Social capital and knowledge sharing in the public sector
74
Ha M. Tri, Doan B. Son. HCMCOUJS-Social Sciences, 11(1), 74-88
Social capital and knowledge sharing in the public sector
Ha Minh Tri1*, Doan Bao Son2
1International University, Vietnam National University Ho Chi Minh City, Vietnam
2Faculty of Aeronautical Electronics - Telecommunications, Vietnam Aviation Academy, Vietnam
*Corresponding author: [email protected]
ARTICLE INFO
ABSTRACT
DOI:10.46223/HCMCOUJS.
soci.en.11.1.1903.2021
This research proposes and validates
a
model
hypothesizing the relationships between social capital and
knowledge sharing in the public sector. We employed a survey
design using a questionnaire to collect data utilizing a convenient
sampling method. The study gathered 389 questionnaires from
civil servants and officials working in departments and agencies
in the public sector in Tien Giang province. The research
hypotheses were developed and validated using the Structural
Model Modeling (SEM) approach. The research results show that
all hypothesized relationships were confirmed except the link
between structural social capital and tacit knowledge sharing.
Our study can be considered a pioneering study to investigate the
influence of social capital on both tacit knowledge sharing and
explicit knowledge sharing in the public sector. This work also
enriches the growing body of knowledge regarding social capital
and knowledge sharing in the public sector. Our research also
offers suggestions to leaders in the public sector concerning
relevant measures and policies that enable employees to share
knowledge by promoting social capital.
Received: May 5th, 2021
Revised: June 6th, 2021
Accepted: June 12th, 2021
Keywords:
explicit knowledge sharing;
public sector; social capital; tacit
knowledge sharing
1. Introduction
Over the last few years, public and private organizations have shown increased interest in
managing knowledge and human resource. Knowledge sharing is increasingly important, not
only as a means to obtaining personal benefits but also as a vehicle for improving the
performance of both individuals and organizations (Kim & Lee, 2005). While there are widely
recognized agreement that there are various ways that societies can gain knowledge, those
communities that leverage social capital are acknowledged as the main vehicles by which this
knowledge is made available (Chow & Chan, 2008; Kim & Lee, 2010). Furthermore, several key
factors influencing knowledge sharing and creation include social dynamics that stem from each
individual and group’s interpersonal and group relationships (Van den Hooff & Huysman, 2009).
Studies on social capital and knowledge sharing have investigated the influence of social
networks and shared goals on knowledge sharing attitudes. This indirect effect on knowledge
sharing intention has been documented by researchers like Chow and Chan (2008), who assert
that social networks and goals have a substantial impact on knowledge sharing attitudes.
Ganguly, Talukdar, and Chatterjee (2019) found that social capital (both cognitive and
relational) are important prerequisites for sharing tacit knowledge, impacting organizational
innovation capability. Hau, Kim, Lee, and Kim (2013) argue that social capital affects the intent
Ha M. Tri, Doan B. Son. HCMCOUJS-Social Sciences, 11(1), 74-88
75
to disseminate explicit knowledge and tacit knowledge (TN). Zaqout and Abbas (2012)
demonstrate the role of trust and social networks in promoting explicit knowledge and TN
exchange as well as in developing tacit knowledge and improving work performance.
It is critical to discern the various influences on employees’ desire to disseminate
knowledge, which aids in enhancing the sharing of information between public-sector employees
and agencies. Since there is so much research about organizational knowledge sharing, some of
the variables may influence knowledge sharing in organizations. However, the majority of studies
have examined knowledge sharing in the private sector. To date, there are limited studies that
have examined knowledge sharing in the public sector (Amayah, 2013). Public organizations and
knowledge-based organizations are known as public-sector organizations today (Willem &
Buelens, 2007). Firms in the private sector, as well as government agencies, must therefore see
that information is as critical to their organizations as it is to the businesses in the private sector
(Willem & Buelens, 2007). To meet more demanding competition for resources and services,
public organizations are challenged now more than ever before (Luen & Al-Hawamdeh, 2001).
The importance of developing processes to improve knowledge sharing cannot be overstated:
These processes will enable current employees to learn from and benefit from the knowledge and
experience of their senior colleagues prior to retirement (Amayah, 2013).
Knowledge management in the public sector in Vietnam tends to move slower than in the
private sector. Specifically, public organizations do not have effective mechanisms and solutions
to encourage civil servants and public employees to create and share knowledge. They turn each
individual’s ability into the organization’s knowledge and the civil service to bring advantages
and operational performance to them. Furthermore, no study has looked specifically at social
capital dimensions and knowledge sharing relationships, including explicit knowledge sharing
and tacit knowledge sharing in the public sector. Such a comprehensive examination of these
relationships would address that research gap. The specific objective of this work is to study the
link between the social capital dimensions and knowledge sharing among individual members in
the public sector.
2. Literature review and hypotheses development
Social capital is effectively a theory appreciating ‘it’s not what you know, but who you
know’ and developed on the strength of weak ties, as defined by M. S. Granovetter (1973).
According to this theory, resources accessed through weak ties will be more valuable than those
accessed through stronger relational ones. This was explained by developing strong ties between
similar individuals who, by definition, would have similar contacts and thus access to similar
resources.
