A Weighted Statistical Network Modeling Approach to Product Competition Analysis
A Weighted Statistical Network Modeling Approach to Product Competition Analysis
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DOI：
10.1155/2022/9417869
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发表时间：
2022-01
期刊：
影响因子：
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通讯作者：
Yaxin Cui;Faez Ahmed;Zhenghui Sha;Lijun Wang;Yan Fu;N. Contractor;Wei Chen
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文献类型：
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作者：
Yaxin Cui;Faez Ahmed;Zhenghui Sha;Lijun Wang;Yan Fu;N. Contractor;Wei Chen
统计网络模型已被用于研究不同产品之间的竞争以及产品属性如何影响客户决策。然而，在使用基于网络的方法的现有研究中，产品竞争被视为二元的（即，是否存在关系），而在现实中，产品之间的竞争强度可能会有所不同。在本文中，我们采用一个统计网络模型的产品竞争力建模，重点是产品属性如何影响哪些产品被认为是在一起，哪些产品最终被客户购买。我们首先展示了如何客户的考虑和选择可以聚合为加权网络。然后，我们提出了一个加权网络建模方法，通过扩展的值指数随机图模型，研究产品的功能和网络结构对产品竞争关系的影响。该方法包括模型的构建，解释和验证，在一个逐步的过程。我们的研究结果表明，加权网络模型在预测产品竞争和市场份额方面优于常用的二进制网络基线。此外，传统上，当使用二进制网络模型来研究产品竞争时，根据选择的二进制网络的截止值，所得到的估计客户偏好可能是不一致的。解释客户偏好时的这种不一致性是二元网络模型的缺点，但可以通过提出的加权网络模型很好地解决。最后，本文首次尝试研究顾客的购买偏好（即，聚集的选择决策）和汽车竞争（即，客户的共同考虑决定）一起使用加权有向网络。
Statistical network models have been used to study the competition among different products and how product attributes influence customer decisions. However, in existing research using network-based approaches, product competition has been viewed as binary (i.e., whether a relationship exists or not), while in reality, the competition strength may vary among products. In this paper, we model the strength of the product competition by employing a statistical network model, with an emphasis on how product attributes affect which products are considered together and which products are ultimately purchased by customers. We first demonstrate how customers’ considerations and choices can be aggregated as weighted networks. Then, we propose a weighted network modeling approach by extending the valued exponential random graph model to investigate the effects of product features and network structures on product competition relations. The approach that consists of model construction, interpretation, and validation is presented in a step-by-step procedure. Our findings suggest that the weighted network model outperforms commonly used binary network baselines in predicting product competition as well as market share. Also, traditionally when using binary network models to study product competitions and depending on the cutoff values chosen to binarize a network, the resulting estimated customer preferences can be inconsistent. Such inconsistency in interpreting customer preferences is a downside of binary network models but can be well addressed by the proposed weighted network model. Lastly, this paper is the first attempt to study customers’ purchase preferences (i.e., aggregated choice decisions) and car competition (i.e., customers’ co-consideration decisions) together using weighted directed networks.