朋友与陌生人网络中的社会学习

Jurui Zhang, Yong Liu, Yubo Chen
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引用次数: 84

摘要

网络和嵌入的关系是人们如何沟通和形成信念的关键决定因素。社交媒体的爆炸式增长大大增加了消费者社交学习的范围和影响。本文研究了朋友网络和陌生人网络中的观察学习。消费者在接收到关于产品质量的私人信号并观察他人的行为后决定是否采用某种产品。与朋友网络相比,陌生人网络对产品的偏好具有更大的异质性。我们表明,当网络较小时,观察朋友的行为有助于消费者对质量做出更准确的推断。然而,随着网络的发展,陌生人网络变得更加有效。这些结果的基础是网络异质性对社会学习的两个相互竞争的影响。这是个体偏好效应,当过去行为的偏好因素更确定时,人们可以做出更好的质量判断;社会从众效应,当人们跟随他人的行为时,私人信号在质量判断中没有得到充分利用。我们发现级联效应更可能发生在朋友网络中而不是陌生人网络中。对于一个高质量的公司来说,当网络规模足够大或私人信号足够准确时,陌生人网络比朋友网络产生更大的销售额。我们还研究了利用广告影响消费者的专家和公司的存在。最后,我们展示了高度同质或异构的网络如何阻碍观察学习。
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Social Learning in Networks of Friends versus Strangers
Networks and the embedded relationships are critical determinants of how people communicate and form beliefs. The explosion of social media has significantly increased the scope and impact of social learning among consumers. This paper studies observational learning in networks of friends versus strangers. A consumer decides whether to adopt a product after receiving a private signal about product quality and observing the actions of others. The preference for the product has greater heterogeneity in the stranger-network than in the friend-network. We show that when the network is small, observing friends' actions helps the consumer make more accurate inferences about quality. As the network grows, however, the stranger-network becomes more effective. Underlying these results are two competing effects of network heterogeneity on social learning. These are the individual preference effect, which allows one to make a better quality judgment when the preference element of past actions is more certain, and the social conforming effect, wherein private signals are underused in quality judgment as people follow others' actions. We find cascading is more likely to occur in the friend-network than in the stranger-network. For a high-quality firm, the stranger-network generates greater sales than the friend-network when the network size is sufficiently large or the private signal is sufficiently accurate. We also examine the existence of experts and firms using advertising to influence consumers. Finally, we show how networks that are highly homogeneous or heterogeneous could impede observational learning.
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