Social influence tactics in ecommerce onboarding: The role of social proof and reciprocity in affecting user registrations Abstract   Given the increasing competition to attract new customers, ecommerce providers (i.e., etailers) are being urged to optimize their website onboarding journeys. Design mechanisms employing social influence tactics seem to be promising avenues to effectively improve user onboarding experiences. Specifically, two social influence tacticsxe2x80x94namely, reciprocity and social proofxe2x80x94are widely used in practice to reliably enhance user registrations. Yet, researchers have only tentatively examined how these combined tactics affect user onboarding behavior. While combining both tactics may hold synergistic potential, it may also be counterproductive, as too much social influence may backfire. We address this research gap by investigating the distinct and joint effects of reciprocity and social proof on user registration behavior. Drawing on two forms of reciprocity (i.e., utility and monetarybased reciprocity), we conduct an online experiment (Nxe2x80xafxe2x80xaf249) and a related randomized field experiment (Nxe2x80xafxe2x80xaf475,495) to compare both reciprocity variants in combination with social proof and investigate their efficacy for actual user onboarding decisions (i.e., user registrations). Our results show that both social influence tactics and both reciprocity variants have positive effects on usersu0027 registration behavior if applied individually. However, when both tactics are employed together, the effect of reciprocity is moderated by social proof such that social proof nullifies the effect of monetarybased reciprocity whereas it amplifies the effect of utilitybased reciprocity. Our study thereby contributes to a more advanced understanding of the interactive relationship between reciprocity and social proof and their joint effect on user onboarding decisions.

To Be or Not to Be: Analyzing & Modeling Social Recommendation in Online Social Networks Firms are now considering to offer rewards to customers who recommend the firmsu0027 productsservices in online social networks (OSN). However, the pros and cons of such social recommendation scheme are still unclear. Thus, it is difficult for firms to design rewarding schemes. Via empirical analysis of data, we first identify key factors that affect the spreading of a firmu0027s product in OSNs. These findings enable us to develop an accurate (i.e., with a high validation accuracy) mathematical model on social recommendations. In particular, our model captures how users decide whether to recommend an item, which is a key factor but often ignored by previous social recommendation models such as the Independent Cascade model. We also design algorithms to infer model parameters. Using these parameters in our model, we uncover conditions when social recommendation can (or cannot) improves a firmu0027s profit. These conditions help a firm to design rewarding schemes. Finally, we extend our model to an online setting and design reinforcement learning algorithms for a firm to dynamically optimize its rewarding schemes.