On Evolutionary Analysis of Customer Purchasing Behavior by the Supervision of E-Commerce Platforms

Abstract

As Internet technology undergoes rapid development and widespread adoption, e-commerce emerges as a pivotal component of the platform economy, permeating various facets of daily life. However, due to the influence of time, space, and other factors, the problem of integrity becomes severe in the real trading environment. As the platforms, sellers, and consumers are the main participants and their decision-making is restricted by historical experiences and contextual conditions, they exhibit constrained rationality. Utilizing evolutionary game theory, the study constructs a tripartite game model that analyses the influence of relevant parameters on the behavior of the participants. To deal with the behaviors of the participants, we built a simulation system on MATLAB to demonstrate the effects of beginning circumstances and associated parameter adjustments on the evolution outcomes for participants. Through theoretical analysis and numerical simulation analysis, we identify that the e-commerce platforms should standardize the good faith behavior of sellers by increasing the punishment, which can reduce the malicious return behavior of consumers. Sellers can mitigate the probability of fraud by improving production technology. Consumers can improve their learning to avoid returning products. This research provides a theoretical framework and decision support for e-commerce platforms, and it also promotes the long-term growth of online transactions.