Nahapiet and Ghoshal (1998) were instrumental in establishing social capital as a valid
construct within management research, proposing a model of social capital composed of a
number of components that could be used to explain both micro- and macro-level behavior of
individuals and groups. The model classified the concept into three distinct components:
cognitive, structural, and relational, all of which will be discussed in greater detail in the
following sections. While the preceding emphasizes that social capital theory’s primary purpose
was to provide insight into obtaining valuable resources, it may also serve as a control
mechanism and help explain how communities behave.
2.1. Social capital dimensions
Social capital is defined as “the sum of the actual and potential resources embedded
within, available through, and derived from the network of relationships possessed by an
76
Ha M. Tri, Doan B. Son. HCMCOUJS-Social Sciences, 11(1), 74-88
individual or social unit” (Nahapiet & Ghoshal, 1998, p. 243). Adler and Kwon (2002)
conceptually constructed a model of social capital that classifies the various forms, origins, and
effects of the concept. They defined social capital as “the goodwill available to individuals or
groups whose source is found in the structure and content of a person’s social relations” (p. 23).
Furthermore, social capital can be categorized into three interrelated components: structural,
relational, and cognitive (Nahapiet & Ghoshal, 1998).
Social capital can be defined as the number of networks a person or group maintains or
the network composition of an organization. It is viewed through a network’s size, the number of
network ties, and stability (Inkpen & Tsang, 2005). Cognitive social capital is defined as the
collective knowledge and awareness possessed by people that allows them to form an agreement
and identify patterns and patterns across participants (Nahapiet & Ghoshal, 1998). For one facet
of the dimension, we have mutually agreed upon goals and a network culture that members
participate in (Inkpen & Tsang, 2005). While in relational social capital, we focus on the way
actors’ personal ties are connected to relational outcomes of interactions, we are interested in
structural results of interactions. Previous research, for example, that of Hau et al. (2013), has
shown that the significant role of social ties, shared goals, and social trust in social capital are
corroborated by further analysis.
2.1.1. Structural social capital and relational social capital
A relationship (as opposed to the material) holds structural social capital, which is
presented in social interaction ties. These ties help increase trust, called relational social capital
(Tsai & Ghosal, 1998). Research conducted previously has asserted that social interaction
appears to be the foundational building block for forming trusting relationships (M. Granovetter,
1985). The intimate connection grows between the two actors as they divulge personal
information over time, and as this happens, the actors will discern each other as trustworthy
(Gabarro, 1978). Actors can learn about and relate to one another through frequent and close
social interactions, obtain relevant information, and collectively form a common viewpoint. As a
result, when an actor occupies a central position in a social interaction network, he is more likely
to be perceived as trustworthy by the network’s other actors (Tsai & Ghosal, 1998). Therefore, it
is relevant to formulate the hypothesis as below.
H1: Structural social capital will be positively related to relational social capital
2.1.2. Cognitive social capital and structural social capital
The relationship between structural social capital and cognitive social capital is predicated
on the premise that social interaction is critical in configuring common goals and values and
enabling an organization to communicate these goals and values (Tsai & Ghosal, 1998). To study
cognitive accuracy in the minds of individuals, Krackhardt (1990) performed a comprehensive
investigation of the entire organizational social structure and found the structure of members’
social interactions has an impact on the formation of a shared vision. Many aspects of
organizational socialization (such as informal social interaction) have been described in terms of
organizational values in the literature (Van Maanen & Schein, 1979). Actors understand and
adopt the language, codes, values, and practices of their organizations as a result of acting in
accordance with the concepts that arise from social interaction. However, these actors could also
generate new values and new perspectives that they have in common as well as entirely new
visions relying on their shared interests and understandings. Units within a multi-building firm
may have various interests and goals, but those goals and interests may differ from those of other
units within the corporation. An individual member of a unit can have a collective orientation that
aligns with the goals and plans they pursue (Tsai & Ghosal, 1998). Accordingly, we propose:
Ha M. Tri, Doan B. Son. HCMCOUJS-Social Sciences, 11(1), 74-88
77
H2: Structural social capital will be positively related to cognitive social capital
2.1.3. Relational social capital and cognitive social capital
By supporting and enhancing a group’s shared values and showing a clear common
vision, the cognitive dimensions of social capital can encourage people to form trusting
relationships (Tsai & Ghosal, 1998). The kind of relationship one develops with someone who
trusts you indicates that “Common goals and values have brought and kept them together”
(Barher, 1983, p. 21). In the view of Ouchi (1980, p. 138), “Common values and beliefs provide
the harmony of interests that erase the possibility of opportunistic behavior”. As Sitkin and Roth
(1993) asserted, there is a positive relationship between trusting relationships and value
congruence; that is, people’s values and the organization’s values are similar. Using a
cooperative framework and goal, individuals within the organization tend to trust each other, as
they can expect that everyone is working toward a cooperative framework (Tsai & Ghosal,
1998). Therefore, it is appropriate to formulate the hypothesis as follows.
H3: Cognitive social capital will be positively related to relational social capital
2.2. Social capital and knowledge sharing
Structural social capital consists of relationships, density, configuration, and network
relevance (Nahapiet & Ghosal, 1998). Network relationships allow access to resources
embedded in social relationships, and knowledge is by far the most precious asset embedded in
that relationship (Ganguly et al., 2019). Employees in organizations will establish many face-to-
face relationships and social networks that increase the opportunities for those connections.
