Despite the long history of studying instant messaging usage in organizations, we know very little about how today's people participate in group chat channels and interact with others. In this short note, we aim to update the existing knowledge on how group chat is used in the context of today's organizations. We have the privilege of collecting a total of 4300 publicly available group chat channels in Slack from an R\&D department in a multinational IT company. Through qualitative coding of 100 channels, we identified 9 channel categories such as project based channels and event channels. We further defined a feature metric with 21 features to depict the group communication style for these group chat channels, with which we successfully trained a machine learning model that can automatically classify a given group channel into one of the 9 categories. In addition, we illustrated how these communication metrics could be used for analyzing teams' collaboration activities. We focused on 117 project teams as we have their performance data, and further collected 54 out of the 117 teams' Slack group data and generated the communication style metrics for each of them. With these data, we are able to build a regression model to reveal the relationship between these group communication styles and one indicator of the project team performance.
翻译:尽管在各组织中研究即时信息使用情况的历史悠久,但我们对今天人们如何参与团体聊天渠道并与其他组织互动知之甚少。在本简短的说明中,我们的目标是更新关于今天各组织中如何使用团体聊天的现有知识。我们有幸从一家多国IT公司R ⁇ D部门收集了来自Slack的4300个公开团体聊天渠道。通过100个频道的质量编码,我们确定了9个频道类别,如基于项目的频道和活动频道。我们进一步定义了具有21个特征的特征指标,以描绘这些团体聊天渠道的团体通信风格。我们成功地培训了一种机器学习模式,可以将特定团体聊天渠道自动分类为9个类别之一。此外,我们演示了这些通信指标如何用于分析团队协作活动。我们注重117个项目团队的绩效数据,还进一步收集了117个团队中的54个,为每个团队制作了通信风格指标。通过这些数据,我们可以建立一个回归模型,以揭示这些团体通信风格与项目团队业绩的一个指标之间的关系。