social trust is positively associated with their quality of
knowledge sharing.
3.1.3. Social Identification
People with a high level of emotional identification have a high level of loyalty and
belongingness towards organizations, and also show willingness to maintain committed
relationships and helpful behaviors with the organizational members. From the perspective of social
capital theory, Nahapiet and Ghoshal (1998) indicated that identification was a social capital resource
that could change members’ motivation to share knowledge. This finding also coincided with the fact
that a high level of personal networks were always associated with a strong and positive social
identification in an organization (Bartol and Srivastava 2002). On the contrary, contradictory and
negative identities toward an organization would make barriers for members to share knowledge
and information. Accordingly, Hypotheses 3a and 3b are given, as follows:
Hypothesis 3a (H3a). An individual’s perceived social identification is positively associated with their
intensity of knowledge sharing.
Hypothesis 3b (H3b). An individual’s perceived social identification is positively associated with their quality
of knowledge sharing.
3.2. Technical Factors and Knowledge Sharing
3.2.1. IT Support
IT support has been a platform for effective knowledge management, and it has also been a
foundation for knowledge sharing. IT support here meant an integrated IT infrastructure with
Intranet, Internet, hardware, software, and databases. IT support was not only reflected from the IT
infrastructure of an organization, but it has also been related to utilization by users. Many researchers
have argued that IT utilization has been a fundamental and critical enabler for knowledge sharing
(Machlup 1984; Davenport and Prusak 2000). IT infrastructure has played key role as knowledge
management system (KMS) in the knowledge sharing process, which is a foundation for knowledge
management, and many researchers have examined the positive effects of IT support on knowledge
sharing capabilities (Kim and Lee 2006). Accordingly, Hypotheses 4a and 4b are given, as follows:
Hypothesis 4a (H4a). An individual’s perceived IT support is positively associated with their intensity of
knowledge sharing.
Hypothesis 4b (H4b). An individual’s perceived IT support is positively associated with their quality of
knowledge sharing.
3.2.2. End-User Focus
The importance of end-user focus for technology-based instruments or applications is the
focused of much research (Davenport and Prusak 2000; Butler and Murphy 2007). Effective
knowledge sharing requires IT utilization to be integrative, easy to use, easy to access, and searchable
(Bartol and Srivastava 2002). In addition, technical systems are required to update fast and to be easy-
Adm. Sci. 2018, 8, 21 7 of 16
to-use, in order to allow users to actively access to knowledge sharing activities (Durst 1999; Gardner
and Laskin 2011). Hypotheses 5a and 5b are given, as follows:
Hypothesis 5a (H5a). An individual’s perceived end-user focus is positively associated with their intensity of
knowledge sharing.
Hypothesis 5b (H5b). An individual’s perceived end-user focus is positively associated with their quality of
knowledge sharing.
3.2.3. Smart Device Utilization
Smart devices, alternatively called ubiquitous devices, can be viewed as computer-based
devices, which ultimately are used for getting information from networks. Through the utilization of
smart devices, the Internet has been linked both to increases and decreases in knowledge sharing.
Nowadays, smart devices are used broadly in the university, in order to check webpages, SNSs, e-
mail, and other activities, all of which are online knowledge sharing activities. Thus, the smart device
has become important platform of knowledge sharing, besides the computer (Ellison et al. 2007).
Accordingly, Hypotheses 6a and 6b are given, as follows:
Hypothesis 6a (H6a). An individual’s perceived smart device utilization is positively associated with their
intensity of knowledge sharing.
Hypothesis 6b (H6b). An individual’s perceived smart device utilization is positively associated with their
quality of knowledge sharing.
3.3. Knowledge Sharing and Individual Creativity
Members in organization may get creative ideas when they share their ideas with others and
when they discuss ideas. Lee et al. (2011) argued that knowledge sharing was a critical facilitator of
creative ideas, and was a primary factor to facilitate organizational creativity and innovation.
Furthermore, knowledge sharing could also stimulate individual creativity (Chen and Chen 2010),
because knowledge sharing could help collaboration within an organization, and could also improve
the domain knowledge (Amin et al. 2011). All of this could be explained by the fact that the
knowledge resource is the most important factor for facilitating individual creativity. By knowledge
sharing, people can get high quality knowledge and information, and combine them with their own
knowledge, which would finally result in creative ideas and new knowledge (Amin et al. 2011).
