Referral Intention vs. Continuous Referral Intention: Incentive Mechanism in Multi-tasking Social Referral Programs

Xu Li, Qiqi Jiang, Kanliang Wang

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


Social referral reward programs (SRRPs) aim to incentivize existing customers to recommend a product or service to others. Based on the reward threshold, we classify social referrals into two categories: single-tasking social referral (SSR) and multi-tasking social referral (MSR). Considering that MSR involves multiple responders, we explore how to design an effective reward mechanism in this new context. Our primary interests in outcomes include users’ willingness to recommend and their continuous referral intentions. Drawing from fairness theory and loss aversion theory, we propose three reward models based on the keeping percentage of rewards obtained, i.e., keep-it-all (KIA), discounted keep-it-all (DKIA), and all-or-nothing (AON). We designed an experiment to test the hypotheses regarding the effects of reward types on referral intention and continuous intention. This study will provide important implications for research and practice in designing an effective reward mechanism in MSR.
Original languageEnglish
Title of host publicationAMCIS 2023 Proceedings
EditorsPaul Pavlou, Vishal Midha, Animesh Animesh, Traci Carte, Alexandre Graeml, Alanah Mitchell
Number of pages5
Place of PublicationAtlanta, GA
PublisherAssociation for Information Systems. AIS Electronic Library (AISeL)
Publication date2023
Article number12
Publication statusPublished - 2023
Event29th Americas Conference on Information Systems. AMCIS 2023 - Panama City, Panama
Duration: 10 Aug 202312 Aug 2023
Conference number: 29


Conference29th Americas Conference on Information Systems. AMCIS 2023
CityPanama City
Internet address


  • Social referral reward programs
  • Perceived fairness
  • Multi-tasking social referral
  • Willingness to recommend
  • Continuous intention

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