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Development Characteristics and Evolution Mechanism of Homestay Agglomeration in Mogan Mountain, China

Author

Listed:
  • Fei Long

    (Institute of Geographic Sciences and Natural Resources Research, Key Laboratory of Regional Sustainable Development Modeling, Chinese Academy of Sciences, Beijing 100101, China
    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China)

  • Jiaming Liu

    (Institute of Geographic Sciences and Natural Resources Research, Key Laboratory of Regional Sustainable Development Modeling, Chinese Academy of Sciences, Beijing 100101, China
    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China)

  • Shuying Zhang

    (Institute of Geographic Sciences and Natural Resources Research, Key Laboratory of Regional Sustainable Development Modeling, Chinese Academy of Sciences, Beijing 100101, China
    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China)

  • Hu Yu

    (Institute of Geographic Sciences and Natural Resources Research, Key Laboratory of Regional Sustainable Development Modeling, Chinese Academy of Sciences, Beijing 100101, China
    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China)

  • Hou Jiang

    (College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
    State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China)

Abstract

The homestay business has contributed to the development of the tourism industry, and it has revitalized tourism resources and boosted the tourism economy. This article takes the Mogan Mountain homestay agglomeration of Yangtze River Delta, the most developed and actively invested in business in the industry, as a research site focus to discuss the recent trends in the Chinese homestay business. Standard deviational ellipse analysis, spatial analysis of nearest-neighbor, and density are empirically tested with the data from the inn/homestay channel of Qunar.com, which is a widely acclaimed website with high page views. Based on the above researches, this paper uses the standard deviation ellipse analysis, the nearest neighbor analysis, and the kernel density estimation analysis method to explore the development characteristics of the Mogan Mountain homestay agglomeration. Combined with the Butler tourist destination life cycle model, it can be concluded that the Mogan Mountain homestay agglomeration area experienced a stage of initial development (2007–2011) and rapid development (2012–2015) and now is in a mature development stage (2016 up to now). This study proposes five actors: physical geographical conditions, traffic location conditions, tourism self-organization mechanism, policy influence, and community residents’ willingness to develop and significantly drive the evolution of Mogan Mountain homestay agglomeration. The formation and evolution of the Mogan Mountain homestay agglomeration are the results of the accumulation of resource-driven effect, scale effect, and differentiation effect at different stages. The unique organization and incentive mechanism promote regional sustainable development.

Suggested Citation

  • Fei Long & Jiaming Liu & Shuying Zhang & Hu Yu & Hou Jiang, 2018. "Development Characteristics and Evolution Mechanism of Homestay Agglomeration in Mogan Mountain, China," Sustainability, MDPI, vol. 10(9), pages 1-18, August.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:9:p:2964-:d:164813
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    References listed on IDEAS

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    1. Yan Yu & Qianwen Han & Wenwu Tang & Yanbin Yuan & Yan Tong, 2018. "Exploration of the Industrial Spatial Linkages in Urban Agglomerations: A Case of Urban Agglomeration in the Middle Reaches of the Yangtze River, China," Sustainability, MDPI, vol. 10(5), pages 1-18, May.
    2. Leo Huang, 2008. "Bed and breakfast industry adopting e-commerce strategies in e-service," The Service Industries Journal, Taylor & Francis Journals, vol. 28(5), pages 633-648, June.
    3. Bing Xia & Suocheng Dong & Duoxun Ba & Yu Li & Fujia Li & Haimeng Liu & Zehong Li & Minyan Zhao, 2018. "Research on the Spatial Differentiation and Driving Factors of Tourism Enterprises’ Efficiency: Chinese Scenic Spots, Travel Agencies, and Hotels," Sustainability, MDPI, vol. 10(4), pages 1-22, March.
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    Cited by:

    1. Xin Zhang & Jiaming Liu & He Zhu & Zongcai Huang & Shuying Zhang & Ping Li, 2021. "A Comparative Study of Customer Perceptions of Urban and Rural Bed and Breakfasts in Beijing: An Analysis of Online Reviews," Sustainability, MDPI, vol. 13(20), pages 1-15, October.
    2. Puwei Zhang & Li Wu & Rui Li, 2023. "Development Drivers of Rural Summer Health Tourism for the Urban Elderly: A Demand- and Supply-Based Framework," Sustainability, MDPI, vol. 15(13), pages 1-27, July.
    3. Jianzhuang Zheng & Lingyan Huang, 2022. "Characterizing the Spatiotemporal Patterns and Key Determinants of Homestay Industry Agglomeration in Rural China Using Multi Geospatial Datasets," Sustainability, MDPI, vol. 14(12), pages 1-21, June.
    4. Xuefeng Ma & Jiaxin Tan & Jiekuan Zhang, 2022. "Spatial–Temporal Correlation between the Tourist Hotel Industry and Town Spatial Morphology: The Case of Phoenix Ancient Town, China," Sustainability, MDPI, vol. 14(17), pages 1-13, August.
    5. Lei-Yi Peng & Jia Lu & Jian-Ji Luo & Yu-Xuan Wang, 2022. "A Combination of FDM, DEMATEL, and DANP for Disclosing the Interrelationship of Influencing Factors in Rural Homestay Business: Empirical Evidence from China," Sustainability, MDPI, vol. 14(16), pages 1-21, August.
    6. Linfeng Zou & Weimin Gui, 2020. "Simulation and prediction of geologic hazards and the impacts on homestay buildings in scenery spots through BIM," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-14, September.

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