Accordingly, Hypotheses 7a and 7b are given, as follows:
Hypothesis 7a (H7a). An individual’s perceived intensity of knowledge sharing is positively associated with
their individual creativity.
Hypothesis 7b (H7b). An individual’s perceived quality of knowledge sharing is positively associated with
their individual creativity.
4. Research Methodology
4.1. Survey Methodology
There were 213 samples that were selected for the first data analysis among the KAIST (Korea
Advanced Institute of Science and Technology) students. From them, 9 of the 213 samples were
excluded because of the incomplete and unsatisfied responses. Therefore, 204 samples were selected
for the final data analysis.
4.2. Measurement Items
The questionnaire was conducted with a multi-item method, and each item was measured based
on the seven-point Likert scale, ranging from ‘strongly disagree’ to ‘strongly agree’. The
measurement items that were used to operationalize the construct were adopted from the relevant
prior studies; they had already been validated in other prior literature. The questionnaire for this
research is shown in the Appendix A.
Adm. Sci. 2018, 8, 21 8 of 16
This study was comprised of six independent variables, two mediating variables, and one
dependent variable.
Among them, the independent variables were divided into two dimensions, the social and
technical. The social dimension consisted of three variables, namely, social interaction ties, social
trust, social identification, and the technical dimension was composed of IT support, end-user focus,
and smart device utilization. The social interaction ties were based on the studies of Tsai and Ghoshal
(1998) and Chiu et al. (2006). Social trust and identification were modified from the researches of Chiu
et al. (2006). The items for IT support were based on Kim and Lee (2006), and the items of the end-
user focus were modified from the work of Kim and Lee (2006). Finally, smart device utilization was
a new item that was based on self-developed items.
In terms of the mediating and dependent variables, intensity and quality of knowledge sharing
were relatively based on the research of Chiu et al. (2006) and Kim and Lee (2006). Finally, the items
that were developed to measure the individual creativity were from Scott and Bruce (1994), and Zhou
and George (2001).
4.3. Data Analysis
The data analysis in this study was performed using the PLS (partial least square) method and
several other statistical methods. The application of the statistical methods followed the reliability
and validity test, the assessment of the measurement model, and the assessment of the structural
model.
Firstly, confirmatory factor analysis (CFA) was applied so as to test the adequacy of the
measurement model, which was assessed on the criteria of the model fit, convergent validity, and
discriminant validity
Cronbach’s alpha was used to assess the internal reliability. The value of Cronbach’s alpha
ranged from 0.70 to 0.88, which exceeded the Nunnally’s criterion of 0.7. To check the convergent
validity, a confirmatory factor analysis (CFA) was conducted and it checked the parameter estimates
and their associated t-values. All of the measurement items were valid (p <0.001) and higher than 0.7,
which also demonstrated unidimensionality. The composite reliability (CR) was also checked, the
lowest value of CR was above 0.83, which exceeded the recommended value of 0.7. The average
variance extracted (AVE) was also calculated and each AVE was above 0.6, exceeding the threshold
value of 0.5.
The discriminant validity was assessed so as to evaluate whether the measures of the constructs
were distinct and whether the indicators were loaded on the appropriate construct. The square root
of the AVE was checked to be greater than all of the inter-construct correlations, which presented
evidence of sufficient discriminant validity (Chin 1998). Table 2 shows that the diagonal elements,
the square root of AVE, were greater than their corresponding off-diagonal elements.
Table 2. Discriminant validity: correlations and average variance extracted (AVE).
