“Choose what suits you”: The role of relative competency strength in shaping job applicants’ reactions and strategies toward AI-based interview - Jiaxuan Wang; Jinghao Zhang; Julie N.Y. Zhu; Liying Bai (Computers in Human Behavior Reports)
The increasing adoption of Artificial Intelligence (AI) in work contexts significantly breeds organizational practices of AI-based personnel recruitment and selection in recent years. Despite its benefits for organizations, whether job applicants favor the adoption of AI-based interview remains unclear. In the present research, we draw on expectancy theory to propose a contingent model explaining when and why applicants are more willing to accept an AI-based interview (vs. human-based interview). We introduce relative competency strength to identify whether AI-based interviews fit their applicants' unique competency. Across three experimental studies (total N = 760), we found that AI-based interview (vs. human-based interview) induced both higher uniqueness neglect expectations and higher fairness expectations of applicants. Moreover, applicants' relative competency strength moderated the impacts on both expectations separately. Specifically, applicants with higher cognitive competency strength had stronger fairness expectations and applicants with higher non-cognitive competency strength had stronger uniqueness neglect expectations, which further differentiated their subsequent job seeking strategies. Overall, our research implies that job applicants' reactions toward AI-based interviews depend on their recognition of their relative competency strength, suggesting an adaptive approach to job applications.