Impact of Incentive Mechanism in Online Referral Programs: Evidence from Randomized Field Experiments

Jaehwuen Jung, Ravi Bapna, Alok Gupta, Soumya Sen

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


Despite the growing popularity of online referral programs, a minimal amount is known regarding the theoretical foundations that drive the key actions associated with successful referrals. In this paper, we study which type of referral reward structure is most effective in maximizing word-of-mouth by conducting two randomized experiments in mobile gaming context. Specifically, we examine the effect of three incentive schemes: selfish reward (inviter gets all the reward), equal-split reward (50-50 split), and generous reward (invitee gets all the reward). Consistent across the two experiments, we find that pro-social referral incentive schemes, namely the equal-split and generous schemes, tend to dominate purely selfish schemes in creating WOM. Our mechanism-level analysis shows that both equal-split and generous schemes result in higher number of conversions by significantly increasing the invitee’s likelihood to accept referrals, which we further show that is partially due to selective and better targeted referrals. Our results contribute to the understanding of the optimal design of online referral programs and provide important implications for designing effective referral reward schemes in the digital world.

Original languageEnglish (US)
Pages (from-to)59-81
Number of pages23
JournalJournal of Management Information Systems
Issue number1
StatePublished - Apr 2 2021

Bibliographical note

Publisher Copyright:
© 2021 Taylor & Francis Group, LLC.


  • Social contagion
  • eWOM
  • mechanism design
  • online incentives
  • online referrals
  • referral incentives
  • viral marketing


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