The purpose of this paper is to investigate how the effects of referral rewards in referral reward programs (RRPs) are moderated through perceived social risk of a recommender.
A total of 717 consumers are accessed through Amazon's Mechanical Turk worker panel. The authors use t-test and analysis of variance to test the proposed hypotheses.
The findings show that consumers with high perceived social risk balance financial rewards with social risks, while low social risk consumers largely ignore these social risk elements surrounding a referral decision.
The inclusion of perceived social risk provides the opportunity to fully understand how a consumer goes about balancing social risk and referral rewards in making referral decisions. The concept of social risk has not been previously applied to this context.
