IDEAS home Printed from https://ideas.repec.org/a/inm/ormksc/v39y2020i1p5-32.html
   My bibliography  Save this article

Sponsorship Disclosure and Consumer Deception: Experimental Evidence from Native Advertising in Mobile Search

Author

Listed:
  • Navdeep S. Sahni

    (Stanford Graduate School of Business, Stanford University, Stanford, California 94305)

  • Harikesh S. Nair

    (Stanford Graduate School of Business, Stanford University, Stanford, California 94305
    JD.com, Mountain View, California 94043)

Abstract

Recent advances in advertising technology have lead to the development of “native advertising,” which is a format of advertising that mimics the other nonsponsored content on the medium. Whereas advertisers have rapidly embraced the format on a variety of digital media, regulators have expressed serious concerns about whether this format materially deceives consumers because the advertising disclosure is incomplete or inappropriate. This has reignited a longstanding debate about the distinction between advertising and content in media markets, and how it affects consumers. This paper contributes to this debate by providing empirical evidence from a randomized experiment conducted on native advertising at a mobile restaurant-search platform. We experimentally vary the format of paid-search advertising, the extent to which ads are disclosed to over 200,000 users, and track their anonymized browsing behavior including clicks and conversions. The research design we propose uses comparisons of revealed preferences under experimentally manipulated treatment and control conditions to assess the potential for consumer confusion and deception. A design based on revealed preference speaks to the “material” standard of regulators, helps assess confusion while avoiding directly questioning consumers, and may be useful in other settings. Implementing the design, we find that native advertising benefits advertisers and detect no evidence of deception under typically used formats of disclosure currently used in the paid-search marketplace. Further investigation shows that the incremental conversions due to advertising are not driven by users clicking on the native ads. Rather, the benefits from advertising are driven by users seeing the ads and later clicking on the advertiser’s “organic” listings. Thus, we find little support of native advertising tricking users into clicking and driving them to advertisers as typically feared; instead, users seem to view ads and deliberately evaluate the advertisers. Furthermore, mere exposure seems sufficient to produce most of the incremental effect of advertising.

Suggested Citation

  • Navdeep S. Sahni & Harikesh S. Nair, 2020. "Sponsorship Disclosure and Consumer Deception: Experimental Evidence from Native Advertising in Mobile Search," Marketing Science, INFORMS, vol. 39(1), pages 5-32, January.
  • Handle: RePEc:inm:ormksc:v:39:y:2020:i:1:p:5-32
    DOI: 10.1287/mksc.2018.1125
    as

