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http://dx.doi.org/10.14695/KJSOS.2021.24.2.75

Happy Applicants Achieve More: Expressed Positive Emotions Captured Using an AI Interview Predict Performances  

Shin, Ji-eun (Department of psychology, Chonnam National University)
Lee, Hyeonju (Psychological solution planning part, MIDAS IT)
Publication Information
Science of Emotion and Sensibility / v.24, no.2, 2021 , pp. 75-80 More about this Journal
Abstract
Do happy applicants achieve more? Although it is well established that happiness predicts desirable work-related outcomes, previous findings were primarily obtained in social settings. In this study, we extended the scope of the "happiness premium" effect to the artificial intelligence (AI) context. Specifically, we examined whether an applicant's happiness signal captured using an AI system effectively predicts his/her objective performance. Data from 3,609 job applicants showed that verbally expressed happiness (frequency of positive words) during an AI interview predicts cognitive task scores, and this tendency was more pronounced among women than men. However, facially expressed happiness (frequency of smiling) recorded using AI could not predict the performance. Thus, when AI is involved in a hiring process, verbal rather than the facial cues of happiness provide a more valid marker for applicants' hiring chances.
Keywords
Happiness; Emotion; Facial Expression; Language; Artificial Intelligence;
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