Variable
AVE
SIT
SI
IT
EF
SDU
IKS
QKS
IC
Social Trust (
ST
)
0.614387
0.7835
Social Interaction
Ties (SIT) 0.69128 0.320629 0.831432
Social
Identification (SI) 0.709861 0.182286 0.528636 0.842532
IT Support (IT)
0.793187
0.248476
0.147863
0.189136
0.89061
End
-
user Focus
(EF) 0.658468 0.219714 0.114117 0.098641 0.139565 0.81146
Smart Device
Utilization (SDU) 0.636148 0.307299 0.114412 0.201135 0.632071 0.227027 0.791295
Intensity of
Knowledge
Sharing (IKS)
0.701408 0.280467 0.437818 0.350336 0.173657 0.078168 0.339639 0.837501
Quality of
Knowledge
Sharing (QKS)
0.682872 0.469848 0.508585 0.499505 0.241864 0.316561 0.349437 0.56622 0.826361
Adm. Sci. 2018, 8, 21 9 of 16
Individual
Creativity (IC) 0.619493 0.069712 0.232795 0.322877 0.175175 0.300178 0.15141 0.205357 0.42306
The detailed hypotheses testing results are presented in Table 3. Among the 14 hypotheses, most
were supported. The variance explained (R2) by the paths was examined and the results are
presented in Table 4. The R2 for the final dependent variable, individual creativity, was 0.63.
Additionally, the R2 for the quality of knowledge sharing, which was the most important
independent factor influencing individual creativity, was 0.82. The R2 value indicated that the model
explained a substantial amount of variance for the online knowledge contribution.
Table 3. Hypotheses testing results.
Hypotheses
T-Value
Result (Two Tails)
H1a 0.571489 Not supported
H1b 2.291318
Supported
(
p <
0.05)
*
H2a 2.282699
Supported
(
p <
0.05)
*
H2b 1.9643
Supported
(
p <
0.05)
*
H3a 0.774949 Not supported
H3b 2.106269
Supported
(
p <
0.05)
*
H4a 1.979202
Supported
(
p <
0.05)
*
H4b 0.346368 Not supported
H5a 0.374359 Not supported
H5b 1.796683
Supported
(
p <
0.1)
H6a 1.966251
Supported
(
p <
0.05)
*
H6b 0.428985 Not supported
H7a 0.079318 Not supported
H7b 2.0156138
S
upported
(
p <
0.05)
*
Table 4. R square.
Intensity of Knowledge Sharing
Quality of Knowledge Sharing
Individual Creativity
R Square
0.29832
0.56642
0.308347
5. Discussion
5.1. Summary of Results
After the empirical analysis, a rich set of results were obtained. The most important result was
that the quality of knowledge sharing was positively associated with individual creativity and played
a mediating role between socio-technical factors and individual creativity, however the intensity of
knowledge sharing was not.
Firstly, the results indicated that social interaction ties, IT support, and end-user focus were
positively associated with intensity of knowledge sharing, and that the social interaction ties, social
trust, social identification, and smart device utilization were positively associated with quality of
knowledge sharing. The social interaction ties increased the individuals’ intensity of knowledge
sharing. This finding was similar to Tsai and Ghoshal (1998), who found that social interaction ties
had a strong effect on trust in the context of resource exchange and production innovation within the
organization. Social trust and social identification did not have a significant impact on intensity of
knowledge sharing, but had an impact on the intensity of knowledge sharing. One possible
explanation might have been that individuals were willing to share their personal knowledge because
of the close and frequent interaction among members, fairness in exchanging knowledge, and strong
feelings toward university, without necessarily trusting other members in the university. Another
possible explanation was that trust and social identification were not crucial in less risky knowledge
sharing relationships.
Adm. Sci. 2018, 8, 21 10 of 16
Secondly, IT support and end-user focus were positively associated with intensity of knowledge
sharing, but not with the quality of knowledge sharing. This result coincided with other research that
was about the positive effects of IT on knowledge sharing. However, it did not influence the quality
of knowledge sharing, which meant that IT support and end-user focus were not enablers of the
quality of knowledge sharing. It could have been explained by the fact that IT infrastructure was a
platform of knowledge sharing, but did not influence the quality of knowledge sharing, which was
more related to the knowledge sharers’ motivation. However, on the contrary of the expectation,
smart device utilization was positively associated with the quality of knowledge sharing, but not
with the intensity of knowledge sharing. One explanation was that the smart device had the mobility
that could help students to find the knowledge timely and appropriately, which increased quality of
knowledge sharing. On the other hand, the individuals’ smart device utilization did not change the
perceived intensity of knowledge sharing.
