IDEAS home Printed from https://ideas.repec.org/a/inm/ormnsc/v70y2024i11p7961-7983.html
   My bibliography  Save this article

Participation vs. Effectiveness in Sponsored Tweet Campaigns: A Quality-Quantity Conundrum

Author

Listed:
  • Jing Peng

    (School of Business, University of Connecticut, Storrs, Connecticut 06269)

  • Christophe Van den Bulte

    (The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104)

Abstract

We investigate the participation and effectiveness of paid endorsers in sponsored tweet campaigns. We manipulate the financial pay rate offered to endorsers on a Chinese paid endorsement platform, where payouts are contingent on participation rather than engagement outcomes. Hence, our design can distinguish between variation in participation and variation in outcomes, even if people select to endorse only specific tweets. Also, the lack of compensation for effort allows one to attribute differences in outcomes to precontractual selection rather than postcontractual behavior. The main finding is that endorsers exhibited adverse selection. Several observed and unobserved endorser characteristics associated with a higher propensity to participate had a negative association with being an effective endorser given participation. This adverse selection results in a conundrum when trying to recruit a sizable number of high-quality endorsers. Only 9.5%–11.8% of the endorsers were above the median in both the propensity to participate and the propensity to be effective compared to a benchmark of 25% in the absence of any association. A simulation analysis of various targeting approaches that leverages our data of actual endorsements and outcomes shows that targeting candidate endorsers by scoring and ranking them using models taking into account adverse selection on observables improves campaign outcomes by 13%–55% compared to models ignoring adverse selection.

Suggested Citation

  • Jing Peng & Christophe Van den Bulte, 2024. "Participation vs. Effectiveness in Sponsored Tweet Campaigns: A Quality-Quantity Conundrum," Management Science, INFORMS, vol. 70(11), pages 7961-7983, November.
  • Handle: RePEc:inm:ormnsc:v:70:y:2024:i:11:p:7961-7983
    DOI: 10.1287/mnsc.2019.01897
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mnsc.2019.01897
    Download Restriction: no

    File URL: https://libkey.io/10.1287/mnsc.2019.01897?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
    ---><---

