Bozana Zekan

Discussion on the growth limits and carrying capacity of tourism destinations is not new. Already for decades, carrying capacity has been at the core of sustainable tourism development and aims at offering ‘time/space-specific answers’ for individual localities of various European regions. There are many definitions of this concept and the calculation of a single ‘magic number’ quantifying the carrying capacity is infeasible for reasons such as differences in the thresholds established by visitors and residents, ecological limits, various resources, etc. The discussion about carrying capacity in the context of regional sustainability is linked to human activities impacting a region. This impact has to be within the region's ecological limits and consistent with the region's social and economic constraints in order to ensure adequate supporting functions for the population living in the region. This means that regions should learn as much as possible about the impact of tourism on their destinations in order to develop solid and adequate policies for regional and tourism development. This paper therefore introduces a novel methodology for assessing carrying capacity in tourism destinations, which (a) is specific enough to cater to destination-specific needs, as verified by pilot-testing on various representative case studies, and (b) is general enough to be applicable to any tourism destination throughout European regions. The results emphasize the importance of such a hands-on actionable methodology, while at the same time underlining the importance of dialogue between different stakeholder groups. The value added of the developed methodology is that it simultaneously addresses regional sustainability and tourism development, while acknowledging the fact that there is no single metric or value for carrying capacity. Finally, it is applicable to various types of destinations.

Destination managers aim at assuring high visitor satisfaction. Efficiency in satisfaction building has been a neglected issue in tourism research. In this study the authors examine the levels of efficiency that ski resorts attain in pursuing this objective. Configurations of satisfaction dimensions lead to some level of overall satisfaction. If high ratings for all dimensions are not necessary for achieving top overall satisfaction, destination managers get room for efficiency improvement. In a mixed-methods approach the authors analyze data from a sample of 54 Austrian, French, German, Italian, and Swiss ski resorts based on a survey totaling 12,234 cases. The two methods, qualitative comparative analysis (QCA) and data envelopment analysis (DEA), complement each other. An individual satisfaction item contributes to overall satisfaction depending on the simultaneous value of other items. QCA shows the holistic effect of such item configurations. DEA extracts the differences with regard to efficient satisfaction building and paves the way for resort benchmarking by proposing best-fitting benchmarking partners. Results indicate that destinations need not deliver top service quality in all satisfaction dimensions to achieve above average overall satisfaction. Thirty-three out of 54 resorts turn out to be inefficient in their satisfaction-building efforts.

This study employs a GVAR model on the passenger numbers of the top 20 busiest airports of the world and the Asia-Pacific and Latin America-Caribbean regions. With air passenger numbers representing a demand measure, country-level proxies for economic drivers are included as domestic and foreign variables. In terms of ex-ante forecast accuracy, the GVAR model performs best for several airports – yet not for the entirety of airports – compared to four benchmarks for horizons one and three quarters ahead. It also achieves several second and third ranks for these and two other horizons and when all horizons are evaluated jointly. Considering the connectivity of airports is worthwhile to achieve accurate and economically interpretable air passenger demand forecasts.

Airbnb has a major role to play in the competitiveness of the overall accommodation sector of individual destinations and it is rather unlikely that this role will diminish in the post-COVID-19 recovery of the tourism industry. Therefore, the present study motivates the Airbnb sector to look back at its past performance for insights that can be used in setting post-pandemic targets. In particular, this research assesses competitiveness of the Airbnb listings of 28 European cities by including hotel-related data as uncontrollable input variables within interactive data envelopment analysis modeling. The contribution lies in joining Airbnb listings and hotels into the benchmarking discussion and efficiency analysis, along with looking beyond the cumulative number of listings by dissecting the overall sector into commercial and private listings—something that has not been attempted as of yet, in spite of the ever-growing body of literature on the sharing economy.

