Word-of-Mouth Recommendations in an Automobile Market System

Abstract

Improving design in the context of market systems requires an understanding of how consumers learn about and evaluate competing products. Marketing models frequently assume that consumers choose the product with the highest utility, which provides businesses insights into how to design and price their products to maximize profits. While recent research has shown the impacts of consumer interactions within social networks on their purchasing decisions, they typically model market systems using a top-down approach. This paper applies an agent-based modeling approach with social network models to investigate the extent to which word-of-mouth (WOM) communications are influential in changing consumer preferences and producer market performance. Using a random network, we study the effects of the number of referrals for a product and the degrees of similarity between the senders and receivers of referrals on purchase decisions. In addition, the eigenvector centrality metric is used to analyze the spread of WOM referrals. The simulation results show that the most influential consumers in the network can create significant shifts in the market share, and a statistical analysis reveals a significant change in the system-level metrics of interest for the competing firms when WOM recommendations are included. The findings incentivize producers to invest in supporting their product development efforts with rigorous social networks analysis so as to increase their market success.