Paper Type
Short
Paper Number
1395
Description
AI interviews have emerged as a critical tool for businesses to perform talent screening. Interviewees frequently use impression management tactics to cope with interview stress. However, the influence of information transparency—a fundamental feature of AI system—on the selection of these tactics remains unclear. Based on transparency research and the Transactional Theory of Stress, we propose that different levels of transparency (through information type combinations) impact interviewees’ perceived safety, thereby affecting their choice of impression management tactics. Furthermore, this study explores the moderating effect of AI familiarity. An experimental approach is proposed to test the conceptual model. This study contributes to the literature by establishing a taxonomy for transparency and shedding light on the nuanced effects of transparency on interviewees’ perceptions and behaviors. The findings are expected to provide insights for refining AI interview systems and management practices, paving the way for future research.
Recommended Citation
Liu, YingLing; Wang, Yi; and Zhang, Meng, "Information Transparency and Impression Management in AI Interviews: The Mediating Role of Perceived Safety" (2024). PACIS 2024 Proceedings. 12.
https://aisel.aisnet.org/pacis2024/track13_hcinteract/track13_hcinteract/12
Information Transparency and Impression Management in AI Interviews: The Mediating Role of Perceived Safety
AI interviews have emerged as a critical tool for businesses to perform talent screening. Interviewees frequently use impression management tactics to cope with interview stress. However, the influence of information transparency—a fundamental feature of AI system—on the selection of these tactics remains unclear. Based on transparency research and the Transactional Theory of Stress, we propose that different levels of transparency (through information type combinations) impact interviewees’ perceived safety, thereby affecting their choice of impression management tactics. Furthermore, this study explores the moderating effect of AI familiarity. An experimental approach is proposed to test the conceptual model. This study contributes to the literature by establishing a taxonomy for transparency and shedding light on the nuanced effects of transparency on interviewees’ perceptions and behaviors. The findings are expected to provide insights for refining AI interview systems and management practices, paving the way for future research.
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