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15. Islands' Tourism Seasonality: A Data Analysis of Mediterranean Islands' Tourism Comparing Seasonality Indicators (2008–2018).

Author

Ruggieri, Giovanni and Platania, Marco

Abstract

Tourism seasonality as a tourist demand concentration in specific seasons is still a big challenge for Mediterranean islands' tourism policies. Overtourism occurs during high tourist seasons, and undertourism occurs during low tourist seasons, creating undesirable economic, social, and environmental effects on sustainability. However, the imbalance of tourism demand, specifically in fragile territories such as islands, needs specific policies based on scientific evidence. This article examines tourism seasonality dynamics in the Mediterranean islands, thoroughly analysing tourist overnights on island destinations. The adopted methodology uses a set of statistical indicators, such as peak factor intensity, calculated in a time series analysis from 2008 to 2018. The results of this article allow us to understand the importance for the Mediterranean islands to plan a resilient, sustainable, and harmonious tourism policy, using strategies to contain seasonal peaks and creating new tourist demand in the low season months. [ABSTRACT FROM AUTHOR]

Published
2024
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27. Interfirm Network Analysis in Marginal Tourist Destinations: The Mediator Company in Business Relationships

Author

Ruggieri, Giovanni, Varvaro, Gaspare, Iannolino, Salvatore, and Ruggieri G., Varvaro G., Iannolino S.

Subjects
Behavioral Economics, tourism destination, Economics, cooperation, Settore SECS-P/06 - Economia Applicata, network analysis, development, network analysis, tourism destination, cooperation, development, tourism enterprises, tourism enterprises, Tourism
Abstract

The literature on tourist destinations and the use of empirical approaches evidence the need to adopt this model to increase tourism economies. In model application, the enterprises, and local stakeholders, sometimes represent limitations or opportunities. This article opens the view to tourist destination networks, described as relational structures that can influence and determine the destination-building process. The Network Analysis methodology offers a better understanding of inter-firm relational dynamics when applied in a small context, and in this case, the application was undertaken on the island of Sicily, in Menfi town and its hinterland. Findings show the presence of a company that assumes a central role in the network, maintaining a solid web of relationships between others. In this way, a mediator company is essential in remote tourist destinations. Policymakers need to facilitate the development of tourism companies, thereby increasing the relationality and business network ties among them.

Published
2022

28. A Relational Approach to Networks in a Tourism Destination: Business and Family Networks in San Vito Lo Capo, Sicily

Author

Ruggieri, Giovanni, Iannolino, Salvatore, and Ruggieri G., Iannolino S.

Subjects
Tourism and Travel, social network analysis, tourism destination, tourism business, tourism destination, social network analysis, Geography, Economics, tourism business, Settore SECS-P/06 - Economia Applicata, Tourism
Abstract

This article constructs a relational framework using the principles of the Network Approach to examining the business exchange structure of a tourist destination. Network Analysis is the methodology to analyse the metrics of collaboration and cooperation among destination companies. The model was applied in a remote tourist destination named San Vito Lo Capo on the island of Sicily, where tourism has significantly expanded in the last twenty years. The focus is on how groupings of small companies within family relations can govern and be responsible for tourism destination cooperation. As the main result, the existence was identified, of a relational framework where three clusters of families with a high density of exchanges emerge. These families can influence the tourism business at the destination, guaranteeing cooperation among other business companies. The findings show the existence and the importance of informal business networks and the contribution of Network Analysis to understanding the structure and cohesiveness of a tourist destination.

Published
2022

40. IJIR Table of Contents Vol. 2

Author

Ruggieri, Giovanni, Calò, Patrizia, Griffin, Kevin A., and Raj, Razaq

Subjects
Tourism and Travel, Geography, Economics, Tourism
Published
2021

48. IJIR Title Page and Table of Contents Vol. 1(1)

Author

Ruggieri, Giovanni, Calò, Patrizia, Raj, Razaq, and Griffin, Kevin A.

Subjects
Tourism and Travel, research, Geography, Economics, islands, International Economics, Tourism
Published
2020

49. Approaching explainable recommendations for personalized social learning the current stage of the educational platform”WhoTeach”

Author

Cataldo Musto, Daniele Magazzeni, Salvatore Ruggieri, Giovanni Semeraro, Marconi, L, Matamoros Aragon, R, Zoppis, I, Manzoni, S, Mauri, G, Epifania, F, Marconi L., Matamoros Aragon R. A., Zoppis I., Manzoni S., Mauri G., Epifania F., Cataldo Musto, Daniele Magazzeni, Salvatore Ruggieri, Giovanni Semeraro, Marconi, L, Matamoros Aragon, R, Zoppis, I, Manzoni, S, Mauri, G, Epifania, F, Marconi L., Matamoros Aragon R. A., Zoppis I., Manzoni S., Mauri G., and Epifania F.

Abstract

Learning and training processes are starting to be affected by the diffusion of Artificial Intelligence (AI) techniques and methods. AI can be variously exploited for supporting education, though especially deep learning (DL) models are normally suffering from some degree of opacity and lack of interpretability. Explainable AI (XAI) is aimed at creating a set of new AI techniques able to improve their output or decisions with more transparency and interpretability. Deep attentional mechanisms proved to be particularly effective for identifying relevant communities and relationships in any given input network that can be exploited with the aim of improving useful information to interpret the suggested decision process. In this paper we provide the first stages of our ongoing research project, aimed at significantly empowering the recommender system of the educational platform”WhoTeach” by means of explainability, to help teachers or experts to create and manage high-quality courses for personalized learning. The presented model is actually our first tentative to start to include explainability in the system. As shown, the model has strong potentialities to provide relevant recommendations. Moreover, it allows the possibility to implement effective techniques to completely reach explainability3

Published
2020

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