Digital Age Research Center, University of Klagenfurt, Klagenfurt, Austria.
Department of Management Control and Strategic Management, University of Klagenfurt, Klagenfurt, Austria.
PLoS One. 2023 Aug 28;18(8):e0290578. doi: 10.1371/journal.pone.0290578. eCollection 2023.
Organisations rely upon group formation to solve complex tasks, and groups often adapt to the demands of the task they face by changing their composition periodically. Previous research has often employed experimental, survey-based, and fieldwork methods to study the effects of group adaptation on task performance. This paper, by contrast, employs an agent-based approach to study these effects. There are three reasons why we do so. First, agent-based modelling and simulation allows to take into account further factors that might moderate the relationship between group adaptation and task performance, such as individual learning and task complexity. Second, such an approach allows to study large variations in the variables of interest, which contributes to the generalisation of our results. Finally, by employing an agent-based approach, we are able to study the longitudinal effects of group adaptation on task performance. Longitudinal analyses are often missing in prior related research. Our results indicate that reorganising well-performing groups might be beneficial, but only if individual learning is restricted. However, there are also cases in which group adaptation might unfold adverse effects. We provide extensive analyses that shed additional light on and help explain the ambiguous results of previous research.
组织依赖于群体形成来解决复杂的任务，并且群体通常通过定期改变其组成来适应他们所面临的任务的要求。以前的研究经常采用实验、调查和实地工作方法来研究群体适应对任务绩效的影响。相比之下，本文采用基于代理的方法来研究这些影响。我们这样做有三个原因。首先，基于代理的建模和模拟允许考虑可能调节群体适应和任务绩效之间关系的其他因素，例如个体学习和任务复杂性。其次，这种方法允许研究感兴趣的变量的大变化，这有助于推广我们的结果。最后，通过采用基于代理的方法，我们能够研究群体适应对任务绩效的纵向影响。纵向分析在以前的相关研究中经常缺失。我们的结果表明，对表现良好的群体进行重组可能是有益的，但前提是限制个体学习。然而，也有一些情况下群体适应可能会产生不利影响。我们提供了广泛的分析，进一步阐明并帮助解释了以前研究的模糊结果。