One of the weaknesses of past tourism benchmarking studies is treatment of all indicators as equally important, which is oftentimes a consequence of lack of data. Therefore, implications derived from such analyses may not be given a full attention by the affected stakeholders, as in real life situations, they are more likely to allocate a different weight on different objectives for their organizations/destinations. This is where the current study comes in: it delves into inspecting the impact of managerial judgment (i.e., weights) in city destination benchmarking by applying data envelopment analysis (DEA). A rather interesting finding is that the benchmarking partners are allocated based on the weighting of each objective. Thus, this clearly points toward the importance of taking the stakeholders’ judgment into account if aiming at a more complete interpretation of the efficiency scores—an area that is indisputably fully unexplored within the destination benchmarking domain to date.

Tourism is a comprehensive, highly diversified and strategically important industry that plays a strong driving role in national economic and social development. Cities have become the key nodes of world tourism development. This chapter focuses on the development of tourism in more than 100 cities in Europe and expounds upon the main trends of world tourism development as reported by European Cities Marketing and the World Tourism Cities Federation, the two leading organizations in the field of city tourism. Regarding the pattern of global tourism development, the share of the European international tourism market keeps shrinking, tourism in American cities remains stable and tourism in Asia-Pacific region cities continues to expand under the influence of globalization. For Europe, the underlying causes of the changing nature of city tourism in Europe are analysed in more detail. Greater global mobility, the information boom and increased knowledge-sharing between people of recent years have facilitated the development of urban structures and led to higher levels of city tourism demand. This growth, however, may prove to be a burden for cities and comes at a price if it is not planned properly.

This study models European Airbnb occupancy from January 2017 to December 2020 for 43 European countries within an econometric panel-data framework. Besides typical economic covariates, the COVID-19 Stringency Index is employed to allow for the recent pandemic. The counterfactual observations created by two versions of the econometric model (one with and one without European aggregates of macroeconomic controls) are benchmarked against traditional univariate time-series models and a seasonal Markov-switching autoregression. For pandemic times, the latter performs very well in terms of several forecast accuracy measures due to its ability of (a) detecting the pandemic-induced structural break and (b) accounting for seasonal patterns in the data. The econometric models also predict well as they outperform the time-series models in the majority of cases. Hence, the panel-data approach is suitable when looking for economically interpretable Airbnb occupancy forecasts, even during the time of the pandemic.

The development of indicators and metrics systems has been identified as being of paramount importance by many tourism boards and international tourism organizations. This article discusses the bottom-up, micro-level approach of TourMIS, which is a platform for exchanging tourism statistics among tourism organizations, for collecting measures of sustainable urban tourism development. The authors provide a synthesis of various frameworks for sustainable tourism indicators for subnational regions and cities, concluding that it is more feasible to analyse existing sustainable tourism indicators than to introduce new measures lacking in direct practical applicability for the organizations. The application of data envelopment analysis (DEA) for benchmarking urban tourism destinations is then demonstrated by assessing measures available in TourMIS. Findings include inefficiency scores that suggest both managerial and political implications. Furthermore, the concept of a virtual reference destination assisting managers and politicians to analyse their destination’s strengths and weaknesses is introduced.

Using spatial panel data comprising a cross section of 1,461 continuously active Airbnb listings obtained from AirDNA, as well as time series data from NYC and Company and the OECD covering the time period September 2014 to June 2016, the present study quantifies own price, cross price, and income elasticities of Airbnb demand to New York City within an empirical tourism demand framework. The particular goal of the study is to establish whether the relationship between Airbnb and the traditional accommodation industry is of a substitutional or of a complementary nature. Employing a one-way fixed-effects spatial Durbin model, it can be concluded that demand is price-inelastic for Airbnb accommodation in New York City, which is a luxury good, and that the city's traditional accommodation industry as well as neighboring Airbnb listings are substitutes for the investigated Airbnb listings. The estimation results are robust against several alternative specifications of the regression equation.