Finally, the quality of knowledge sharing had mediating effects, as shown in Table 4. The results
showed that the quality of knowledge sharing played a strong mediating role between those social
and technical factors and individual creativity. It meant that individual creativity could be improved
through visible support by increasing the members’ social networks, building a culture of trust and
identification, and encouraging the use of a smart device for knowledge sharing.
Additionally, by examining the relationship between knowledge sharing and individual
creativity, an individual’s flow through knowledge sharing was enabled. According to the results,
the quality of knowledge sharing was the major factor that facilitated individual creativity, rather
than the intensity of knowledge sharing. It indicated that quality was more important than intensity
or volume, in terms of knowledge.
5.2. Limitations and Future Research
First, the definition of creativity that was used in this research was very generalized. In fact,
creativity could be classified with many dimensions and categories. Thus, future research could make
contributions by investigating the effects of knowledge sharing on a different creativity.
Based on the facts that there was limited research on the effects of knowledge sharing on an
individual level, this research focused on individual creativity. However, further research should
notice that individual creativity is one of the most antecedents of organizational innovation and
performance. Thus, it was very important to understand how knowledge sharing improved
organizational performance through individual creativity.
There could have been a sample bias inherent in most of the online survey-based research. As
the survey was conducted on the Internet using a self-motivated questionnaire, on people who were
very active and altruistic, and this may have biased the results.
Because the survey was only conducted among the KAIST students, KAIST was the only sample
organization. The results could be different in relation to the way knowledge sharing took place, as
this result was not able to be generalized into various types of higher education institutions. Future
research that would compare different types of services may be needed.
5.3. Implications for Theory and Practice
5.3.1. Theoretical Implication
The effects of knowledge sharing have been discussed in many previous studies, but they were
mainly focused on organizational performance. However, in terms of creativity and innovation, there
was limited literature available to explore. The relationship between knowledge sharing and
individual creativity was, firstly, investigated in this study with the socio-technical view on
knowledge sharing. This study was theoretically important, because it bridged the gap between
knowledge sharing with individual creativity, which had thorough backgrounds. By developing the
research model under the socio-technical view, a strong framework was offered for explaining
knowledge sharing’s effects on individual creativity.
Adm. Sci. 2018, 8, 21 11 of 16
This study was important as it was the first research article that investigated the relationship
between knowledge sharing and individual creativity. Theoretically, this was a pioneering study that
adopted a socio-technical theory and a social capital theory into the effects of knowledge sharing on
individual creativity research. This was a theoretically important contribution, because the socio-
technical theory was used frequently in recent research on knowledge sharing. Focusing on the
integrative effect of social and technical factors on knowledge sharing, this gaves shape to causal
relationships and the path from major components of socio-technical factors to individual creativity
through knowledge sharing. By adopting this model, the social interaction ties, social trust, social
identification, and smart device utilization could contribute to individual creativity through the
quality of knowledge sharing.
Moreover, it was important to clarify whether the intensity of knowledge sharing or the quality
of knowledge sharing, and the results showed that the quality of knowledge sharing only had
mediating effect between socio-technical factors and individual creativity. This finding established
the exact difference in intensity and quality of knowledge sharing, and figured out the quality sides’
important mediating role. This study found that the antecedent factors of knowledge sharing could
boost individual creativity.
This study also made up the limitation of knowledge sharing in the public sector, especially in
higher education institutions, where individual creativity was especially important. Accordingly, the
results better explained the knowledge sharing’s effects on individual creativity in a higher education
institution. This empirical study on real contributors will enrich the understanding of both
knowledge sharing and individual creativity.
5.3.2. Practical Implication
Our findings offered guidance and insights for practitioners and leaders who were trying to
boost individual creativity. As the individual creativity was an important source of organization, and
knowledge sharing could contribute to the individual creativity, more specifically, the more qualified
knowledge was attained from knowledge sharing, the more the individual creativity improved.
Through this study, socio-technical factors were suggested for practitioners and academics in higher
education systems to focus on. Particularly, the practitioners needed to strive to increase the quality
of knowledge sharing so as to boost individual creativity.
