Supplementary file 1_When algorithms evaluate people: how AI video interviews relate to employability confidence and organizational attractiveness.docx

AI video interviews are one such setting; they are largely one-directional, opaque, and often provide no feedback about how applicants are evaluated. This study examines how such AI-mediated evaluation relates to two socially consequential perceptions among university students: confidence in their own employability and trust-related evaluations of organizations that deploy AI-based assessment.

Drawing on Situated Expectancy-Value Theory (SEVT), we distinguish between expectancy beliefs—whether one can succeed—and value beliefs—whether the outcome is worth pursuing, and develop a dual-pathway model in which expectancy-related individual factors (AI literacy, metacognition, need for cognition, and technology anxiety) are associated with perceived employability, while value-related process perceptions (procedural justice and algorithmic transparency) are associated with organizational attractiveness, with social comparison orientation and AI usage experience as boundary conditions.