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Sending mixed signals: How congruent versus incongruent signals of popularity affect product appeal

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  • Moldovan, Sarit
  • Shoham, Meyrav
  • Steinhart, Yael

Abstract

A high volume of sales or online reviews can make a product seem more popular and established and consequently enhance its appeal. But is it advisable to display both metrics? We focus on the interplay between volume of sales and number of reviews and explore what happens when these signals are perceived as congruent versus incongruent. Five experimental studies and an analysis of field data demonstrate that consumers find products with congruent (vs. incongruent) ratios of reviews to sales more appealing. We distinguish between two types of incongruities: when the volume of sales clearly exceeds that of the reviews (over-purchased products) versus many reviews compared to sales (over-reviewed products). We argue that both reduce consumer confidence in the product’s merit, but that the latter has a more pronounced impact. However, the effects are attenuated when contextual cues explain the incongruities.

Suggested Citation

  • Moldovan, Sarit & Shoham, Meyrav & Steinhart, Yael, 2023. "Sending mixed signals: How congruent versus incongruent signals of popularity affect product appeal," International Journal of Research in Marketing, Elsevier, vol. 40(4), pages 881-897.
  • Handle: RePEc:eee:ijrema:v:40:y:2023:i:4:p:881-897
    DOI: 10.1016/j.ijresmar.2023.08.008
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    as
    1. Daniel He & Shiri Melumad & Michel Tuan Pham & Vicki G MorwitzEditor & Amna KirmaniEditor & Chris JaniszewskiAssociate Editor, 2019. "The Pleasure of Assessing and Expressing Our Likes and Dislikes," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 46(3), pages 545-563.
    2. Hélène Deval & Susan P. Mantel & Frank R. Kardes & Steven S. Posavac, 2013. "How Naive Theories Drive Opposing Inferences from the Same Information," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 39(6), pages 1185-1201.
    3. Hinz, Oliver & Skiera, Bernd & Barrot, Christian & Becker, Jan, 2011. "Seeding Strategies for Viral Marketing: An Empirical Comparison," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 56543, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    4. Ramon Caminal & Xavier Vives, 1996. "Why Market Shares Matter: An Information-Based Theory," RAND Journal of Economics, The RAND Corporation, vol. 27(2), pages 221-239, Summer.
    5. Bennett, Peter D & Harrell, Gilbert D, 1975. "The Role of Confidence in Understanding and Predicting Buyers' Attitudes and Purchase Intentions," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 2(2), pages 110-117, Se.
    6. Sarah Clemente & Eric Dolansky & Antonia Mantonakis & Katherine White, 2014. "The effects of perceived product-extrinsic cue incongruity on consumption experiences: The case of celebrity sponsorship," Marketing Letters, Springer, vol. 25(4), pages 373-384, December.
    7. Yubo Chen & Jinhong Xie, 2008. "Online Consumer Review: Word-of-Mouth as a New Element of Marketing Communication Mix," Management Science, INFORMS, vol. 54(3), pages 477-491, March.
    8. Tingting Fan & Leilei Gao & Yael Steinhart & Darren W Dahl & J Jeffrey Inman & L J Shrum, 2020. "The Small Predicts Large Effect in Crowdfunding," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 47(4), pages 544-565.
    9. Alan T. Sorensen, 2017. "Bestseller Lists and the Economics of Product Discovery," Annual Review of Economics, Annual Reviews, vol. 9(1), pages 87-101, September.
    10. Stephen X. He & Samuel D. Bond, 2015. "Why Is the Crowd Divided? Attribution for Dispersion in Online Word of Mouth," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 41(6), pages 1509-1527.
    11. Herr, Paul M & Kardes, Frank R & Kim, John, 1991. "Effects of Word-of-Mouth and Product-Attribute Information on Persuasion: An Accessibility-Diagnosticity Perspective," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 17(4), pages 454-462, March.
    12. Bikhchandani, Sushil & Hirshleifer, David & Welch, Ivo, 1992. "A Theory of Fads, Fashion, Custom, and Cultural Change in Informational Cascades," Journal of Political Economy, University of Chicago Press, vol. 100(5), pages 992-1026, October.
    13. Topaloglu, Omer & Gokalp, Omer N., 2018. "How brand concept affects consumer response to product recalls: A longitudinal study in the U.S. auto industry," Journal of Business Research, Elsevier, vol. 88(C), pages 245-254.