Employees will then be more likely to be willing to share skills and thoughts with those with
whom they have a special relationship (Chow & Chan, 2008). While creating and sharing
knowledge in an organization is correlated with the social network structure, the building of a
social network is the process of the knowledge creation and sharing process (Borgatti & Cross,
2003). Because of the close relationships and network structure of the structural social capital,
actors can more easily communicate with one another, thus increasing the flow of knowledge
throughout the entire network (Reagans & McEvily, 2003). In particular, strong social networks
with a high level of intensity, in which participants have frequent and close interactions, will
help promote the spread of tacit knowledge (Ganguly et al., 2019).
As a result, social interactions are crucial to the successful tacit knowledge-sharing
process (Nonaka, 1994). In the course of the conversion and creation of knowledge in
organizations, information is disseminated through “socialization”, which necessitates numerous
social interactions between employees (Nonaka, 1994). However, compared to explicit
knowledge, which is mostly transferred or shared through “combination,” which depends more
on transactional interactions, social interactions have very little to do with it (Hau et al., 2013).
Based on these arguments, it is reasonable to formulate the hypotheses as below.
H4: Structural social capital is positively related to explicit knowledge sharing
H5: Structural social capital is positively related to tacit knowledge sharing
The relational social capital represented by trust and credibility is essential for a thick
social network structure that promotes knowledge acquisition and assimilation (Ganguly et al.,
2019). Trust among organizational members is a factor that improves interactive relationships
and promotes successful knowledge sharing (Chow & Chan, 2008). Trusting others and allowing
others to trust are essential for knowledge-sharing initiatives. If the organization members have
trusted relationships, they are willing to allow colleagues to access their resources. Trust also
increases the quality of knowledge sharing (Zaqout & Abbas, 2012). Inkpen and Tsang (2005)
78
Ha M. Tri, Doan B. Son. HCMCOUJS-Social Sciences, 11(1), 74-88
assert that trust is essential to the coherence of relationships between parties. It acts as a check on
opportunistic behavior and provides the basis for free knowledge exchange. In addition,
interpersonal trust will promote tacit knowledge sharing (Foos, Schum, & Rothenberg, 2006).
By establishing a common identity that facilitates the free exchange of knowledge and
reduces the cost of learning, relational embeddedness eliminates barriers and promotes learning
(Dyer & Nobeoka, 2000). Trust contributes to knowledge sharing by instilling a sense of security
that the knowledge will not be used for purposes other than those for which it was intended.
Operating systems that are common allows for the implementation of a shared communication
protocol, which promotes the flow of information. To aid in clarifying and controlling
information, relational nexus performs three key roles in knowledge sharing: clarification,
control, and motivation (Dhanaraj, Lyles, Steensma, & Tihanyi, 2004). Trust can be
conceptualized in two dimensions: benevolent trust and competence trust, according to Levin
and Cross (2004). The empirical study conducted on this topic claims that perceived
competency-based trust helps facilitate tacit knowledge sharing. In contrast, benevolence-based
trust can facilitate both explicit knowledge sharing and tacit knowledge sharing. Their results
suggest that relational social capital is crucial to forming dense social networks to act as a
productive precursor of knowledge acquisition and assimilation (Ganguly et al., 2019).
Therefore, it is appropriate to formulate hypotheses as follows.
H6: Relational social capital is positively related to explicit knowledge sharing
H7: Relational social capital is positively related to tacit knowledge sharing
Cognitive social capital includes the goals, vision, and values shared by members of the
organization. When trying to discern knowledge to be gathered and evaluated, participants’
shared goals, language, and understanding are influential (Kogut & Zander, 1996). Similarly,
Inkpen and Tsang (2005) assert that a common vision and a set of mutually agreed upon goals
serve as a glue that motivates actors across an intra-corporate network to embrace and
successfully integrate newly acquired knowledge. Social capital is comprised of various
elements, all of which work together to shape how people behave. One of these elements is
culture, which is defined as a shared set of values, beliefs, and norms. Together, these various
elements mold how people behave cognitively (Wasko & Faraj, 2005). Individuals exchanging
tacit knowledge through exchange mechanisms related to socialization need a common
understanding and experience, and a mode of knowledge transfer involves shared experiences
and thinking processes (Nonaka, 1994).
Groups must have a common goal to foster mutual understanding and exchange of ideas.
It’s thus possible to consider goals shared by two or more people to be the powerful force that
holds them together and encourages them to share their knowledge. A goal shared by all
members of an organisation can be obtained through cooperation and knowledge sharing (Chow
& Chan, 2008). Therefore, it is relevant to formulate hypotheses as follows.
H8: Cognitive social capital is significantly associated with explicit knowledge sharing
H9: Cognitive social capital is significantly associated with tacit knowledge sharing
2.3. Explicit knowledge sharing and tacit knowledge sharing
Explicit knowledge is objective knowledge; in other words, it is written down and can be
delivered to others, as well as made explicit using official avenues (Alavi & Leidner, 2001).
