Abstract

To explore the dynamic evolution of brand competition networks and effectively help private brand firms manage production and operation risks, this study, drawing on consumers’ real-time implicit preferences, uses the Stochastic Actor-Oriented Model and analyzes platform consumers’ clickstream data to build a dynamic brand competition network. It also applies the Social Network Analysis software SIENA to examine the driving mechanisms of individual brand attributes and network structural attributes on network evolution. The study finds brands with common competitive targets, similar age-group consumers or higher sales are more competitive, while those targeting the elderly, serving high-tier members or with similar membership tiers have lower competition probability. It provides references for brand strategy optimization and customer management, future research may analyze preference disparities across click behaviors.

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