    14. Richard F. J. Haans & Constant Pieters & Zi-Lin He, 2016. "Thinking about U: Theorizing and testing U- and inverted U-shaped relationships in strategy research," Strategic Management Journal, Wiley Blackwell, vol. 37(7), pages 1177-1195, July.
    15. Young-Jin Lee & Kartik Hosanagar & Yong Tan, 2015. "Do I Follow My Friends or the Crowd? Information Cascades in Online Movie Ratings," Management Science, INFORMS, vol. 61(9), pages 2241-2258, September.
    16. Jiewen Hong & Angela Y. Lee, 2008. "Be Fit and Be Strong: Mastering Self-Regulation through Regulatory Fit," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 34(5), pages 682-695, August.
    17. Ina Garnefeld & Tabea Krah & Eva Böhm & Dwayne D. Gremler, 2021. "Online reviews generated through product testing: can more favorable reviews be enticed with free products?," Journal of the Academy of Marketing Science, Springer, vol. 49(4), pages 703-722, July.
    18. Zhuang, Mengzhou & Cui, Geng & Peng, Ling, 2018. "Manufactured opinions: The effect of manipulating online product reviews," Journal of Business Research, Elsevier, vol. 87(C), pages 24-35.
    19. Abhijit V. Banerjee, 1992. "A Simple Model of Herd Behavior," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 107(3), pages 797-817.
    20. Reimer, Thomas & Benkenstein, Martin, 2016. "When good WOM hurts and bad WOM gains: The effect of untrustworthy online reviews," Journal of Business Research, Elsevier, vol. 69(12), pages 5993-6001.
    21. Langan, Ryan & Besharat, Ali & Varki, Sajeev, 2017. "The effect of review valence and variance on product evaluations: An examination of intrinsic and extrinsic cues," International Journal of Research in Marketing, Elsevier, vol. 34(2), pages 414-429.
    22. Froot, Kenneth A & Scharftstein, David S & Stein, Jeremy C, 1992. "Herd on the Street: Informational Inefficiencies in a Market with Short-Term Speculation," Journal of Finance, American Finance Association, vol. 47(4), pages 1461-1484, September.
    23. Yi Zhao & Sha Yang & Vishal Narayan & Ying Zhao, 2013. "Modeling Consumer Learning from Online Product Reviews," Marketing Science, INFORMS, vol. 32(1), pages 153-169, May.
    24. Ana Babić Rosario & Kristine Valck & Francesca Sotgiu, 2020. "Conceptualizing the electronic word-of-mouth process: What we know and need to know about eWOM creation, exposure, and evaluation," Journal of the Academy of Marketing Science, Springer, vol. 48(3), pages 422-448, May.
    25. repec:oup:jconrs:v:47:y:2021:i:5:p:654-674. is not listed on IDEAS
    26. Jiménez, Fernando R. & Mendoza, Norma A., 2013. "Too Popular to Ignore: The Influence of Online Reviews on Purchase Intentions of Search and Experience Products," Journal of Interactive Marketing, Elsevier, vol. 27(3), pages 226-235.
    27. Gordon Burtch & Yili Hong & Ravi Bapna & Vladas Griskevicius, 2018. "Stimulating Online Reviews by Combining Financial Incentives and Social Norms," Management Science, INFORMS, vol. 64(5), pages 2065-2082, May.
    28. David Godes & Dina Mayzlin, 2004. "Using Online Conversations to Study Word-of-Mouth Communication," Marketing Science, INFORMS, vol. 23(4), pages 545-560, June.
    29. Hahn, Jinyong & Todd, Petra & Van der Klaauw, Wilbert, 2001. "Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design," Econometrica, Econometric Society, vol. 69(1), pages 201-209, January.
    30. Georgios Zervas & Davide Proserpio & John W. Byers, 2021. "A first look at online reputation on Airbnb, where every stay is above average," Marketing Letters, Springer, vol. 32(1), pages 1-16, March.
    31. Fama, Eugene F & MacBeth, James D, 1973. "Risk, Return, and Equilibrium: Empirical Tests," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 607-636, May-June.
    32. Moldovan, Sarit & Muller, Eitan & Richter, Yossi & Yom-Tov, Elad, 2017. "Opinion leadership in small groups," International Journal of Research in Marketing, Elsevier, vol. 34(2), pages 536-552.
    33. Khare, Adwait & Labrecque, Lauren I. & Asare, Anthony K., 2011. "The Assimilative and Contrastive Effects of Word-of-Mouth Volume: An Experimental Examination of Online Consumer Ratings," Journal of Retailing, Elsevier, vol. 87(1), pages 111-126.
    34. Moon, Sangkil & Kim, Moon-Yong & Iacobucci, Dawn, 2021. "Content analysis of fake consumer reviews by survey-based text categorization," International Journal of Research in Marketing, Elsevier, vol. 38(2), pages 343-364.
    35. Michael Luca & Georgios Zervas, 2016. "Fake It Till You Make It: Reputation, Competition, and Yelp Review Fraud," Management Science, INFORMS, vol. 62(12), pages 3412-3427, December.
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