随着信息技术的不断发展，个体间的交互更加便捷，同时各类网络平台的不断兴起，使得个体之间的决策变得越来越相互依赖。新产品扩散的研究自Bass模型起，主要是对市场整体扩散过程及结果进行描述和预测，但这些方法均存在一定的局限性，包括完全连接的网络结构、未考虑个体之间的异质性等。本研究从社会网络的视角来研究同伴影响在个体新产品采纳行为中的作用，并通过社会网络将个体行为与市场扩散过程相连接。对于此类研究中难以区分的同质性所带来的内生性问题，本研究通过PSM模型进行了有效的控制。同时，本文使用超过120万社交网络用户的真实数据集，包含完整的社会网络、个体属性及采纳行为等信息，通过数据分析、静态与动态PSM模型相结合的方法，证实了在新产品的扩散中同伴影响的作用。此外，研究发现关系强度对同伴影响具有正向的调节作用。本文对后续从社会网络视角进行产品扩散的研究提供了方法及依据。
With the continuous development of information technology, the communication between individuals becomes easier. Meanwhile, the emergence of various types of e-commerce platforms makes the decisionsof individuals become more and more interdependent. The researches of innovation diffusion (starting from the classical Bass Model)focus on the diffusion process from the perspective of market level. However, the existing studies have some limitations, such as the completely connected network which divorced from practice,and the overlook of the heterogeneity between individuals. Thus, more researchers focus the innovation diffusion from the perspective from individual level, and connect individuals' adoption behavior with markets' diffusion results. And, this connection should not lack the help of social networks. Luckily, benefited from the improvement of accessibility of social network data, the studies are able to realize.This study focuses on the influence of peers on the individual behavior of new product adoption, and connects the individual behavior and market diffusion process via social networks. A unique dataset contains more than 1.2 million users' information is taken, which come from the biggest online social network in China. It has several information including a complete social network, individual attributes and adoption behavior. Firstly, a shuffle test is used to identify the peer influence. But, the endogenous from homophilycan not be ignored. Then, the PSM model with both static and dynamic model is used to control the endogenous effectively. And the effect of peer influence is found to exist in adoption process. Furthermore, it accounts for 72% of the total factors. In addition, it is found that the network structure of individuals impacts the peer influence, and the relationship between the tie strength and peer influence has a positive moderating effect. A method and empirical evidence is provided for the future studies.
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