In the context of exercise psychology, the mediating relationship between peer support, self-efficacy and self-regulation, and adolescents’ exercise adherence was to be explored. Methods: A questionnaire was distributed among 2200 teenagers from twelve middle schools in Shanghai. The “process” program in SPSS and the bootstrap method were applied to construct and analyze the direct and indirect effects of peer support on adolescents’ exercise adherence. Results: Peer support directly affected adolescents’ exercise adherence (β = 0.135, p < 0.001, effect size of 59%) and self-efficacy (β = 0.493, p < 0.001, effect size accounted for 42%), and self-regulation (β = −0.184, p < 0.001, effect size of 11%) influenced exercise adherence indirectly. In addition, self-efficacy and self-regulation could impose a chain-mediated effect on peer support and exercise adherence (effect size of 6%). Conclusion: Peer support could promote adolescents’ exercise adherence. Self-efficacy and self-regulation are mediating factors of peer support on exercise adherence in teenagers, self-regulation as well as self-efficacy-imposed chain-mediating effects on peer support and adolescents’ exercise adherence.

Tourist flows, crucial information within online travelogues, reveal the interactive relationships between different tourist destinations and serve as the nerve center and link of the tourism system. This study takes Hangzhou, China, as a case to investigate the spatial network structure of its tourist flows. Firstly, a BERT-BiLSTM-CRF model and pan-attraction database are built to extract tourist attractions from online travelogues and create the tourist flow matrix. Then, this study uses social network analysis (SNA) to examine the structure of the tourist flow network from a county-level perspective. Additionally, GIS spatial analysis methods are applied to analyze the evolution of the tourist gravity center and standard deviation ellipse (SDE) of the network. The results reveal that the identification performances of the tourist flow extraction model this study proposed are significantly better than those of previous mainstream models, with an F1 value of 0.8752. Furthermore, the tourist flow network in Hangzhou displays a relatively sparse and unbalanced distribution, forming a “Core–Semi-Periphery–Periphery” structure. Lastly, from 2020 to 2022, the network’s gravity center experienced a shift towards the southwest, paralleled by an initial expansion and subsequent contraction of the SDE in the same southwest direction. These findings provide valuable insights into the spatial network structure of tourism in Hangzhou and can serve as a reference for policymakers to promote the “all-for-one” tourism.

Maintaining tourism competitiveness is crucial for destinations’ long-term success. However, studies often lack longitudinal analyses that capture the evolving determinants of tourism competitiveness in both domestic and international markets. Taking China as a case study, this research introduces an innovative framework that integrates spatiotemporal machine learning with multimodal big data to examine dynamic factors influencing domestic and international tourism competitiveness. Several findings emerge: (1) overall, online popularity is essential for domestic tourism competitiveness, whereas the natural environment underpins international tourism competitiveness; (2) temporally, tourism infrastructure, government support, and online tourist perception are becoming more critical for tourism competitiveness over time, while tourism attractions’ role is waning; and (3) developed destinations are increasingly leveraging tourism infrastructure to enhance competitive advantage, and less-developed areas are benefiting more from online visibility. This research further contextualizes the dynamic nature of tourism competitiveness and offers strategies for sustainable tourism development. Also available in Chinese. See Supplemental Material for details.

In the context of consumer economics, it is imperative to consider the functionality of sports venues based on customer demand. However, traditional survey methods are time-consuming, resource-intensive, and coverage-limited. This paper conducted sentiment mining based on Internet big data, deep learning, topic analysis, and social network analysis to capture the satisfaction of consumers and its influencing factors. Findings indicate that activity, courses, and facilities are core factors driving positive comments. Coaches, environment, and activities are key determinants influencing neutral evaluations. Attitude, integrity, and qualifications can trigger negative reviews. The findings offer insights into developing consumer-friendly service for sports venues.