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Navigating the Efficiency Landscape: A Data Envelopment Analysis of Tourist Resorts in Jiangsu Province for Optimized Socio-Economic Benefits

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  • Guang Chu

    (School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China
    Huangshan Park Ecosystem Observation and Research Station, Ministry of Education, Huangshan 245899, China)

  • Liangjian Yang

    (School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China
    Huangshan Park Ecosystem Observation and Research Station, Ministry of Education, Huangshan 245899, China)

  • Jinhe Zhang

    (School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China
    Huangshan Park Ecosystem Observation and Research Station, Ministry of Education, Huangshan 245899, China)

  • Tian Wang

    (School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China
    Huangshan Park Ecosystem Observation and Research Station, Ministry of Education, Huangshan 245899, China)

  • Yingjia Dong

    (School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China
    Huangshan Park Ecosystem Observation and Research Station, Ministry of Education, Huangshan 245899, China)

  • Zhangrui Qian

    (School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China
    Huangshan Park Ecosystem Observation and Research Station, Ministry of Education, Huangshan 245899, China)

Abstract

Tourist resorts stand out as a focal point in the academic discourse on tourism, garnering significant attention within the tourism academic community. Assessing the efficiency of these resorts serves as a crucial tool for steering their management strategies, optimizing resource allocation, and contributing to regional economic development. This study centers on tourist resorts in Jiangsu Province, employing the data envelopment analysis method to gauge their tourism efficiency. The research delves into the impact of decomposing the efficiency of tourist resorts and investigates the spatiotemporal dynamic patterns of various efficiencies. Key findings indicate that: (1) The overall tourism efficiency of tourist resorts in Jiangsu Province registers as low, with an average of only 0.119, signaling ample room for improvement towards optimal levels. Among different efficiencies, scale efficiency exhibits the highest average value, followed by pure technical efficiency, with comprehensive efficiency ranking the lowest. (2) The comprehensive efficiency of tourist resorts in Jiangsu Province is influenced by the combined effects of various decomposition efficiencies. Notably, pure technical efficiency plays a more substantial role in overall efficiency compared to scale efficiency. (3) Spatial differentiation in efficiency values is evident among tourist resorts in Jiangsu Province. High-efficiency areas, particularly the southern Jiangsu region, display concentrated clusters, emphasizing a pronounced agglomeration of scale efficiency. In contrast, the central and northern regions of Jiangsu witness a rising number of tourist resorts demonstrating pure technical efficiency and high overall efficiency. (4) Over the research period, the focus of various efficiency factors in tourist resorts shifted towards the north, albeit without significant deviation. Simultaneously, the standard deviation ellipse area of various efficiencies exhibits a general trend of expansion. Drawing from these research outcomes, the article recommends practical measures such as enhancing the diversity of vacation resort services, establishing interactive mechanisms, and attracting management talent. These suggestions aim to provide actionable guidance for the development of tourist resorts, contributing to their sustained growth and success.

Suggested Citation

  • Guang Chu & Liangjian Yang & Jinhe Zhang & Tian Wang & Yingjia Dong & Zhangrui Qian, 2024. "Navigating the Efficiency Landscape: A Data Envelopment Analysis of Tourist Resorts in Jiangsu Province for Optimized Socio-Economic Benefits," Sustainability, MDPI, vol. 16(4), pages 1-21, February.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:4:p:1653-:d:1340401
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    References listed on IDEAS

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    1. Palmer, Adrian & Mathel, Virginie, 2010. "Causes and consequences of underutilised capacity in a tourist resort development," Tourism Management, Elsevier, vol. 31(6), pages 925-935.
    2. Jan Frančeškin & Štefan Bojnec, 2022. "Economic efficiency of coastal hotel companies," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 35(1), pages 4425-4436, December.
    3. Hwai-Shuh Shieh & Jin-Li Hu & Teng-Yi Liu, 2017. "An environment-adjusted dynamic efficiency analysis of international tourist hotels in Taiwan," Current Issues in Tourism, Taylor & Francis Journals, vol. 20(16), pages 1749-1767, December.
    4. Maria Francesca Cracolici & Peter Nijkamp & Piet Rietveld, 2008. "Assessment of Tourism Competitiveness by Analysing Destination Efficiency," Tourism Economics, , vol. 14(2), pages 325-342, June.
    5. Aurélie Corne & Olga Goncalves & Nicolas Peypoch, 2020. "Evaluating the performance drivers of French ski resorts: A hierarchical approach," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 41(3), pages 389-405, April.
    6. Zhou, P. & Ang, B.W. & Poh, K.L., 2008. "A survey of data envelopment analysis in energy and environmental studies," European Journal of Operational Research, Elsevier, vol. 189(1), pages 1-18, August.
    7. Fangdong Cao & Zhenfang Huang & Cheng Jin & Min Xu, 2016. "Influence of Chinese economic fluctuations on tourism efficiency in national scenic areas," Tourism Economics, , vol. 22(5), pages 884-907, October.
    8. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    9. Carlos Pestana Barros & Laurent Botti & Nicolas Peypoch & Bernardin Solonandrasana, 2011. "Managerial efficiency and hospitality industry: the Portuguese case," Applied Economics, Taylor & Francis Journals, vol. 43(22), pages 2895-2905.
    10. Jun Yu & Xiaobin Zhang & Hak-Seon Kim, 2023. "Using Online Customer Reviews to Understand Customers’ Experience and Satisfaction with Integrated Resorts," Sustainability, MDPI, vol. 15(17), pages 1-14, August.
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