Proceedings of the 39th Hawaii International Conference on System Sciences - 2006
Exploring Knowledge Sharing in Virtual Teams:
A Social Exchange Theory Perspective
Sheng Wu
Cathy S. Lin
Tung-Ching Lin
Dept. of Information Management,
Dept. of Information Management,
Dept. of Information Management,
Southern Taiwan University of Technology
National University of Kaohsiung
National Sun Yat-Sen University
[email protected]
[email protected]
[email protected]
Abstract
In the Knowledge Economics Age, knowledge is
seen as a critical resource.
To enhance the
knowledge value, businesses have to promote
knowledge sharing as the path to gaining competitive
advantages. Further, with the rapid progress in
network technology, new business models have
emerged to adapt to the changing environment; the
virtual team, a kind of new business style, has become
prevalent for many businesses in emerging
information technologies.
In this study, we study how the virtual team
members effectively share their knowledge through
the network technology.
Based on the Social
Exchange Theory and a model of shared knowledge,
we explore the critical factors and causal
relationships among knowledge sharing on virtual
team.
The findings show that the total eight
hypotheses have been confirmed and are valid.
Thus, these findings could be good references for both
academics and in practice. Based upon the research
findings, implications and limitations are discussed.
Keywords: Knowledge Sharing, Social Exchange
Theory, Virtual Team
1.
Introduction
Competing in the Knowledge Economics Age,
businesses have faced a whole new regimentation
since its development. Knowledge is seen as a
critical resource in modern society, while the
traditional production element is becoming the
secondary resource for businesses. Thus, to enhance
the knowledge value, businesses have to promote
knowledge sharing as the path to gaining competitive
advantages that will benefit the business.
According to Davenport (1997), knowledge
sharing, which is different from other knowledge
activities such as individual learning or knowledge
acquisition, is often unnatural. In other words,
knowledge hoarding and mutual suspicion of
knowledge acquired from others are the more natural
tendencies; people are not willing to share their
knowledge when they hold knowledge in great
account. A recent survey showed that the biggest
challenge organizations will face in knowledge
management is "changing people’s behavior"
(Ruggles, 1998). Thus, it is critical for businesses to
investigate how such knowledge sharing affects
employees’ behavior.
With the rapid progress in network technology,
new business models have emerged to adapt to the
changing environment; the virtual team, a kind of new
business style, has become prevalent for many
businesses in emerging information technologies.
The virtual team enables geographically separated
teams to work together for the duration of a specific
task. A group of people who work across space,
time, and organizational boundaries with links
strengthened by network communications and
information technology, substitutes for conventional
face-to-face contact.
The team coordination,
meetings, and tasks are accomplished via shared
network communications and information systems.
Dispersed team members can achieve specific team
missions without being limited by geography or time
constraints. As virtual teams become more prevalent,
many corporations have begun to question the way
the teams operate. A majority of the past research
investigates how advanced information technology
and telecommunications support virtual teams’
cooperative performance, emphasizing the importance
of information technology. Past researches’ focuses
included how the system introduction affects group
behavior (Tan, et al., 2000; Franz, 1999; Gorton &
Motwani, 1996), how the media characteristics affect
team performance and perception (Burke &
0-7695-2507-5/06/$20.00 (C) 2006 IEEE
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Proceedings of the 39th Hawaii International Conference on System Sciences - 2006
Chidambaram, 1999; Warkentin, et al., 1997), and so
on. However, these works fail to consider the social
interaction fields thus further research efforts should
be directed towards the social interaction study of
virtual teams because virtual teams may face the same
challenges
as
conventional
face-to-face
communications.
Considering the constraints on the physical
locations and intra-organizations in the virtual team, it
is important to achieve communication effectiveness
and information sharing (Keller, 1986; Brown &
Eisenhardt, 1995). In contrast, the face-to-face
group possesses the convenience of free-flowing
communications to build interpersonal relationships,
which help members to efficiently complete group
tasks. The two different kinds of groups form
distinct atmospheres. For example, since trust is an
important factor in both face-to-face groups and
virtual teams, Jarvenpaa et al. (1998) found that trust
has transformed into “swift trust” for the virtual team
members, which highly influences a virtual team
environment.
