Detection of Social Identification in Workgroups from a Passively-sensed WiFi Infrastructure

Social identification: how much individuals psychologically associate themselves with a group has been posited as an essential construct to measure individual and group dynamics. Studies have shown that individuals who identify very differently from their workgroup provide critical cues to the lack of social support or work overloads. However, measuring identification is typically achieved through time-consuming and privacy-invasive surveys. We hypothesize that the extremities in-group norm affects individuals' behaviors, thus more likely to give rise to negative appraisals. As a more convenient and less-invasive technique, we propose a method to predict individuals who are increasingly different in identifying themselves with their working peers using mobility data passively sensed from the WiFi infrastructure. To test our hypothesis, we collected WiFi data of 62 college students over a whole semester. Students provided regular self-reports on their identification towards a workgroup as ground truth. We analyze the contrasts between groups' mobility patterns and build a classification model to determine students who identify very differently from their workgroup. The classifier achieves approximately 80% True Positive Rate (TPR), 73% True negative rate (TNR), and 78% Accuracy (ACC). Such a mechanism can help distinguish students who are more likely to struggle with negative workgroup appraisals and enable interventions to improve their overall team experience.