Enhancing recommendation acceptance: Resolving the personalization–privacy paradox in recommender systems: A privacy calculus perspective

Wang, Yedi; Zhu, Jiaji; Liu, Renhuai; Jiang, Yushi

doi:10.1016/j.ijinfomgt.2024.102755

International Journal of Information Management

2024

DOI: 10.1016/j.ijinfomgt.2024.102755

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Enhancing recommendation acceptance: Resolving the personalization–privacy paradox in recommender systems: A privacy calculus perspective

Yedi Wang,

Jiaji Zhu,

Renhuai Liu

et al.

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Cited by 6 publications

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Enhancing recommendation acceptance: Resolving the personalization–privacy paradox in recommender systems: A privacy calculus perspective

Cited by 6 publications

References 113 publications

User-tailored privacy: unraveling the influences of psychological needs and message framing on app users’ privacy Disclosure intentions

User-tailored privacy: unraveling the influences of psychological needs and message framing on app users’ privacy Disclosure intentions

Exploring the influence of technology regulatory policy instruments on public acceptance of algorithm recommender systems

Influence of perceived risk on consumers’ intention and behavior in cross-border e-commerce transactions: A case study of the Tmall Global platform

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