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Effects of Online Recommendations on Consumers’ Willingness to Pay

Author

Listed:
  • Gediminas Adomavicius

    (Information and Decision Sciences, Carlson School of Management, University of Minnesota, Minneapolis, Minnesota 55455)

  • Jesse C. Bockstedt

    (Information Systems and Operations Management, Goizueta Business School, Emory University, Atlanta, Georgia 30322)

  • Shawn P. Curley

    (Information and Decision Sciences, Carlson School of Management, University of Minnesota, Minneapolis, Minnesota 55455)

  • Jingjing Zhangc

    (Operations and Decision Technologies, Kelley School of Business, Indiana University, Bloomington, Indiana 47405)

Abstract

Recommender systems are an integral part of the online retail environment. Prior research has focused largely on computational approaches to improving recommendation accuracy, and only recently researchers have started to study their behavioral implications and potential side effects. We used three controlled experiments, in the context of purchasing digital songs, to explore the willingness-to-pay judgments of individual consumers after being shown personalized recommendations. In Study 1, we found strong evidence that randomly assigned song recommendations affected participants’ willingness to pay, even when controlling for participants’ preferences and demographics. In Study 2, participants viewed actual system-generated recommendations that were intentionally perturbed (introducing recommendation error), and we observed similar effects. In Study 3, we showed that the influence of personalized recommendations on willingness-to-pay judgments was obtained even when preference uncertainty was reduced through immediate and mandatory song sampling prior to pricing. The results demonstrate the existence of important economic side effects of personalized recommender systems and inform our understanding of how system recommendations can influence our everyday preference judgments. The findings have significant implications for the design and application of recommender systems as well as for online retail practices. The online appendix is available at https://doi.org/10.1287/isre.2017.0703 .

Suggested Citation

  • Gediminas Adomavicius & Jesse C. Bockstedt & Shawn P. Curley & Jingjing Zhangc, 2018. "Effects of Online Recommendations on Consumers’ Willingness to Pay," Information Systems Research, INFORMS, vol. 29(1), pages 84-102, March.
  • Handle: RePEc:inm:orisre:v:29:y:2018:i:1:p:84-102
    DOI: isre.2017.0703
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    3. Xuan Bi & Mochen Yang & Gediminas Adomavicius, 2024. "Consumer Acquisition for Recommender Systems: A Theoretical Framework and Empirical Evaluations," Information Systems Research, INFORMS, vol. 35(1), pages 339-362, March.
    4. Weiquan Wang & May Wang, 2019. "Effects of Sponsorship Disclosure on Perceived Integrity of Biased Recommendation Agents: Psychological Contract Violation and Knowledge-Based Trust Perspectives," Information Systems Research, INFORMS, vol. 30(2), pages 507-522, June.
    5. Molaie, Mir Majid & Lee, Wonjae, 2022. "Economic corollaries of personalized recommendations," Journal of Retailing and Consumer Services, Elsevier, vol. 68(C).
    6. Liangfei Qiu & Arunima Chhikara & Asoo Vakharia, 2021. "Multidimensional Observational Learning in Social Networks: Theory and Experimental Evidence," Information Systems Research, INFORMS, vol. 32(3), pages 876-894, September.
    7. Xuan Bi & Gediminas Adomavicius & William Li & Annie Qu, 2022. "Improving Sales Forecasting Accuracy: A Tensor Factorization Approach with Demand Awareness," INFORMS Journal on Computing, INFORMS, vol. 34(3), pages 1644-1660, May.
    8. Lawrence Bunnell & Kweku-Muata Osei-Bryson & Victoria Y. Yoon, 2020. "RecSys Issues Ontology: A Knowledge Classification of Issues for Recommender Systems Researchers," Information Systems Frontiers, Springer, vol. 22(6), pages 1377-1418, December.
    9. Ruiqi Rich Zhu & Cheng He & Yu Jeffrey Hu, 2023. "The Effect of Product Recommendations on Online Investor Behaviors," Papers 2303.14263, arXiv.org, revised Nov 2023.
    10. Jansen, Thomas & Moura, Francisco Tigre, 2024. "WOM, eWOM and WOMachine: The evolution of consumer recommendations through a systematic review of 194 studies," IU Discussion Papers - Marketing & Communication 3 (Juni 2024), IU International University of Applied Sciences.
    11. Geneviève Bassellier & Jui Ramaprasad, 2023. "All External Reference Prices Are Not the Same: How Magnitude, Source, and Fairness Shape Payment for Digital Goods," Information Systems Research, INFORMS, vol. 34(4), pages 1761-1774, December.
    12. Kevin Bauer & Andrej Gill, 2024. "Mirror, Mirror on the Wall: Algorithmic Assessments, Transparency, and Self-Fulfilling Prophecies," Information Systems Research, INFORMS, vol. 35(1), pages 226-248, March.
    13. 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).

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