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Influence of Sustainable Environment Based on a SWOT-PEST Model on Sports Tourism Service Integration Development

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  • Yuhu Zhao

    (School of Physical Education, Changzhou University, Changzhou 213164, China)

  • Xiangping Mei

    (Huaide College, Changzhou University, Jingjiang 214500, China)

  • Jianqiang Guo

    (School of Physical Education, Changzhou University, Changzhou 213164, China)

Abstract

The rapid growth of the social economy allows the masses to have more free time to enjoy a material and civilized life, and also allows many families to participate in sports tourism activities. However, as the development of sports tourism has just started, it still has some deficiencies in service integration. Meanwhile, the concept of sustainable development has been applied to sports tourism services. Through the survey on service attitudes and quality of the service staff by selected tourists, it was found that, according to the concept of sustainable development, 140 tourists have described service attitude and quality as very good and good, and only 35 tourists made other choices. In addition, to solve the shortcomings of sports tourism, this paper studied sports tourism services according to the sustainable environment of a SWOT-PEST model. Through expert scoring, the SWOT-PEST model was scored and compared with the model previously used. The score after use was above 4.3 points, and the score before use was below 4 points. Therefore, the research methods in this paper were valuable for research into sports tourism services.

Suggested Citation

  • Yuhu Zhao & Xiangping Mei & Jianqiang Guo, 2023. "Influence of Sustainable Environment Based on a SWOT-PEST Model on Sports Tourism Service Integration Development," Sustainability, MDPI, vol. 15(2), pages 1-14, January.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:2:p:1632-:d:1035620
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    References listed on IDEAS

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    1. Renzo Cordina & Martin Joseph Gannon & Ross Croall, 2019. "Over and over: local fans and spectator sport tourist engagement," The Service Industries Journal, Taylor & Francis Journals, vol. 39(7-8), pages 590-608, June.
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    Cited by:

    1. Doğan Küçük & Nursal Arıcı, 2023. "Deep Learning-Based Sentiment and Stance Analysis of Tweets About Vaccination," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 19(1), pages 1-18, January.

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