The purpose of this study is to propose a suitable measure for evaluating the effectiveness of regional destination management towards building a successful destination image. The study introduces a new methodology that examines the similarities and differences of the views between the tourism demand side as expressed by tourists’ opinions on TripAdvisor and the supply side as expressed by local policy makers. Analysis of quantitative and qualitative survey data reveals measurable discrepancies caused by local strategy and policy decisions and by attributes of the destinations. Destination management organizations could conduct similar surveys to evaluate the effectiveness and overall success of local branding and destination promotion strategies. This is one of the few studies focusing on the individuals with key roles in local tourism management, comparing the perspectives of the demand and supply side and identifying reasons for failure, based on factors acknowledged by the local policy makers.

The objective of this paper is to analyse the changes that occurred in the regional disparities and sectoral specialisation of the Greek regions due to the economic crisis. The research problem is to identify the effect that the crisis had on the developmental perspectives of the regions and on regional policy priorities. In this framework, we explore the regional disparities, along with the allocation and specialisation of economic sectors in two separate time periods: the pre-crisis period (2000–2007) and the crisis period (2008–2014). The variable used is regional employment in the branches of economic activity. The methods used are Coefficient of Variation, Location Quotients and Shift-Share Analysis. According to the results, we classify the spatial units into categories and we propose means of regional policy. The results show that the disparities increased during the first period of the crisis and declined in the next, without, however, reaching the levels of 2000. In the first period the dynamic economic sectors are concentrated mainly in the metropolitan region of Attica and in the island region of South Aegean, while local advantages are shown in several regions except Attica. During the period of crisis, Attica and South Aegean lost their sectoral dynamism, while a few regions resisted. Regarding the local share effects, the more urbanised regions show negative local shares. The rest of the regions exhibit local advantages. Thus, according to these results, a concluding remark is that the more traditional activities seem to be more resilient, unlike the modern activities, which seem more sensitive to the crisis and are located mainly in the large cities and the most urbanised regions of the country. Regarding the proposed regional policy means, infrastructure improvement is indicated for most of the urbanised regions in order to improve their developmental environment. For the other regions, a more balanced sectoral structure must be promoted. Of course, in order to propose more targeted policy measures, it is important that regional development features (according to the classification of the regions and the proposed policy measures) be adapted to smaller areas and to a greater number of narrower economic sectors, rather than simply applying them at the regional level. This is also true of the effect that some other factors such as human capital and innovative capacity have on regional resilience. Future research will focus on this.

This study investigates the impact of two consecutive economic shocks—the 2009–2018 economic crisis and the 2019–2022 COVID-19 pandemic—on the resilience and recovery of Greece’s 13 NUTS II-level Administrative Regions. Using secondary data on employment per sector from the Greek Statistical Authority and Eurostat, resilience is assessed through resistance and recovery indices, complemented by shift-share analysis to identify sectoral and local influences on regional performance. Regions with robust agricultural bases displayed consistent resistance and recovery, while tourism-dependent regions transitioned from high to low resistance during the pandemic. Urban centres like Attiki showed enhanced resilience due to concentrated economic activity and local advantages, forming a high-resistance, fast-recovery cluster. Consequently, regions with local advantages are more resilient in times of crisis. Sectors developing locally due to these advantages strengthen economic resilience, whereas dynamic sectors at the national level relying on external environments are more vulnerable, recovering more slowly and impacting host regions. Strong concentration economies alongside local advantages further bolster resilience. Strengthening local economic structures and diversifying activities are crucial for mitigating disparities. This study underscores the importance of tailored sustainable policies to enhance resilience and ensure long-term regional sustainability.

Culinary authenticity is central to the tourist experience, offering sensory and symbolic access to a destination’s culture (Bessière, 1998). It enhances satisfaction and deepens engagement with pl...

A significant amount of research has been conducted regarding the resilience of the regions and the factors that contribute to allow them to face challenges, crises, or disasters. The rise of promising sectors like Machine learning (ML) and Artificial Intelligence (AI) can enhance this research using computing power in regional economic, social, and environmental data analysis to find patterns and create prediction models. Through Machine Learning, the following research introduces the use of models that can predict the performance of a region in disasters. A case study of the performance of USA Counties during the Covid19 first wave period of the pandemic and the related restrictions that were applied by the authorities was used in order to reveal the obvious or hidden parameters and factors that affected their resilience, in particular their economic response, and other interesting patterns between all the involved attributes. This paper aims to contribute to a methodology and to offer useful guidelines in how regional factors can be translated and processed by data and ML/AI tools and techniques. The proposed models were evaluated on their ability to predict the economic performance of each county and in particular the difference of its unemployment rate between March and June of 2020. The former is based on several economic, social, and environmental data -up to that point in time- using classifiers like neural networks and decision trees. A comparison of the different models' execution was performed, and the best models were further analyzed and presented. Further execution results that identified patterns and connections between regional data and attributes are also presented. The main results of this research are i) a methodological framework of how regional status can be translated into digital models and ii) related examples of predictive models in a real case. An effort was also made to decode the results in terms of regional science to produce useful and meaningful conclusions, thus a decision tree is also presented to demonstrate how these models can be interpreted. Finally, the connection between this work and the strong current trend of regional and urban digitalization towards sustainability is established.

This paper investigates the impact of demographic changes on regional specialisation across Greek NUTS2 regions from 2001 to 2021. It explores the relationship between age-specific variations in the economically active population and gross value added (GVA) across different economic sectors. Using demographic and GVA data alongside spatial analysis techniques such as spatial regression models, median Local Moran’s I, and location quotient, the study identifies spatial autocorrelation patterns. Findings reveal strong correlations between the economically active population and GVA, highlighting demographic factors’ crucial role in regional economic performance. Over the period 2001 to 2021, the 15–24 and 25–34 age groups generally experienced declining growth rates in total, male, and female economically active populations, while the 45–54 and 55–64 age groups saw increases. Regions adjacent to those with higher economically active populations showed high–high clustering. Especially, Attiki and Kentriki Makedonia, the two metropolitan regions of Greece, influence neighbouring regions through economic spillover effects, fostering economic sector clustering and emphasising regional specialisation. These findings highlight the complex interplay between demographic shifts and regional economic outcomes, noting spatial disparities and the need for targeted policy interventions. The study provides empirical evidence demonstrating how demographic changes influence regional economic development across different regions.