Team knowledge representation: a network perspective.

Area of Science:

• Organizational Behavior
• Social Network Analysis
• Knowledge Management

Background:

• Traditional team knowledge measures often oversimplify complex knowledge configurations within teams.
• Differences in knowledge distribution (isolates, cliques, densities) significantly influence team dynamics, even with similar aggregate knowledge levels.
• Existing methods fail to capture the relational structure of knowledge sharing and expertise within teams.

Purpose of the Study:

• To propose and validate a network perspective for conceptualizing and measuring team knowledge.
• To demonstrate the advantages of a network approach in representing team knowledge structure and content.
• To expand the explanatory value of team knowledge research through advanced network analysis.

Main Methods:

• A network analysis approach was developed to compute, analyze, and visually represent team knowledge.
• The proposed methodology was applied to a sample of 57 teams.
• Network metrics such as isolation and centrality were utilized to assess knowledge distribution.

Main Results:

• Team knowledge network structures, including isolation and centrality, were significantly associated with task and strategy coordination.
• The proportion of team knowledge cliques was also linked to specific network structures.
• These associations remained significant after controlling for shared team knowledge, highlighting the importance of network configuration.

Conclusions:

• Network analysis provides a robust framework for understanding the relationship between team knowledge structure and team outcomes.
• The proposed network approach complements existing measures and offers practical applications for researchers and managers.
• Visualizing and measuring team knowledge networks can illuminate how relational structures influence team coordination, cohesion, and performance.