    References listed on IDEAS

    as
    1. Raghuram Iyengar & Christophe Van den Bulte & Thomas W. Valente, 2011. "Opinion Leadership and Social Contagion in New Product Diffusion," Marketing Science, INFORMS, vol. 30(2), pages 195-212, 03-04.
    2. Prasad A. Naik & Murali K. Mantrala & Alan G. Sawyer, 1998. "Planning Media Schedules in the Presence of Dynamic Advertising Quality," Marketing Science, INFORMS, vol. 17(3), pages 214-235.
    3. Camerer, Colin F & Hogarth, Robin M, 1999. "The Effects of Financial Incentives in Experiments: A Review and Capital-Labor-Production Framework," Journal of Risk and Uncertainty, Springer, vol. 19(1-3), pages 7-42, December.
    4. Xiao, Ping & Tang, Christopher S. & Wirtz, Jochen, 2011. "Optimizing referral reward programs under impression management considerations," European Journal of Operational Research, Elsevier, vol. 215(3), pages 730-739, December.
    5. Norris I. Bruce, 2008. "Pooling and Dynamic Forgetting Effects in Multitheme Advertising: Tracking the Advertising Sales Relationship with Particle Filters," Marketing Science, INFORMS, vol. 27(4), pages 659-673, 07-08.
    6. Sharad Goel & Ashton Anderson & Jake Hofman & Duncan J. Watts, 2016. "The Structural Virality of Online Diffusion," Management Science, INFORMS, vol. 62(1), pages 180-196, January.
    7. Shan Huang & Sinan Aral & Yu Jeffrey Hu & Erik Brynjolfsson, 2020. "Social Advertising Effectiveness Across Products: A Large-Scale Field Experiment," Marketing Science, INFORMS, vol. 39(6), pages 1142-1165, November.
    8. Laura J. Kornish & Qiuping Li, 2010. "Optimal Referral Bonuses with Asymmetric Information: Firm-Offered and Interpersonal Incentives," Marketing Science, INFORMS, vol. 29(1), pages 108-121, 01-02.
    9. Berman, Barry, 2016. "Referral marketing: Harnessing the power of your customers," Business Horizons, Elsevier, vol. 59(1), pages 19-28.
    10. Eleanor Singer & Cong Ye, 2013. "The Use and Effects of Incentives in Surveys," The ANNALS of the American Academy of Political and Social Science, , vol. 645(1), pages 112-141, January.
    11. Ernst Fehr & Klaus M. Schmidt, 1999. "A Theory of Fairness, Competition, and Cooperation," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 114(3), pages 817-868.
    12. Yacheng Sun & Xiaojing Dong & Shelby McIntyre, 2017. "Motivation of User-Generated Content: Social Connectedness Moderates the Effects of Monetary Rewards," Marketing Science, INFORMS, vol. 36(3), pages 329-337, May.
    13. Jean Tirole, 1988. "The Theory of Industrial Organization," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262200716, December.
    14. Dean Karlan & Jonathan Zinman, 2009. "Observing Unobservables: Identifying Information Asymmetries With a Consumer Credit Field Experiment," Econometrica, Econometric Society, vol. 77(6), pages 1993-2008, November.
    15. Powell, David & Goldman, Dana, 2021. "Disentangling moral hazard and adverse selection in private health insurance," Journal of Econometrics, Elsevier, vol. 222(1), pages 141-160.
    16. Patrick Puhani, 2000. "The Heckman Correction for Sample Selection and Its Critique," Journal of Economic Surveys, Wiley Blackwell, vol. 14(1), pages 53-68, February.
    17. Gary Chamberlain, 1980. "Analysis of Covariance with Qualitative Data," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 47(1), pages 225-238.
    18. 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.
    19. Frank M. Bass & Norris Bruce & Sumit Majumdar & B. P. S. Murthi, 2007. "Wearout Effects of Different Advertising Themes: A Dynamic Bayesian Model of the Advertising-Sales Relationship," Marketing Science, INFORMS, vol. 26(2), pages 179-195, 03-04.
    20. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    21. 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).
    22. William Greene, 2009. "Models for count data with endogenous participation," Empirical Economics, Springer, vol. 36(1), pages 133-173, February.
    23. Vijay Viswanathan & Sebastian Tillmanns & Manfred Krafft & Daniel Asselmann, 2018. "Understanding the quality–quantity conundrum of customer referral programs: effects of contribution margin, extraversion, and opinion leadership," Journal of the Academy of Marketing Science, Springer, vol. 46(6), pages 1108-1132, November.
    24. Jaap H. Abbring & James J. Heckman & Pierre-André Chiappori & Jean Pinquet, 2003. "Adverse Selection and Moral Hazard In Insurance: Can Dynamic Data Help to Distinguish?," Journal of the European Economic Association, MIT Press, vol. 1(2-3), pages 512-521, 04/05.