Among the social factors, social interaction ties, social trust, and social identification, all had
increased the individual creativity through the quality of knowledge sharing. Thus, higher education
institutions should focus more on the social capital inside the organization, so as to make an
appropriate environment for individual creativity, which would finally contribute to the
organization level innovation and improve the performance of the organization. This social capital
could be raised up by the intended support of organizations. For instance, the university should
support formal and informal communities inside the university, to let the students make more social
interaction ties. The increase in social interaction ties would result in the knowledge ‘gateway’ of
individuals, to make knowledge sharing smoother and increase the chance to find qualified
knowledge. Social trust and social identification could be cultivated by the organizational culture, for
instance, organizational history has been an important aspect of organizational culture, which was
found to increase the members’ belongingness and the trust among the members. That was why
many famous universities collected the history of the university and reorganized the history for
differentiating the history of universities for contributing to forming its specific culture. Based on our
study, these activities were not only contributing to the belongings and culture of universities, but it
also contributed to individual creativity through knowledge sharing. Thus, universities should make
use of this social capital to making contributions to qualified knowledge sharing, and to increase
individual creativity, which finally contributed to the individuals’ and organizational performance.
Among the technical factors, only the smart device utilization was related to the quality of
knowledge sharing. However, it did not ignore the importance of IT support and end-user focus,
which played an important role in forming the platform of knowledge sharing. However, IT support
and end-user focus mainly contributed to the intensity of knowledge sharing, not the quality of
Adm. Sci. 2018, 8, 21 12 of 16
knowledge sharing, which could be explained by the fact that KAIST already had enough IT support
and end-user focus that they were not critical factor for the quality of knowledge sharing. While smart
device utilization was the new factor in creativity research, results showed that it was positively
related to creativity by the mediating effect of knowledge sharing, thus organizations should diffuse
the smart devices to allow members access to knowledge sharing process, to increase their creativity.
However, the smart device was not only related to knowledge sharing among the organization, thus
organizations also had to regulate the utilization of the smart device for knowledge gain and sharing,
such as encouraging access to the organization related resources and online community, but also by
restricting accessing to entertainment or SNS that was not related to the organizational knowledge
sharing.
Finally, the implications for firms and/or policy makers were that the social capital formation
within an organization was important for knowledge sharing. That is, an organizational culture that
built social trust and interaction ties should be fostered. Microsoft, for example, has been able to
increase its new product development and financial performance significantly by innovating its
organizational culture of social trust and knowledge sharing. Such organizational culture policies are
necessary to support the competitiveness of education systems, improving the knowledge
management processes. This study found that, to increase individual creativity, the quality of
knowledge sharing should be getting more attention instead of just encouraging students to share
more knowledge.
Conflicts of Interest:
Appendix A
Table A1. Measurement items. KAIST—Korea Advanced Institute of Science and Technology.
Social Interaction
Ties
I maintain close social relationships with
some members in the university community.
(Tsai and Ghoshal 1998)
(Chiu et al. 2006)
I spend a lot of time interacting with some
members in the university community.
I know some members in the university
community on a personal level.
I have frequent communication with some
members in the university community.
Social Trust
I believe that other members in KAIST are
honest and reliable. (Gefen, Karahanna et al. 2003;
Cabrera and Cabrera 2005)
(Chiu et al. 2006)
I believe that other members in KAIST are
knowledgeable and competent in their area.
I expect that students in my personal
network will help each other.
Social
Identification
I
believe I am similar to my friends in
KAIST.
(Cabrera and Cabrera 2005)
(Chiu et al. 2006)
I am happy to spend time with the group of
my friends.
I perceive an overlap between my self
-
identity and my friends group in KAIST.
I feel feelings of
belongingness towards the
group of my friends.
IT support
The KAIST’s IT infrastructure facilitates
knowledge sharing.
(Kim and Lee 2006)
Knowledge/ information available in the
KAIST’s IT is relevant.
Knowledge/ information available in the
KAIST’s IT is up-to-date.
Adm. Sci. 2018, 8, 21 13 of 16
End-user Focus
I regularly use the Internet, e
-
mail, and the
organization’s intranet
(Kim and Lee 2006)
In KAIST, IT infrastructure is designed to be
user-friendly
It is easy for me to use IT infrastructure
without extra training.