Prior researches have studied knowledge sharing,
but very few of them focused on virtual teams. One
such study is Jarvenpaa & Staples (2000) and Staples
& Jarvenpaa (2002); they conducted an exploratory
study and focused on the use of collaborative
electronic media for knowledge sharing. However,
past studies reveal that the theoretical foundation
needs to be strengthened. In this study, we adopt the
social exchange theory as the theoretical background
and extend the model developed by Nelson and
Cooprider (1996) to study the interpersonal
interactions between virtual team members.
Furthermore, we endeavor to explore the critical
factors and causal relationships of knowledge sharing
on virtual teams.
In the next section, we develop the theoretical
underpinnings for the research question and advance
hypotheses. The methodology used for the empirical
study is then described, followed by a description of
the results and a discussion of the potential
implications for practitioners and future research.
2.
Theoretical Background
2.1 Virtual Teams
The use of virtual teams is increasingly
becoming part of everyday work life for businesses
due to the emergence of information technology and
network telecommunications. Virtual teams can be
generally defined as a collection of co-workers who
come from a variety of organizational departments or
business units to achieve a common purpose or goal.
They are often dispersed across space, time, and
organizational boundaries. These teams have a low
frequency of face-to-face contact and collaborate
through the use of emerging computer and
communications technologies to accomplish a specific
task or project (Lipnack & Stamps, 2000; Igbaria, et
al., 1999; Speier & Palmer, 1998; Townsend et al.,
1998; Geber, 1995). Some scholars tend to define a
virtual team as a global group consisting of members
from different countries and cultural backgrounds
(Maznevski & Chudoba, 2000; Jarvenpaa & Leidner,
1999; Kristof et al., 1995).
The
advancement
of
communication
technology makes virtual teams indispensable to
companies in this age. Lipnack & Stamps (1997)
claimed that both human and organizational factors
are critical to ensuring that virtual teams operate
successfully and effectively.
They proposed a
“people/purpose/link” model where nine virtual team
principles were deduced for the practice architecture
of virtual teams’ management. As virtual teams
entered the development stage of the team life cycle,
members share leadership, undertake interdependent
tasks, and engage in various interactions without
boundaries. In this study, we define virtual teams as
"a group of individuals who come from different
corporate organizations to form a learning
task-orientated team. Once the specific objectives
have been accomplished, virtual teams will be
disbanded."
Coworkers are assembled using a
combination of telecommunications and information
technologies in order to overcome geographical
distances and time differences.
2.2 Social Exchange Theory
One theoretical perspective based on social
exchange theory (SET) (Blau, 1964; Homans, 1958;
Thibaut & Kelley, 1978) provides the theoretical
foundations to develop the research model of this
study. SET is one of the most widely used model
dealing with interpersonal interactions involving
behavior, affection, products, and communications
from social psychological perspective (Blau, 1964;
Homans, 1961). A social exchange is a relationship
in which the participants have exhibited behavior in
each other’s presence on repeated occasions, created
products for each other, or communicated with each
other (Thibaut & Kelley, 1959). The theory has been
successfully applied to many areas, including
marketing (e.g., Anderson & Narus, 1984; Dwyer, et
al., 1987; Morgan & Hunt, 1994), management
(Konovsky & Pugh, 1994), and so on.
SET views interpersonal interactions from a
cost-benefit
perspective,
considering
these
interactions as similar to an economic exchange,
except that a social exchange deals with the exchange
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Proceedings of the 39th Hawaii International Conference on System Sciences - 2006
of intangible social costs and benefits (such as respect,
honor, friendship, and caring) and is not governed by
explicit rules or agreements. Social exchanges are
similar to economic exchanges in that they both
assume that an individual’s exchange behavior
depends on the reciprocal and equivalent rewards
gained in return. The major difference between
social and the economic exchanges is that social
exchanges give no guarantee that the reciprocal
rewards in return will be equivalent to the cost
invested. However, unlike in an economic exchange,
there are no rules or agreements that govern the
interaction. Therefore, the belief that the other party
will reciprocate can only be established in a social
exchange because each party feels obligated to
maintain a cooperative relationship with the other
party (Thibaut & Kelley, 1959; Blau, 1964; Kelley &
Thibaut, 1978). By the definition of Emerson (1981),
the exchange relationship as a kind of “productive
exchange relation”, and Dixson (2000) described
knowledge sharing is treated as a kind of exchange
behavior. Taken as a whole, this study focus on the
exchange relationship to understand how the members
share their knowledge to accomplish the team tasks.
