相关实验视频
Updated: Jun 29, 2025

06:48
The HoneyComb Paradigm for Research on Collective Human Behavior
Published on: January 19, 2019
9.3K
经验塑造了团队行为相互依存,团队协作和大规模多人在线游戏中的表现之间的非线性
Carlos Carrasco-Farré1, Nancy Hakobjanyan2
1Toulouse Business School, Toulouse, France. c.carrasco-farre@tbs-education.es.
Scientific reports
|April 3, 2024
概括
大规模多人在线游戏 (MMOG) 的团队表现取决于行为上的相互依赖和经验. 过度的协作可能会阻碍经验较少的团队,而风险较高的团队表现更差.
科学领域:
- 行为科学是一种行为科学.
- 管理研究 管理研究
- 计算社会科学 计算社会科学
背景情况:
- 团队管理文献提供了对绩效预测因素的见解.
- 大规模多人在线游戏 (MMOG) 提供大规模的细粒度数据进行分析.
研究的目的:
- 检查MMOG中的团队表现的定量预测因素.
- 研究玩家体验对行为相互依赖和表现的缓和效应.
主要方法:
- 分析了超过14万场球队模式的比赛,涉及超过50万名球员.
- 行为相互依赖,经验和团队表现的统计检查.
- 使用MMOG数据复制和扩展现有的团队管理研究.
主要成果:
- 证实了行为相互依赖和团队表现之间的曲线关联.
- 对于经验较少的团队来说,确定了一个反转的U形模式,在这种模式中,过度的协作是反作用的.
- 观察了经验丰富的团队的线性关系,并发现风险较高的团队表现更差.
结论:
- 玩家体验可以缓和行为相互依赖对MMOG团队表现的影响.
- MMOG数据为行为和管理研究提供了有价值,可复制和可通用的资源.
- 电子竞技数据可以推进社会科学研究,因为它具有数字化,大规模和颗粒性质.
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