Study on the Behavioral Motives of Algorithmic Avoidance in Intelligent Recommendation Systems
Year: 2024
Type: article
Study design: Observational Study
Abstract: Through an exploration of the underlying mechanisms driving users' algorithmic avoidance in intelligent recommendation systems, this study aims to facilitate a positive interaction between users and technology, providing theoretical guidance for the efficient operations of enterprises using intelligent recommendation systems. The research integrates the theories of information ecology and psychological resistance, establishing a model of influencing factors on users' algorithmic avoidance in intelligent recommendation systems. Utilizing a structural equation model, the study conducts analysis and validation on data collected from 506 questionnaires. The findings reveal that algorithmic transparency and perceived manipulation significantly impact the users' algorithmic avoidance in intelligent recommendation systems. The sense of being manipulated emerges as a crucial psychological factor leading to algorithmic avoidance, playing a complete mediating role in the influence of information quality, homogeneous recommendation, and algorithmic transparency on algorithmic avoidance.
DOI: 10.4018/jgim.352857
Authors: Xiao Cheng, Guochao Peng
Institution: Sun Yat-sen University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4401805187