Based upon the aforementioned understandings, it is
appropriate to adopt the social exchange theory to
explore the sharing behavior in virtual teams for the
following reasons: (a) social behavior is a series of
exchanges, (b) individuals attempt to maximize their
rewards and minimize their costs, and (c) when
individuals receive rewards from others, they feel
obligated to reciprocate (LaGaipa, 1977; Nye, 1979;
Emerson, 1981).
2.3 Knowledge Sharing
Knowledge, which is information whose validity
has been established through tests of proof
(Liebeskind, 1996), has emerged as a strategically
significant resource of firms.
Tacit and Explicit Knowledge
In Nonaka & Takeuchi’s definition (1995),
knowledge includes both tacit and explicit knowledge.
Tacit knowledge is personal, context-specific
knowledge, and therefore is hard to formalize and
communicate; explicit knowledge can be described as
knowledge that is transmittable informally, through
systematic language. Polanyi (1966) also claimed
that the only way to acquire tacit knowledge was
through apprenticeship and experience. Thus, this
study introduces the concept of knowledge
representativeness, which refers to the degree to
which knowledge can be expressed in verbal,
symbolic, or written form, to generate a new and more
concrete definition of tacit and explicit knowledge,
using Polanyi’s concept. That is, we consider the
representativeness of knowledge to be a continuum.
According to this rationale, tacit knowledge is defined
as "knowledge that cannot be expressed in verbal,
symbolic, or written form", while explicit knowledge
is "knowledge that exists in symbolic or written
form".
Nelson & Cooprider’s Model of Shared Knowledge
Since organizational knowledge is inherently
created by and resides with individuals (Nonaka &
Konno, 1998), a major management issue arises in
how to transform individual know-how into
organizational knowledge,. Especially the virtual
team members who come from different corporate
organizations often own distinct knowledge domains,
it is very important for virtual team members to share
their know-how during the collaborative process so
that they can effectively solve the problems and
complete the task efficiently. Hence, how to foster
team members exchanging their tacit and/or explicit
knowledge with each other is a fundamental issue
nowadays.
Nelson and Cooprider (1996) have
proposed a model of shared knowledge from the
interpersonal interactions perspective, which built the
relationship of mutual trust and mutual influence as
the important antecedents that lead to share their
knowledge.
Consequently, knowledge sharing behavior is
viewed in this study as "the degree to which virtual
team members actually share their mutual knowledge
with fellow members for project tasks." Based upon
the social exchange theory and Nelson & Cooprider’s
model, this research proposes factors that lead to
knowledge sharing behavior between virtual team
members.
3.
Research Model and Hypotheses
In this study, the research model incorporates
factors that lead to knowledge sharing among virtual
team members (figure 1). The independent variables
affecting knowledge sharing behavior include mutual
communication and understanding. The mediating
variables are mutual influence, trust, commitment,
and conflict. The research variables and associated
hypotheses are described below.
Mutual Communication to Mutual Trust
Based on the social exchange theory and related
literature, Homans! )1958) posited that the
relationships among team members will develop and
function smoothly if the team has built good
communications.
With regard to the causal
relationships between communication and trust, Etgar
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Proceedings of the 39th Hawaii International Conference on System Sciences - 2006
(1979) found that instant communication increases
trust among members and decreases contention.
Anderson et al. (1987) noticed that good
communication establishes trust among members; the
same findings from prior research have confirmed
such causal relationships (Anderson & Narus, 1990;
Jarvenpaa & Leidner, 1999; Suchan & Hayzak, 2001;!
Eggert, 2001). The marketing study conducted by
Morgan & Hunt!)1994) found that communication
had a positive impact on trust. In this study, we posit
that if the virtual team members engage in mutual
communication, this will be positively associated with
their mutual trust. Hence, this leads to the first
hypothesis:
[H1] The mutual communication among
virtual team members is positively associated
with their mutual trust.
Mutual Communication to Mutual Influence
Communication is an antecedent of mutual trust
and influence. Neslson & Cooprider (1996) argued
that there is a positive relationship between mutual
communication and mutual influence. Since mutual
communication is the antecedent of mutual trust and
mutual influence, Nelson & Cooprider (1996) found
H2(+)
Mutual
Communication
Mutual
Influence
that if virtual team members have more
communication, then this would lead them to increase
their mutual influence. This leads to the second
hypothesis:
[H2] The mutual communication among
virtual team members is positively associated
with their mutual influence.
