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Asynchronous Video Interviews (AVIs) incorporating Artificial Intelligence (AI)-assisted assessment has become popular as a pre-employment screening method. The extent to which applicants engage in deceptive impression management (IM) behaviors during these interviews remains uncertain. Furthermore, the accuracy of human detection in identifying such deceptive IM behaviors is limited. This study seeks to explore differences in deceptive IM behaviors by applicants across video interview modes (AVIs vs Synchronous Video Interviews (SVIs)) and the use of AI-assisted assessment (AI vs non-AI). The study also investigates if video interview modes affect human interviewers' ability to detect deceptive IM behaviors.

The authors conducted a field study with four conditions based on two critical factors: the synchrony of video interviews (AVI vs SVI) and the presence of AI-assisted assessment (AI vs Non-AI): Non-AI-assisted AVIs, AI-assisted AVIs, Non-AI-assisted SVIs and AI-assisted SVIs. The study involved 144 pairs of interviewees and interviewers/assessors. To assess applicants' deceptive IM behaviors, the authors employed a combination of interviewee self-reports and interviewer perceptions.

The results indicate that AVIs elicited fewer instances of deceptive IM behaviors across all dimensions when compared to SVIs. Furthermore, using AI-assisted assessment in both video interview modes resulted in less extensive image creation than non-AI settings. However, the study revealed that human interviewers had difficulties detecting deceptive IM behaviors regardless of the mode used, except for extensive faking in AVIs.

The study is the first to address the call for research on the impact of video interview modes and AI on interviewee faking and interviewer accuracy. This research enhances the authors’ understanding of the practical implications associated with the use of different video interview modes and AI algorithms in the pre-employment screening process. The study contributes to the existing literature by refining the theoretical model of faking likelihood in employment interviews according to media richness theory and the model of volitional rating behavior based on expectancy theory in the context of AVIs and AI-assisted assessment.

The purpose of this paper is to propose a model to understand how and when employees’ perceived privacy violations and procedural injustice interact to predict intent to leave in the context of the use of social networking sites (SNSs) monitoring.

This study was conducted in a field setting of Facebook to frame the hypotheses in a structural equation model with partial least squares-structural equation modeling. Variables were measured empirically by administering questionnaires to full-time employed Facebook users who had experienced SNS monitoring.

The results showed that when an employee believed that he/she had more ability to control his/her SNS information, he/she was less likely to perceive that his/her privacy had been invaded; and when an employee believed that the transparency of the SNS data collection process was higher, he or she was more likely to perceive procedural justice in SNS monitoring.

This research draws attention to the importance of intent to leave in the absence of perceived procedural justice under SNS monitoring, and the partial mediation of the perception of justice or injustice by perceived privacy violations.

For employers, the author recommends that employers come to know how to conduct SNS monitoring and data collection with limited risk of employee loss.

For employees, the author suggests that SNS users learn how to control their SNS information and make sure to check their privacy settings on the SNS that they use frequently.

This study provided an initial examination and bridged the gap between employer use of SNS monitoring and employee reactions by opening a mediating and moderating black box that has rarely been assessed.