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Does it pay to be honest? The effect of retailer-provided negative feedback on consumers’ product choice and shopping experience

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  • Merle, Aurélie
  • St-Onge, Anik
  • Sénécal, Sylvain

Abstract

This research aims at investigating the potential double-edged sword effect of a retailer’s negative feedback, which may not only lead consumers to alter their purchase decisions, but also provide a shopping experience that is more effortful and thus be of less utilitarian value. Three experiments were performed involving 678 participants. Overall, results suggest a double-edged sword effect of negative feedback in online and offline retail contexts. When compared to no feedback, neutral feedback, or positive feedback, negative feedback leads consumers to change their initial product choice whatever their choice uncertainty and whatever the feedback source (human advisor or algorithmic advisor). However, it also leads to the perception of more cognitive effort and reduced utilitarian value, resulting in lower purchase and word-of-mouth intentions. The only situation in which negative feedback does not degrade the utilitarian value of the shopping experience is when consumers are highly uncertain about their initial product choice.

Suggested Citation

  • Merle, Aurélie & St-Onge, Anik & Sénécal, Sylvain, 2022. "Does it pay to be honest? The effect of retailer-provided negative feedback on consumers’ product choice and shopping experience," Journal of Business Research, Elsevier, vol. 147(C), pages 532-543.
    • Handle: RePEc:eee:jbrese:v:147:y:2022:i:c:p:532-543
    DOI: 10.1016/j.jbusres.2022.03.031
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    as
    1. Mosteller, Jill & Donthu, Naveen & Eroglu, Sevgin, 2014. "The fluent online shopping experience," Journal of Business Research, Elsevier, vol. 67(11), pages 2486-2493.
    2. Einhorn, Hj & Hogarth, Rm, 1981. "Behavioral Decision-Theory - Processes Of Judgment And Choice," Journal of Accounting Research, Wiley Blackwell, vol. 19(1), pages 1-31.
    3. Park, Cheol & Lee, Thae Min, 2009. "Information direction, website reputation and eWOM effect: A moderating role of product type," Journal of Business Research, Elsevier, vol. 62(1), pages 61-67, January.
    4. Babin, Barry J & Darden, William R & Griffin, Mitch, 1994. "Work and/or Fun: Measuring Hedonic and Utilitarian Shopping Value," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 20(4), pages 644-656, March.
    5. Sniezek, Janet A. & Buckley, Timothy, 1995. "Cueing and Cognitive Conflict in Judge-Advisor Decision Making," Organizational Behavior and Human Decision Processes, Elsevier, vol. 62(2), pages 159-174, May.
    6. Yaniv, Ilan & Kleinberger, Eli, 2000. "Advice Taking in Decision Making: Egocentric Discounting and Reputation Formation," Organizational Behavior and Human Decision Processes, Elsevier, vol. 83(2), pages 260-281, November.
    7. Campbell, Margaret C & Kirmani, Amna, 2000. "Consumers' Use of Persuasion Knowledge: The Effects of Accessibility and Cognitive Capacity on Perceptions of an Influence Agent," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 27(1), pages 69-83, June.
    8. Anika Stuppy & Nicole L Mead & Stijn M J Van Osselaer & JoAndrea Hoegg & Eileen Fischer & Kirmani Amna, 2020. "I Am, Therefore I Buy: Low Self-Esteem and the Pursuit of Self-Verifying Consumption [The Origins of Deference: When Do People Prefer Lower Status?]," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 46(5), pages 956-973.
    9. Friestad, Marian & Wright, Peter, 1994. "The Persuasion Knowledge Model: How People Cope with Persuasion Attempts," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 21(1), pages 1-31, June.
    10. Michael Flacandji & Nina Krey, 2020. "Remembering shopping experiences: The Shopping Experience Memory Scale," Post-Print hal-03219821, HAL.
    11. Berkeley J. Dietvorst & Joseph P. Simmons & Cade Massey, 2018. "Overcoming Algorithm Aversion: People Will Use Imperfect Algorithms If They Can (Even Slightly) Modify Them," Management Science, INFORMS, vol. 64(3), pages 1155-1170, March.
    12. Flacandji, Michaël & Krey, Nina, 2020. "Remembering shopping experiences: The Shopping Experience Memory Scale," Journal of Business Research, Elsevier, vol. 107(C), pages 279-289.
    13. Chang, En-Chi & Tseng, Ya-Fen, 2013. "Research note: E-store image, perceived value and perceived risk," Journal of Business Research, Elsevier, vol. 66(7), pages 864-870.
    14. Andrew Prahl & Lyn Van Swol, 2017. "Understanding algorithm aversion: When is advice from automation discounted?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(6), pages 691-702, September.
    15. Urbany, Joel E & Dickson, Peter R & Wilkie, William L, 1989. "Buyer Uncertainty and Information Search," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 16(2), pages 208-215, September.
    16. Elaine Chan & Jaideep Sengupta, 2013. "Observing Flattery: A Social Comparison Perspective," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 40(4), pages 740-758.
    17. Overby, Jeffrey W. & Lee, Eun-Ju, 2006. "The effects of utilitarian and hedonic online shopping value on consumer preference and intentions," Journal of Business Research, Elsevier, vol. 59(10-11), pages 1160-1166, October.
    18. Heller, Jonas & Chylinski, Mathew & de Ruyter, Ko & Mahr, Dominik & Keeling, Debbie I., 2019. "Let Me Imagine That for You: Transforming the Retail Frontline Through Augmenting Customer Mental Imagery Ability," Journal of Retailing, Elsevier, vol. 95(2), pages 94-114.
    19. Stacey R. Finkelstein & Ayelet Fishbach, 2012. "Tell Me What I Did Wrong: Experts Seek and Respond to Negative Feedback," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 39(1), pages 22-38.
    20. Eric J. Johnson & John W. Payne, 1985. "Effort and Accuracy in Choice," Management Science, INFORMS, vol. 31(4), pages 395-414, April.
    21. Logg, Jennifer M. & Minson, Julia A. & Moore, Don A., 2019. "Algorithm appreciation: People prefer algorithmic to human judgment," Organizational Behavior and Human Decision Processes, Elsevier, vol. 151(C), pages 90-103.
    22. Johnson, Eric J. & Payne, John W. & Bettman, James R., 1988. "Information displays and preference reversals," Organizational Behavior and Human Decision Processes, Elsevier, vol. 42(1), pages 1-21, August.
    23. Mary Steffel & Elanor F Williams & Vicki MorwitzEditor & Andrea MoralesAssociate Editor, 2018. "Delegating Decisions: Recruiting Others to Make Choices We Might Regret," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 44(5), pages 1015-1032.
    24. Maxham, James III, 2001. "Service recovery's influence on consumer satisfaction, positive word-of-mouth, and purchase intentions," Journal of Business Research, Elsevier, vol. 54(1), pages 11-24, October.
    25. Young Eun Huh & Joachim Vosgerau & Carey K. Morewedge, 2014. "Social Defaults: Observed Choices Become Choice Defaults," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 41(3), pages 746-760.
    26. Basso, Kenny & dos Santos, Cristiane Pizzutti & Albornoz Gonçalves, Manuela, 2014. "The impact of flattery: The role of negative remarks," Journal of Retailing and Consumer Services, Elsevier, vol. 21(2), pages 185-191.
    27. Bonaccio, Silvia & Dalal, Reeshad S., 2006. "Advice taking and decision-making: An integrative literature review, and implications for the organizational sciences," Organizational Behavior and Human Decision Processes, Elsevier, vol. 101(2), pages 127-151, November.
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