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The purpose of this article is to describe the characteristics and the consumption behaviour of tourists coming from different countries and choosing different holiday typologies (cultural, seaside, mountains, etc.), by evaluating their expenditure in terms of consumption functions and productive sectors. The analysis — which uses the results of the survey on tourist expenditure carried out in the Veneto region, with particular reference to international tourism, from May 1994 to April 1995 —, can be suitably extended over the local scale. A multisectoral‐biregional input output model (VERDITOUR) has been implemented to measure the economic role of each segment and the plot of interactions going from tourist expenditure habits to the tourism industry.

This paper analyses the consumption habits of tourists coming from different origins and choosing Italy as a holiday destination, in terms of expenditure level and composition. The economic role of both international and domestic demand and the plot of interactions going from tourist expenditure behaviour to the tourism industry have been measured. Furthermore, the impacts which localised expenditure (total and by item) can generate in each regional economic system are discussed, and estimates of the “centralising” and “leakage” effects are presented. The balance between these effects selects the net centralising regions of economic benefits deriving from tourism demand localised everywhere, with respect to those regions producing a net leakage of the positive effects generated by the demand localised in them.

The purpose of this paper is to review recent studies published from 2007 to 2015 on tourism and hotel demand modeling and forecasting with a view to identifying the emerging topics and methods studied and to pointing future research directions in the field.

Articles on tourism and hotel demand modeling and forecasting published mostly in both science citation index and social sciences citation index journals were identified and analyzed.

This review finds that the studies focused on hotel demand are relatively less than those on tourism demand. It is also observed that more and more studies have moved away from the aggregate tourism demand analysis, whereas disaggregate markets and niche products have attracted increasing attention. Some studies have gone beyond neoclassical economic theory to seek additional explanations of the dynamics of tourism and hotel demand, such as environmental factors, tourist online behavior and consumer confidence indicators, among others. More sophisticated techniques such as nonlinear smooth transition regression, mixed-frequency modeling technique and nonparametric singular spectrum analysis have also been introduced to this research area.

The main limitation of this review is that the articles included in this study only cover the English literature. Future review of this kind should also include articles published in other languages. The review provides a useful guide for researchers who are interested in future research on tourism and hotel demand modeling and forecasting.

This review provides important suggestions and recommendations for improving the efficiency of tourism and hospitality management practices.

The value of this review is that it identifies the current trends in tourism and hotel demand modeling and forecasting research and points out future research directions.

Image has been found to be one of the important influences in the selection of consumers' choice for visiting and investing in a destination leading to sustainable development. Important determinants of tourism namely, knowledge of destination attractions and image association were employed in this research based on previous studies in a number of fields. The research reported in this paper presents the results of an empirical test of the determinants related to tourism using Liverpool as a case study because of its selection as the European Capital of Culture (CoC) for 2008. European Capital of Culture scheme has among its many objectives the idea of sustainable development for the chosen CoC. Combination of data collection methods was used for the research. The paper contributes to the ongoing debate on destination image association by providing empirical evidence through the case study or Liverpool as well as how consumers relate to a destination and especially a CoC. One of the major findings of the study was the identification of image association clusters with regard to Liverpool as a CoC. We brand this clusters as the ‘tangible attractions cluster’ and ‘intangible attractions cluster’. The results of this research provide important implications for strategic image management and can aid in designing and implementing sustainable marketing programs for creating and enhancing tourism destination images.

This paper aims to compare the forecasting performance of different models with and without big data predictors from search engines and social media.

Using daily tourist arrival data to Mount Longhu, China in 2018 and 2019, the authors estimated ARMA, ARMAX, Markov-switching auto-regression (MSAR), lasso model, elastic net model and post-lasso and post-elastic net models to conduct one- to seven-days-ahead forecasting. Search engine data and social media data from WeChat, Douyin and Weibo were incorporated to improve forecasting accuracy.

Results show that search engine data can substantially reduce forecasting error, whereas social media data has very limited value. Compared to the ARMAX/MSAR model without big data predictors, the corresponding post-lasso model reduced forecasting error by 39.29% based on mean square percentage error, 33.95% based on root mean square percentage error, 46.96% based on root mean squared error and 45.67% based on mean absolute scaled error.

Results highlight the importance of incorporating big data predictors into daily demand forecasting for tourism attractions.

This study represents a pioneering attempt to apply the regularized regression (e.g. lasso model and elastic net) in tourism forecasting and to explore various daily big data indicators across platforms as predictors.

This paper aims to examine tourists’ preferences for package holidays offering different bundles of activities at a winter mountain destination. A winter mountain destination is usually chosen for snow sports, particularly skiing, but increasingly more tourists want to fully exploit their holiday opportunity with an authentic and comprehensive experience of the place. After collecting qualitative data on how tourists spend their typical day, quantitative research is conducted to segment the demand on the basis of tourists’ preferences for bundles of activities undertaken during a winter mountain holiday.

