First impressions matter. When we meet people for the first time, we quickly form impressions about them based on their nonverbal behavior and spoken words. Specifically in the workplace, impressions affect key outcomes like being hired or promoted, and are critical in entire sectors of the economy including sales, service, and hospitality.
Research in organizational psychology and nonverbal communication has revealed some of the connections existing between nonverbal behavior and impressions in the workplace, including links between immediacy behavior and hiring decisions or ratings of supervisors. However, and despite the fact that impressions at work are ubiquitous, much of the existing research in this domain has been done in the laboratory, for single organizational situations, and based on single interactions. Furthermore, one of the fundamental goals of organizational behavior research -- how to make these findings useful for training and improvement of skills by employees -- has often been disconnected from laboratory studies given the lack of methods and tools to systematically understand favorable impressions and related variables in the field for multiple situations, and embedded with the training process.
This is what UBImpressed aims to achieve through a multidisciplinary research approach involving academics in Work and Organizational Psychology, Ubiquitous Computing, and Social Computing, in close partnership with an international hospitality management school. The project integrates nonverbal communication research with mobile computing, perceptual computing, and machine learning to understand what nonverbal behaviors are related to conveying favorable impressions in different domains within the organizational context; which of these impression-related behavioral cues can be robustly extracted and analyzed by automatic means over multiple physical settings; and how favorable first impressions in the workplace can be trained by integrating expert knowledge with automatic sensing, analysis, and visualization technology.
The project is funded by the Sinergia interdisciplinary program of the Swiss National Science Foundation (SNSF)