Accelerating Platform User Growth: Pinduoduo's Multi‐Motivational Selfish Referral Strategy

ABSTRACT Referral strategies, whereby existing platform users recruit new users and thereby strengthen same‐side network effects, have long been central to digital platforms' efforts to grow the installed user base. Current referral strategies often require the referee to complete a transaction as a prerequisite for the referrer to receive the reward, something which reduces the immediacy of positive reinforcement, thereby weakening the incentive of participation. Drawing on interview and platform‐related data from users on Pinduoduo, the fastest‐growing e‐commerce platform in China, we study a new form of selfish referral strategy that is unbounded by dyadic relations and time delays. We develop a theoretical model of a multi‐motivational selfish referral strategy that consists of three interrelated phases: the initiating phase, the continuing phase, and the aborting phase. Within each phase, we reveal the compositions and interrelationships of psychological, social, and technological motivational mechanisms. We contribute to research on platform user growth and referral strategies. We conclude by discussing the theoretical and practical implications of our model.