Explicit knowledge is widely available, and it can be found in several different formats,
including files, library collections, and databases (Nonaka & Takeuchi, 1995). However, it is
difficult to precisely identify and quantify the extent of tacit knowledge, which is thought to be
Ha M. Tri, Doan B. Son. HCMCOUJS-Social Sciences, 11(1), 74-88
79
derived from experience and primarily in the minds of individuals (Hlupic, Pouloudi, & Rzevski,
2002). It has also been described as casual, free-flowing, private, and mysterious, and
compelling. The issue here is that tacit knowledge is nearly impossible to represent in words
accurately, and this makes it extremely difficult to put it in databases (Zaqout & Abbas, 2012).
Organizational knowledge creation entails creating new content or updating existing
content in the organization’s tacit knowledge and explicit knowledge (Pentland, 1995).
Information is generated, shared, increased, enlarged, and substantiated in organizational settings
by social and collaborative processes, as well as the individual’s cognitive processes, such as
thinking, pondering, and consideration (Nonaka, 1994). Nonaka (1994) suggested that the
process of generating and creating knowledge can be categorized into four processes:
socialization, externalization, combination, and internalization, with the addition of transferring
knowledge between tacit knowledge and explicit knowledge. Externalization is the process of
taking existing tacit knowledge and turning it into new explicit knowledge (e.g., articulation of
best practices or lessons learned) (Reychav & Weisberg, 2009). Because of this conversion,
individuals are able to gain knowledge that, when expressed in terms of technical or academic
data, is known as technical and educational documentation, such as manuals, documents, and
patents (Nonaka, 1994).
Tacit knowledge is integrative, challenging to express, and inferable by action. It’s fine-
grained, and it gives the explicit knowledge more meaning and understanding. Tacit knowledge
explains how the whole is better than the sum of its parts. Tacit knowledge, in essence, gives
sense to many of an organization’s stated practices (Dhanaraj et al., 2004). Fundamentally, tacit
knowledge refers to highly individualized information that is difficult to record or formalize
(Nonaka, 1991). In addition, Hau et al. (2013) believe that sharing one’s explicit knowledge
takes less work than sharing one’s tacit knowledge. A person who is eager to offer his or her tacit
knowledge is undoubtedly willing to share his or her explicit knowledge (Hau et al., 2013). Tacit
knowledge positively sharing promotes explicit knowledge sharing, according to Dhanaraj et al.
(2004) and Chumg, Cooke, Fry, and Hung (2015). On the other hand, Reychav and Weisberg
(2009) demonstrated that people who are eager to disclose their tacit knowledge are also likely to
reveal their explicit knowledge. Thus, it is reasonable to propose a hypothesis as below.
H10: Tacit knowledge sharing is positively associated with explicit knowledge sharing
Figure 1. Hypothesized model
80
Ha M. Tri, Doan B. Son. HCMCOUJS-Social Sciences, 11(1), 74-88
3. Methodology
3.1. Data collection and sampling
This study adopts a survey design that uses a questionnaire to collect data. Respondents
of this study were civil servants and public employees working in public departments in Tien
Giang. Our study used a convenient sampling method to collect a sample size of 450 (Hair,
Black, Babin, & Anderson, 2014; Kline, 2015). Before data collection, a pre-test of the
questionnaire was performed with a small group of respondents (n = 10) to ensure the wording,
content as well as special or technical terminologies that could be difficult to understand for
respondents. The pre-test results show that cognitive and structural social capital dimensions
may cause confusion. Consequently, our questionnaire provides a detailed explanation of these
two terms. Data collection took place between March and April of 2021. With 450
questionnaires distributed, 389 were returned, resulting in an 86.4 percent response rate.
3.2. Measures
Our study uses a five-point Likert scale, scoring from 1 denoting “strongly disagree” to 5
denoting “strongly agree” to measure observed variables. Measurement scales were adapted
from validated literature. Specifically, both structural social capital and relational social capital
include four items adapted from Chow and Chan (2008) and Nahapiet and Ghoshal (1998).
Cognitive social capital includes four items adapted from Chow and Chan (2008), and Miller,
Besser, and Malshe (2007). Explicit knowledge sharing and tacit knowledge sharing, including
six items and seven items, respectively, were adapted from Reychav and Weisberg (2009) and Z.
Wang and Wang (2012).
