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A tale of two recommender systems: The moderating role of consumer expertise on artificial intelligence based product recommendations

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  • Chinchanachokchai, Sydney
  • Thontirawong, Pipat
  • Chinchanachokchai, Punjaporn

Abstract

Recommender systems are used in e-Commerce websites to make product recommendations or deliver personalized content to users. We constructed a beer recommendation program using review data from existing online community to test the hypotheses. This research aims to bridge the gap between marketing and computer science by investigating the moderating effects of consumer knowledge (expertise) on the performance and evaluation of two widely-used recommendation systems – user-based collaborative filtering and content-based. The results show that expert consumers prefer user-based collaborative filtering systems, whereas there is no difference between the two systems among novice consumers. Theoretical and managerial implications are discussed.

Suggested Citation

  • Chinchanachokchai, Sydney & Thontirawong, Pipat & Chinchanachokchai, Punjaporn, 2021. "A tale of two recommender systems: The moderating role of consumer expertise on artificial intelligence based product recommendations," Journal of Retailing and Consumer Services, Elsevier, vol. 61(C).
    • Handle: RePEc:eee:joreco:v:61:y:2021:i:c:s0969698921000941
    DOI: 10.1016/j.jretconser.2021.102528
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    Cited by:

    1. Guo, Wenhao & Tian, Jin & Li, Minqiang, 2023. "Price-aware enhanced dynamic recommendation based on deep learning," Journal of Retailing and Consumer Services, Elsevier, vol. 75(C).
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    3. Molaie, Mir Majid & Lee, Wonjae, 2022. "Economic corollaries of personalized recommendations," Journal of Retailing and Consumer Services, Elsevier, vol. 68(C).
    4. Kim, Jungkeun & Kim, Jeong Hyun & Kim, Changju & Park, Jooyoung, 2023. "Decisions with ChatGPT: Reexamining choice overload in ChatGPT recommendations," Journal of Retailing and Consumer Services, Elsevier, vol. 75(C).
    5. Zhang, Junhui & Balaji, M.S. & Luo, Jun & Jha, Subhash, 2022. "Effectiveness of product recommendation framing on online retail platforms," Journal of Business Research, Elsevier, vol. 153(C), pages 185-197.
    6. Fan, Hua & Han, Bing & Gao, Wei, 2022. "(Im)Balanced customer-oriented behaviors and AI chatbots' Efficiency–Flexibility performance: The moderating role of customers’ rational choices," Journal of Retailing and Consumer Services, Elsevier, vol. 66(C).

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