IDEAS home Printed from https://ideas.repec.org/a/eee/jomega/v105y2021ics0305048321001055.html
   My bibliography  Save this article

Reach maximization for social lotteries

Author

Listed:
  • Fontecha, John E.
  • Walteros, Jose L.
  • Nikolaev, Alexander

Abstract

Social influence programs promoting behavior change have shown that people are more likely to adjust their behavior when simple information on the benefits of a targeted behavior is followed by “social proof” that referent others have already adopted this behavior. We argue that the impact of such programs, in particular those that promote a pro-environmental behavior, can be enhanced with direct incentives (e.g., monetary prizes) distributed in a random manner to adopters of the favored behavior according to their efforts. We formally model “social lottery” as a type of social influence program, and solve the problem of maximizing its expected impact. A social lottery rewards the promoted behavior and distributes the incentives fairly—based mainly on the individuals’ verifiable commitment to the program (e.g., their achievements in energy-saving) as opposed to their network positions. We define “reach” as a measure of exposure of individuals to Word-of-Mouth spread of information, and explain that the impact of a social influence program on a population (the conversion rate of non-adopters to adopters) can be viewed as a direct consequence of enhancing its reach. We then introduce reach maximization as a fundamental approach to maximizing the impact of social influence programs, where the influence spread is not driven by direct diffusion and where influence is delivered over multiple rounds. We show how our methodology can be applied to design each individual round of a social lottery. Further, we illustrate how our methodology can be used to design a social lottery program for fostering energy-conscious behavior, and perform computational experiments to showcase its effectiveness and scalability.

Suggested Citation

  • Fontecha, John E. & Walteros, Jose L. & Nikolaev, Alexander, 2021. "Reach maximization for social lotteries," Omega, Elsevier, vol. 105(C).
    • Handle: RePEc:eee:jomega:v:105:y:2021:i:c:s0305048321001055
    DOI: 10.1016/j.omega.2021.102496
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0305048321001055
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.omega.2021.102496?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Samadi, Mohammadreza & Nagi, Rakesh & Semenov, Alexander & Nikolaev, Alexander, 2018. "Seed activation scheduling for influence maximization in social networks," Omega, Elsevier, vol. 77(C), pages 96-114.
    2. Emma L Giles & Shannon Robalino & Elaine McColl & Falko F Sniehotta & Jean Adams, 2014. "The Effectiveness of Financial Incentives for Health Behaviour Change: Systematic Review and Meta-Analysis," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-16, March.
    3. Uri Gneezy & Stephan Meier & Pedro Rey-Biel, 2011. "When and Why Incentives (Don't) Work to Modify Behavior," Journal of Economic Perspectives, American Economic Association, vol. 25(4), pages 191-210, Fall.
    4. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    5. 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.
    6. Frederiks, Elisha R. & Stenner, Karen & Hobman, Elizabeth V., 2015. "Household energy use: Applying behavioural economics to understand consumer decision-making and behaviour," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 1385-1394.
    7. United Nations UN, 2015. "Transforming our World: the 2030 Agenda for Sustainable Development," Working Papers id:7559, eSocialSciences.
    8. Tommy Gärling & Maria Andersson & Martin Hedesström & Anders Biel, 2017. "An Experimental Study of Influences of Performance-Related Payments on Timing of Delegated Stock Purchases," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 18(1), pages 78-85, January.
    9. Brown, Jacqueline Johnson & Reingen, Peter H, 1987. "Social Ties and Word-of-Mouth Referral Behavior," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 14(3), pages 350-362, December.
    10. Sebastian Martin, 2017. "Word-of-mouth in the health care sector: a literature analysis of the current state of research and future perspectives," International Review on Public and Nonprofit Marketing, Springer;International Association of Public and Non-Profit Marketing, vol. 14(1), pages 35-56, March.
    11. Jinhee Kim & Soora Rasouli & Harry J. P. Timmermans, 2018. "Social networks, social influence and activity-travel behaviour: a review of models and empirical evidence," Transport Reviews, Taylor & Francis Journals, vol. 38(4), pages 499-523, July.
    12. Robin Cubitt & Chris Starmer & Robert Sugden, 1998. "On the Validity of the Random Lottery Incentive System," Experimental Economics, Springer;Economic Science Association, vol. 1(2), pages 115-131, September.
    13. George B. Dantzig & Philip Wolfe, 1960. "Decomposition Principle for Linear Programs," Operations Research, INFORMS, vol. 8(1), pages 101-111, February.
    14. Mundaca, Luis & Luth Richter, Jessika, 2015. "Assessing ‘green energy economy’ stimulus packages: Evidence from the U.S. programs targeting renewable energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 1174-1186.
    15. Boudet, Hilary S. & Flora, June A. & Armel, K. Carrie, 2016. "Clustering household energy-saving behaviours by behavioural attribute," Energy Policy, Elsevier, vol. 92(C), pages 444-454.
    16. Sinan Aral & Dylan Walker, 2011. "Creating Social Contagion Through Viral Product Design: A Randomized Trial of Peer Influence in Networks," Management Science, INFORMS, vol. 57(9), pages 1623-1639, February.
    17. Hunt Allcott & Todd Rogers, 2014. "The Short-Run and Long-Run Effects of Behavioral Interventions: Experimental Evidence from Energy Conservation," American Economic Review, American Economic Association, vol. 104(10), pages 3003-3037, October.
    18. Stephen P. Borgatti, 2006. "Identifying sets of key players in a social network," Computational and Mathematical Organization Theory, Springer, vol. 12(1), pages 21-34, April.
    19. Corey M. Angst & Ritu Agarwal & V. Sambamurthy & Ken Kelley, 2010. "Social Contagion and Information Technology Diffusion: The Adoption of Electronic Medical Records in U.S. Hospitals," Management Science, INFORMS, vol. 56(8), pages 1219-1241, August.
    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. Fontecha, John E. & Nikolaev, Alexander & Walteros, Jose L. & Zhu, Zhenduo, 2022. "Scientists wanted? A literature review on incentive programs that promote pro-environmental consumer behavior: Energy, waste, and water," Socio-Economic Planning Sciences, Elsevier, vol. 82(PA).

