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Study on the Behavioral Motives of Algorithmic Avoidance in Intelligent Recommendation Systems

Study on the Behavioral Motives of Algorithmic Avoidance in Intelligent Recommendation Systems

Xiao Cheng, Guochao Peng
Copyright: © 2024 |Volume: 32 |Issue: 1 |Pages: 22
ISSN: 1062-7375|EISSN: 1533-7995|EISBN13: 9798369324523|DOI: 10.4018/JGIM.352857
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MLA

Cheng, Xiao, and Guochao Peng. "Study on the Behavioral Motives of Algorithmic Avoidance in Intelligent Recommendation Systems." JGIM vol.32, no.1 2024: pp.1-22. http://doi.org/10.4018/JGIM.352857

APA

Cheng, X. & Peng, G. (2024). Study on the Behavioral Motives of Algorithmic Avoidance in Intelligent Recommendation Systems. Journal of Global Information Management (JGIM), 32(1), 1-22. http://doi.org/10.4018/JGIM.352857

Chicago

Cheng, Xiao, and Guochao Peng. "Study on the Behavioral Motives of Algorithmic Avoidance in Intelligent Recommendation Systems," Journal of Global Information Management (JGIM) 32, no.1: 1-22. http://doi.org/10.4018/JGIM.352857

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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.