Intelligent Exchange of Sustainable Tourist Habits among the EU Member States

Fátima Leal () and Micaela Pinho
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Fátima Leal: Research on Economics, Management and Information Technologies, REMIT, Portucalense University, 4200-072 Porto, Portugal
Micaela Pinho: Research on Economics, Management and Information Technologies, REMIT, Portucalense University, 4200-072 Porto, Portugal

Administrative Sciences, 2024, vol. 14, issue 6, 1-17

Abstract: Despite much research being conducted within the scope of sustainable tourism, more progress has yet to be made in defining how close or far different countries are from achieving this goal. Consequently, this paper aims to evaluate and compare the commitment of citizens, as tourists, from the 27 member states of the European Union to sustainable tourism. A map of sustainability was developed through the use of machine learning algorithms. A cluster analysis was performed, followed by a sustainable rating. The main findings indicate the existence of three country segments among the European Union member states according to the involvement of its citizens as tourists with sustainable practices: highly committed, moderately committed, and uncommitted. Based on these segments, we proposed a recommendation system that suggests the top-five countries where tourists could exchange sustainable tourism habits based on the idea of contagion or imitation behaviours among individuals across an extensive set of everyday decisions. The results reveal significant variations in sustainable tourism practices across member states, highlighting both challenges and opportunities for harmonisation. By implementing this recommendation system, we facilitate the adoption of sustainable habits among tourists and stakeholders, driving a more unified approach to sustainability in the multiple tourism destinations. This study shows no convergence between the 27 European Union member states regarding sustainable tourism. Therefore, political policies are necessary so that all citizens converge on sustainable tourist habits and the European Union contributes, as a whole, to sustainable tourism.

Keywords: sustainable tourism; clustering; machine learning; tourists’ sustainable behaviours; sustainability information availability; recommendation systems (search for similar items in EconPapers)
JEL-codes: L M M0 M1 M10 M11 M12 M14 M15 M16 (search for similar items in EconPapers)
Date: 2024
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