Understanding team viability -- a team's capacity for sustained and future success -- is essential for building effective teams. In this study, we aggregate features drawn from the organizational behavior literature to train a viability classification model over a dataset of 669 10-minute text conversations of online teams. We train classifiers to identify teams at the top decile (most viable teams), 50th percentile (above a median split), and bottom decile (least viable teams), then characterize the attributes of teams at each of these viability levels. We find that a lasso regression model achieves an accuracy of .74--.92 AUC ROC under different thresholds of classifying viability scores. From these models, we identify the use of exclusive language such as `but' and `except', and the use of second person pronouns, as the most predictive features for detecting the most viable teams, suggesting that active engagement with others' ideas is a crucial signal of a viable team. Only a small fraction of the 10-minute discussion, as little as 70 seconds, is required for predicting the viability of team interaction. This work suggests opportunities for teams to assess, track, and visualize their own viability in real time as they collaborate.
翻译:团队了解团队可行性 -- -- 团队持续和未来成功的能力 -- -- 是建立有效团队的关键所在。在本研究中,我们汇总了组织行为文献中的特征,以对在线团队669 10分钟的文本对话数据集进行可行性分类模型培训。我们培训分类人员,以辨别最高级十分位(最可行的团队)、第50百分位(在中位分)和最底层十分位(最低可行的团队)的团队,然后确定团队在这些可行性层面的每个层面的特征。我们发现,对于预测团队互动的可行性而言,只需要10分钟讨论的一小部分(只有70秒)的准确度。我们从这些模型中确定使用独家语言,如“但”和“例外”,以及第二人称,这是探测最可行的团队的最预测性特征,表明与他人的积极参与是团队的关键信号。在10分钟的讨论中,只有一小部分(只有70秒)是预测团队互动可行性所需要的。这为团队评估、跟踪和直视自身可行性提供了机会。