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Anonymity, Social Image, and the Competition for Volunteers: A Case Study of the Online Market for Reviews

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  • Wang Zhongmin

    (Northeastern University)

Abstract

This paper takes a first step toward understanding the working of the online market for reviews. Most online review firms rely on unpaid volunteers to write reviews. Can a for-profit online review firm attract productive volunteer reviewers, limit the number of ranting or raving reviewers, and marginalize fake reviewers? This paper sheds light on this issue by studying reviewer productivity and restaurant ratings at Yelp, where reviewers are encouraged to establish a social image, and two competing websites, where reviewers are completely anonymous. Using a dataset of nearly half a million reviewer accounts, we find that the number (proportion) of prolific reviewers on Yelp is an order of magnitude larger than that on either competing site, more productive reviewers on all three websites are less likely to give an extreme rating, and restaurant ratings on Yelp tend to be much less extreme than those on either competing site.

Suggested Citation

  • Wang Zhongmin, 2010. "Anonymity, Social Image, and the Competition for Volunteers: A Case Study of the Online Market for Reviews," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 10(1), pages 1-35, May.
    • Handle: RePEc:bpj:bejeap:v:10:y:2010:i:1:n:44
    DOI: 10.2202/1935-1682.2523
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    References listed on IDEAS

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    Cited by:

    1. Thomas J. Miles, 2015. "Do Attorney Surveys Measure Judicial Performance or Respondent Ideology? Evidence from Online Evaluations," The Journal of Legal Studies, University of Chicago Press, vol. 44(S1), pages 231-267.
    2. Weijia (Daisy) Dai & Ginger Jin & Jungmin Lee & Michael Luca, 2018. "Aggregation of consumer ratings: an application to Yelp.com," Quantitative Marketing and Economics (QME), Springer, vol. 16(3), pages 289-339, September.
    3. Gordon Burtch & Anindya Ghose & Sunil Wattal, 2015. "The Hidden Cost of Accommodating Crowdfunder Privacy Preferences: A Randomized Field Experiment," Management Science, INFORMS, vol. 61(5), pages 949-962, May.
    4. Luís Cabral & Lingfang (Ivy) Li, 2015. "A Dollar for Your Thoughts: Feedback-Conditional Rebates on eBay," Management Science, INFORMS, vol. 61(9), pages 2052-2063, September.
    5. Ni Huang & Yili Hong & Gordon Burtch, 2015. "Digital Social Visibility, Anonymity and User Content Generation: Evidence from Natural Experiments," Working Papers 15-04, NET Institute.
    6. Uttara M. Ananthakrishnan & Beibei Li & Michael D. Smith, 2020. "A Tangled Web: Should Online Review Portals Display Fraudulent Reviews?," Information Systems Research, INFORMS, vol. 31(3), pages 950-971, September.
    7. Michael Luca, 2011. "Reviews, Reputation, and Revenue: The Case of Yelp.com," Harvard Business School Working Papers 12-016, Harvard Business School, revised Mar 2016.
    8. Zhihong Ke & De Liu & Daniel J. Brass, 2020. "Do Online Friends Bring Out the Best in Us? The Effect of Friend Contributions on Online Review Provision," Information Systems Research, INFORMS, vol. 31(4), pages 1322-1336, December.
    9. Hoon S. Choi & Michele Maasberg, 2022. "An empirical analysis of experienced reviewers in online communities: what, how, and why to review," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(3), pages 1293-1310, September.
    10. Anna Kerkhof & Johannes Münster, 2021. "Detecting coverage bias in user-generated content," ECONtribute Discussion Papers Series 057, University of Bonn and University of Cologne, Germany.
    11. Theodoros Lappas & Gaurav Sabnis & Georgios Valkanas, 2016. "The Impact of Fake Reviews on Online Visibility: A Vulnerability Assessment of the Hotel Industry," Information Systems Research, INFORMS, vol. 27(4), pages 940-961, December.
    12. Lafky, Jonathan, 2014. "Why do people rate? Theory and evidence on online ratings," Games and Economic Behavior, Elsevier, vol. 87(C), pages 554-570.
    13. Kenju Kamei & Louis Putterman, 2018. "Reputation Transmission Without Benefit To The Reporter: A Behavioral Underpinning Of Markets In Experimental Focus," Economic Inquiry, Western Economic Association International, vol. 56(1), pages 158-172, January.
    14. Lafky, Jonathan & Wilson, Alistair J., 2020. "Experimenting with incentives for information transmission: Quantity versus quality," Journal of Economic Behavior & Organization, Elsevier, vol. 169(C), pages 314-331.
    15. , 2015. "Quality Versus Quantity in Information Transmission: Theory and Experimental Evidence," Working Paper 539, Department of Economics, University of Pittsburgh, revised Jan 2015.
    16. Kerkhof, Anna, 2019. "Advertising and Content Differentiation: Evidence from YouTube," VfS Annual Conference 2019 (Leipzig): 30 Years after the Fall of the Berlin Wall - Democracy and Market Economy 204468, Verein für Socialpolitik / German Economic Association.
    17. Anna Kerkhof & Johannes Münster, 2021. "Detecting Coverage Bias in User-Generated Content," CESifo Working Paper Series 8844, CESifo.
    18. Anna Kerkhof, 2020. "Advertising and Content Differentiation: Evidence from YouTube," CESifo Working Paper Series 8697, CESifo.
    19. Alistair Wilson & Jonathan Lafky, 2015. "Quality Versus Quantity in Information Transmission: Theory and Experimental Evidence," Working Paper 540, Department of Economics, University of Pittsburgh, revised Jan 2015.
    20. Jonathan Lafky & Alistair J. Wilson, 2015. "Quality vs. Quantity in Information Transmission: Theory and Experimental Evidence," CESifo Working Paper Series 5426, CESifo.

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