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

• 成果类型: Article
• 署名作者: Shi, Zhan; Whinston, Andrew B.
• 署名单位: Arizona State University; Arizona State University-Tempe; University of Texas System; University of Texas Austin
• 刊物名称: JOURNAL OF MANAGEMENT INFORMATION SYSTEMS
• ISSN/ISSBN: 0742-1222
• DOI: 10.2753/MIS0742-1222300207
• 发表日期: 2013
• 页码: 185-212
• 关键词: recommender systems; simple-model; contagion; diffusion; identification; INFORMATION; INNOVATION; cascades; BEHAVIOR

摘要

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 paper, 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 data set 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 prediction of the conventional observational learning model. Drawing on 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 than the plain proportion does. Repeated check-ins by friends are found to have a pronounced effect. Our empirical result calls for the revisiting of observational learning in a social network setting. It also suggests that practitioners should incorporate network proximity when designing social recommendation products and conducting promotional campaigns in a social network.