Competitiveness Evaluation of Tourist Attractions Based on Artificial Neural Network

Lou, Bingna; Chen, Nan; Ma, Lan

doi:10.18280/ria.340513

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…The use of AI in the tourism industry also has a positive impact on destination competitiveness. For example, Lou et al (2020) and Ezzat et al (2022) emphasize that AI has an important role in increasing the competitiveness of tourist attractions and destinations. Moreover, research (Filieri et al, 2021;Ezzat et al, 2022) generally underlines that AI is quite important in shaping the competitiveness of leading tourism destinations.…”

Section: Literature Reviewmentioning

confidence: 99%

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Evaluation of top tourism destinations according to their AI development with the entropy-TOPSIS-integrated method

Yılmaz,

Ülker,

Ülker

2024

WHATT

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PurposeThis study aims to determine and evaluate the artificial intelligence (AI) development and competitiveness of the top 20 countries that receive the highest number of tourists with the entropy technique for order of preference by similarity to the ideal solution (TOPSIS)-integrated method.Design/methodology/approachThis study is based on Global AI Index data published by Tortoise Media. Based on this index, according to the World Tourism Organization (UNWTO) report, the top 20 destinations that will host the highest number of tourists in 2022 were evaluated in seven different subpillars, which are talent, infrastructure, operating environment, research, development, government strategy and commercial. These seven subpillars of the index were considered as criteria, and the top 20 tourist destinations were included in the research as decision alternatives.FindingsThe analysis results show that the three most important AI criteria are operating environment, infrastructure and government strategy. Furthermore, the first three countries with the best AI performance according to the weighted criteria were the USA, China and the UK, respectively.Practical implicationsConsidering that AI technologies will direct tourist behavior in a world where technology is rapidly developing, it is recommended that the countries that receive the highest number of tourists improve their AI performance.Originality/valueWhen the relevant literature is examined, there is a limited number of studies examining the AI development and competitiveness of the top tourist destinations and weighting the Global AI Index values. Therefore, this study contributes to the gap in the relevant literature.

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Section: Literature Reviewmentioning

confidence: 99%

“…The use of AI in the tourism industry also has a positive impact on destination competitiveness. For example, Lou et al. (2020) and Ezzat et al.…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluation of top tourism destinations according to their AI development with the entropy-TOPSIS-integrated method

Yılmaz,

Ülker,

Ülker

2024

WHATT

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“…The accuracy of the predictions achieved was between 80% and 90%, thus the neural networks were suitable for predicting precipitation. Lou et al [15] also utilized backpropagation to determine the competitiveness evaluation of tourist attractions, with an accuracy rate of 97.3%.…”

Section: Introductionmentioning

confidence: 99%

Study of Climate Change in the Mandalika International Circuit Area Using Neural Network Backpropagation

Mandailina¹,

Nurhalimah²,

Mehmood³

et al. 2022

RIA

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Climate change is a global phenomenon that also causes small-scale effects. This study aims to determine future climate changes in the Mandalika International Circuit area using the artificial neural network of the MATLAB GUI-based backpropagation method. The simulation stage used daily rainfall intensity data collected in the Mandalika International Circuit area from 2012-2021 (365 data). A preliminary analysis concluded that the Mandalika International Circuit area is dominated by a very wet climate according to the Schmidt-Ferguson classification, which occurred in 2012, 2013, 2017, and 2021. This study used two architectural models with two and three hidden layers. The TRAINRP training function and the LOGSIG activation function were utilized at each hidden layer. Between the two architectures, the better architecture was selected, namely the 100-50-10-1 (three hidden layers) that resulted in an accuracy rate of 99.90% and an MSE of 0.0412376 achieved in the 258th iteration. These results indicate that the area has a very wet climate with the highest rain intensity in March and the lowest in January. The results of this study show that the backpropagation method can be used to help formulate an alternative policy on the measures for handling and mitigating extreme climate change in upcoming periods, especially during international events at the Mandalika International Circuit area.

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“…The use of ANN has been widely carried out in various scientific fields, due to possessing more capabilities to perform classification, prediction, projection, and segmentation functions, which are popularly and heavily relied on for problem-solving. Based on its applications in competitive fields, the contesting power of a company to the competitiveness of the country was observed in Ülengin et al (2011Ülengin et al ( ), Önsel Ekici et al (2016, Kolkova (2020), Lou et al (2020), Rahmat and Sen (2021). The implementation of this network in the fields related to the increase in product innovation was also found by Kinne and Kinne and Lenz (2021), Wang et al (2021), Y.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 91%

A Conceptual Framework for Innovation Product and Marketing Strategy at Precast Construction Company in Indonesia to Increase Competitiveness Using SWOT and Neural Network Model

Proceedings of the International Conference on Industrial Engineering and Operations Management

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The competitiveness of national infrastructure is influenced by the strength of the precast concrete industry, leading to the need for its productivity to be boosted. Although many interminable challenges have been unfolding, the domestic precast concrete companies are presently attempting to penetrate the foreign markets through thorough preparation, specifically in organizational competitiveness. This is due to their potential competition with a wider market, compared to the domestic. The number of foreign construction companies, specifically precast concrete services, also continues to grow in the Indonesian domestic market and largely occupy business fields, due to the low competitiveness of local organizations, which has been identified as a future potential threat. Therefore, this study aims to analyze the competitiveness of local companies in the Indonesian domestic market. This needs to be highly evaluated as well as importantly and carefully planned to avoid low organizational competitive levels. A qualitative method was used with SWOT analysis and neural network mapping, which were supported by previous literature. The results show that precast products and marketing strategies should be able to have long-term planning in achieving market size, for the minimization of external threats.

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Competitiveness Evaluation of Tourist Attractions Based on Artificial Neural Network

Cited by 5 publications

References 15 publications

Evaluation of top tourism destinations according to their AI development with the entropy-TOPSIS-integrated method

Evaluation of top tourism destinations according to their AI development with the entropy-TOPSIS-integrated method

Study of Climate Change in the Mandalika International Circuit Area Using Neural Network Backpropagation

A Conceptual Framework for Innovation Product and Marketing Strategy at Precast Construction Company in Indonesia to Increase Competitiveness Using SWOT and Neural Network Model

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