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Free ad(vice): internet influencers and disclosure regulation

Author

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  • Matthew Mitchell
Abstract
Consumers rely on intermediaries (“influencers”) such as social media recommendations to provide information about products. The advice may be mixed with endorsement in a way that is unobservable to the follower, creating a trade‐off for influencers between the best advice and the most revenue. This article models the dynamic relationship between an influencer and a follower. The relationship evolves between periods of less and more revenue. The model can provide insight into policies such as the Federal Trade Commission's mandatory disclosure rules. An opt‐in policy may be superior: it deregulates influencers who are reaping the rewards of past good advice.

Suggested Citation

  • Matthew Mitchell, 2021. "Free ad(vice): internet influencers and disclosure regulation," RAND Journal of Economics, RAND Corporation, vol. 52(1), pages 3-21, March.
    • Handle: RePEc:bla:randje:v:52:y:2021:i:1:p:3-21
    DOI: 10.1111/1756-2171.12359
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    References listed on IDEAS

    as
    1. Burguet, Roberto & Caminal, Ramon & Ellman, Matthew, 2015. "In Google we trust?," International Journal of Industrial Organization, Elsevier, vol. 39(C), pages 44-55.
    2. Alexandre Cornière & Greg Taylor, 2014. "Integration and search engine bias," RAND Journal of Economics, RAND Corporation, vol. 45(3), pages 576-597, September.
    3. Kaya, Ayça, 2009. "Repeated signaling games," Games and Economic Behavior, Elsevier, vol. 66(2), pages 841-854, July.
    4. Hugo Hopenhayn & Gerard Llobet & Matthew Mitchell, 2006. "Rewarding Sequential Innovators: Prizes, Patents, and Buyouts," Journal of Political Economy, University of Chicago Press, vol. 114(6), pages 1041-1068, December.
    5. Qingmin Liu, 2011. "Information Acquisition and Reputation Dynamics," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 78(4), pages 1400-1425.
    6. Yingni Guo, 2016. "Dynamic Delegation of Experimentation," American Economic Review, American Economic Association, vol. 106(8), pages 1969-2008, August.
    7. Liu, Qingmin & Skrzypacz, Andrzej, 2014. "Limited records and reputation bubbles," Journal of Economic Theory, Elsevier, vol. 151(C), pages 2-29.
    8. Klein, Benjamin & Leffler, Keith B, 1981. "The Role of Market Forces in Assuring Contractual Performance," Journal of Political Economy, University of Chicago Press, vol. 89(4), pages 615-641, August.
    9. Inderst, Roman, 2015. "Regulating commissions in markets with advice," International Journal of Industrial Organization, Elsevier, vol. 43(C), pages 137-141.
    10. Sha Yang & Anindya Ghose, 2010. "Analyzing the Relationship Between Organic and Sponsored Search Advertising: Positive, Negative, or Zero Interdependence?," Marketing Science, INFORMS, vol. 29(4), pages 602-623, 07-08.
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    Cited by:

    1. Foerster, Manuel & Hellmann, Tim & Vega-Redondo, Fernando, 2024. "Strategic use of social media influencer marketing," UC3M Working papers. Economics 43985, Universidad Carlos III de Madrid. Departamento de Economía.
    2. Mohamed Mostagir & James Siderius, 2023. "Strategic Reviews," Management Science, INFORMS, vol. 69(2), pages 904-921, February.
    3. Andrea Gallice & Edoardo Grillo, 2022. "Legitimize through Endorsement," Carlo Alberto Notebooks 680 JEL Classification: C, Collegio Carlo Alberto.
    4. Bin Shen & Ming Cheng & Renlong He & Minglei Yang, 2024. "Selling through social media influencers in influencer marketing: participation-based contract versus sales-based contract," Electronic Commerce Research, Springer, vol. 24(2), pages 1095-1118, June.
    5. Isaac Owusu Asante & Yushi Jiang & Xiao Luo, 2023. "Does it matter how I stream? Comparative analysis of livestreaming marketing formats on Amazon Live," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-12, December.
    6. Ron Berman & Aniko Oery & Xudong Zheng, 2023. "Influence or Advertise: The Role of Social Learning in Influencer Marketing," Cowles Foundation Discussion Papers 2358, Cowles Foundation for Research in Economics, Yale University.
    7. Cao, Zike & Belo, Rodrigo, 2023. "Effects of Explicit Sponsorship Disclosure on User Engagement in Social Media Influencer Marketing," SocArXiv b8tsg, Center for Open Science.
    8. Daniel Ershov & Yanting, He & Stephan Seiler, 2023. "How Much Influencer Marketing Is Undisclosed? Evidence from Twitter," CESifo Working Paper Series 10743, CESifo.

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