Smart Device
Utilization
In the past week, on average, approximately
how many minutes per day have you spent
on smart device?
(Ellison et al. 2007)
Smart device has become part of my daily
routine.
I feel out of touch when I do not have smart
device for a while.
I would be sorry if smart device shut down.
Intensity of
Knowledge
Sharing
In KAIST, knowledge is shared frequently
among members.
(Chiu et al. 2006)
Members share their knowledge and
expertise voluntarily in KAIST.
Members
share knowledge with people
from other divisions in KAIST.
Quality of
Knowledge
Sharing
The knowledge shared by members in
KAIST is relevant to the topics.
(Chiu et al. 2006)
The knowledge shared by members in
KAIST is easy to understand.
The
knowledge shared by members in
KAIST is accurate.
The knowledge shared by members in
KAIST is complete.
The knowledge shared by members in
KAIST is reliable.
The knowledge shared by members in
KAIST is timely.
Individual
Creativity
I am a good
source of creative ideas
.
(Shin and Zhou 2003)
I come up with new and practical ideas to
improve performance.
I am not afraid to take risks
I promote and champion ideas to others
Acknowledgements: This work was supported by Soonchunhyang University Research Grant No. 20180408.
Conflicts of Interest: The author declares no conflict of interest.
References
Amabile, Teresa M. 1988. A model of creativity and innovation in organizations. Research in Organizational
Behavior 10: 123–67.
Amin, Aamir, Shuib Basri, Mohd Fadzil Hassan, and Mubashir Rehman. 2011. Occupational stress, knowledge
sharing and GSD communication barriers as predictors of software engineer’s creativity. Paper presented
at 2011 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM),
Singapore, 6–9 December.
Andrews, Kate M., and Brian L. Delahaye. 2000. Influences on knowledge processes in organizational learning:
The psychosocial filter. Journal of Management Studies 37: 797–810.
Bartol, Kathryn M., and Abhishek Srivastava. 2002. Encouraging knowledge sharing: The role of organizational
reward systems. Journal of Leadership & Organizational Studies 9: 64–76.
Adm. Sci. 2018, 8, 21 14 of 16
Bergami, Massimo, and Richard P. Bagozzi. 2000. Self-categorization, affective commitment and group self-
esteem as distinct aspects of social identity in the organization. British Journal of Social Psychology 39: 555–
77.
Bhatt, Ganesh D. 2001. Knowledge management in organizations: examining the interaction between
technologies, techniques, and people. Journal of Knowledge Management 5: 68–75.
Blau, Peter M. 1964. Exchange and Power in Social Life. New York: Transaction Publishers.
Bostrom, Robert P., and J. Stephen Heinen. 1977. MIS Problems and failures: a sociotechnical perspective part I:
The cause. MIS Quarterly 1: 17–32.
Butler, Tom, and Ciaran Murphy. 2007. Understanding the design of information technologies for knowledge
management in organizations: a pragmatic perspective. Information Systems Journal 17: 143–63.
Cabrera, Elizabeth F., and Angel Cabrera. 2005. Fostering knowledge sharing through people management
practices. The International Journal of Human Resource Management 16: 720–35.
Centobelli, Piera, Roberto Cerchione, and Emilio Esposito. 2017. Knowledge management in startups: systematic
literature review and future research agenda. Sustainability 9: 361.
Chen, Jui-Kuei, and I-Shuo Chen. 2010. Critical creativity criteria for students in higher education: taking the
interrelationship effect among dimensions into account. Quality & Quantity 46: 1057–75.
Chin, Wynne W. 1998. The partial least squares approach for structural equation modeling. Modern Methods for
Business Research 295: 295–336.
Chiu, Chao-Min, Meng-Hsiang Hsu, and Eric TG Wang. 2006. Understanding knowledge sharing in virtual
communities: An integration of social capital and social cognitive theories. Decision Support Systems 42:
1872–88.
Choe, Jong-min. 2004. The consideration of cultural differences in the design of information systems. Information
& Management 41: 669–84.