Mutual Understanding to Mutual Trust
Cohen & Gibson!)1999) believed that mutual
understanding and trust are critical to the performance
of virtual teams since these factors help build trust
and help lessen the communication problems that
Denning,
arise from a lack of face-to-face contact.
et. al. (2002) pointed out that knowledge sharing can
only experience where groups have organized
themselves well, which will shape the environment of
mutual understanding and trust that encourages them
sharing knowledge so that people who do not know
can learn from those who do know. This leads to the
third hypothesis:
[H3] The mutual understanding among virtual
team members is positively associated with
their mutual trust.
H6(+)
H1(+)
Mutual
Understanding
H3(+)
H4(+)
Mutual
Trust
Mutual
Commitment
H7(+)
Knowledge
Sharing
H5(-)
H8(-)
Mutual
Conflict
Figure 1: Research Model
Mutual Trust to Mutual Commitment
Based on the social exchange theory, mutual
commitment and the cooperative relationship are
unable to thrive if there is a lack of mutual trust
(McDonald, 1981). Furthermore, Achrol! )1991)
indicated that trust has a significant influence on
commitment. Hrebiniak!)1974) conceived that trust
enables high performance, and that trust is followed
by commitment in the relationship. Hence, this
leads to the fourth hypothesis:
[H4] The mutual trust among virtual team
members is positively associated with their
mutual commitment.
Mutual Trust to Mutual Conflict
Conflict is one of the key elements of
relationship exchange.
Within a collaborative
business environment, when people anticipate that
conflict will occur, such conflict will subsequently be
adjusted by mutual trust (Dwyer et al, 1987). The
study by Morgan & Hunt (1994) provides useful
insights in this area of research; the stronger mutual
trust is within a team, the fewer conflicts will be
caused by contention. Anderson & Narus (1990)
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Proceedings of the 39th Hawaii International Conference on System Sciences - 2006
believed that stronger mutual trust could reduce
mutual conflict. This leads to the fifth hypothesis:
[H5] The mutual trust among virtual team
members is negatively associated with their
mutual conflict.
Mutual Influence to Knowledge Sharing
Mutual influence means both parties are able to
influence their counterpart; both have a specific
ability to influence the other. Cohen & Bradford
(1989) believed the development of mutual influence
is based on the team members’ interactions. Neison
& Cooprider (1996) believed that mutual influence is
an important factor associated with knowledge
sharing. Anderson & Narus (1990) claimed that
partners will rely on each other through cooperative
interactions. Such reliance will create a kind of
influential relationship. This is an essential process
for partners to understand each other. Through this
social mechanism, knowledge sharing will then
develop. This leads to the sixth hypothesis:
[H6] The mutual influence among virtual team
members is positively associated with their
knowledge sharing behavior.
Mutual Commitment to Knowledge Sharing
Blau (1964) claimed that, due to their mutual
commitment, team members would continuously
show his or her trustworthiness during the exchange
relationship. Münch (1993) believed that once the
exchange relationship was established in a team, more
and more contributions will be made, team members
will share mutual goals and benefits, and closer
interactions would gradually be established. Team
members will form a self-contained group, and the
mutual commitment among them will be strengthened.
Mohr & Spekman (1994) recognized that
commitment is a vital requirement for the
collaborative relationship.
In sum, this study
investigated whether stronger mutual commitment
leads to better knowledge sharing. This leads to the
seventh hypothesis:
[H7] The mutual commitment among virtual
team members is positively associated with
their knowledge sharing behavior.
Mutual Conflict to Knowledge Sharing
Finally, according to social exchange theory,
Blau (1964) claimed that if exchange is asymmetric in
the organization, it would cause power disunity,
creating the potential for conflict. Anderson &
Narus (1984, 1990) also found that contentions cause
conflict in the collaborative relationship. The level
of the conflict depends on the frequency, strength, and
duration of the contentions (Reve & Stern, 1979).
The results of this study demonstrate that serious
conflicts existing in the virtual team will cause mutual
disagreements to arise and are negatively associated
with the knowledge sharing intention. This leads to
the eighth hypothesis:
[H8] The mutual conflict among virtual team
members is negatively associated with their
mutual knowledge sharing behavior.
