Loading...
2 results
Search Results
Now showing 1 - 2 of 2
- Sustainable competitiveness of tourism in the Algarve region. Critical stakeholders’ perception of the supply sectorPublication . Farinha, Fátima; Bienvenido-Huertas, David; Duarte Pinheiro, Manuel; Da Silva, Elisa Maria De Jesus; Lança, Rui; Oliveira, Miguel José; Batista, RicardoThe Algarve region, located in the south of Portugal, is a well-known tourism destination that seeks to be sustainable and competitive. The local administration looks to establish a collaborative network, where stakeholders take a crucial role. The research aims to appeal to the accommodations and food services stakeholders to have a shared vision of the issues and priorities related to sustainable tourism development. Their perception is a critical factor in making decisions regarding the region’s competitiveness. Algarve’s two major and leading associations of the tourism supply sector AIHSA and AHETA were invited to participate in the study. Based on the responses of an online questionnaire, an artificial intelligence algorithm was applied to the data to identify the common and divergent aspects. The conceptual model developed is based on a simplified model of psychological ownership. The results highlight a convergent perspective regarding sustainability challenges, namely, natural resources and biodiversity, safety, and supply chain. However, hotels and restaurants do not reflect the same perception regarding sustainability initiatives, e-tourism, or free internet access. These divergences are essential results since they indicated which issues require local authorities’ priority intervention.
- Comparison of artificial intelligence algorithms to estimate sustainability indicatorsPublication . Bienvenido-Huertas, David; Farinha, Fátima; Oliveira, Miguel José; Da Silva, Elisa Maria De Jesus; Lança, Ruithe monitoring of sustainability indicators allows behavioural tendencies of a region to be controlled, so that adequate policies could be established in advance for a sustainable development. However, some data could be missed in the monitoring of these indicators, thus making the establishment of sustainability policies difficult. This paper therefore analyses the possibility to forecast the sustainability indicators of a region by using four different artificial intelligent algorithms: linear regression, multilayer perceptron, random forest, and M5P. the study area selected was the Algarve region in Portugal, and 180 monitored indicators were analysed between 2011 and 2017. the results showed that M5P is the most appropriate algorithm to estimate sustainability indicators. M5P was the algorithm obtaining the best estimations in a greater number of indicators. Nevertheless, the results showed that MP5 was not the best option for all indicators, since in some of them, the use of other algorithms obtained better results, thus reflecting the need of an individual previous study of each indicator. With these algorithms, it is possible for public bodies and institutions to evaluate the sustainable development of the region and to have reliable information to take corrective measures when needed, thus contributing to a more sustainable future.