(A) bayesian learning model for measuring social influence

Abstract

As online sharing of music through social media emerges as a promising mode for diffusion of new music, firms are now interested in identifying individuals who have strong influence on other listeners. However, previous studies on disproportionate social influence have neglected the fact that in real-world social networks, individuals not only occupy certain social positions, but also have heterogeneous social relationships with others. Exploiting coexistence of asymmetric and symmetric relations in the microblogging service Twitter, this study simultaneously examines the effects of network structure such as indegree centrality and dyadic properties of interpersonal relationships, including communication frequency and network embeddedness, on the diffusion of new music. To the best of my knowledge, this is the first empirical research that proposes a new viral marketing strategy that reflects the concurrent impact of social position and of dyadic relation characteristics on disproportionate peer influence. I develop a model for an individual’s information sharing decision where each Twitter user updates her/his belief about the quality of musicians in a Bayesian learning fashion. Multiple information sources for learning are classified into eight different types of online peers. Utilizing Twitter users’ actual behavior data related to new musicians of the audition reality show, I provide an empirical demonstration of estimating each peer type’s social influence and practical managerial insights based on estimated results. Results of a what-if scenario analysis show that the strategy deploying the proposed model outperforms traditional word-of-mouth (WOM) marketeing strategies, including hub targeting and random seeding, in its return on investment (ROI).