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The good, the bad, and the ugly: impact of analytics and artificial intelligence-enabled personal information collection on privacy and participation in ridesharing

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  • Xusen Cheng
  • Linlin Su
  • Xin (Robert) Luo
  • Jose Benitez
  • Shun Cai

Abstract

Big data analytics (BDA) and artificial intelligence (AI) may provide both bright and dark sides that may affect user participation in ridesharing. We do not know whether the juxtaposed sides of these IT artefacts influence users’ cognitive appraisals, and if so, to what extent will their participative behaviour be affected. This paper contributes to the IS research by uncovering the interplay between the dark and bright sides of BDA and AI and the underlying mechanisms of cognitive appraisals for user behaviour in ridesharing. We performed two phases of the study using mixed-methods. In the first study, we conduct 21 semi-structured interviews to develop the research model. The second study empirically validated the research model using survey data of 332 passengers. We find that the usage of BDA and AI on ridesharing platforms have a bright side (usefulness, “the good”) but also a dark side (uncertainty and invasion of privacy, “the bad and the ugly”). The bright side generates perceived benefits, and the dark side shape perceived risks in users, which discount the risks from the benefits of using the ridesharing platform. Privacy control exerts a positive effect on the perceived benefits to encourage individuals to use the ridesharing platform.

Suggested Citation

  • Xusen Cheng & Linlin Su & Xin (Robert) Luo & Jose Benitez & Shun Cai, 2022. "The good, the bad, and the ugly: impact of analytics and artificial intelligence-enabled personal information collection on privacy and participation in ridesharing," European Journal of Information Systems, Taylor & Francis Journals, vol. 31(3), pages 339-363, May.
    • Handle: RePEc:taf:tjisxx:v:31:y:2022:i:3:p:339-363
    DOI: 10.1080/0960085X.2020.1869508
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    Cited by:

    1. Lin, Shunzhi & Lin, Jiabao, 2023. "How organizations leverage digital technology to develop customization and enhance customer relationship performance: An empirical investigation," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    2. Wu, Min & Wang, Nanxi & Yuen, Kum Fai, 2023. "Can autonomy level and anthropomorphic characteristics affect public acceptance and trust towards shared autonomous vehicles?," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    3. Wei, Xinyi & Chu, Xiaoyuan & Geng, Jingyu & Wang, Yuhui & Wang, Pengcheng & Wang, HongXia & Wang, Caiyu & Lei, Li, 2024. "Societal impacts of chatbot and mitigation strategies for negative impacts: A large-scale qualitative survey of ChatGPT users," Technology in Society, Elsevier, vol. 77(C).

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