Table 1
Constructs and their responding measures
Constructs and indicators
1. Structural social capital (SSC)
Loading AVE CR
SSC1 In general, I get along splendidly with my coworkers
SSC2 My coworkers are aware of the expertise I possess
0.654
0.807
I’m aware of which knowledge might be useful to which
coworker
0.560 0.834
SSC3
0.829
0.689
I am confident that everyone in my department knows of
relevant knowledge that is available to them
SSC4
2. Relational social capital (RSC)
My colleagues are my extended family, and I am very close to
RSC1
them
0.777
My coworkers will always be there to help me if I am in
trouble
RSC2
0.687
0.827
0.817
0.607 0.860
RSC3 My coworkers will help me if I do require assistance
I have total trust in my colleagues because I know they will
help me when I need it
RSC4
Ha M. Tri, Doan B. Son. HCMCOUJS-Social Sciences, 11(1), 74-88
81
Constructs and indicators
3. Cognitive social capital (CSC)
Loading AVE CR
Every time I’m at work, my colleagues and I always come to
an agreement on what is critical in the workplace
CSC1
CSC2
CSC3
CSC4
0.719
0.801
0.822
0.789
My coworkers and I always get along well when it comes to
working goals and vision
0.614 0.864
My coworkers and I are never in a poor mood when working
toward the organisation’ shared goals and missions
Personal socializing is something that I like to offer my
colleagues
4. Explicit knowledge sharing (EKS)
My coworkers frequently make use of reports and official
EKS1
0.733
0.790
0.811
0.856
0.867
documents that already exist for the purposes of sharing them
My coworkers routinely share reports and official papers that
they have prepared on their own
EKS2
My coworkers routinely acquire reports and formal documents
from others
EKS3
0.660 0.906
Knowledge-sharing strategies are regularly used to motivate
people in my organization
EKS4
My coworkers are routinely given a variety of training and
development opportunities
EKS5
My coworkers benefit from IT solutions that have been
invested in knowledge sharing
EKS6
5. Tacit knowledge sharing (TKS)
My coworkers routinely share their experience-based
expertise
TKS1
0.802
0.760
0.777
My coworkers routinely gather information from others based
on their own experiences
TKS2
My coworkers routinely communicate their know-where or
know-whom information with others
TKS3
My coworkers routinely communicate their know-where or
know-whom information with others
TKS4
0.760 0.577 0.872
0.696
My coworkers routinely share their expertise-based
knowledge
TKS5
My coworkers routinely gather information from others
depending on their areas of expertise
TKS6
When it feels essential, my coworkers will share lessons
learned from previous failures
TKS7
Source: Authors’ calculation
82
Ha M. Tri, Doan B. Son. HCMCOUJS-Social Sciences, 11(1), 74-88
3.3. Data analysis
Data analysis employs a two-step approach provided by Anderson and Gerbing (1988).
Data is analyzed using IBM AMOS 24 (Arbuckle, 2016). The first step is to establish a
measurement model to confirm a Confirmatory Factor Analysis (CFA) procedure. Tests of
reliability and validity are performed to determine how reliable and valid the measurement
model is. The next step is to evaluate a structural model by performing a path analysis to confirm
a set of hypotheses.
4. Empirical findings
4.1. Description of sample characteristics
Table 2 depicts the demographic information of 389 respondents, including gender, age,
educational level, and work experience.
Table 2
Characteristics of sample respondents
Characteristics
Frequency
Percent
Gender:
- Female
- Male
192
197
49.4
50.6
Age:
- 21 - 25
- 26 - 30
- 31 - 35
- 36 - 40
- Over 40
23
144
168
49
5.9
37.0
43.2
12.6
1.3
5
Educational level:
- Intermediate
- College
- University
- Master
23
54
150
159
3
5.8
13.9
38.6
40.9
0.8
- Other
Working experience (year):
- 0 - 3
- 3 - 5
- 5 - 10
- 10 - 15
- Over 15
31
102
129
75
8.0
26.2
33.2
19.3
13.4
52
N = 389
Source: Authors’ calculation
4.2. Measurement model
Analysis of model fit measures shows that CMIN/df = 2.379, p-value = 0.000, CFI =
0.945, TLI = 0.936, SRMR = 0.0390 and RMSEA = 0.060. These results pass the cut-off values
as suggested by Hu and Bentler (1999). Therefore, our data fit the model well. Table 3 indicates
Ha M. Tri, Doan B. Son. HCMCOUJS-Social Sciences, 11(1), 74-88
83
that Composite Reliability (CR) of all six latent variables ranges from 0.834 (SSC) to 0.906
(EKS) and Average Variance Extracted (AVE) ranges from 0.560 (SSC) to 0.660 (EKS). This
implies that the model achieves convergent validity. In addition, none of the correlations
between the construct exceed the square root of the AVE of each construct, implying that all
latent variables or constructs receive adequate discriminant validity (Fornell & Larcker, 1981).
Table 3
Results of discriminant validity
CR AVE MSV Square root of AVE
TKS
1
EKS
SSC
RSC
CSC
0.872 0.577 0.521
0.906 0.660 0.521
0.834 0.560 0.361
0.860 0.607 0.361
0.864 0.614 0.490
0.760
0.813
0.748
0.779
0.784
TKS
EKS
SSC
RSC
CSC
0.722***
1
0.388*** 0.493***
1
0.402*** 0.512*** 0.601***
1
0.700*** 0.700*** 0.469*** 0.421***
1
***Correlation is significant at the 0.01 level
Source: Authors’ calculation
4.3. Structural model
An analysis of the structural model is subsequently carried out to verify all proposed
hypotheses. The subsequent step involves an estimation that uses maximum likelihood
estimation to validate all the hypothesized relationships postulated from the research model
(Byrne, 2016). Model fit measures reveal that CMIN/df = 2.379, p-value = 0.000, CFI = 0.945,
TLI = 0.936, SRMR = 0.039, RMSEA = 0.060. It can be concluded that our data fit the model
well, as seen in these results (Hu & Bentler, 1999). Hypothesis testing was performed and
summarised in Table 4. Other than H5, all remaining hypotheses are statistically significant at
different levels.
