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The downside of artificial intelligence (AI) in green choices: How AI recommender systems decrease green consumption

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  • Kai Wang
  • Lin Lu
  • Junyi Fang
  • Yiwei Xing
  • Zelin Tong
  • Lei Wang

Abstract

Extant research shows that marketers are increasingly utilizing artificial intelligence (AI) recommender systems to aid consumers in purchasing decisions across various products, but little research has examined the potential negative impact of AI recommenders on consumers' green consumption intentions compared with human recommender. To address this gap, three studies reveal that AI (vs. human) recommenders reduce consumers' motivation for impression management in green consumption contexts, resulting in decreased intention to purchase green products. This negative effect of AI recommenders on green consumption can be mitigated by priming consumer's environmental identity. This research contributes to the literature of green consumption and AI usage, while also providing practical implications for marketers to manage consumer's reduced green consumption intentions resulting from AI recommenders.

Suggested Citation

  • Kai Wang & Lin Lu & Junyi Fang & Yiwei Xing & Zelin Tong & Lei Wang, 2023. "The downside of artificial intelligence (AI) in green choices: How AI recommender systems decrease green consumption," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 44(6), pages 3346-3353, September.
    • Handle: RePEc:wly:mgtdec:v:44:y:2023:i:6:p:3346-3353
    DOI: 10.1002/mde.3882
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    References listed on IDEAS

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    1. Arun T. M. & Puneet Kaur & Stefano Bresciani & Amandeep Dhir, 2021. "What drives the adoption and consumption of green hotel products and services? A systematic literature review of past achievement and future promises," Business Strategy and the Environment, Wiley Blackwell, vol. 30(5), pages 2637-2655, July.
    2. Yong Zhang & Jiayu Ao & Jiayue Deng, 2019. "The Influence of High–Low Power on Green Consumption: The Moderating Effect of Impression Management Motivation," Sustainability, MDPI, vol. 11(16), pages 1-16, August.
    3. Diana GHEORGHE, 2022. "Development of Artificial Intelligence in the Automotive Field," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 26(2), pages 29-36.
    4. Shirley Bluvstein Netter & Priya Raghubir, 2021. "Tip to Show Off: Impression Management Motivations Increase Consumers’ Generosity," Journal of the Association for Consumer Research, University of Chicago Press, vol. 6(1), pages 120-129.
    5. Dixon, Darcie & Mikolon, Sven, 2021. "Cents of self: How and when self-signals influence consumer value derived from choices of green products," International Journal of Research in Marketing, Elsevier, vol. 38(2), pages 365-386.
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    Cited by:

    1. Li, Sinan & Huang, Xinmin & Sheng, Yunying & Chen, Kai, 2025. "The interactive effect of recommendation subjects and message types on consumers' suboptimal food purchase intentions," Journal of Retailing and Consumer Services, Elsevier, vol. 84(C).
    2. Rajeev K. Goel & Michael A. Nelson, 2024. "Do college anti‐plagiarism/cheating policies have teeth in the age of AI? Exploratory evidence from the Internet," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 45(4), pages 2336-2347, June.

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