This project investigates social interaction in personnel selection interviews enhanced by digital technology (trained machine learning algorithms for verbal and nonverbal behavior) using videos interviews.
First, the selection interview is the gateway to employment and thus the potential beginning of one of the fundamental social ties in modernity: the work relationship. We explore a new format by which selection interviews are conducted in an online, asynchronous manner.
Second, the selection interview is an important personnel selection procedure, which itself is an important component of strategic talent management. The digitalization of talent management is rapidly expanding in practice, but is currently poorly understood in research.
Third, video interviews are a novel experience for many applicants. Machine learning techniques can be used to extract the applicants’ behaviors recorded on the videos and to some degree infer their personality and social skills. This information can then be fed back to the applicants, potentially changing their subjective experience of the video interview.
The study will yield four main sets of outputs. First, the primary data from the study will lead to publications in scientific journals or conference proceedings in human-computer interaction and organizational psychology or human resources. Second, the data will be used to adapt an existing data collection platform and to improve the quality of algorithms to infer verbal and nonverbal behavior from videos. Third, data about user experiences will inform the development of evidence-based coaching programs for improving applicants’ performance. Fourth, the rich set of data and experience generated will constitute fruitful avenues for further research by the applicant team.