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