4.
4.1.
Research Methodology
Subjects
To relate the research purpose to virtual teams,
we conducted an empirical study at the National Sun
Yat-Sen Cyber University (http://cu.nsysu.edu.tw/).
Before students enroll in the Cyber University, they
need to be equipped with network capability and be
fundamentally computer literate. These on-the-job
students who major in Management Information
Systems take courses in asynchronous network
learning. At the beginning of the course, they have
to learn how to operate the cyber classroom using the
web interface so that every student can study without
difficulty.
4.2.
Research Procedure
The students in the Cyber University are only
temporarily assembled; everyone comes from
different corporate organizations and different
locations. Since they have diverse learning hours,
they have to do the lessons and projects in the
coordinated and cooperative working environment of
a virtual team. In this cyber campus, unique online
bulletin board is provided for team members
discussing and sharing information with each other in
order to accomplish the tasks and achieve mutual
goals.
Each team group composes of five to seven
members. Upon the course requirement, every team
member is asked to discuss their project on online
team bulletin board. After fifteen weeks teamwork,
every team group needs to accomplish a term report
concerning the real information management cases.
To address this study, a field survey was conducted of
the cyber university students upon their completion of
the project. The purpose of this survey was to
investigate the team members’ knowledge sharing
behavior during their semester collaboration. A total
of 156 surveys were sent to the subjects; incomplete
questionnaires were discarded, leaving 148 usable
samples (a net response rate of 94.87%).
4.3.
Measurement Development
Seven constructs were measured in this study:
mutual communication, mutual understanding, mutual
trust, mutual influence, mutual commitment, mutual
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Proceedings of the 39th Hawaii International Conference on System Sciences - 2006
conflict, and knowledge sharing. Through the use of
standardized
response
category
in
survey
questionnaire, the Likert scales is typically a
seven-point scale, ranking from "strongly disagree
(=1)" to "strongly agree (=7)". Table 1 provided
operational definitions and measurement items for
these constructs.
Table 1: Operationalization and Measurement Items of Constructs
Construct
Mutual
communication
Mutual
understanding
Operational Definition and Measurement Items
Reference source
Lee & Kim (1999)
The degree that virtual team members communicate swimmingly with each other.
We {have peaceful and pleasant communication during team collaboration / understand very well about what members try to
express during team collaboration / easily come to common consensus during team collaboration / keep no secrets from each
team members/ usually have communication problems during team collaboration}.
Lee & Kim (1999)
The degree that virtual team member know his partner well with each other.
I think our team members understand each others’ {background / position / expertise / contribution}.
Anderson & Narus (1990),
Doney & Canon (1997)
I think our team members {are obliging and positive to solve the problems / treat each other sincerely / will voluntarily help each
other to fix the problems which related to team jobs / will not do something hurt each other}.
The degree that the ability of virtual team members to affect the executing tasks of each Anderson & Narus (1990),
Doney & Canon (1997)
other.
The degree that virtual team member believe his partner well with each other.
Mutual trust
Mutual influence
Mutual
commitment
Mutual conflict
Knowledge
sharing
5.
I think {our team has high mutual influence during team collaboration / team members easily be influenced by each other during
team collaboration / I have power to influence team members during team collaboration}.
Meyer & Allen (1997)
The degree that virtual team member commit to each other within the group.
As long as I {get adequate response from team members / can accumulate my knowledge in my team / can learn what I want to
learn in my team / can achieve self-development in my team}, I will do my best contributing my knowledge in the bulletin board.
Brown & Day (1981)
The degree that the interaction among virtual team members when they happened divide.
We seldom have {contentions / arguments / complaints} during team collaboration.
Bock & Kim (2002)
The degree that virtual team members share the tacit and explicit knowledge.
1) Tacit (Including: experience curve, the real working experience, professional judgments, unique opinions, the accumulative
working experience, the accumulative professional knowledge, the unique opinions and judgments).
2) Explicit (Copies from articles published in books, periodicals, magazines, websites, documents, manuals, handout materials
and so on)
Data Analysis and Results
The results of the measurement model analyses
are presented first. This is followed by a formal test
of the hypotheses. To assess the research hypotheses,
this research relied extensively on the confirmatory
factor analysis (CFA) using LISREL 8.30 and the
sample correlation matrix (Joreskog & Sorbom, 1993).
