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首页> 外文期刊>Journal of management information systems >Network Structure and Observational Learning: Evidence from a Location-Based Social Network
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Network Structure and Observational Learning: Evidence from a Location-Based Social Network

机译:网络结构和观察性学习:来自基于位置的社交网络的证据

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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.
机译:近年来,基于位置/启用社交网络的蓬勃发展,人们可以“签到”他们正在访问的物理场所并与朋友共享。在本文中,我们假设朋友进行的“签到”可帮助用户了解访问场地的潜在收益。我们认为,这种“网络学习”过程与传统的观察式学习模型有一个微妙而又重要的方式不同:代理商从匿名的网络朋友那里学到的东西比他们的网络朋友更好,而不是匿名的其他人,代理商从他们的体验商品中得到的味道更好知情的。实证分析是在一个独特的数据集上进行的,在该数据集中,我们观察到了明确的人际关系及其随之而来的签到。关键结果是,签入朋友的比例与新访问的可能性没有正相关,从而拒绝了常规观察学习模型的预测。利用社会学和计算机科学方面的文献,我们表明,通过简约的接近度度量对朋友的签入进行加权可以产生比普通比例更直观的结果。发现朋友反复入住会产生明显影响。我们的经验结果要求在社交网络环境中重新进行观察性学习。它还建议从业者在设计社交推荐产品和在社交网络中进行促销活动时应考虑网络邻近性。

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