Community-based sport-leisure participation and perceived community psychological resilience: a three-wave RI-CLPM study of community identification and collective efficacy
Objective In modern risk societies, community psychological resilience has become an important concern for community psychology and behavioral science. Yet less is known about how everyday community-based sport-leisure participation contributes to residents’ perceived community psychological resilience over time. This study examined whether residents’ community-based sport-leisure participation predicts subsequent perceived community psychological resilience and whether this association is explained by community identification and collective efficacy. Methods A three-wave longitudinal survey with a 4-month time lag was conducted among 386 residents in Hangzhou, China. Data were analyzed using a Random Intercept Cross-Lagged Panel Model (RI-CLPM) to separate within-person state dynamics from stable between-person differences. Results Within-person increases in community-based sport-leisure participation positively predicted subsequent perceived community psychological resilience (T1 → T2: β = 0.18, p < 0.01; T2 → T3: β = 0.21, p < 0.01). The theory-guided standardized sequential indirect effect through community identification and collective efficacy was also significant [standardized indirect effect = 0.065, 95% CI (0.032, 0.105)]. Conclusion The findings provide longitudinal predictive evidence consistent with a behavior–identity–efficacy pathway linking residents’ sport-leisure participation with perceived community psychological resilience. Given the observational design and modest effect sizes, the pathway should be interpreted as one prospective social-psychological association rather than as evidence of a causal intervention effect. The study positions community-based sport-leisure participation as an everyday behavioral context embedded in social relationships, with implications for community-based health promotion, social connection, and resilience-related perceptions.
This publication proposes a definition and a classification of agile software development approaches and analyses ten software development methods that can be characterized as being "agile" against the defined criterion.
P. Abrahamsson, O. Salo, Jussi Ronkainen et al.· arXiv.org· 727 citations· ⚡54
The study shows that agile practices improve both informal and formal communication, but indicates that, in larger development situations involving multiple external stakeholders, a mismatch of adequate communication mechanisms can sometimes even hinder the communication.
M. Pikkarainen, Jukka Haikara, O. Salo et al.· Empirical Software Engineeri...· 401 citations· ⚡48
The results indicate that software engineering work practices are chosen opportunistically, adapted and configured to provide value under the constrains imposed by the startup context.
Nicolò Paternoster, Carmine Giardino, M. Unterkalmsteiner et al.· Information and Software Tec...· 394 citations· ⚡54
It is proved that any global minimizer of the trajectory balance objective can define a policy that samples exactly from the target distribution, and empirically demonstrate the benefits of the trajectories balance objective for GFlowNet convergence, diversity of generated samples, and robustness to long action sequenc...
Esmeralda S. Whitammer, Moksh Jain, Emmanuel Bengio et al.· Neural Information Processin...· 302 citations· ⚡60
Related blog posts
MIT News · Artificial Intelligence· news.mit.eduOct 8, 2026
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.