Jointly Predicting Job Performance, Personality, Cognitive Ability, Affect, and Well-Being

Current methods for assessing individual well-being in team collaboration at the workplace rely often on manually collected surveys. This limits continuous real-world data collection and proactive measures to improve team member workplace satisfaction. We propose a method to automatically derive social signals related to individual well-being in team collaboration from raw audio and video data collected in teamwork contexts. The goal is to develop computational methods and measurements to facilitate the mirroring of individuals’ well-being to themselves. We are focusing on how speech behavior is perceived by team members to improve their well-being. Our main contribution is the assembly of an integrated toolchain to perform multi-modal extraction of robust speech features in noisy field settings and to find which features are predictors of self-reported satisfaction scores. We apply the toolchain to a case study where we collected videos of 22 teams with 56 participants collaborating over a four-day period in a team project. Our audiovisual speaker diarization extracts individual speech features in a noisy environment. As the dependent variable team members filled out a daily PERMA (positive emotion, engagement, relationships, meaning, and accomplishment) survey. These well-being scores have been predicted with speech features extracted from the videos using machine learning. The results suggest that the proposed toolchain is able to automatically predict individual well-being in teams, leading to better teamwork and happier team members.

Purpose/significanceStudying the correlation between short video users’ personality traits and their sharing motivation can enrich the theoretical research on social sharing motivation and provide a reference for short-video content management and platform construction.Methods/processBased on uses and gratifications theory and personality traits theory, a structural model affecting short-video users’ sharing motivations was proposed. A total of 579 valid questionnaires were collected from a social network, and the proposed hypotheses were tested using SPSS and Amos software.Results/conclusionThe results show that the personality traits of short-video users affect their sharing motivation and that their specific sharing motivation also differ due to their personality traits. At the same time, the research results also confirm the Matthew effect of “the rich getting richer” and the social compensation effect of “the poor getting richer” in the context of social platforms that host short videos.