@article{4647,
  author = {Bo Li},
  title = {Optimizing Sports Tourism Development Through Search Algorithms: Strategies for Market Integration and Economic Growth},
  journal = {Journal of Information & Systems Management},
  year = {2026},
  volume = {16},
  number = {1},
  doi = {https://doi.org/10.6025/jism/2026/16/1/11-20},
  url = {https://www.dline.info/jism/fulltext/v16n1/jismv16n1_2.pdf},
  abstract = {The paper examines the integration of sports and tourism as a driver of economic development and public
health improvement in China. It outlines how the fusion of physical activity with leisure and travel meets
evolving consumer demands and supports national strategic goals. The study proposes leveraging search
algorithms particularly hash function based approaches to optimize sports tourism planning, market
prediction, and digital platform integration. Methodologically, the research evaluates algorithm
performance on datasets of varying sizes (50,000 to 200,000 records), demonstrating high reliability (up
to 0.94) and improved computational efficiency, reducing processing time from 100 to 30 seconds for 3,000
complex data points. The analysis also evaluates tourist satisfaction factors such as comfort, ease of search,
and acceptability across multiple teams. Drawing on recent literature, the paper affirms that data-driven
strategies, supported by AI and big data, can significantly enhance destination management and market
responsiveness. Despite promising results, the author acknowledges a gap in assessing the algorithm's
adaptability across diverse contexts. Overall, the study presents a practical, technology enabled framework
to advance sports tourism through intelligent search systems, contributing to both economic growth and
sustainable tourism development.},
}