    Download full text from publisher

    File URL: https://doi.org/10.1287/mksc.2018.1125
    Download Restriction: no

    File URL: https://libkey.io/10.1287/mksc.2018.1125?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Randall Lewis & David Reiley, 2014. "Online ads and offline sales: measuring the effect of retail advertising via a controlled experiment on Yahoo!," Quantitative Marketing and Economics (QME), Springer, vol. 12(3), pages 235-266, September.
    2. Edelman, Benjamin & Gilchrist, Duncan S., 2012. "Advertising disclosures: Measuring labeling alternatives in internet search engines," Information Economics and Policy, Elsevier, vol. 24(1), pages 75-89.
    3. Russo, J Edward & Metcalf, Barbara L & Stephens, Debra, 1981. "Identifying Misleading Advertising," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 8(2), pages 119-131, September.
    4. Navdeep S Sahni & Harikesh S Nair, 2020. "Does Advertising Serve as a Signal? Evidence from a Field Experiment in Mobile Search," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 87(3), pages 1529-1564.
    5. Navdeep S. Sahni, 2015. "Effect of temporal spacing between advertising exposures: Evidence from online field experiments," Quantitative Marketing and Economics (QME), Springer, vol. 13(3), pages 203-247, September.
    6. Burke, Raymond R, et al, 1988. "Deception by Implication: An Experimental Investigation," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 14(4), pages 483-494, March.
    7. Sridhar Narayanan & Kirthi Kalyanam, 2015. "Position Effects in Search Advertising and their Moderators: A Regression Discontinuity Approach," Marketing Science, INFORMS, vol. 34(3), pages 388-407, May.
    8. Navdeep Sahni, 2015. "Erratum to: Effect of temporal spacing between advertising exposures: Evidence from online field experiments," Quantitative Marketing and Economics (QME), Springer, vol. 13(3), pages 249-250, September.
    9. Beales, Howard & Craswell, Richard & Salop, Steven C, 1981. "The Efficient Regulation of Consumer Information," Journal of Law and Economics, University of Chicago Press, vol. 24(3), pages 491-539, December.
    10. Thomas Blake & Chris Nosko & Steven Tadelis, 2015. "Consumer Heterogeneity and Paid Search Effectiveness: A Large‐Scale Field Experiment," Econometrica, Econometric Society, vol. 83, pages 155-174, January.
    11. Navdeep S. Sahni, 2015. "Erratum to: Effect of temporal spacing between advertising exposures: Evidence from online field experiments," Quantitative Marketing and Economics (QME), Springer, vol. 13(3), pages 249-250, September.
    12. Avi Goldfarb & Catherine Tucker, 2011. "Search Engine Advertising: Channel Substitution When Pricing Ads to Context," Management Science, INFORMS, vol. 57(3), pages 458-470, March.
    13. Navdeep Sahni, 2015. "Effect of temporal spacing between advertising exposures: Evidence from online field experiments," Quantitative Marketing and Economics (QME), Springer, vol. 13(3), pages 203-247, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Cao, Zike & Belo, Rodrigo, 2023. "Effects of Explicit Sponsorship Disclosure on User Engagement in Social Media Influencer Marketing," SocArXiv b8tsg, Center for Open Science.
    2. Prabirendra Chatterjee & Bo Zhou, 2021. "Sponsored Content Advertising in a Two-Sided Market," Management Science, INFORMS, vol. 67(12), pages 7560-7574, December.
    3. Brett R. Gordon & Robert Moakler & Florian Zettelmeyer, 2023. "Predictive Incrementality by Experimentation (PIE) for Ad Measurement," Papers 2304.06828, arXiv.org.
    4. Zimand-Sheiner, Dorit & Ryan, Tanya & Kip, Sema Misci & Lahav, Tamar, 2020. "Native advertising credibility perceptions and ethical attitudes: An exploratory study among adolescents in the United States, Turkey and Israel," Journal of Business Research, Elsevier, vol. 116(C), pages 608-619.
    5. Yue Wu & Esther Gal-Or & Tansev Geylani, 2022. "Regulating Native Advertising," Management Science, INFORMS, vol. 68(11), pages 8045-8061, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Caio Waisman & Navdeep S. Sahni & Harikesh S. Nair & Xiliang Lin, 2019. "Parallel Experimentation and Competitive Interference on Online Advertising Platforms," Papers 1903.11198, arXiv.org, revised Feb 2024.