Cummings, Jonathon N. 2004. Work groups, structural diversity, and knowledge sharing in a global
organization. Management Science 50: 352–64.
Davenport, Thomas H., and Laurence Prusak. 2000. Working Knowledge: How Organizations Manage What They
Know. Boston: Harvard Business Press.
Dewett, Todd, and Melissa L. Gruys. 2007. Advancing the case for creativity through graduate business
education. Thinking Skills and Creativity 2: 85–95.
DiPietro, William R., and Emmanuel Anoruo. 2006. Creativity, innovation, and export performance. Journal of
Policy Modeling 28: 133–39.
Dong, Yuntao, Kathryn M. Bartol, Zhi-Xue Zhang, and Chenwei Li. 2017. Enhancing employee creativity via
individual skill development and team knowledge sharing: Influences of dual-focused transformational
leadership. Journal of Organizational Behavior 38: 439–58.
Du Plessis, Marina. 2005. Drivers of knowledge management in the corporate environment. International Journal
of Information Management 25: 193–202.
Durst, Samantha L. 1999. Assessing the effect of family friendly programs on public organizations. Review of
Public Personnel Administration 19: 19–33.
Dyer, Jeffrey H., and Nile W. Hatch. 2006. Relation-specific capabilities and barriers to knowledge transfers:
Creating advantage through network relationships. Strategic Management Journal 27: 701–19.
Ellison, Nicole B., Charles Steinfield, and Cliff Lampe. 2007. The benefits of Facebook “friends:” Social capital
and college students’ use of online social network sites. Journal of Computer-Mediated Communication 12:
1143–68.
Fairweather, James S. 2000. Diversification or homogenization: How markets and governments combine to shape
American higher education. Higher Education Policy 13: 79–98.
Fink, Kerstin, and Christian Ploder. 2009. Knowledge management toolkit for SMEs. International Journal of
Knowledge Management 5: 46–60.
Foss, Nicolai J., and Torben Pedersen. 2002. Transferring knowledge in MNCs: the role of sources of subsidiary
knowledge and organizational context. Journal of International Management 8: 49–67.
Gardner, Howard E., and Emma Laskin. 2011. Leading Minds: An Anatomy of Leadership. New York: Basic Books.
Gefen, David, Elena Karahanna, and Detmar W. Straub. Trust and TAM in online shopping: An integrated model.
MIS Quarterly 27: 51–90.
Gilad, Benny. 1984. Entrepreneurship: The issue of creativity in the market place. The Journal of Creative Behavior
18: 151–61.
Adm. Sci. 2018, 8, 21 15 of 16
Hansen, Morten T. 2002. Knowledge networks: Explaining effective knowledge sharing in multiunit companies.
Organization Science 13: 232–48.
Jackson, Susan E., Chih-Hsun Chuang, Erika E. Harden, and Yuan Jiang. 2006. Toward developing human
resource management systems for knowledge-intensive teamwork. Research in Personnel and Human
Resources Management 25: 27–70.
Khan, Zaheer, Yong Kyu Lew, and Rudolf R. Sinkovics. 2015a. International joint ventures as boundary spanners:
technological knowledge transfer in an emerging economy. Global Strategy Journal 5: 48–68.
Khan, Zaheer, Oded Shenkar, and Yong Kyu Lew. 2015b. Knowledge transfer from international joint ventures
to local suppliers in a developing economy. Journal of International Business Studies 46: 656–75.
Kim, Soonhee, and Hyangsoo Lee. 2006. The Impact of Organizational Context and Information Technology on
Employee Knowledge-Sharing Capabilities. Public Administration Review 66: 370–85.
Lee, Pauline, Nicole Gillespie, Leon Mann, and Alexander Wearing. 2010. Leadership and trust: Their effect on
knowledge sharing and team performance. Management Learning 41: 473–91.
Lee, Kun Chang, Dae Sung Lee, Young Wook Seo, and Nam Young Jo. 2011. Antecedents of team creativity and
the mediating effect of knowledge sharing: bayesian network approach to PLS modeling as an ancillary
role. Intelligent Information and Database Systems 545–55.
