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

Shafiee, Sanaz; Ghatari, Ali Rajabzadeh; Hasanzadeh, Alireza; Jahanyan, Saeed

doi:10.1007/s40558-022-00236-7

Cited by 8 publications

(5 citation statements)

References 64 publications

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“…The application of ISM starts with exploring the issues influencing a problem, then proceeds with a group-oriented problem-solving approach. Usually, the issues related to a system are generated through a literature review and are modified, dropped, or selected based on expert consensus [68].…”

Section: Background Of Ismmentioning

confidence: 99%

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Interpretive Structural Modeling of Barriers to Sustainable Tourism Development: A Developing Economy Perspective

Hussain,

Sun,

Ramzan

et al. 2024

Sustainability

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Sustainable tourism development (STD) has been much touted in academia and policy circles; however, developing economies are still struggling to adopt STD. This phenomenon motivated us to trace the barriers hindering STD and their interrelationships in the context of developing economies. This study identified barriers through a systematic literature review and expert opinions. A six-tier hierarchical structure was developed using interpretive structural modeling (ISM). The Matrice d’Impacts croisés multiplication appliquée á un classement (MICMAC) was applied to assess the driving and dependence power. The findings illustrate that all barriers are significant and influence STD; however, the most critical are ‘lack of government interest’, ‘funding constraints’, and ‘lack of integration among institutions and public–private people partnerships (4P)’. The findings of this research will be helpful for managers and policymakers to comprehend the driving and dependent power and categorization of barriers and devise pragmatic strategies to mitigate the barriers and promote STD.

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Section: Background Of Ismmentioning

confidence: 99%

“…MICMAC emphasizes the evaluation of an item through its indirect paths compared to its direct affiliations. The degrees of driving and dependence of variables are demonstrated through MICMAC analysis [68]. Hybrid ISM-MICMAC analysis yields more accurate and detailed findings with respect to a problem to support decision making.…”

Section: Background Of Micmacmentioning

confidence: 99%

Interpretive Structural Modeling of Barriers to Sustainable Tourism Development: A Developing Economy Perspective

Hussain,

Sun,

Ramzan

et al. 2024

Sustainability

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“…The effective factors were identified through a review of the research literature and by surveying experts [1].…”

Section: Theoretical Considerationsmentioning

confidence: 99%

Managing Consumer Behaviour. Visitor Satisfaction Regarding the Tourist Offer in Berzasca

Loredana¹,

Dinu²,

Popovici³

et al. 2022

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“…These aspects can help to measure the performance, impact, and potential of smart tourism destinations and services. Some possible methods to analyze smart tourism are data analysis that can help to identify patterns, trends, correlations, and causal relationships among different variables related to smart tourism [1], survey analysis that helps to evaluate the effectiveness and quality of smart tourism offerings and activities [2], case study analysis that can be utilized to learn from best practices and lessons learned from realworld scenarios [2,3] and benchmarking analysis to identify strengths and weaknesses of smart tourism development and management [4].…”

Section: Introductionmentioning

confidence: 99%

A seven-year analysis of the tourism sector of India with a prediction model—A case study of smart tourism

Lipsa,

Dash

2024

Smart. Tour.

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<p>The tourism sector requires an in-depth analysis and forecasting to bring a clear picture of various factors that affect the visit of foreign tourists to certain countries. In this context, the work carried out in this paper provides an in-depth analysis of the number of tourists to India and the revenue generated from them from the year 2014 to 2020. Furthermore, the analysis of different states of India to which the tourists visited the most and quarterly analysis of the tourists to India are also presented. The impact of corona pandemic on the tourism sector of India is also shown by comparing the number of tourists in 2019 and 2020. Support vector regression (SVR) is trained with the historical data of the number of tourists from 2001 to 2016 and validated for 2017 to 2019. This trained model is used to forecast the number of tourists from 2020 to 2023 to study the impact of corona pandemic on the number of foreign tourists to India. Similarly, the historical data of foreign exchange fees from 2001 to 2016 is used to train the model while it is validated with data from 2017 to 2019. This train model is used to predict the Foreign Exchange Earning (FEE) for the years 2020 to 2022. The actual FEE is compared with the predicted FEE to show the impact of the coronavirus pandemic on the revenue generated from tourism by India.<strong></strong></p>

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

Cited by 8 publications

References 64 publications

Interpretive Structural Modeling of Barriers to Sustainable Tourism Development: A Developing Economy Perspective

Interpretive Structural Modeling of Barriers to Sustainable Tourism Development: A Developing Economy Perspective

Managing Consumer Behaviour. Visitor Satisfaction Regarding the Tourist Offer in Berzasca

A seven-year analysis of the tourism sector of India with a prediction model—A case study of smart tourism

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