    2. Kirthi Kalyanam & John McAteer & Jonathan Marek & James Hodges & Lifeng Lin, 2018. "Cross channel effects of search engine advertising on brick & mortar retail sales: Meta analysis of large scale field experiments on Google.com," Quantitative Marketing and Economics (QME), Springer, vol. 16(1), pages 1-42, March.
    3. Weijia Dai & Hyunjin Kim & Michael Luca, 2023. "Frontiers: Which Firms Gain from Digital Advertising? Evidence from a Field Experiment," Marketing Science, INFORMS, vol. 42(3), pages 429-439, May.
    4. Brett R. Gordon & Florian Zettelmeyer & Neha Bhargava & Dan Chapsky, 2019. "A Comparison of Approaches to Advertising Measurement: Evidence from Big Field Experiments at Facebook," Marketing Science, INFORMS, vol. 38(2), pages 193-225, March.
    5. Stephan Seiler & Song Yao & Wenbo Wang, 2017. "Does Online Word of Mouth Increase Demand? (And How?) Evidence from a Natural Experiment," Marketing Science, INFORMS, vol. 36(6), pages 838-861, November.
    6. Garrett A. Johnson & Randall A. Lewis & David H. Reiley, 2017. "When Less Is More: Data and Power in Advertising Experiments," Marketing Science, INFORMS, vol. 36(1), pages 43-53, January.
    7. Navdeep S. Sahni & Dan Zou & Pradeep K. Chintagunta, 2017. "Do Targeted Discount Offers Serve as Advertising? Evidence from 70 Field Experiments," Management Science, INFORMS, vol. 63(8), pages 2688-2705, August.
    8. Brett R. Gordon & Robert Moakler & Florian Zettelmeyer, 2023. "Predictive Incrementality by Experimentation (PIE) for Ad Measurement," Papers 2304.06828, arXiv.org.
    9. Hana Choi & Carl F. Mela & Santiago R. Balseiro & Adam Leary, 2020. "Online Display Advertising Markets: A Literature Review and Future Directions," Information Systems Research, INFORMS, vol. 31(2), pages 556-575, June.
    10. Brett R Gordon & Kinshuk Jerath & Zsolt Katona & Sridhar Narayanan & Jiwoong Shin & Kenneth C Wilbur, 2019. "Inefficiencies in Digital Advertising Markets," Papers 1912.09012, arXiv.org, revised Feb 2020.
    11. Johannes Hermle & Giorgio Martini, 2022. "Valid and Unobtrusive Measurement of Returns to Advertising through Asymmetric Budget Split," Papers 2207.00206, arXiv.org.
    12. Anja Lambrecht & Catherine Tucker & Caroline Wiertz, 2018. "Advertising to Early Trend Propagators: Evidence from Twitter," Marketing Science, INFORMS, vol. 37(2), pages 177-199, March.
    13. Thomas W. Frick & Rodrigo Belo & Rahul Telang, 2023. "Incentive Misalignments in Programmatic Advertising: Evidence from a Randomized Field Experiment," Management Science, INFORMS, vol. 69(3), pages 1665-1686, March.
    14. Navdeep Sahni, 2015. "Effect of temporal spacing between advertising exposures: Evidence from online field experiments," Quantitative Marketing and Economics (QME), Springer, vol. 13(3), pages 203-247, September.
    15. Chadwick J. Miller & Daniel C. Brannon & Jim Salas & Martha Troncoza, 2021. "Advertising, incentives, and the upsell: how advertising differentially moderates customer- vs. retailer-directed price incentives’ impact on consumers’ preferences for premium products," Journal of the Academy of Marketing Science, Springer, vol. 49(6), pages 1043-1064, November.
    16. Omid Rafieian & Hema Yoganarasimhan, 2021. "Targeting and Privacy in Mobile Advertising," Marketing Science, INFORMS, vol. 40(2), pages 193-218, March.
    17. Wesley R. Hartmann & Daniel Klapper, 2018. "Super Bowl Ads," Marketing Science, INFORMS, vol. 37(1), pages 78-96, January.
    18. Chen He & Tobias J. Klein, 2023. "Advertising as a Reminder: Evidence from the Dutch State Lottery," Marketing Science, INFORMS, vol. 42(5), pages 892-909, September.
    19. Ruichang Lu & Qiaowei Shen & Tenghui Wang & Xiaojun Zhang, 2022. "Frenemies: Corporate Advertising Under Common Ownership," Management Science, INFORMS, vol. 68(6), pages 4645-4669, June.
    20. Omid Rafieian, 2023. "Optimizing User Engagement Through Adaptive Ad Sequencing," Marketing Science, INFORMS, vol. 42(5), pages 910-933, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:ormksc:v:39:y:2020:i:1:p:5-32. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.