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Recommending AI based on Quantified Self: Investigating the mechanism of consumer acceptance of AI recommendations

Author

Listed:
  • Aoxue Li

    (Hefei University of Technology)

  • Zhengping Ding

    (Hefei University of Technology
    Key Laboratory of Philosophy and Social Sciences for Cyberspace Behaviour and Management)

  • Chunhua Sun

    (Hefei University of Technology
    Key Laboratory of Philosophy and Social Sciences for Cyberspace Behaviour and Management)

  • Yezheng Liu

    (Hefei University of Technology
    Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education)

Abstract

Rapid advancement and widespread use of Quantified Self technology have contributed to the development of artificial intelligence (AI) recommendations. This study is aimed at exploring how users’ perception of the technology affordance of Quantified Self is associated with their intention to accept AI-recommended products. We distinguish four key technology affordances (information richness, personalization, visibility, and metavoicing) of the Quantified Self platform on affordance theory and explore their impact on consumer responses. A one-group design experiment with a pretest was conducted, and 360 participants were recruited to participate in the experiment and fill in the questionnaires. Finally, 344 valid questionnaires were obtained. The findings indicate the following: (1) information richness, personalization, visibility, and metavoicing positively impact purchase intention through trust and behavioral control. (2) Appearance concern positively moderated the relationships between metavoicing and trust and between metavoicing and behavioral control and negatively moderated the relationships between information richness and behavioral control and between personalization and behavioral control. Hence, this study has theoretical significance and practical implications for enriching the knowledge on how to introduce Quantified Self to improve the conversion rate of AI recommendations.

Suggested Citation

  • Aoxue Li & Zhengping Ding & Chunhua Sun & Yezheng Liu, 2024. "Recommending AI based on Quantified Self: Investigating the mechanism of consumer acceptance of AI recommendations," Electronic Markets, Springer;IIM University of St. Gallen, vol. 34(1), pages 1-15, December.
    • Handle: RePEc:spr:elmark:v:34:y:2024:i:1:d:10.1007_s12525-024-00739-7
    DOI: 10.1007/s12525-024-00739-7
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    More about this item

    Keywords

    Quantified Self; Technology affordance; Trust; Behavioral control; Purchase intention; Appearance concern;
    All these keywords.

    JEL classification:

    • M2 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Economics

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