Several common measures were used to assess the
model’s overall goodness of fit: chi-square/degree of
freedom, goodness-of-fit index (GFI), normed fit
index (NFI), non-normed fit index (NNFI),
comparative fit index (CFI), and standardized root
mean square residual (SRMSR) (Bentler, 1989, Chau,
1997). By using these measures, this research is able
to assess the measurement model and determine
whether the measured variables reliably reflect the
theoretical constructs. Further, this research can
check the overall goodness-of-fit of the proposed
research model.
5.1.
Assessment of Measurement Model
The model was further assessed the construct
reliability and validity. The first step in scale
validation was to examine the goodness-of-fit of the
overall CFA model. For models with good fit, it is
suggested that chi-square normalized by degrees of
freedom (χ2/df) should be less than 3 (Bentler, 1989;
Wheaton, et al., 1977), GFI, NFI, NNFI, and CFI
should all exceed 0.9, and SRMSR should be less than
0.1. For the current CFA model, χ2/df is 1.55
(χ2=394.57; df=254), GFI was 0.82, NFI is 0.84,
NNFI is 0.91, CFI is 0.92, and SRMSR is 0.06. An
adequate model fit is suggested.
Next, convergent validity was evaluated for the
measurement scales using the criteria suggested by
Fornell & Larcker (1981) and Fornell (1982): (1)
composite reliabilities should exceed 0.6, and (2)
average variance extracted (AVE) by each construct
should exceed the variance due to measurement error
for that construct (i.e., AVE should exceed 0.50).
Composite reliabilities of research constructs ranged
between 0.67 and 0.91; and AVE ranged from 0.50 to
0.77 (Table 2) that are greater than variance due to
measurement error. Hence, all the conditions for
convergent validity were closely met.
Finally, Fornell & Larcker (1981) recommended
a stronger test of discriminant validity, where the AVE
for each construct should exceed the squared
correlation between that and any other construct.
The factor correlation matrix in Table 2 indicates that
the largest squared correlation between any pair of
constructs is 0.42 (mutual communication and mutual
trust), while the smallest AVE is 0.50. Hence, the
test of discriminate validity was also met.
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Proceedings of the 39th Hawaii International Conference on System Sciences - 2006
Table 2: Scale Properties and Correlations
Factor Correlations
X1
X2
X3
X4
X5
X6
X1
0.84
0.51
1
X2
0.93
0.77
0.54
1
X3
0.85
0.65
0.48
0.56
1
X4
0.87
0.64
0.65
0.55
0.48
1
X5
0.91
0.73
0.47
0.46
0.46
0.57
1
X6
0.79
0.56
-0.51
-0.37
-0.30
-0.52
-0.53
1
Y1
0.67
0.50
0.32
0.18
0.31
0.25
0.17
-0.25
X1: Mutual Communication, X2: Mutual Understanding, X3: Mutual Influence,
X4: Mutual Trust, X5: Mutual Commitment, X6: Mutual Conflict, Y1: Knowledge Sharing
a
Composite Reliability (CR) = ( standardized loading)2 / ( standardized loading)2 + İj
b
Average Variance Extracted (AVE) = (standardized loading2) / (standardized loading)2 + İj
Construct
5.2.
a
AVEb
CR
Assessment of Model Fit and Evaluation
of Hypotheses
The eight hypotheses presented earlier were
tested collectively using the structural equation
modeling (SEM) approach performed in LISREL.
This approach is particularly appropriate for testing
theoretically justified models (Joreskog & Sorbom,
1993). Each indicator was modeled in a reflective
manner (as that in CFA), the seven constructs were
linked as hypothesized (see Figure 2), and model
0.65**
Mutual
Communication
Mutual
Influence
Y1
1
estimation was done by using the maximum
likelihood technique.
The goodness-of-fit of the structural model was
comparable to that of the previous CFA model.
Model Ȥ2/df was 1.64 (Ȥ2=438.41; df=266), GFI was
0.81, NFI was 0.82, NNFI was 0.90, CFI was 0.91,
and SRMSR was 0.06 (see Figure 2). These metrics
provided evidence of adequate fit between the
hypothesized model and the observed data.