    25. Michael Braun & Wendy W. Moe, 2013. "Online Display Advertising: Modeling the Effects of Multiple Creatives and Individual Impression Histories," Marketing Science, INFORMS, vol. 32(5), pages 753-767, September.
    26. Heike M. Wolters & Christian Schulze & Karen Gedenk, 2020. "Referral Reward Size and New Customer Profitability," Marketing Science, INFORMS, vol. 39(6), pages 1166-1180, November.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. 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.
    3. Lili Wang & Zoey Chen, 2022. "The effect of incentive structure on referral: the determining role of self-construal," Journal of the Academy of Marketing Science, Springer, vol. 50(5), pages 1091-1110, September.
    4. Che-Wei Liu & Guodong (Gordon) Gao & Ritu Agarwal, 2019. "Unraveling the “Social” in Social Norms: The Conditioning Effect of User Connectivity," Information Systems Research, INFORMS, vol. 30(4), pages 1272-1295, April.
    5. Heike M. Wolters & Christian Schulze & Karen Gedenk, 2020. "Referral Reward Size and New Customer Profitability," Marketing Science, INFORMS, vol. 39(6), pages 1166-1180, November.
    6. Muller, Eitan & Peres, Renana, 2019. "The effect of social networks structure on innovation performance: A review and directions for research," International Journal of Research in Marketing, Elsevier, vol. 36(1), pages 3-19.
    7. Meyners, Jannik & Barrot, Christian & Becker, Jan U. & Bodapati, Anand V., 2017. "Reward-scrounging in customer referral programs," International Journal of Research in Marketing, Elsevier, vol. 34(2), pages 382-398.
    8. Massimiliano Bratti & Alfonso Miranda, 2010. "Endogenous Treatment Effects for Count Data Models with Sample Selection or Endogenous Participation," DoQSS Working Papers 10-05, Quantitative Social Science - UCL Social Research Institute, University College London, revised 10 Dec 2010.
    9. Gregory S. Crawford & Nicola Pavanini & Fabiano Schivardi, 2018. "Asymmetric Information and Imperfect Competition in Lending Markets," American Economic Review, American Economic Association, vol. 108(7), pages 1659-1701, July.
    10. Sarah Gelper & Ralf van der Lans & Gerrit van Bruggen, 2021. "Competition for Attention in Online Social Networks: Implications for Seeding Strategies," Management Science, INFORMS, vol. 67(2), pages 1026-1047, February.
    11. Massimiliano Bratti & Alfonso Miranda, 2011. "Endogenous treatment effects for count data models with endogenous participation or sample selection," Health Economics, John Wiley & Sons, Ltd., vol. 20(9), pages 1090-1109, September.
    12. Bedsworth, Fredrick & Neal, Daniel R. & Portillo, Javier E. & Willardsen, Kevin, 2021. "Asymmetric information and insurance: An experimental approach," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 92(C).
    13. Elisabeth Nindl, 2014. "An empirical assessment of Fairtrade: A perspective for low- and middle-income countries?," Department of Economics Working Papers wuwp160, Vienna University of Economics and Business, Department of Economics.
    14. Jing Peng, 2023. "Identification of Causal Mechanisms from Randomized Experiments: A Framework for Endogenous Mediation Analysis," Information Systems Research, INFORMS, vol. 34(1), pages 67-84, March.
    15. Longxiu Tian & Fred M. Feinberg, 2020. "Optimizing Price Menus for Duration Discounts: A Subscription Selectivity Field Experiment," Marketing Science, INFORMS, vol. 39(6), pages 1181-1198, November.
    16. Giampiero Marra & Rosalba Radice & Till Bärnighausen & Simon N. Wood & Mark E. McGovern, 2017. "A Simultaneous Equation Approach to Estimating HIV Prevalence With Nonignorable Missing Responses," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 484-496, April.
    17. Ryan Dew & Nicolas Padilla & Anya Shchetkina, 2024. "Your MMM is Broken: Identification of Nonlinear and Time-varying Effects in Marketing Mix Models," Papers 2408.07678, arXiv.org.
    18. Ike Silver & Deborah A. Small, 2024. "Put Your Mouth Where Your Money Is: A Field Experiment Encouraging Donors to Share About Charity," Marketing Science, INFORMS, vol. 43(2), pages 392-406, March.
    19. Xiang Hui & Meng Liu & Tat Chan, 2023. "Targeted incentives, broad impacts: Evidence from an E-commerce platform," Quantitative Marketing and Economics (QME), Springer, vol. 21(4), pages 493-517, December.
    20. Ceren Kolsarici & Demetrios Vakratsas, 2015. "Correcting for Misspecification in Parameter Dynamics to Improve Forecast Accuracy with Adaptively Estimated Models," Management Science, INFORMS, vol. 61(10), pages 2495-2513, October.

    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:ormnsc:v:70:y:2024:i:11:p:7961-7983. 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.