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Cited by 1Digital Marketing and Social MediaExperimental Behavioral Economics StudiesConsumer Market Behavior and Pricing

Can Reward Uncertainty Encourage Social Referrals? Evidence from a Large-Scale Field Experiment

Li, Andy Tao, Belo, Rodrigo, Li, Ting·2026·Management Science
DOI 10.1287/mnsc.2024.05685
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Abstract

Social referral programs, in which individuals recommend products or services within their networks in return for rewards, have been widely adopted across digital platforms. This study explores the impact of incorporating uncertainty into the rewards of such programs, focusing on how senders and recipients perceive and react to uncertain rewards. We run a randomized experiment involving more than 160,000 users of a telecommunications operator in China and examine the effectiveness of different referral reward schemes. We find that referral programs are most successful when senders are incentivized with uncertain rewards and recipients are guaranteed certain rewards. Specifically, introducing uncertainty in the sender’s reward leads to a 20.9% increase in total referrals with recipients of these invitations more likely to engage in subsequent referrals and profitable in-app activities. In contrast, uncertainty in the recipient’s reward results in a 12.3% decrease in total referrals with invited recipients showing a lower propensity to make further referrals and reduced postreferral engagement. Additional online experiments identify distinct mechanisms driving these asymmetric effects: For senders, the uncertainty alleviates feelings of guilt, enhancing referral sharing and, thus, increasing the total number of referrals. For recipients, the adverse effects of uncertainty stem primarily from diminished perceptions of fairness and social pressure, and these deter engagement in the referral process. Our study sheds light on the complex dynamics of reward uncertainty in referral programs, offering novel insights into how it can be optimized to foster more engaged referral networks. This paper was accepted by Anindya Ghose, information systems. Funding: A.T. Li acknowledges support from the National Natural Science Foundation of China [Grants 72301269 and 72332007] and the Fundamental Research Funds for the Central Universities [Grant WK2040000085]. R. Belo acknowledges funding by Fundação para a Ciência e a Tecnologia (UID/00124/2025, UID/PRR/124/2025, Nova School of Business and Economics, DOI: https://doi.org/10.54499/UID/00124/2025 ) and LISBOA2030 (DataLab2030 - LISBOA2030-FEDER-01314200). T. Li acknowledges support from the Erasmus Research Institute of Management (ERIM) Convergence AI, Data & Digitalisation (Convergence AI Lab – Immersive Tech) Gravitation Program Public Values in the Algorithmic Society (AlgoSoc). Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.05685 .

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Crossref · 10.1287/mnsc.2024.05685

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Cites

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1 of the 51 references couldn’t be matched to a known paper.

Research works printed but not linked: 2

  1. Reynolds DB, 2009, J. Bus. Econom. Res., 7, 63
  2. Xu H, 2020, Amer. J. Indust. Bus. Management, 10, 1586

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Authors
Li, A. T., Belo, R., & Li, T.
Affiliations
  • University of Science and Technology of China (CN)
  • NOVA School of Business and Economics (PT)
  • Erasmus University Rotterdam (NL)
Corresponding author
Belo, Rodrigo
ORCID
Venue
Management Science
Publisher
Institute for Operations Research and the Management Sciences (INFORMS)
Published
2026-02-26
ISSN
  • 0025-1909 (ISSN-L)
  • 1526-5501
DOI
10.1287/mnsc.2024.05685
OpenAlex
W7131643171
Semantic Scholar
5103880181a70c90cad1e2bd6a22f4e0707fbdec
Cited by
1
References
51
Citations by year
2026: 1
FWCI
14.08
Type
journal article
Language
en
Open access
Locations
  • Management Science (publishedVersion)
  • SSRN Electronic Journal (acceptedVersion)
  • EUR Research Repository (Erasmus University Rotterdam) (submittedVersion, other-oa)
Topics
  • Social Sciences › Sociology and Political Science › Digital Marketing and Social Media
  • Social Sciences › Safety Research › Experimental Behavioral Economics Studies
  • Social Sciences › Business, Management and Accounting › Marketing › Consumer Market Behavior and Pricing
Keywords
reward uncertainty, referral rewards, guilt, perceived fairness, social pressure, user engagement, telecommunications companies, digital platforms

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