Collective Attention and Collective Intelligence: The Role of Hierarchy and Team Gender Composition

等级制度 团队构成 作文(语言) 心理学 集体智慧 多级模型 团队效能 组织结构 集体行为 社会心理学 现存分类群 工作(物理) 结果(博弈论) 知识管理 社会学 计算机科学 管理 政治学 微观经济学 工程类 经济 法学 哲学 机器学习 生物 机械工程 进化生物学 语言学 人类学
作者
Anita Williams Woolley,Rosalind M. Chow,Anna Mayo,Christoph Riedl,Jin Wook Chang
出处
期刊:Organization Science [Institute for Operations Research and the Management Sciences]
卷期号:34 (3): 1315-1331 被引量:28
标识
DOI:10.1287/orsc.2022.1602
摘要

Collective intelligence (CI) captures a team’s ability to work together across a wide range of tasks and can vary significantly between teams. Extant work demonstrates that the level of collective attention a team develops has an important influence on its level of CI. An important question, then, is what enhances collective attention? Prior work demonstrates an association with team composition; here, we additionally examine the influence of team hierarchy and its interaction with team gender composition. To do so, we conduct an experiment with 584 individuals working in 146 teams in which we randomly assign each team to work in a stable, unstable, or unspecified hierarchical team structure and vary team gender composition. We examine how team structure leads to different behavioral manifestations of collective attention as evidenced in team speaking patterns. We find that a stable hierarchical structure increases more cooperative, synchronous speaking patterns but that unstable hierarchical structure and a lack of specified hierarchical structure both increase competitive, interruptive speaking patterns. Moreover, the effect of cooperative versus competitive speaking patterns on collective intelligence is moderated by the teams’ gender composition; majority female teams exhibit higher CI when their speaking patterns are more cooperative and synchronous, whereas all male teams exhibit higher CI when their speaking involves more competitive interruptions. We discuss the theoretical and practical implications of our findings for enhancing collective intelligence in organizational teams. Funding: This work was supported by the National Science Foundation [Grant VOSS-1322254], Division of Information and Intelligent Systems [Grant 1322241], Army Research Office [Grant W911NF-20-1-0006], Defense Advanced Research Projects Agency [Grant W911NF-20-1-0006], and Army Research Laboratory [Grant W911NF-19-2-0135]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/orsc.2022.1602 .
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