Comparing job applicant deception in asynchronous vs synchronous video interviews, with and without AI-assisted assessments

Hung-Yue Suen, Kuo-En Hung
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Abstract

PurposeAsynchronous 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.Design/methodology/approachThe 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.FindingsThe 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.Originality/valueThe 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.
比较求职者在有人工智能辅助评估和没有人工智能辅助评估的异步与同步视频面试中的欺骗行为
目的 作为一种就业前筛选方法,结合人工智能(AI)辅助评估的异步视频面试(AVI)已变得十分流行。在这些面试中,求职者在多大程度上会做出欺骗性的印象管理(IM)行为仍不确定。此外,人工检测识别此类欺骗性印象管理行为的准确性也很有限。本研究旨在探讨不同视频面试模式(AVI 与同步视频面试 (SVI))和使用人工智能辅助评估(人工智能与非人工智能)的申请人在欺骗性 IM 行为方面的差异。本研究还调查了视频面试模式是否会影响人类面试官发现欺骗性即时通讯行为的能力。作者根据两个关键因素:视频面试的同步性(AVI vs SVI)和人工智能辅助评估的存在(人工智能 vs 非人工智能),进行了四种条件的实地研究:非人工智能辅助的 AVI、人工智能辅助的 AVI、非人工智能辅助的 SVI 和人工智能辅助的 SVI。研究涉及 144 对面试者和面试官/评估员。研究结果表明,与 SVI 相比,AVI 在所有维度上引起的欺骗性即时通讯行为较少。此外,在两种视频访谈模式中使用人工智能辅助评估,与非人工智能环境相比,图像创建的范围更小。然而,研究表明,无论使用哪种模式,人类面试官都难以发现欺骗性即时通讯行为,只有在 AVIs 中的大量伪造行为除外。原创性/价值这项研究首次回应了研究视频面试模式和人工智能对被面试者伪造行为和面试官准确性的影响的呼吁。这项研究加深了作者对在就业前筛选过程中使用不同视频面试模式和人工智能算法的实际影响的理解。本研究根据媒体丰富性理论完善了就业面试中造假可能性的理论模型,并在 AVI 和人工智能辅助评估的背景下完善了基于期望理论的自愿评级行为模型,从而为现有文献做出了贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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