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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. [ABSTRACT FROM AUTHOR]

The prices of cash crops impact the livelihoods of millions of households in developing countries. While the influence of speculators on global commodity prices determined through derivatives exchanges is extensively discussed, the contribution of hedgers to short‐term changes in futures prices has largely been disregarded in the financialization of commodities discourse over the past two decades. This results in a failure to account for the interconnected activities of increasingly consolidated lead firms within physical global value chains (GVCs) and derivatives markets. This article examines the pricing and hedging strategies of lead firms in the coffee, cocoa and cotton GVCs in relation to their activities on commodity derivatives markets. Based on Open Interest data as an indicator of derivatives markets activity, a measure of buying and selling pressure by trader categories is applied in a Generalized ARCH (GARCH) model. The findings of this article show that hedgers' activities allow speculators to drive global benchmark prices so that they can benefit through combinations of financial hedging and physical trading strategies. As these practices of lead firms contribute to the transmission of futures prices along GVCs, smallholders and other actors in cash crops in producer countries are exposed to heightened price changes. [ABSTRACT FROM AUTHOR]

The sharing economy has allowed people from all over the world to more effectively utilize their assets. Owners or controllers of assets in the sharing economy are free to set any price they want subject to prevailing market demand as long as they operate in an imperfectly competitive market environment. This paper examines how various characteristics of an Airbnb listing (size, number of photos, ratings, host responsiveness, superhost status, distance from city centre, etc.) affect the prices of accommodation and determines which factors strongly affect price using weighted least squares (WLS) and quantile regression. A hedonic pricing model was developed and applied to data from the cities of Barcelona, Madrid, and Seville to determine how the different characteristics of an Airbnb listing affect the price of accommodation in these major three Spanish tourist cities. The estimation results, which are resilient to various robustness checks, indicate that overall rating as well as characteristics indicative of the size of the accommodation have the strongest positive influence on price, while the number of reviews and distance from the city centre have the strongest negative influence on price. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Recently, blockchain and cryptocurrencies have become topics of discussion in both research and industry. Iansiti and Lakhani perceive blockchain as a foundational technology rather than a disruptive one, since potentially new economic and social systems can be based on blockchain. Therefore, understanding blockchain and contemplating its impact on the tourism and hospitality industry is essential. The tourism and hospitality industry has to focus not on the technology itself but on how it can be used for the benefit of consumers and suppliers, while at the same time creating new tourism products or systems. The purpose of this study is to explore and identify use cases for blockchain for the tourism and hospitality industry. In addition, an outlook on potential future blockchain applications given the current COVID-19 pandemic is provided. [ABSTRACT FROM AUTHOR]

The present study develops a methodology to assess transportation-related CO2 emissions of European city tourism. In doing so, not only is travel distance considered, but also the chosen transportation modes and the particularities of the different cities' source markets. The major contribution of this study is the implementation of this learning methodology into a decision support system for destination management organizations of cities. Based on a sample from 2018 of 48 European cities with at least 40 source markets, the range of aggregate transportation-related CO2 emissions of European city tourism is estimated. Moreover, a longitudinal analysis of the exemplary city of Vienna covering the period of 1990 to 2018 is carried out. Finally, some policy recommendations of how destination management organizations can contribute to make the estimated transportation-related CO2 emissions even more precise and on how to make European city tourism more environmentally sustainable are drawn. [ABSTRACT FROM AUTHOR]

The present study investigates the role of ecotourism as a potential catalyst for land sparing in Latin America, with a particular focus on indigenous and community-based ecotourism. The research question is investigated within a comprehensive empirical land sparing–agricultural expansion framework, which uses the Jevons paradox as its theoretical foundation. It also allows for environmental governance and includes several socioeconomic control variables. In doing so, a panel data set based on secondary data from institutional sources and comprising 10 Latin American countries for the period from 1995 to 2015 is employed, which resulted in 209 observations in total. Panel estimation results show that there is only moderate evidence of land sparing associated with ecotourism, when it occurs on indigenous peoples' and local communities' land. To achieve land sparing through ecotourism, titling land to indigenous peoples and local communities as stakeholders is therefore crucial, but this beneficial effect should not be overestimated. [ABSTRACT FROM AUTHOR]

This study uses geotagged photos from Instagram to identify differences between the popular places in Vienna for residents and visitors. Moreover, we explore whether geotagged data can be useful in determining tourism demand in Vienna. The spatial analysis of 627,632 geotagged photos reveals the top-50 locations in Vienna for all-, local-, and visiting-Instagram users based on three popularity indicators (numbers of likes, comments, and photos). The results show that the top locations unique to local users are closely linked to activities residents usually pursue or location types they usually visit at their place of dwelling. In using geotagged photos to predict actual tourist arrivals to Vienna, we conclude that only the popularity indicators number of likes and number of comments based on the location ID "Vienna, Austria" for visitors to Vienna should be used and not the number of photos, since this indicator does not automatically generate engagement. [ABSTRACT FROM AUTHOR]

Facebook is a popular social media platform used by both the demand and the supply sides of the tourism industry. Since there is a huge amount of information on the Internet, which can lead to information overload, individuals tend to apply the principle of least effort in attempting to obtain useful information as quickly and easily as possible. One of the easiest ways to retrieve travel information is by visiting the Facebook pages of destinations. This study investigates the foundations of the usefulness of Facebook Statistics: in particular of likes on DMO Facebook pages as a potential predictor of tourism demand, in addition to previous arrival numbers. In- and out-of-sample results show that the DMOs of Graz, Innsbruck, Salzburg, and Vienna can already utilize likes as an expedient leading indicator for demand, albeit not the only one. These findings are recommended to be incorporated into the DMOs' marketing efforts. [ABSTRACT FROM AUTHOR]

The emergence of peer-to-peer (P2P) accommodation (e.g. Airbnb) has steadily increased the pressure on the traditional accommodation sector. Although Airbnb listings are perceived as being more affordable than hotels, this has not yet been conclusively demonstrated. Therefore, the aim of this study is to investigate whether significant price dependencies exist between the Airbnb and traditional accommodation sectors and to analyze the underlying pricing strategies. For this purpose, the Estonian capital city of Tallinn is used as a case example. Airbnb data, prices and locations of hotels in Tallinn, as well as spatial information such as distance to points of interest (POIs), and so on, are used in hedonic price regression models. The results show that Airbnb pricing positively depends on characteristics of the listing and the number of POIs within an optimal 650 m radius, which is obtained from a simulation study. Also, prices of hotels and of other Airbnb listings within the same radius positively impact Airbnb listing prices. Finally, Airbnb accommodations are shown to indeed be the more affordable alternative. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]