A dimensional exploration and scale development study of algorithm aversion

Lin Wang, Xia Li, Huiyu Zhu and Yang Zhao

Journal of the Operational Research Society, 2025, vol. 76, issue 8, 1531-1552

Abstract: The increased use of AI in business has spurred an explosion in algorithm aversion research. The absence of scientific measurement instruments has caused the empirical research on the structural dimensions and measurement scales of algorithm aversion to stagnate, and the field is currently just in the exploratory stages of investigation. The results of experimental research and polls on algorithm aversion and appreciation may not be as broadly applicable as they may be because roughly two thirds of them used U.S. samples. Thus, extending from previous research, this work applies grounded theory to investigate the dimensionality of the structural dimensions of algorithm aversion using data from Chinese user interviews as well as the MicroBlog, Zhihu, and CSDN corpus. The scale was tested through the processes of questionnaire, exploratory factor analysis, and validation factor analysis to construct the scale of algorithmic aversion. The study finds five dimensions of algorithm aversion: Algorithm power gameplay, Algorithm user lock-in, Algorithm cognitive bias, Algorithm recommendation preference, and Recommendation algorithm adoption. The scale has a good level of validity and reliability and comprises 22 items. The findings of this study will support theoretical underpinnings for AI marketing and practical research on algorithm aversion in recommendation systems.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/01605682.2024.2419544 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tjorxx:v:76:y:2025:i:8:p:1531-1552

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tjor20

DOI: 10.1080/01605682.2024.2419544

Access Statistics for this article

Journal of the Operational Research Society is currently edited by Tom Archibald

More articles in Journal of the Operational Research Society from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().