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Reach maximization for social lotteries

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  • 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.

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  • 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
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    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. George B. Dantzig & Philip Wolfe, 1960. "Decomposition Principle for Linear Programs," Operations Research, INFORMS, vol. 8(1), pages 101-111, February.
    7. 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.
    8. 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.
    9. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. United Nations UN, 2015. "Transforming our World: the 2030 Agenda for Sustainable Development," Working Papers id:7559, eSocialSciences.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
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    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).

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