    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. 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.
    2. Ricci, Elena Claire & Banterle, Alessandro, 2020. "Do major climate change-related public events have an impact on consumer choices?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 126(C).
    3. Koster, Maurice & Lindner, Ines & Molina, Elisenda, 2010. "Networks and collective action," DES - Working Papers. Statistics and Econometrics. WS ws104830, Universidad Carlos III de Madrid. Departamento de Estadística.
    4. Florian Probst & Laura Grosswiele & Regina Pfleger, 2013. "Who will lead and who will follow: Identifying Influential Users in Online Social Networks," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 5(3), pages 179-193, June.
    5. Xiao Fang & Paul Jen-Hwa Hu & Zhepeng (Lionel) Li & Weiyu Tsai, 2013. "Predicting Adoption Probabilities in Social Networks," Information Systems Research, INFORMS, vol. 24(1), pages 128-145, March.
    6. Rishika Rishika & Jui Ramaprasad, 2019. "The Effects of Asymmetric Social Ties, Structural Embeddedness, and Tie Strength on Online Content Contribution Behavior," Management Science, INFORMS, vol. 65(7), pages 3398-3422, July.
    7. Landsman, Vardit & Nitzan, Irit, 2020. "Cross-decision social effects in product adoption and defection decisions," International Journal of Research in Marketing, Elsevier, vol. 37(2), pages 213-235.
    8. ITO Koichiro & IDA Takanori & TANAKA Makoto, 2015. "The Persistence of Moral Suasion and Economic Incentives: Field experimental evidence from energy demand," Discussion papers 15014, Research Institute of Economy, Trade and Industry (RIETI).
    9. Khim-Yong Goh & Cheng-Suang Heng & Zhijie Lin, 2013. "Social Media Brand Community and Consumer Behavior: Quantifying the Relative Impact of User- and Marketer-Generated Content," Information Systems Research, INFORMS, vol. 24(1), pages 88-107, March.
    10. Christoph Bühren & Maria Daskalakis, 2015. "Do not incentivize eco-friendly behavior - Go for a competition to go green!," MAGKS Papers on Economics 201534, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    11. Panzone, Luca A. & Ulph, Alistair & Zizzo, Daniel John & Hilton, Denis & Clear, Adrian, 2021. "The impact of environmental recall and carbon taxation on the carbon footprint of supermarket shopping," Journal of Environmental Economics and Management, Elsevier, vol. 109(C).
    12. Wouter Vermeer & Otto Koppius & Peter Vervest, 2018. "The Radiation-Transmission-Reception (RTR) model of propagation: Implications for the effectiveness of network interventions," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-21, December.
    13. Claus, Bart & Geyskens, Kelly & Millet, Kobe & Dewitte, Siegfried, 2012. "The referral backfire effect: The identity-threatening nature of referral failure," International Journal of Research in Marketing, Elsevier, vol. 29(4), pages 370-379.
    14. David Godes & Dina Mayzlin, 2004. "Using Online Conversations to Study Word-of-Mouth Communication," Marketing Science, INFORMS, vol. 23(4), pages 545-560, June.
    15. Timothy Gubler & Ian Larkin & Lamar Pierce, 2016. "Motivational Spillovers from Awards: Crowding Out in a Multitasking Environment," Organization Science, INFORMS, vol. 27(2), pages 286-303, April.
    16. Jeremy West & Robert W. Fairlie & Bryan Pratt & Liam Rose, 2021. "Automated Enforcement of Irrigation Regulations and Social Pressure for Water Conservation," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 8(6), pages 1179-1207.
    17. Fontecha, John E. & Nikolaev, Alexander & Walteros, Jose L. & Zhu, Zhenduo, 2022. "Scientists wanted? A literature review on incentive programs that promote pro-environmental consumer behavior: Energy, waste, and water," Socio-Economic Planning Sciences, Elsevier, vol. 82(PA).
    18. Laura Abrardi, 2019. "Behavioral barriers and the energy efficiency gap: a survey of the literature," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 46(1), pages 25-43, March.
    19. Zheyin (Jane) Gu & Yunchuan Liu, 2014. "Consumer Learning on Social Networks and Retailer Digital Platform Strategies Access," Working Papers 14-02, NET Institute.
    20. Qingliang Wang & Fred Miao & Giri Kumar Tayi & En Xie, 2019. "What makes online content viral? The contingent effects of hub users versus non–hub users on social media platforms," Journal of the Academy of Marketing Science, Springer, vol. 47(6), pages 1005-1026, November.

    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:eee:jomega:v:105:y:2021:i:c:s0305048321001055. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/375/description#description .

    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.