Hire me: Computational Inference of Hirability in Employment Interviews Based on Nonverbal Behavior
Hire me: Computational Inference of Hirability in Employment Interviews Based on Nonverbal Behavior
复制标题
雇佣我：基于非语言行为的就业面试中可聘性的计算推断
DOI：
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发表时间：
2014
影响因子：
7.3
通讯作者：
D. Gática
中科院分区：
文献类型：
--
作者：
L. Nguyen;Denise Frauendorfer;M. S. Mast;D. Gática
Understanding the basis on which recruiters form hirability impressions for a job applicant is a key issue in organizational psychology and can be addressed as a social computing problem. We approach the problem from a face-to-face, nonverbal perspective where behavioral feature extraction and inference are automated. This paper presents a computational framework for the automatic prediction of hirability. To this end, we collected an audio-visual dataset of real job interviews where candidates were applying for a marketing job. We automatically extracted audio and visual behavioral cues related to both the applicant and the interviewer. We then evaluated several regression methods for the prediction of hirability scores and showed the feasibility of conducting such a task, with ridge regression explaining 36.2% of the variance. Feature groups were analyzed, and two main groups of behavioral cues were predictive of hirability: applicant audio features and interviewer visual cues, showing the predictive validity of cues related not only to the applicant, but also to the interviewer. As a last step, we analyzed the predictive validity of psychometric questionnaires often used in the personnel selection process, and found that these questionnaires were unable to predict hirability, suggesting that hirability impressions were formed based on the interaction during the interview rather than on questionnaire data.
