Brison, NoémieUCLouvain Psychological Sciences Research Institute, , Louvain-la-Neuve,
Author
Hamel, Jean-FélixUniversité de Tours QualiPsy (UR1901), , Tours,
Author
Demoulin, StéphanieUCLouvain Psychological Sciences Research Institute, , Louvain-la-Neuve,
Author
Abstract
Purpose: This research examines how algorithmic versus human resume screening shapes applicants' perceptions of organisational dehumanisation (OD) and its consequences (i.e., intentions to pursue employment, organisational attractiveness, and negative word-of-mouth). It explores one potential boundary condition of this relationship (i.e., negative attitudes toward artificial intelligence).
Design/methodology/approach: Three experimental studies were conducted on social media (Studies 1-2) or Prolific Academic (Study 3). Study 1 (N = 249) used a vignette experiment manipulating resume screening type (algorithmic vs. human). Studies 2a (N = 210) and 2b (N = 392) employed a 2×2 between-subjects design manipulating screening type and decision favourability (acceptance vs. rejection). These studies also controlled for justice perceptions and examined negative attitudes toward artificial intelligence as a moderator.
Findings: In Study 1, algorithmic resume screening increased OD, which was associated with lower intentions to pursue employment, reduced organisational attractiveness, and more negative word-of-mouth. Studies 2a and 2b replicated these effects regardless of decision favourability and while controlling for justice perceptions. Study 2a also showed stronger effects among applicants with negative attitudes toward artificial intelligence, although this interaction did not replicate in Study 2b.
Originality/value This research contributes to a better understanding of how algorithmic resume screening shapes applicants’ perceptions, notably OD.
Caesens, G., Brison, N., Hamel, J.-F., & Demoulin, S. (2026). The impact of algorithmic resume screening on applicants’ perceptions: the role of organisational dehumanisation. International Journal of Organizational Analysis, ahead of print, 1-18. https://doi.org/10.1108/IJOA-11-2025-6220 (Original work published 2026)