Consumer Heterogeneity and E-Retailing Market Concentration: An Agent-Based Approach

Teng Li ()
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Teng Li: https://echotengli.github.io/

Journal of Artificial Societies and Social Simulation, 2025, vol. 28, issue 4, 3

Abstract: The phenomenon of high concentration in China's e-retail markets remains not fully understood. This study aims to explore its potential origins from the perspective of consumer types. By theoretically examining the impact of consumer type on market structure, this study assumes sellers to be homogeneous, while categorizing consumers based on two dimensions: risk-taking tendency (preference for new products over old ones) and pickiness tendency (likelihood to give negative rather than positive ratings). An agent-based simulation model is developed to examine the trends of market concentration ratios under diverse conditions. The simulation findings indicate that consumers' risk-taking tendency influences market concentration and volatility. Specifically, higher risk preference tends to reduce market concentration, making the market more volatile. Similarly, the higher the consumers' pickiness tendency, the more it hinders market concentration. Additionally, the relative weight of word-of-mouth (WoM) on a product plays a crucial role in market dynamics. When the weight of sales is greater, market concentration is more likely. However, if WoM becomes more influential, positive ratings enhance sales' role in increasing market concentration, while negative ratings suppress it. In other words, when picky consumers dominate the market, higher sales, which means more negative ratings, can actually diminish the product's WoM and subsequently reduce the product's attractiveness. This prevents any product from maintaining a sales advantage, making it hard to achieve high market concentration. This research aligns with existing market structure theories and has implications for e-retailers and administrators.

Keywords: Consumer Type; Market Concentration; Word-Of-Mouth; Consumer Attention; Agent-Based Modeling (search for similar items in EconPapers)
Date: 2025-10-31
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