University of Lincoln
Browse

Developing a model for smart tourism destinations: an interpretive structural modelling approach

Version 2 2024-03-13, 10:25
Version 1 2024-03-01, 12:54
journal contribution
posted on 2024-03-13, 10:25 authored by Sanaz Shafiee, Ali Rajabzadeh GhatariAli Rajabzadeh Ghatari, Alireza Hasanzadeh, Saeed Jahanyan

The smart tourism concept emerged from smart city development and is a particular application area within smart city initiatives. Smart tourism is broadly applied as a strategic tool to enhance the competitiveness of tourism destinations. This study creates a framework to identify, explore, and rate the efective factors of developing smart tourism destinations. The efective factors were identifed through a review of the research literature and by surveying experts. The identifed factors were rated using an interpretative-structural modelling approach. A Cross-impact matrix multiplication applied to classifcation (MICMAC) analysis was used to determine the power and dependence of these factors. The fndings show 20 indexes at ten levels. Financial resources, government support, and smart tourism policies were identifed as the most important factors in modelling smart tourism development. By identifying efective factors for developing smart tourism destinations, the policymakers can encourage innovation of smart destinations, support smart tourism and highlight the multi-faceted contribution of smart destinations to sustainable development.

History

School affiliated with

  • Department of Management (Research Outputs)

Publication Title

Information Technology & Tourism

Volume

24

Issue

4

Publisher

Springer

ISSN

1098-3058

eISSN

1943-4294

Date Submitted

2023-11-01

Date Accepted

2022-09-22

Date of First Publication

2022-11-24

Date of Final Publication

2022-11-24

Date Document First Uploaded

2023-10-31

ePrints ID

57060

Usage metrics

    University of Lincoln (Research Outputs)

    Licence

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC