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تبيين شاخصهای مدل شکوفایی گردشگری شهری.

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  • Additional Information
    • Alternate Title:
      Explaining the Indicators of Urban Tourism Development Model.
    • Abstract:
      Today, indicators have increasingly become a valuable tool for policy making and monitoring. In this regard, the tourism sector, especially urban tourism, needs to adopt efficient indicators to evaluate and formulate policies and ensure development. Therefore, this study aims to explore the indicators of the development model of urban tourism. This is an applied-developmental and exploratory research, which adopts a descriptive-analytical and survey method. Moreover, the dominant approach that governs the research is theoretical analysis. In this paper, firstly, theoretical literature relevant to the subject are explored from a structural and systematic perspective. Then, using the interview method, the factors influencing the development of urban tourism are extracted. The statistical population consists of elites, specialists and experts in urban management and tourism. Using the purposive sampling method, 154 people were selected as the statistical sample and their views were incorporated in the final analysis. In the next step, structural equation modeling approach based on partial least squares is utilized to validate the designed pattern of urban tourism development. Finally, using Smart-PLS3 software, the second-order confirmatory factor analysis was used to validate the designed model. Using this scientific process, the underlying factors affecting urban tourism were identified in five dimensions (economic, socio-cultural, infrastructural, sustainable and institutional). The results suggest the good fit of the model. As such, the "sociocultural" (0.81) index had the highest extracted average variance while “economic" index (0.47) gained the lowest value. [ABSTRACT FROM AUTHOR]
    • Abstract:
      Copyright of Journal of Geography & Regional Development is the property of Ferdowsi University of Mashhad Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)