Table 4
Hypotheses testing of the structural model
Hypothesized relationships
SSC → RSC
Proposed effects
Positive
SRW
Remark
Supported
Supported
Supported
Supported
Not supported
Supported
Supported
Supported
Supported
Supported
H1
H2
H3
H4
H5
H6
H7
H8
H9
H10
0.517***
0.469***
0.179**
0,096*
SSC → CSC
Positive
CSC → RSC
Positive
SSC → EKS
Positive
SSC → TKS
Positive
0.012ns
RSC → EKS
RSC → TKS
CSC → EKS
CSC → TKS
TKS → EKS
Positive
0.163**
0.125**
0.300***
0.641***
0.409***
Positive
Positive
Positive
Positive
Notes: ns not significant; * p < .10; ** p < .05; *** p < .001
Source: Authors’ calculation
84
Ha M. Tri, Doan B. Son. HCMCOUJS-Social Sciences, 11(1), 74-88
4.4. Discussion
Our study proposes and tests a theoretical model that shapes the connection between
social capital and knowledge sharing. Given the increasingly critical role of knowledge sharing
as it can contributes to knowledge application, innovation, and eventually performance (Kim &
Lee, 2005; Nonaka & Taikeuchi, 1995; S. Wang, Noe, & Wang, 2011), our study provides
important evidence regarding how the three social capital dimensions influence both tacit
knowledge sharing and explicit knowledge sharing. Theoretically, no attention has been paid to
systematically examine how the three social capital dimensions affect knowledge sharing in the
public sector. Therefore, our research is a pioneering work contributing significantly to social
capital and knowledge-sharing literature. Furthermore, our findings substantially support our
hypothesized model. Specifically, all hypothesized relationships were confirmed, except the
relationship between structural social capital and tacit knowledge sharing.
In the public sector context, we have asserted that all social capital dimensions are
interrelated. Our findings corroborate previous research. Specifically, structural social capital has
a favorable influence on relational social capital (Gabarro, 1978; Gulati, 1995; M. Granovetter,
1985). Furthermore, Tsai and Ghosal (1998) showed that social interaction positively influences
trustworthiness. At the same time, structural social capital also has a favorable influence on
cognitive social capital. The social contacts of organization members have an impact on the
establishment of a common vision (Krackhardt, 1990). Furthermore, research on organizational
socialization (see, for example, Van Maanen & Schein, 1979) confirms the critical role of
informal social interaction in promoting values learned about organizational norms, as is proven
in the findings of Tsai and Ghosal (1998). Finally, the significant relationship between cognitive
social capital and relational social capital was consistent with the work of Sitkin and Roth
(1993), who contended that trusting relationships originate from value congruence.
In a similar vein, our research results also show that relational social capital and cognitive
social capital significantly influence both explicit knowledge sharing and tacit knowledge
sharing. This result is completely in agreement with Hau et al. (2013), stating that common
beliefs and goals have a favorable impact on sharing explicit knowledge and tacit knowledge. In
addition, Zaqout and Abbas (2012) demonstrated the roles of trust in promoting explicit
knowledge sharing and tacit knowledge sharing among workers, which is also in line with our
findings regarding relational social capital. Several scholars argue that mutual trust between the
knowledge-sharing partners is required for the effective transfer of tacit knowledge (Nonaka &
Takeuchi, 1995). However, some say the absence of shared values, vision, and a shared goal can
lead to conflict and impede trust, which acts as a barrier to the transfer of tacit knowledge.
Additionally, while relational social capital is critical for effective tacit knowledge sharing and
explicit knowledge sharing transfer, cognitive social capital also play a critical role in effective
tacit knowledge sharing and explicit knowledge sharing transfer. This is consistent with the
research conducted by Ganguly et al. (2019) and Zaqout and Abbas (2012).
Interestingly, structural social capital only has a favorable impact on explicit knowledge
sharing but has no significant relationship with tacit knowledge sharing. This result is in line
with prior studies indicating no favorable effect between structural social capital and tacit
knowledge sharing (Ganguly et al., 2019). Additionally, we found that tacit knowledge sharing is
positively associated with explicit knowledge sharing. This finding corresponds with the studies
mentioned above, including studies done by Cohen and Levinthal (1990), Chumg et al. (2015),
Dhanaraj et al. (2004), and Hau et al. (2013). While Cohen and Levinthal (1990) posit that tacit
knowledge is necessary to assist in comprehending explicit knowledge, Dhanaraj et al. (2004)
Ha M. Tri, Doan B. Son. HCMCOUJS-Social Sciences, 11(1), 74-88
85
assert that tacit knowledge can provide the understanding behind explicit procedures in an
organization; also, the tacit knowledge learned from workers will assist in learning new explicit
routines. Similarly, Hau et al. (2013) found that employees’ tacit knowledge-sharing intentions
are viewed as if they are sharing a more valuable resource, which can affect their explicit
knowledge-sharing intentions. Finally, our findings align with Chumg et al. (2015), stating that it
is critical to concentrate on externalizing the knowledge conversion process when creating new
explicit knowledge from tacit knowledge in the virtual organization.
5. Policy implications
Our work provides several policy implications to create and encourage knowledge
sharing by promoting the following dimensions of social capital. As for structural social capital,
managers should encourage employees to actively participate in the network of relationships
within departments as well as the network of relationships between departments to create and
facilitate knowledge transfer.