0.35**
0.73**
Mutual
Understanding
0.14*
Mutual
Trust
0.62**
Mutual
Commitment
-0.64**
0.15*
Knowledge
Sharing
-0.29**
Mutual
Conflict
Model fit: ǒ2=438.41, df=266, ǒ2 / df = 1.64,
GFI=0.81, NFI=0.82, NNFI=0.90, CFI=0.91, IFI=0.91
*P<0.01; **P<0.001
Figure 2: LISREL Analysis of Research Model
Next, the path significance of each
hypothesized association in the research model and
variance explained (R2 value) by each path were
examined. Figure 2 shows the standardized path
coefficient and path significance, as reported by
LISREL. All eight hypothesized paths in the model
were significant at p < 0.01. The virtual team
members’ mutual trust was predicted by mutual
communication (ȕ=0.73) and understanding (ȕ=0.14),
which explained 68% of the mutual trust variance.
Therefore, hypotheses 1 and 3 were not rejected.
Mutual Influence, in turn, was predicted by mutual
communication (ȕ=0.65), which explained 42% of the
mutual communication variance.
Therefore,
hypothesis 2 was not rejected. Mutual commitment,
in turn, was predicted by mutual trust (ȕ=0.62), which
explained 39% of the mutual commitment variance.
Therefore, hypothesis 4 was not rejected. Mutual
conflict, in turn, was predicted by mutual trust
(ȕ=-0.64), which explained 40% of the mutual
conflict variance. Therefore, hypothesis 5 was not
rejected.
Final, the virtual team members’
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Proceedings of the 39th Hawaii International Conference on System Sciences - 2006
knowledge sharing behavior, in turn, were predicted
by mutual influence (ȕ=0.35), commitment (ȕ=0.15)
and conflict (ȕ=-0.29), which explained 23% variance
of the knowledge sharing behavior.
Therefore,
hypothesis 6, 7 and 8 was not rejected.
6.
6.1.
Conclusions and Implications
Research Conclusions
Knowledge is a crucial resource in today’s
organizational environment. Due to the natural
tendency to hoard useful information, knowledge is
not always being fully utilized in organizations.
Therefore, effective mangers encourage knowledge
sharing. However, to achieve successful knowledge
sharing requires investigating the associated factors.
The objective of this study was to assess the
associated factors of mutual communication, mutual
understanding, mutual influence, mutual trust, mutual
commitment, and mutual conflict on knowledge
sharing behavior.
For the great advancement of information
technology, businesses have adopted various methods
to minimize cost and maximize performance, such as
paying more and more attention to the virtual team
model instead of the traditional face-to-face
communication model. Hence, it is important to
investigate the virtual team members’ knowledge
sharing behavior through the advancement of
information technology. This study is based on the
social exchange theory; the proposed research
framework, which included the factors of mutual
communication, mutual understanding, mutual
influence, mutual trust, mutual commitment, and
mutual conflict, is appropriate for studying knowledge
sharing behavior in the virtual team circumstance.
Following the empirical survey and data analysis with
the structural equation model (SEM), the findings
show that knowledge sharing behavior is influenced
by mutual influence, mutual commitment, and mutual
conflict. Mutual communication has an influence on
mutual influence. Mutual commitment and mutual
conflict is influenced by mutual trust. Mutual
communication and mutual understanding have an
influence on mutual trust. In sum, the total eight
hypotheses have been confirmed and are valid. Thus,
these findings could be good references for both
academics and in practice.
6.2.
affecting the mutual influence and trust. Further, the
mediating variables between mutual trust and
knowledge sharing behavior are the mutual
commitment, and conflict.
The findings of this study, in terms of academic
implications, applied the social exchange theory in the
virtual team circumstance. We not only investigated
the important factors associated with knowledge
sharing behavior, but also discovered the causal
relationships. Therefore, the research framework in
this study is significant for future work. With regard
to the practical implications, the insights gained from
this study are useful for businesses attempting to
understand sharing behavior among employees. The
findings also could be a valuable contribution for
businesses trying to increase sharing behavior.
Jarvenpaa et al. (1998) and Jarvenpaa & Leidner!
)1999) posited that communication and trust are
widely recognized as the critical factors in virtual
team cooperation; obviously, this is in line with the
findings of this study.
6.3.
7.
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