社会网络与扩散模型的整合及采用过程中枢纽价值的评价

J. Goldenberg, O. Lowengart, Daniel Shapira
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引用次数: 5

摘要

本文分析研究了社交中心对新产品渗透的影响。将个人层面的社会网络考虑聚合到市场层面,我们确定了枢纽对扩散影响的边界条件。我们的研究结果表明,播种中心对潜在未来销售的额外净现值(NPV)衡量的扩散具有差分加速效应。在封闭形式解决方案的基础上,我们发现消费者购买新产品的决定几乎完全是由口碑传播引起的,培育少数社交联系比普通人高10倍的中心,可能有助于启动一个有价值的扩散过程,在这个过程中,NPV增加了几十个百分点。另一方面,播种这种高度连接的枢纽只增加了不到1%的净现值。通过研究以社会关系的数量和强度为特征的社会影响类别,我们发现枢纽的“影响范围”对净现值的影响大于其联系强度。我们将重点放在集线器细分市场的采用演变上,发现该细分市场的产品生命周期比整个市场的生命周期短两到三倍。我们发现,枢纽与非枢纽度的比例对缩短生命周期长度的影响最为显著,其影响超过了其他影响(即个体邻居中枢纽的平均比例、外部影响强度或口碑传播)。我们使用来自在线社交网络的经验数据来检验所提出的分析框架。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Integrating the Social Network to Diffusion Model and Evaluation of the Value of Hubs in the Adoption Process
In this paper we analytically study the effect of social hubs on the penetration of new products. Aggregating individual-level social network considerations to the market level, we identify boundary conditions for hubs' effect on diffusion. Our results demonstrate that seeding hubs has a differential accelerating effect on diffusion measured by the additional net present value (NPV) of potential future sales. On the basis of closed-form solutions, we find that where consumers’ decisions to purchase a new product are almost entirely induced by word-of-mouth communications, seeding a small number of hubs whose social-connectedness is about 10 times greater than that of ordinary individuals, may help initiate a valuable diffusion process in which the NPV is increased by several tens of percentage points. On the other hand, seeding such highly connected hubs adds less than 1% to the NPV. Tapping into a category of social influence that is characterized by the number and intensity of social ties, we find that a hub’s “area of influence” has greater impact on NPV than its tie intensity. Focusing on the evolution of adoption in a segment of hubs, we show that the product life cycle in this segment is about two to three times shorter than the life cycle in the entire market. We find that the ratio of hub-to-non-hub degree has the most significant impact on reducing life cycle length, and its effect exceeds other effects (i.e., the average proportion of hubs among individuals' neighbors, the intensity of external influence, or word-of-mouth communications). We examine the proposed analytical framework using empirical data from an online social network.
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