The effects of relevance of previous work and multiple memberships on advice network centrality and the moderating role of TMS
Sunwoo Lee, Korea University
» Full paper: ilera-2019-paper-280-Lee.pdf
As increasing the importance of knowledge and information as the resources of organizations, managing and controlling specific knowledge of the organization is one of the key factors to maintain competitive advantage. In particular, due to the increased complexity and specialization of tasks, the need of systems and management to utilize their knowledge effectively has been extensively emphasized. In the organization, the utilizing expertise within team projects through organizational learning, communication systems, and knowledge management encourages knowledge sharing, cooperation, and mutual learning among employees that stimulates the creation of novel ideas and knowledge. In addition, it contributes to organizational performances and innovation success. Therefore, organizations need to concentrate on understanding how their knowledge is shared and what the process is to make knowledge flow more effectively, particularly in the team, which is formed to conduct their strategy in the short run and long run (Alavi & Leidner, 2001; Okhuysen & Eisenhardt, 2002).
Utilizing expertise and knowledge has been explained by network perspective among individuals, business units and organizations because a wide variety of individuals’ networks lead to knowledge transfer and provide opportunities for all members to learn and collaborate with other colleagues (Tsai & Ghoshal, 1998). For instances, the knowledge networks of individuals as the sources of social capital have positive effects on individual promotion and organizational innovation and effectiveness (e.g., Brass, Galaskiewicz, Greve, & Tsai, 2004; Burt, 1997; Coleman, 1988; Dess & Shaw, 2001; Seibert, Kraimer, & Liden, 2001). Therefore, to integrate and utilize members’ expertise through the process of asking for advice, the team members have to know who has the valuable knowledge and information. However, in the prior studies with the team network, it has been focused on the effects of a structural feature of the given network on performance and the relationship between the position within this network and performance such as the closure network and structural holes (Coleman, 1988; Burt, 1997; Leana & Van Buren, 1999). Although the forming individual’s network is the behavior and intention of an actor to share knowledge and ask for information from a specific alters in a workplace, there is a lack of studies on how the network is built by actors and which predictors influence the forming ties of actors (Mehra, Kilduff, & Brass, 2001). Some researchers also proposed the limitation that it has a tendency to more concentrate on studying a network’s structural characteristics to predict performance rather than asking how individuals generate networks in organizations (Monge and Contractor, 2001).
In this paper, we describe the utilizing knowledge and information in project team with the view of the advice network and focus on the advice network centrality by examining when and why a member turns to others for seeking advice. The advice network define as the relationship with team members or organization members which is determined by the flow of expertise, knowledge, and information, in addition, the centrality of advice network is the degree of helping other team members and taking part in sharing knowledge (Sparrowe, Liden, Wayne, & Kraimer, 2001). Individuals who lie in this centrality tend to more share their knowledge than the other. In other words, the higher degree of his/her centrality signifies that other team members ask much more sharing his/her information and know-how in project teams.
First, a member’s relevance of previous work is related with advice network centrality. A member may consider quality of expertise that they receive when they seek advice from others (Nebus, 2006), and thus, the member will approach to other members who are perceived as experts. In addition, high status members (e.g., multiple memberships) are more likely to be in central positions in advice network. Expectation states theory suggest that people tend to infer ability of others from social characteristics, such that they expect high performance from high status members and defer their view to the high status members (Bunderson, 2003). Further, we present a contingency model in which the effects of expertise level and social status depend on the team context (e.g., transactive memory system; TMS). Transactive memory system is defined as shared mental model of knowing who knows what among team members (Wegner, 1987). Under the high level of TMS, expertise will become a more salient and accessible cue to team members, and as a result, they will be more attentive to the expert members. Yet, the low level of TMS may cause disagreement about who knows what and suffer identifying expert members in teams. This leads non expertise related cues to be more noticeable factor for members seeking advice. Therefore we propose the following hypotheses with the theoretical model.
- H1: A member’s relevance of previous work will be positively related with advice network centrality.
- H2: Multiple memberships of a team member will be positively related with advice network centrality.
- H3: Transactive memory system will moderate the positive relationship between a member’s relevance of previous work and advice network centrality, such that the relationship will be stronger when TMS is high than when it is low.
- H4: Transactive memory system will moderate the positive relationship between multiple memberships of a team member and advice network centrality, such that the relationship will be weaker when TMS is high than it is low.
Using multi-wave survey data from 26 project teams with 95 members, we analyze our theoretical model with hierarchical linear modeling (random intercept and random slop). The results show that a member’s relevance of previous work (Hypothesis 1) and the multiple memberships (Hypothesis 2) are positively associated with advice network centrality. Therefore, hypothesis 1 and 2 are supported. For testing for the moderator hypotheses, however, the hypothesis 3 and 4 are not supported. We could not found that the relationship the multiple membership and advice network centrality is weakened when TMS is high. Finally, in this paper, we discuss implications of these finding for research and practice.