Expressly Different: Discursive Diversity and Team Performance

Katharina Lix, Amir Goldberg, Sameer B. Srivastava and Melissa A. Valentine
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Katharina Lix: Stanford University
Amir Goldberg: Stanford University
Sameer B. Srivastava: University of California, Berkeley
Melissa A. Valentine: Stanford University

Research Papers from Stanford University, Graduate School of Business

Abstract: How does diversity among members of a team affect their performance? Prior research has found diversity to be a double-edged sword that sometimes boosts and in other cases dampens performance. Yet empirical evidence on the link between diversity and performance remains mixed, and theoretical progress in understanding the contingencies has begun to stall. To help reinvigorate research in this field, we propose a novel conceptualization of team cognitive diversity and introduce a language-based technique to measure it. We focus on a particular aspect of cognitive diversity--discursive diversity--that reflects realized, rather than potential, divergence among group members; expressed, rather than latent, differences in the way members construe and ultimately communicate about a given set of topics; and temporal variation in construals and expressions over a team’s life cycle rather than the assumption of stability. We use the tools of natural language processing to develop a time-varying measure of discursive diversity. Using data from 117 remote teams of freelance software developers who collaborate via an online communication tool, we find that discursive diversity is generally associated with better team performance. However, levels of discursive diversity fluctuate significantly over teams’ life cycles. In more fine-grained analyses, we find that discursive diversity’s effects on performance are contingent on time: it is positive when a team’s next milestone is distant but turns negative as the next milestone approaches. We discuss implications of this work for research on diversity and cultural heterogeneity and the potential for computational methods to inform the design and implementation of diversity and inclusion initiatives in organizations.

Date: 2019-03
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