Understanding the power-law nature of participation in community sports organizations

TL;DR

This study uncovers that participation in community sports follows a power-law distribution, introduces a behavioral model HFBI to explain this, and reveals burst-quiet participation patterns with implications for intervention strategies.

Contribution

The paper presents a novel behavioral model HFBI that explains power-law participation patterns and introduces insights into individual participation dynamics in community sports.

Findings

Participation frequency follows a power-law distribution.

The HFBI model accurately fits observed participation data.

Participation intervals also follow a power-law distribution.

Abstract

The improvement of living standards and awareness of chronic diseases have increased the importance of community sports organizations in promoting the physical activity levels of the public. However, limited understanding of human behavior in this context often leads to suboptimal resource utilization. In this study, we analyzed the participation behavior of 2,956 members with a time span of 6 years in a community sports organization. Our study reveals that, at the population level, the participation frequency in activities adheres to a power-law distribution. To understand the underlying mechanisms driving crowd participation, we introduce a novel behavioral model called HFBI (Habit-Formation and Behavioral Inertia), demonstrating a robust fit to the observed power-law distribution. The habit formation mechanism indicates that individuals who are more engaged are more likely to maintain participation, while the behavioral inertia mechanism suggests that individuals' willingness to participate in activities diminishes with their absences from activities. At the individual level, our analysis reveals a burst-quiet participation pattern, with bursts often commencing with incentive activities. We also find a power-law distribution in the intervals between individual participations. Our research offers valuable insights into the complex dynamics of human participation in community sports activity and provides a theoretical foundation to inform intervention design. Furthermore, the flexibility of our model enables its application to other data exhibiting power-law properties, broadening its potential impact beyond the realm of community sports.