To promote relational social capital, managers should allocate more resources in building
trust and enhancing cooperation among cadres and civil servants within and between departments.
To develop and nurture trust, it is necessary to create a truly open and consistent working
atmosphere. To promote collaboration at work, managers need to focus on and create favorable
conditions for the working environment, facilities, equipment, and working facilities for
employees. The more trust employees have the more knowledge they can share and benefit each
other.
To promote cognitive social capital, managers are responsible for establishing the
organization’s vision and communicating it to all employees to encourage collaboration to
enhance knowledge sharing. Public organizations need to build organizational culture, create
consensus and cohesion among members of the organization so that everyone has a voice, and
promotes ideas and initiatives in building visions and goals. This, in turn, inspires them to
overcome difficulties and challenges to achieve effective work performance.
Limitations and future research
This work also has several limitations. First, this research has not analyzed and tested the
role of control variables, for example, age, gender, educational level or experience. Future
research should add these control variables in the analysis to better understand the possible
differences in these variables that may be useful policy implications.
Second, our study uses cross-sectional data, which only collects data at one single point
of time to examine the link between social capital and knowledge sharing. Future research may
use a longitudinal design to examine the possible changes over time to have better insights into
more in-depth factors that may affect knowledge sharing in the public sector.
Third, our study develops a theoretical model and only focuses on the public sector.
Future research may expand the context to include other sectors, such as the business sector, to
provide a complete view of social capital and knowledge sharing.
References
Adler, P. S., & Kwon, S. W. (2002). Social capital: Prospects for a new concept. Academy of
Management Review, 27(1), 17-40.
Alavi, M., & Leidner, D. E. (2001). Knowledge management and knowledge management
systems: Conceptual foundations and research issues. MIS Quarterly, 25(1), 107-136.
86
Ha M. Tri, Doan B. Son. HCMCOUJS-Social Sciences, 11(1), 74-88
Amayah, A. (2013). Determinants of knowledge sharing in a public sector organization. Journal
of Knowledge Management, 17(3), 454-471.
Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review
and recommended two-step approach. Psychological Bulletin, 103(3), 411-423.
Arbuckle, J. L. (2016). IBM SPSS AMOS 24 User’s guide. New York, NY: IBM Corp.
Barher, B. (1983). The logic and limits of trust. New Brunswick, NJ: Rutgers University Press.
Borgatti, S. P., & Cross, R. (2003). A relational view of information seeking and learning in
social networks. Management Science, 49(4), 432-445.
Byrne, B. M. (2016). Structural equation modeling with Amos: Basic concepts, applications, and
programming (3rd ed.). NewYork, NY: Routledge/Taylor & Francis Group.
Chow, W. S., & Chan, L. S. (2008). Social network, social trust and shared goals in
organizational knowledge sharing. Information & Management, 45(7), 458-465.
Chumg, H. F., Cooke, L., Fry, J., & Hung, I. H. (2015). Factors affecting knowledge sharing in
the virtual organisation: Employees’ sense of well-being as a mediating effect. Computers
in Human Behavior, 44(C), 70-80.
Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning
and innovation. Administrative Science Quarterly, 35(1), 128-152.
Dhanaraj, C., Lyles, M. A., Steensma, H. K., & Tihanyi, L. (2004). Managing tacit and explicit
knowledge transfer in IJVs: The role of relational embeddedness and the impact on
performance. Journal of International Business Studies, 35(5), 428-442.
Dyer, J. H., & Nobeoka, K. (2000). Creating and managing a high‐performance knowledge‐
sharing network: The Toyota case. Strategic Management Journal, 21(3), 345-367.
Foos, T., Schum, G., & Rothenberg, S. (2006). Tacit knowledge transfer and the knowledge
disconnect. Journal of Knowledge Management, 10(1), 6-18.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable
variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
Gabarro, J. J. (1978). The development of trust, influence, and expectations. In A. G. Athos & J.
J. Gabarro (Eds.), Interpersonal behaviors: Communication and understanding in
relationships (pp. 290-303). Englewood Cliffs, NJ: Prentice-Hall.
Ganguly, A., Talukdar, A., & Chatterjee, D. (2019). Evaluating the role of social capital, tacit
knowledge sharing, knowledge quality and reciprocity in determining innovation
capability of an organization. Journal of Knowledge Management, 23(6), 1105-1135.
Granovetter, M. S. (1973). The strength of weak ties. American Journal of Sociology, 78(6),
1360-1380.
Granovetter, M. (1985). Economic action and social structure: The problem of
embeddedness. American Journal of Sociology, 91(3), 481-510.
Gulati, R. (1995). Does familiarity breed trust? The implications of repeated ties for contractual
choice in alliances. Academy of Management Journal, 38(1), 85-112.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2014). Multivariate data analysis.
Edinburgh Gate, UK: Pearson New International Edition.
Ha M. Tri, Doan B. Son. HCMCOUJS-Social Sciences, 11(1), 74-88
87
Hau, Y. S., Kim, B., Lee, H., & Kim, Y. G. (2013). The effects of individual motivations and
social capital on employees’ tacit and explicit knowledge sharing intentions. International
Journal of Information Management, 33(2), 356-366.
