The paradox of knowledge: how heterogeneity shapes online medical team performance

Purpose – The heterogeneity of intellectual resources is one of the most critical static characteristics shaping team performance, while interactions among team members significantly influence how this characteristic is utilized. This study aims to explore the impact of knowledge heterogeneity within online medical teams (OMTs) on team performance and the underlying mechanisms driving this effect. Additionally, we seek to examine how intra-team interactions might moderate the relationships. Design/methodology/approach – We collected OMT consultation records from 2017 to 2023 of a leading online health platform in China. Using this data, we constructed a panel dataset for analysis. Findings – We found that OMTs' knowledge heterogeneity is negatively associated with team performance, including economic revenue and patient satisfaction. Leader-initiated interaction intensifies the negative relationship between OMT knowledge heterogeneity and team performance, while member-initiated interaction significantly mitigates this negative effect. We further validated that the mechanism underlying the performance decline caused by OMT knowledge heterogeneity is a reduction in team engagement. This reduction in engagement becomes more pronounced with greater leader-initiated interactions, whereas increased member-initiated interactions help alleviate this negative dynamic. We also find that only interactions initiated by senior leaders amplify the negative effect of OMT knowledge heterogeneity, whereas those initiated by junior leaders do not. Originality/value – This study is the first to focus on OMT knowledge heterogeneity and reveal its detrimental effects and the underlying mechanism in the context of OMT services. Our findings also underscore how intra-team interaction interplays with the process through which OMT knowledge heterogeneity shapes team performance. These insights can help platform managers better understand the impact of OMT knowledge heterogeneity, with the aim of optimizing OMT service performance and platform operation.