Destination managers aim at increasing and maintaining visitor satisfaction. In this study the authors examine the levels of efficiency which ski resorts attain in pursuing this objective for a sample of 54 Austrian, French, German, Italian, and Swiss ski resorts based on a survey totaling 12.234 cases. Configurations of resort attributes lead to some level of overall satisfaction. An individual satisfaction item contributes to overall satisfaction depending on the simultaneous value of other items. Qualitative Comparative Analysis (QCA) is tailor-made for demonstrating the holistic effect of such item configurations. Data Envelopment Analysis (DEA) extracts the differences with regard to efficient satisfaction building and paves the way for resort bench-marking by proposing best-fitting bench-marking partners. As results indicate, destinations need not deliver top service quality in all satisfaction dimensions to achieve above average overall satisfaction.

The COVID-19 pandemic has been a major shock to the global tourism industry. Given its peculiarity, this paper analyzes one of the most intriguing questions in the Airbnb literature – the pricing of Airbnb listings – by taking advantage of a difference-in-differences methodology that largely draws on variations in country-level policy responses to the pandemic. Relying on a dataset containing weekly information from 130,999 continuously active listings across 27 European countries from 2019 to 2020, this study first investigates the exogenous impact of response policies (proxied by the COVID-19 Stringency Index) on demand. Secondly, accounting for the endogeneity of both demand and prices, this research analyzes pricing responses to demand variations. Results show that: i) increases in the COVID-19 Stringency Index cause significant declines in Airbnb demand; ii) increases in demand cause, on average, increases in Airbnb prices; and iii) pricing strategies between commercial and private hosts differ substantially.

Data envelopment analysis (DEA) is a non-parametric, deterministic method for evaluating performance. DEA estimates best practice production frontiers and measures the relative efficiency of peer entities. These entities are called decision-making units (DMUs) and are assumed to be homogeneous. This assumption implies that all DMUs (e.g., hotels, Airbnb listings, destinations) pursue the same or at least similar goals (e.g., increasing occupancy rate, increasing guest satisfaction). Then, their performances are evaluated. DEA has been successfully used in various contexts, with continuous applications across disciplines and sectors as diverse as agriculture, Airbnb listings, banking, education, hospitals, hotels, transportation and travel agencies. The literature also highlights the method’s superior benchmarking abilities over other operations research techniques. This characteristic alone makes DEA interesting to decision makers. It gives them an opportunity to improve the performance of their DMUs. Benchmarking is all about improvement.

Airbnb is arguably the world’s most popular accommodation sharing platform. Its impact on demand and supply within the tourism and hospitality industry is nowadays unquestionable. The present study delves into inspecting the efficiency of Airbnb listings of European cities, as, in spite of the success of Airbnb as a whole, it cannot be presupposed that all listings are equally successful. More specifically, data envelopment analysis (DEA) is employed in this first comprehensive benchmarking attempt within the domain of the sharing economy to date. This article also makes a contribution to robustness by introducing an interactivity note to the base model, thus, inspecting the results for corroboration/discrepancies and going beyond the static analyses that are common in DEA modeling. Ultimately, this is done with the goal of highlighting opportunities for inefficient Airbnb listings to properly utilize their inputs and therefore become more competitive.

The COVID-19 pandemic has restricted both business and social life over the last two years. Stop-and-go policies enacted as containment measures have further impacted the global economy, and tourism in particular. Tourism demand shows only weak signs of a sustainable recovery. The medium-term outlook remains highly uncertain, and yet few studies have addressed the development of the tourism and leisure industries in the years ahead. In this context, we forecast demand in selected EU countries in terms of total expenditure on outbound travel (tourism imports) using a panel pooled Fully Modified Ordinary Least Squares (FMOLS) approach. Baseline and downside scenarios are elaborated to project demand for foreign travel until 2025.

There are many erroneous but pervasive ‘truths’ about tourism. This study assesses individuals' capacity to question these myths alongside their self-perceptions of their critical thinking skills. The research used a survey with 1493 respondents from 22 universities across 16 countries/territories to test the Dunning Kruger effect, which suggests an inverse relationship between self-belief and competence. The data provides strong evidence of the Dunning Kruger effect insofar as those more likely to believe in tourism myths also had a greater tendency to overestimate their capabilities, and vice versa. We discuss the possible causes and the implications for tourism education, identifying potential interventions at different points along learners' developmental journeys to help ensure a more sustainable future for tourism scholarship and practice.