Dirk-jan Kamann (p), Petra Gyurácz-Németh

Roberto Patuelli

Facing the Covid-19 pandemic and related Government measures, hotel managers of quite different types of hotels share the very same mechanisms in their response. A nine month four wave panel study used a sensemaking approach applying grounded theory. The narratives resulting from the 47 interviews revealed that (1) managers show isomorphism in sharing the same topics, themes and underlying dimensions on their path to find an adequate answer to survive; (2) the way they implement their response in more detailed policies however shows significant differences. This more idiosyncratic behaviour by top decision makers can be explained by different background characteristics – size, multi-site, market segment, location, ownership and in particular leadership style given specific local network embeddedness. Anchoring control is the key; creating adaptability in employment, operations, marketing and supplier relations provides the means to secure financial and mental health. Successful implementation creates relief; inability to negate events leads to anxiety. Flexibility and rapid decision making are considered essential to meet daily changing challenges; decentralised decision authority proves an advantage. Smaller hotels prove to be more flexible, especially when they are well connected with local networks; hotel chains may offer financial security at the one hand, but may lead to complete closure in favour of other chain members on the other hand.

Riccardo Curtale (p), Filipe Batista e Silva, Paola Proietti, Ricardo Barranco

Dirk-jan Kamann

Tourism was one of the most negatively affected sectors by the outbreak of the COVID-19 and the restrictions put in place by national authorities to contain it. The heterogeneous impact on travel demand across EU regions in EU27 in 2020 underlined a place-specific vulnerability. This study aims at assessing the determinants of regional tourism vulnerability in the context of the pandemic and providing new insight for tourism policy aiming at a more resilient tourism activity. We investigate the vulnerability of European tourism destinations at the NUTS2 level, by considering the percentage loss of night spent in response to the demand shock induced by the pandemic. Nights spent were retrieved from Eurostat's experimental data source on Collaborative economy platforms, which records the number of nights spent by all guests booking accommodation via four different platforms: Airbnb, Booking.com, Tripadvisor, and Expedia for 2019 and 2020 at NUTS2 level. Results from a two-steps fractional response model conducted on 233 NUTS 2 regions from EU countries show that, controlling for socio-economic variables and COVID-19 related restrictions, it is possible to identify destination-related characteristics explaining vulnerability, such as proximal demand, share of foreign tourism, destination type and population density. Proximal demand and the presence of natural assets seems to mitigate destinations vulnerability, which suffers from high share of foreign tourism and a high population density. Further investigation is needed to obtain a detailed seasonal analysis and to explore the impact of additional variables, such as tourism density.

Paolo Figini, Roberto Patuelli (p)

Riccardo Curtale

The measurement of the economic contribution of tourism in national and regional economies is often left to superficial back-of-the-envelope calculations or is constrained to simply assessing the amount of tourist expenses or tourism employment. Recent research has instead called for a more in-depth analysis of the role of the tourism industry, in particular with regard to its extensive production links with many non-tourism industries. To this aim, this paper aims to analyse the economic contribution of tourism to production, income and employment for a fairly large sample of European countries, by making use of standardized economic reporting tools such as the tourism satellite accounts (TSA) and input-output tables (IOTs). Connecting expense data from TSA to the inter-industry dependencies structure imposed by the IOTs allows us to evaluate the indirect contribution of tourism to the economy by computing production, income and employment multipliers. Multiplicative effects can then be computed also by industry, to evaluate which non-related industries are most influenced by tourism expenditure. We apply our TSA-IOT computation procedure to both pre-COVID tourism data and to (estimates of) pandemic expenditures, in order to assess the extent of income and employment losses, and where these losses are located in terms of tourism and non-tourism industries, in order to guide policymakers in both pre-emptive and ex-post (compensative) policies.