Network Structure and Observational Learning : Evidence from a Location-Based Social Network

In recent years, there has been stellar growth of location-based/enabled social networks in which people can “check-in” to physical venues they are visiting and share with friends. In this article, we hypothesize that the “check-ins” made by friends help users learn the potential payoff of visiting a venue. We argue that this learning-in-a-network process differs from the classic observational learning model in a subtle yet important way: Rather than from anonymous others, the agents learn from their network friends, about whose tastes in experience goods the agents are better informed. The empirical analyses are conducted on a unique dataset in which we observe both the explicit interpersonal relationships and their ensuing check-ins. The key result is that the proportion of checked-in friends is not positively associated with the likelihood of a new visit, rejecting the predication of the conventional observational learning model. It is thus a supporting evidence for our re-visiting observational learning in a social network setting. Drawing upon the literature in sociology and computer science, we show that weighting the friends’ check-ins by a parsimonious proximity measure can yield a more intuitive result. In dealing with the endogeneity problem, we apply the machine learning technique of nonnegative matrix factorization to uncover users’ latent features from the network graph