Research On The Influence Of AI Interviewers’ Design Features On Applicants’ Perceived Interactional Justice

AI-based job interview is defined as a system that uses AI technology to interview and analyze interviewees,in order to screen and find suitable candidates.The outbreak of COVID-19 has accelerated the recruitment industry into the era of intelligent recruitment,leveraged the demand for non-contact interviews,and provided an opportunity for AI-based interviews to play a role.In recent business practices,AI-based interviews are becoming a popular trend and are being used by more and more companies for large-scale,structured initial screening scenarios.Compared with previous interview forms,the interaction between applicants and interviewers in AI-based interviews has undergone a fundamental change,from traditional human-human interaction to human-machine interaction,and the change in the form of interaction brings new interactive experience.AI-based interviews that lack warmth and do not support two-way communication reduce applicants’ perceived interactional justice,which ultimately leads to applicants’ negative attitudes toward interviews and organizations.Under the realistic background that AI-based interviews are becoming the tool of preliminary job interviews for more companies,how to optimize the system to improve applicants’ perceived interactional justice is rarely explored by scholars.In order to improve applicants’ perceived interactional justice,this study comprehensively explores the antecedents and impact of interactional justice in AI-based job interviews.Specifically,based on the research on interactional justice in the organizational justice theory,this study takes the design features of AI interviewers as the starting point,and explores the impact of the design of AI interviewers in three aspects: appearance,language style and feedback form to applicants’ perceived interactional justice,and further explores the impact of the two dimensions of interactional justice,namely interpersonal justice and informational justice,on the overall favorability and organizational attractiveness.In this study,the scenario-based online experiment method was used to verify the proposed research model,and the data of465 valid samples were empirically analyzed using Stata15.0 and Smart PLS3.0.The research results show that designing AI interviewers at the aspects of appearance and language style to highlight their warm and friendly personality can effectively improve interpersonal justice of interactional justice,and designing AI interviewers at the aspect of feedback form to provide informative feedback can effectively improve informational justice of interactional justice.The results also show that the two dimensions of interactional justice,namely interpersonal justice and informational justice,both have a positive impact on the overall favorability,and only informational justice has a positive impact on organizational attractiveness,while interpersonal justice has no direct impact on organizational attractiveness.Overall favorability plays a full mediating role in the relationship between interpersonal justice and organizational attractiveness,and overall favorability plays a partial mediating role in the relationship between informational justice and organizational attractiveness.This research takes the design features of AI interviewers as the starting point,and further deepens the understanding of the influencing factors and mechanism of applicants’ perceived interactional justice,overall favorability,organizational attractiveness in AI-based job interviews.And it enriches the research on the interactional justice in the field of AI-based job interviews.At the same time,the empirical results of this study can also provide guidance for recruitment organizations and AI-based job interviews system designers in terms of strategy and function optimization,so as to help them make more effective use of AI-based interviews to improve applicants’ interview experience and their own competitiveness,and thus promote the popularization and continuous development of AI-based job interviews.