What drives job applicants’ reactions and behavior intention during AI-enabled interview

Abstract

Artificial Intelligence (AI) has brought significant changes to the field of human resources, especially in the interview process. It has facilitated the implementation of remote interviews, improved applicants’ experiences, and contributed to the diversity and inclusiveness of the recruitment process. As organizational research advances, the focus has shifted to applicant reaction, positing their response impact on various outcomes. Organizations must be aware of applicant reactions so that they can be better informed of the potential consequences of their selection procedures. Thus, this research delves into the intricacies of how applicants perceive and respond to AI-enabled interviews and explores various factors that shape applicant and behavior intention.
This research has integrated previous literature and developed a theoretical model that bridges interview type, applicant perceptions, and applicant behavior. The result reveals differences in perceived fairness and social presence between AI interviews and face-to-face interviews in recruitment. Although AI interviews are at a disadvantage in social presence, applicants’ trust in AI is unexpectedly higher. The results also emphasize the importance of social presence and fairness in shaping applicants’ attitudes and behavioral intentions toward organizations. Moreover, this research combines Fuzzy-Set Qualitative Comparative Analysis (fsQCA) and Structural Equation Modeling (SEM) to deeply highlight the intrinsic relationship between AI recruiters' attributes and applicant behavior intention. It is confirmed that social bandwidth and interactivity stimulate positive perception and behavior. Finally, this study extensively explores the psychological factors with a special focus on regulatory focus theory and regulatory fit in the context of AI-enabled interviews. It is found that regulatory fit significantly increases applicants’ feelings of social presence and fairness during their interaction with AI recruiter.
This study has made an important contribution to the field of human resources, providing a comprehensive understanding of the impact of AI interviews through interdisciplinary methodology. It not only reveals the differences between AI interviews and traditional methods in terms of fairness and social interaction but also emphasizes the profound impact of these factors on the decision-making process of applicants. This perspective also provides a new perspective for understanding the psychological motivations and behaviors of applicants and offers valuable insights for organizations on how to optimize AI interview processes to meet the needs of different applicants.

Date of Award	Oct 2024
Original language	English
Awarding Institution	University of Nottingham
Supervisor	Jie YU (Supervisor), Shuiqing Yang (Supervisor) & Qingxin Meng (Supervisor)