Estimating work engagement from online chat tools

The Covid-19 pandemic, caused by the SARS-Cov2- virus, has transformed our lives. To combat the spread of the infection, remote work has become a widespread practice. However, this shift has led to various work-related problems, including prolonged working hours, mental health issues, and communication difficulties. One particular challenge faced by team members is the inability to accurately gauge the work engagement (WE) levels of subordinates, such as their absorption, dedication, and vigor, due to the limited number of in-person interactions that occur in remote work settings. To address this issue, online communication systems utilizing text-based chat tools such as Slack and Microsoft Teams have gained popularity as substitutes for face-to-face communication. In this paper, we propose a novel approach that uses graph neural networks (GNNs) to estimate the work engagement levels (WELs) of users on text-based chat platforms. Specifically, our method involves embedding users in a feature space based solely on the structural information of the utilized communication network, without considering the contents of the conversations that take place. We conduct two studies using Slack data to evaluate our proposal. The first study reveals that the properties of communication networks play a more significant role when estimating WELs than do conversation contents. Building upon this result, the second study involves the development of a machine learning model that estimates WELs using only the architectural features of the employed communication network. In this network representation, each node corresponds to a human user, and edges represent communication logs; i.e., if person A talks to person B, the edge between node A and node B is stretched. Notably, our model achieves a correlation coefficient of 0.60 between the observed and predicted WEL values. Importantly, our proposed approach relies solely on communication network data and does not require linguistic information. This makes it particularly valuable for real-world business situations.