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Destination performance: Introducing the utility function in the Mean-Variance space

Author

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  • Linjia Zhang
  • Laurent Botti

    (CRESEM - Centre de Recherche sur les Sociétés et Environnements en Méditerranées - UPVD - Université de Perpignan Via Domitia)

  • Sylvain Petit

    (UPF - Université de la Polynésie Française)

Abstract

Economic characteristics of home countries can cause considerable variations in the tourism demand. For example, the average level of expenditure per tourist varies from one origin to another and these variations may alter overtime. Thus different tourist nationalities are associated with different level of expenditures and risks. Therefore strategies aimed at minimizing the variations may become an important issue for the policy makers. In this paper, we aim to use the productivity measurement theory in a mean-variance space to a French region (Nord Pas-de-Calais) by introducing the utility function in a mean-variance framework. With this method, we can calculate the optimal portfolio share for each origin and give some useful political advices to the policy decision makers to improve the performance of the tourist sector.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Linjia Zhang & Laurent Botti & Sylvain Petit, 2016. "Destination performance: Introducing the utility function in the Mean-Variance space," Post-Print hal-01831499, HAL.
  • Handle: RePEc:hal:journl:hal-01831499
    Note: View the original document on HAL open archive server: https://hal.archives-ouvertes.fr/hal-01831499
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    References listed on IDEAS

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    Cited by:

    1. Luis Felipe Mendieta-Peñalver & José F Perles-Ribes & Ana B Ramón-Rodríguez & María J Such-Devesa, 2018. "Is hotel efficiency necessary for tourism destination competitiveness? An integrated approach," Tourism Economics, , vol. 24(1), pages 3-26, February.
    2. Walheer, Barnabé & Zhang, Linjia, 2018. "Profit Luenberger and Malmquist-Luenberger indexes for multi-activity decision making units: the case of the star-rated hotel industry in China," RIEI Working Papers 2018-06, Xi'an Jiaotong-Liverpool University, Research Institute for Economic Integration.
    3. Walheer, Barnabé & Zhang, Linjia & Luo, Yingchan, 2020. "Bidirectional technological spillover in the Chinese star-rated hotel sector: An empirical investigation," Economic Modelling, Elsevier, vol. 86(C), pages 210-226.
    4. Hanitra Rakotondramaro & Laurent Botti, 2018. "Optimizing tourist demands with utility efficient frontier," Tourism Economics, , vol. 24(2), pages 157-166, March.
    5. Wenbin Luo, 2018. "Evaluating Tourist Destination Performance: Expanding the Sustainability Concept," Sustainability, MDPI, vol. 10(2), pages 1-16, February.
    6. Tyagi, Akansha & Dhar, Rajib Lochan & Sharma, Jyoti, 2016. "Police culture, tourists and destinations: A study of Uttarakhand, India," Tourism Management, Elsevier, vol. 52(C), pages 563-573.
    7. Hai Dong & Nicolas Peypoch & Linjia Zhang, 2020. "Do contextual factors matter? Evidence from Chinese hotel productivity with heterogeneity," Tourism Economics, , vol. 26(2), pages 257-275, March.
    8. Milenkovski, Ace & Gjorgievski, Mijalce & Nakovski, Dejan, 2018. "Termal/Mineral Springs Industry: Need For Transformation In Function Of Tourism," UTMS Journal of Economics, University of Tourism and Management, Skopje, Macedonia, vol. 9(2), pages 181-187.
    9. Laurent Botti & Sylvain Petit & Linjia Zhang, 2020. "Strategic decision concerning tourist origins portfolio: A decision process based on the ELECTRE method and applied to French Polynesia," Tourism Economics, , vol. 26(5), pages 830-843, August.
    10. Lai, Shuying & Qiu, Jing & Tao, Yuechuan & Zhao, Junhua, 2021. "Risk hedging for gas power generation considering power-to-gas energy storage in three different electricity markets," Applied Energy, Elsevier, vol. 291(C).
    11. 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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    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C67 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Input-Output Models
    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism

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