In the context of rapid digital economic development, e-commerce platforms are exhibiting increasingly concentrated market structures. The paper “Consumer attention and market concentration in e-commerce: an agent-based perspective” by Li et al. (2025), published in the Journal of Economic Interaction and Coordination, employs an agent-based modeling (ABM) approach to investigate a critical question: How does limited consumer attention shape the structure of e-commerce markets?
In an age of information overload, this study reveals that even in markets without product differentiation, the scarcity of consumer attention can spontaneously lead to a “winner-takes-all” outcome. This provides a novel perspective for understanding the emergence of monopolistic tendencies in platform economies.
Background: When Attention Becomes Scarce, Who Wins?
As digital marketplaces grow larger and more complex, several puzzles emerge:
- Why do certain sellers emerge as dominant “superstars”?
- Why do sales of seemingly identical products vary so drastically between sellers?
In digital marketplaces, the core challenge for consumers has shifted from information scarcity to information overload. Although online shopping removes geographical barriers and offers access to virtually unlimited product options, human cognitive limitations prevent consumers from processing all available information. This scarcity of attention becomes a key driver of market dynamics. While most research on market concentration focuses on supply-side factors (such as product differentiation or firm strategies), this study emphasizes the often-overlooked role of consumer behavior in shaping market structure.
Method: Agent-Based Modeling
This study adopts agent-based modeling (ABM), a cutting-edge computational approach that simulates the micro-level decision-making of heterogeneous consumers to observe emergent macro-level market patterns.
Key innovations in the model include:
Key Features of the Model:
- Consumer cognition as parameters: Attention span, decision heuristics, and social connectivity are all modeled.
- Seller homogeneity: All sellers offer identical products—this isolates the effects of consumer-side dynamics.
- Social network embedding: Consumers are embedded in a network and influenced by peer recommendations.
By systematically adjusting these parameters, the model isolates the causal impact of limited attention on market concentration.
Findings
1. Attention Scarcity Drives Concentration
When consumers can only examine a few sellers, the market self-organizes into a highly concentrated structure. Top sellers accumulate most of the market share—even when all products are the same.
2. Equal Products, Unequal Outcomes
Even with perfect product homogeneity, random allocation of attention and positive feedback from social influence generate self-reinforcing dynamics that lead to market concentration.
Two distinct mechanisms of competitive advantage are highlighted:
- Random Competitive Advantage
Early advantages may arise due to chance—such as initial good reviews, temporary promotions, or biases in recommendation algorithms. These advantages are not rooted in superior product quality, but in stochastic consumer discovery. - Cumulative Competitive Advantage
Once a seller gains early attention, they are more likely to receive future clicks, reviews, and algorithmic boosts—creating a feedback loop. This results in a Matthew Effect: the rich get richer.
These mechanisms interact and mutually reinforce each other in simulation, illustrating how small initial differences can snowball into systemic dominance.
Contributions
- Theoretical Innovation: It challenges firm-centric views in traditional industrial organization theory by introducing limited consumer attention as an independent explanatory factor of market concentration.
- Conceptual Framework: It provides a theoretical foundation for integrating the concept of the attention economy into analyses of market structure evolution.
- Methodological Advancement: By employing ABM, the study captures the nonlinear interaction between consumer behavior and algorithmic mechanisms—dynamics often missed in equilibrium-based models.
Implications
- For Platform Designers: Product presentation and recommendation algorithms should help consumers allocate their limited attention efficiently. Striking a balance between promoting top sellers and exposing long-tail sellers is crucial to avoid excessive concentration.
- For Regulators: Policymakers should distinguish attention-driven concentration from traditional forms of monopoly, and assess how platform design may inadvertently exacerbate concentration. Interventions in attention allocation mechanisms may be warranted.
- For Small Sellers: In attention-scarce environments, social proof and network visibility often matter more than product attributes. Sellers should actively manage attention strategies—for instance, through search optimization, reviews, and social sharing.
Conclusion
Overall, this study offers new simulation-based evidence on the formation of concentrated market structures in e-commerce, providing valuable insights for scholars interested in complex systems modeling, digital economics, and behavioral economics.
Reference
Li, T., Wang, S., Zhou, D., et al. (2025). Consumer attention and market concentration in e-commerce: an agent-based perspective. Journal of Economic Interaction and Coordination. https://doi.org/10.1007/s11403-025-00443-5