A109: Analysis of Optimization Pathways for the Structure of Sports Consumption in the Digital Economy

With the rapid development of the digital economy, the structure of sports consumption is undergoing profound transformations. Traditional sports consumption models can no longer meet the increasingly diversified and personalized demands of consumers. The integration of digital technologies presents new opportunities for the transformation of the sports industry. This study aims to explore how digital technologies can optimize the structure of sports consumption, enhance consumer experiences, and promote the sustainable development of the sports industry. Based on survey data from 524 Chinese residents (282 females and 242 males), this study employs statistical analysis using SPSS to conduct an in-depth examination of three key dimensions: the application of digital technologies in sports consumption, consumer willingness toward non-physical sports consumption, and overall sports consumption intention. The study further investigates the impact mechanisms of the digital economy on the structure of sports consumption. The findings indicate that: (1) The application of digital technologies significantly enhances residents' willingness to engage in sports consumption (β=0.453, p < 0.01), particularly in the domain of non-physical consumption, such as online fitness courses and virtual sports event viewing; (2) Traditional sports consumption models face three major challenges, including homogenized offerings, outdated facilities, and high costs; (3) Consumers demonstrate a strong demand for intelligent services, and digital technologies, through personalized recommendations, effectively activate the latent sports consumption market. The results provide new perspectives and empirical evidence for policymakers in formulating "sports + digital" integration policies and for enterprises undergoing digital transformation. However, this study has limitations, such as the restricted regional scope of the sample. Future research should expand the sample coverage and refine the analysis of consumption behavior differences across various demographic groups (e.g., age, income levels, and urban stratification), while further exploring intergenerational differences.