eprintid: 50664 rev_number: 5 eprint_status: archive userid: 23303 importid: 106 dir: disk0/00/05/06/64 datestamp: 2025-03-17 08:59:44 lastmod: 2025-03-17 08:59:44 status_changed: 2025-03-17 08:59:44 type: article metadata_visibility: show creators_name: Dong, Menchang creators_name: Bonnefon, Jean-François creators_name: Rahwan, Iyad creators_idrefppn: 076374645 creators_idrefppn: 154839345 creators_affiliation: Institut Max-Planck de développement humain creators_affiliation: Toulouse School of Management creators_affiliation: Institut Max-Planck de développement humain creators_halaffid: 1002422; 520525; 441569 creators_halaffid: 353294 title: Toward Human-Centered AI Management: Methodological Challenges and Future Directions ispublished: pub subjects: subjects_GESTION abstract: As algorithms powered by Artificial Intelligence (AI) are increasingly involved in the management of organizations, it becomes imperative to conduct human-centered AI management research and understand people's feelings and behaviors when machines gain power over humans. The two mainstream methods – vignette studies and case studies – reveal important but inconsistent insights. Here we discuss the respective limitations of vignette studies (affective forecasting errors, biased media coverage, and question substitution) and case studies (social desirability biases and lack of random assignment and control conditions), which may lead them to overrate negative and positive reactions to AI management, respectively. We further discuss the advantages of a third method for mitigating these limitations: field experiments on crowdsourced marketplaces. A proof-of-concept study on Amazon Mechanical Turk (Mturk; as a world-leading crowdsourcing platform) showed unique human reactions to AI management, which were not perfectly aligned with those in vignette or case studies. Participants (N = 504) did not differ significantly under AI versus human management, in terms of performance, intrinsic motivation, fairness perception, and commitment. We suggest that crowdsourced marketplaces can go beyond human research subject pools and become models of AI-managed workplaces, facilitating timely behavioral research and robust predictions on human-centered work designs and organizations. date: 2024-03 date_type: published publisher: Elsevier id_number: 10.1016/j.technovation.2024.102953 faculty: gestion divisions: CRM keywords: Artificial intelligenceAlgorithmic managementAlgorithm aversionAlgorithm appreciationFuture of workWork designCrowdsourcing language: en has_fulltext: FALSE doi: 10.1016/j.technovation.2024.102953 view_date_year: 2024 full_text_status: none publication: Technovation volume: vol.131 place_of_pub: Amsterdam pagerange: 102953-102953 refereed: TRUE issn: 0166-4972 oai_identifier: oai:tsm.fr:2896 harvester_local_overwrite: pending harvester_local_overwrite: note harvester_local_overwrite: volume harvester_local_overwrite: issn harvester_local_overwrite: creators_idrefppn harvester_local_overwrite: creators_halaffid harvester_local_overwrite: publisher harvester_local_overwrite: place_of_pub oai_lastmod: 2025-03-14T10:56:41Z oai_set: tsm site: ut1 citation: Dong, Menchang, Bonnefon, Jean-François and Rahwan, Iyad (2024) Toward Human-Centered AI Management: Methodological Challenges and Future Directions. Technovation, vol.131. p. 102953.