Hlupic, V., Pouloudi, A., & Rzevski, G. (2002). Towards an integrated approach to knowledge
management: ‘Hard’, ‘soft’ and ‘abstract’ issues. Knowledge and Process
Management, 9(2), 90-102.
Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis:
Conventional criteria versus new alternatives. Structural Equation Modeling:
Multidisciplinary Journal, 6(1), 1-55.
A
Inkpen, A. C., & Tsang, E. W. (2005). Social capital, networks, and knowledge
transfer. Academy of Management Review, 30(1), 146-165.
Kim, S., & Lee, H. (2005). Employee knowledge sharing capabilities in public & private
organizations: Does organizational context matter? Proceedings of the 38th Hawaii
International Conference on System Sciences, 8, 249-258.
Kim, T., & Lee, G. (2010). Examining social capital and knowledge sharing as antecedents of
service innovativeness and business performance in the hotel industry: An application of
the Resource-Based View (RBV) theory. Journal of Tourism Sciences, 34(7), 13-36.
Kline, R. B. (2015). Principles and practice of structural equation modeling. New York, NY:
The Guilford Press.
Kogut, B., & Zander, U. (1996). What firms do? Coordination, identity, and
learning. Organization Science, 7(5), 502-518.
Krackhardt, D. (1990). Assessing the political landscape: Structure, cognition, and power in
organizations. Administrative Science Quarterly, 35(2), 342-369.
Levin, D. Z., & Cross, R. (2004). The strength of weak ties you can trust: The mediating role of
trust in effective knowledge transfer. Management Science, 50(11), 1477-1490.
Luen, T. W., & Al-Hawamdeh, S. (2001). Knowledge management in the public sector:
Principles and practices in police work. Journal of Information Science, 27(5), 311-318.
Miller, N. J., Besser, T., & Malshe, A. (2007). Strategic networking among small businesses in
small US communities. International Small Business Journal, 25(6), 631-665.
Nahapiet, J., & Ghoshal, S. (1998). Social capital, intellectual capital, and the organizational
advantage. Academy of Management Review, 23(2), 242-266.
Nonaka, I. (1991). The knowledge creating company. Harvard Business Review, 69(6), 96-104.
Nonaka, I. (1994). A dynamic theory of organizational knowledge creation. Organization
Science, 5(1), 14-37.
Nonaka, I., & Takeuchi, H. (1995). The knowledge-creating company. New York, NY: Oxford
University Press.
Ouchi, W. G. (1980). Markets, bureaucracies, and clans. Administrative Science Quarterly,
25(1), 129-141.
Pentland, B. T. (1995). Grammatical models of organizational processes. Organization
Science, 6(5), 541-556.
88
Ha M. Tri, Doan B. Son. HCMCOUJS-Social Sciences, 11(1), 74-88
Reagans, R., & McEvily, B. (2003). Network structure and knowledge transfer: The effects of
cohesion and range. Administrative Science Quarterly, 48(2), 240-267.
Reychav, I., & Weisberg, J. (2009). Bridging intention and behavior of knowledge
sharing. Journal of Knowledge Management, 14(2), 285-300.
Sitkin, S. B., & Roth, N. L. (1993). Explaining the limited effectiveness of legalistic “remedies”
for trust/distrust. Organization Science, 4(3), 367-392.
Tsai, W., & Ghoshal, S. (1998). Social capital and value creation: The role of intrafirm
networks. Academy of Management Journal, 41(4), 464-476.
Van den Hooff, B., & Huysman, M. (2009). Managing knowledge sharing: Emergent and
engineering approaches. Information &Management, 46(1), 1-8.
Van Maanen, J., & Schein, E. H. (1979). Toward a theory of organizational socialization. In B.
M. Staw (Ed.), Research in organizational behavior (pp. 209-264). Greenwich, CT: JAI
Press.
Wang, Z., & Wang, N. (2012). Knowledge sharing, innovation and firm performance. Expert
Systems with Applications, 39(10), 8899-8908.
Wang, S., Noe, R. A., & Wang, Z. M. (2011). Motivating knowledge sharing in knowledge
management systems: A quasi-field experiment. Journal of Management, 40(4), 978-1009.
doi:10.1177/0149206311412192
Wasko, M. M., & Faraj, S. (2005). Why should I share? Examining social capital and knowledge
contribution in electronic networks of practice. MIS Quarterly, 29(1), 35-57.
Willem, A., & Buelens, M. (2007). Knowledge sharing in public sector organizations: The effect
of organizational characteristics on interdepartmental knowledge sharing. Journal of
Public Administration Research and Theory, 17(4), 581-606.
Zaqout, F., & Abbas, M. (2012). Towards a model for understanding the influence of the factors
that stimulate university students’ engagement and performance in knowledge
sharing. Library Review, 61(5), 345-361.
Creative Commons Attribution-NonCommercial 4.0 International License.
15 trang
22860
Free
Bạn đang xem tài liệu "Social capital and knowledge sharing in the public sector", để tải tài liệu gốc về máy hãy click vào nút Download ở trên
File đính kèm:
- social_capital_and_knowledge_sharing_in_the_public_sector.pdf
