How rejection letters from AI recruiters shape recruiter's prestige and experienced respect

Introduction Organizations increasingly rely on artificial intelligence (AI) in recruitment, yet little is known about how AI systems should communicate unfavorable outcomes such as rejection. The present research examined how type of rejection letters of an AI recruiter shape applicants' perceptions of the recruiter and their experienced respect. Methods Two experiments were conducted. In Study 1, the type of AI-generated rejection letter (considerate vs. typified) and applicants' employment status (unemployed vs. control) were experimentally manipulated. Study 2 examined the same rejection-letter manipulation while using participants' actual employment status. Across both studies, mechanistic dehumanization, perceived recruiter prestige, and experienced respect were assessed. Results In Study 1, the effects of type of AI rejection letter emerged only among participants positioned as unemployed, suggesting that unemployment status shaped sensitivity to AI-enabled recruitment. In Study 2, considerate versus typified rejection letters directly influenced mechanistic dehumanization. Mechanistic dehumanization, in turn, was negatively associated with perceived recruiter prestige and experienced respect. Unexpectedly, participants' actual employment status did not moderate the effects of rejection-letter type. Exploratory analyses, however, suggested that attitudes toward unemployment influenced the interaction between rejection-letter type and employment status in predicting the outcome variables. Discussion The findings demonstrate that how AI recruiters communicate rejection can shape applicants' perceptions of the recruiter and their experience of respect.