Machlup, Fritz. 1984. Knowledge: Its Creation, Distribution, and Economic Significance. Princeton: Princeton
University Press.
Meek, V. Lynn. 2000. Diversity and marketisation of higher education: Incompatible concepts? Higher Education
Policy 13: 23–39.
Men, Chenghao, Patrick SW Fong, Jinlian Luo, Jing Zhong, and Weiwei Huo. 2017. When and how knowledge
sharing benefits team creativity: The importance of cognitive team diversity. Journal of Management &
Organization 1–18, doi:10.1017/jmo.2017.47.
Mesmer-Magnus, Jessica R., and Leslie A. DeChurch. 2009. Information sharing and team performance: A meta-
analysis. Journal of Applied Psychology 94: 535–46.
Mumford, Michael D. 2000. Managing creative people: Strategies and tactics for innovation. Human Resource
Management Review 10: 313–51.
Nahapiet, Janine, and Sumantra Ghoshal. 1998. Social capital, intellectual capital, and the organizational
advantage. Academy of Management Review 23: 242–66.
Nonaka, Ikujiro, and Hirotaka Takeuchi. 1995. The Knowledge-Creating Company: How Japanese Companies Create
the Dynamics Of Innovation. Oxford: Oxford University Press.
Pan, Shan L., and Harry Scarbrough. 1998. A socio-technical view of knowledge sharing at Buckman
Laboratories. Journal of Knowledge Management 2: 55–66.
Petrides, Lisa A., and Thad R. Nodine. 2003. KM in Education, Defining the Landscape. Half Moon Bay: The Institute
for the Study of Knowledge Management in Education, March.
Quigley, Narda R., Paul E. Tesluk, Edwin A. Locke, and Kathryn M. Bartol. 2007. A multilevel investigation of
the motivational mechanisms underlying knowledge sharing and performance. Organization Science 18: 71–
88.
Scott, Susanne G., and Reginald A. Bruce. Determinants of innovative behavior: A path model of individual
innovation in the workplace. Academy of Management Journal 37: 580–607.
Shin, S. J. and J. Zhou. 2003. Transformational leadership, conservation and creativity: Evidence from Korea.
Academy of Management Journal 45: 703–714.
Shalley, Christina E. 1995. Effects of coaction, expected evaluation, and goal setting on creativity and
productivity. Academy of Management Journal 38: 483–503.
Shalley, Christina E., Jing Zhou, and Greg R. Oldham. 2004. The effects of personal and contextual characteristics
on creativity: Where should we go from here? Journal of Management 30: 933–58.
Sohail, M. Sadiq, and Salina Daud. 2009. Knowledge sharing in higher education institutions: Perspectives from
Malaysia. Vine 39: 125–42.
Son, Seung Yeon, Duck Hyun Cho, and Seung-Wan Kang. 2017. The impact of close monitoring on creativity
and knowledge sharing: The mediating role of leader-member exchange. Creativity and Innovation
Management 26: 256–65.
Stewart, Thomas, and Clare Ruckdeschel. 1998. Intellectual capital: The new wealth of organizations. Performance
Improvement 37: 56–59.
Adm. Sci. 2018, 8, 21 16 of 16
Tsai, Wenpin, and Sumantra Ghoshal. 1998. Social capital and value creation: The role of intrafirm networks.
Academy of Management Journal 41: 464–76.
Krogh, Von G. 1998. Care in knowledge creation. California Management Review 40: 133–153.
Wasko, Molly McLure, and Samer Faraj. 2005. Why should I share? Examining social capital and knowledge
contribution in electronic networks of practice. MIS Quarterly 29: 35–57.
Weiner, Robert Paul. 2000. Creativity & Beyond: Cultures, Values, and Change. New York: The State University of
New York Press.
Whiting, Bruce G. 1988. Creativity and entrepreneurship: How do they relate? The Journal of Creative Behavior 22:
178–83.
Williamson, Bill. 2001. Creativity, the corporate curriculum and the future: a case study. Futures 33: 541–55.
Zhou, Jing, and Jennifer M. George. 2001. When job dissatisfaction leads to creativity: Encouraging the
expression of voice. Academy of Management Journal 44: 682–96.
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).
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