A knowledge-based view of team performance resilience: evidence from esports

Abstract

Purpose This study aims to develop a knowledge-based perspective on team performance resilience by examining how the structuring, integration and reconfiguration of knowledge are associated with teams’ ability to withstand and recover from external shocks. Design/methodology/approach The study uses a quantitative empirical design using data from professional esports teams competing in Counter-Strike: Global Offensive. Exogenous shocks are captured through changes in the competitive map pool, providing a quasi-experimental setting. Team resilience is measured as the ratio of post-shock to pre-shock performance and the speed of recovery. Key knowledge-related variables are operationalized using team-level metrics, and their associations with resilience outcomes are estimated using regression models. Findings The results show that knowledge specialization has an inverted U-shaped relationship with team performance resilience, indicating that increasing specialization enhances resilience, although its benefits diminish as specialization becomes more pronounced. Knowledge integration efficiency, proxied by language similarity, is positively associated with resilience, whereas age diversity is not statistically significant. Knowledge reconfiguration through team turnover is also positively associated with resilience, whereas accumulated team experience is negatively associated with it, consistent with the possibility of knowledge rigidity. Originality/value The originality of this study lies in advancing a knowledge-based perspective on team performance resilience by highlighting three interrelated knowledge tensions: specialization versus diversification, similarity versus diversity and accumulation versus renewal. Empirical evidence from the unique context of professional esports, characterized by objective performance measures and exogenous shocks, provides insights that are transferable to other knowledge-intensive teams operating in dynamic and uncertain environments.