The study uses a mixed method. Two focus groups are included to understand how tourists spend their time at a winter destination; results are then used to identify the components of the holiday, which are then combined in eight packages using an orthogonal array. A questionnaire is administered to a sample of 273 tourists at a well-known mountain destination to measure their preference for different packages. Results are analyzed using factor analysis, conjoint analysis and cluster analysis.

The most significant findings are as follows: winter mountain holidaying is a highly segmented market. Even at a mountain destination strongly associated with skiing, there are many tourists who do not ski and spend their time doing something else; food and beverage, and all their related activities, are at the top of all tourists’ interests, and passionate skiers very highly rate the experience of tasting, eating, understanding and buying local food; and there are four segments of winter mountain holiday tourists who show very differentiated interests for the different activities that can be experienced at a mountain location.

This paper considers what lies beyond sport at winter mountain destinations, and it reveals new possibilities for configuring bundles of activities to attract different segments of tourists.

This article generally deals with the level of acceptance the population of Tyrol showed for the Olympic Games in Innsbruck in the past (1964 and 1976) and in these days (applications for 2002 and 2006). Moreover, it explains the reasons which led to a decrease in sympathy for Olympic Games among the local population during the last years. Particularly most people in the city think, that tourism drives up prices, increases traffic, reduces parking space, destroys the environment and so on. On the other hand, the people in the country‐side, especially in the tourism resorts, have a more positive opinion about tourism, because they see the advantages for them directly. In 1993, the application for the Olympic Winter Games 2002 was negative with 73% of the votes. In 1997, a second referendum for an application for 2006 was held. The result in the province of Tyrol was positive with a majority of 69%, but the voters in the city of Innsbruck rejected the proposal by 53%. The author works out the thesis, that a well organized campaign could have had a positive result already in the first referendum in 1993. He also points out, that one of the main mistakes was the fact that there were no engaged entrepreneurs supporting the idea of Olympic Games, but mainly politicians and functionaries. Other reasons for the rejection and increased scepticism of the population are the concentration on the city of Innsbruck, the lack in clearly demonstrating the advantages in financial income, infrastructure and employment as well as the opinion that tourism only meets the interest of the hotel industry.

The digital revolution is causing profound transformations, characterised by the way companies and customers approach the market, helping to significantly transform the tourism sector. The aim of this study is to investigate how small hospitality businesses use internet in terms of marketing.

After analyzing the literature, which concerns the relationship that has gradually developed over the years between small businesses in the tourism sector and the use of the internet, it was decided to carry out an empirical survey conducted among the subjects managing micro-activities in the hospitality sector. The research hypothesis is that the adoption of the website is conditioned by the size of the company and the type of experience one has with small businesses.

The results show how small hospitality businesses make limited use of booking portals and social networks, and it emerges how digital marketing policies are crucial to achieve success, both in Italy and abroad.

It is believed that this work can provide useful insights that can be useful for the local government, aimed at promoting a greater ability of small businesses to participate more actively in the competition.

The purpose of this paper is to analyse the potential benefits of identifying homogeneous territorial units of the urban-tourism space at a local scale.

The territory is an essential variable for designing tourist activities adapted to the characteristics of each urban-tourism space. However, your consideration presents a series of problems, including the lack of alphanumeric, microscale, georeferenced statistical information. The territorial segmentation of the tourist accommodations supply is approached as a methodology, a technique and an instrument that can be used to apply marketing strategies in coastal tourism areas.

One of the most important results is that territorial segmentation is a methodology and technique that can mitigate this issue because it is well-suited to defining spatial patterns of tourist behaviour through the delimitation of territorial units that have a certain degree of homogeneity.

The idea of territorial segmentation is the ideal technique for understanding tourists and their behaviour in the territory by integrating all the variables that intervene in a trip, the different aspects of the destination and data regarding tourist behaviour, allowing them to be understood at the greatest level of territorial disaggregation and making it a good tool for public and private actors, capable of facilitating intelligent decisions in strategic territorial planning and in defining the marketing approach of tourism companies.

This study attempts to identify and explicate the unique segmentation of the increasingly growing virtual reality (VR) user market based on the user experience. Consequently, it collects five hundred forty-five online survey questionnaires through the Prolific platform and deploys cluster analysis to identify mutually exclusive groups of VR users. The research variable, user experience, contains 16 indicators explained by four dimensions. As a result, this study is able to unveil three mutually exclusive markets which are labeled as (1) beginner, (2) aficionado, and (3) utilitarian. The unique features of these three groups are further compared based on their VR tour behaviors. In the conclusion section, it offers managerial implications for devising novel marketing strategies.