A Weighted Statistical Network Modeling Approach to Product Competition Analysis

Yaxin Cui, Faez Ahmed, Zhenghui Sha, Lijun Wang, Yan Fu, Noshir Contractor, Wei Chen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Article number9417869
JournalComplexity
Volume2022
DOIs
StatePublished - 2022

ASJC Scopus subject areas

  • Computer Science(all)
  • General

Cite this