Online referral systems help firms attract new customers and expand their customer base by leveraging the social relationships of existing customers. We integrate ultimatum game theory, which focuses on fairness, with motivation theories to investigate the effects of social distance and monetary incentives on the performance of three competing designs for online referral systems: rewarding only or primarily the proposer, rewarding only or primarily the responder, and dividing the reward equally or fairly between the proposer and responder. A set of controlled laboratory and randomized field experiments were conducted to test how the fairness of the split of the reward (equal/fair versus unequal/unfair split of the referral bonus) and social distance (small versus large) between the proposer and the responder jointly affect the performance of online referral systems (the proposer sending an offer and the responder accepting the offer). For a large social distance (acquaintances or weak tie relationships), equally splitting the referral bonus results in the best performance. However, for a small social distance (friends or strong tie relationships), an equal split of the referral reward does not improve referral performance, which suggests that under a small social distance, monetary incentives may not work effectively. Face validity and external validity (generalizability) are ensured using two distinct measures of social distance across several contexts. Through the analysis of the interaction effects of fairness and social distance, our research offers theoretical and practical implications for social commerce by showing that the effectiveness of fairness on the success of online social referrals largely depends on social distance.