No abstract

Transition management to tourism sustainability is fundamental for mature and mass tourism destinations. While the literature has largely focused on residents’ evaluations of tourism sustainability, little attention has been devoted to investigate tourists’ perceptions and relevance of environmental practices at the destination. This study fills this gap by analyzing tourists’ evaluations of environmental sustainability experienced during their holiday at a mass and mature tourism destination, by focusing on both tourism-based and complementary products. Mapping and cluster analysis are carried out on a sample of tourists who have chosen an Italian mass and mature tourism destination for their holidays. Findings show that sustainability is a less relevant factor and provides less satisfaction with respect to other aspects. Tourist expectations on environmental sustainability are not met; different perceptions and priorities on environmental sustainability are detected in the different tourist clusters. Managerial and policy strategies can be derived from these results.

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We investigate if and how climate indicators and web-traffic data may improve the estimates of demand functions’ parameters, considering specific origins and destinations. Overall, augmented demand functions show better fit and more reliable price and income elasticities whether the demand is measured with arrivals or with overnights. However, heterogeneity stemming from the main type of tourism (business vs. cultural vs. sea and sun) affects both the web-based and the climate indicators better describing tourists demand as well as their optimal lags. Our findings highlight the utility of such prompt and territorial detailed information for local policymakers, showing, however, how sensitive different demand segments are to policy intervention.

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What happens in forecasting problems when high frequency and high spatial detail data encounter significant publication delays? In this paper, we consider a monthly dynamic panel data model, augmented by Google Trends search query volume data, for tourism demand forecasting at high spatial detail, in which one of the main aspects is represented by a publication delay ranging from 8 to 15 months. Some findings in the tourism literature already specify forecasting/nowcasting applications considering a realistic time delay but not for more than 3 months.

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MICE is one of the most profitable sectors of the tourism industry, with an effective impact on local economies. However, the investigation of its spatial agglomeration has not been fully explored. In this study, we use information on the venues' characteristics and territorial distribution to identify convention firms' agglomeration in Emilia‐Romagna (Italy), through a constrained clustering algorithm. This approach enables the inclusion of both non‐geographical aspects, measured by the Krugman sectoral index, and geographical information. Findings show the existence of developed and developing convention agglomerations, with different MICE offers. Using convention demand data, we improve the cluster's profiles and propose some managerial strategies.