An Experimental Study of Team Size and Performance on a Complex Task.
An Experimental Study of Team Size and Performance on a Complex Task.
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一项针对复杂任务的团队规模和绩效的实验研究。
DOI：
10.1371/journal.pone.0153048
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发表时间：
2016
期刊：
影响因子：
3.7
通讯作者：
Watts DJ
中科院分区：
文献类型：
--
作者：
Mao A;Mason W;Suri S;Watts DJ
团队规模和生产率之间的关系是一个广泛涉及经济学、心理学和管理学的问题。然而，对于复杂的任务，协调工作的潜在收益和成本都随着工人数量的增加而增加，无论是理论论点还是经验证据都不一致地支持较大的团队与较小的团队。同时，实验结果依赖于小组和高度程式化的任务，因此很难推广到现实环境中。在这里，我们缩小了现实世界任务复杂性和实验控制之间的差距，报告了一个在线实验的结果，在这个实验中，47个团队，从n=1到32个，合作完成了一个现实的危机映射任务。我们发现，团队中的个人比独立的员工付出的总体努力更少，部分原因是将他们的努力分配到要求较低(和生产率较低)的子任务上；然而，我们也发现，随着团队规模的增加，团队中的个人合作更多。直接比较这些相互竞争的影响，我们发现最大的团队表现优于同等数量的独立员工，这表明合作的收益超过了努力的损失。重要的是，这些团队的表现也与现场部署的危机地图绘制程序相当，这表明这里描述的这种类型的实验可以帮助解决实际问题，并促进集体智力科学的发展。
The relationship between team size and productivity is a question of broad relevance across economics, psychology, and management science. For complex tasks, however, where both the potential benefits and costs of coordinated work increase with the number of workers, neither theoretical arguments nor empirical evidence consistently favor larger vs. smaller teams. Experimental findings, meanwhile, have relied on small groups and highly stylized tasks, hence are hard to generalize to realistic settings. Here we narrow the gap between real-world task complexity and experimental control, reporting results from an online experiment in which 47 teams of size ranging from n = 1 to 32 collaborated on a realistic crisis mapping task. We find that individuals in teams exerted lower overall effort than independent workers, in part by allocating their effort to less demanding (and less productive) sub-tasks; however, we also find that individuals in teams collaborated more with increasing team size. Directly comparing these competing effects, we find that the largest teams outperformed an equivalent number of independent workers, suggesting that gains to collaboration dominated losses to effort. Importantly, these teams also performed comparably to a field deployment of crisis mappers, suggesting that experiments of the type described here can help solve practical problems as well as advancing the science of collective intelligence.