“If I was going to die I should at least be having fun”: Travel blogs,meaning and tourist experience

Travel blogs are an under-utilised resource for researchers of tourism experiences. They can provide rich insights on how tourists express the transformational effects of their experiences for the self. This study of travel blogs by nineteen British bloggers reveals how elements of the narrative relating to self-reflection and emotions are central to the process of transforming their travel experiences into personally meaningful experiences. Bloggers implicitly and explicitly express how travel contributes to self-identity, signalling self-development. The study contributes to knowledge about the lasting impact of long term travel on people, adding meaning symbolic of an evolving self.     

Domestic tourism expenditures: The non-linear effects of length of stay and travel party size

The independent variables length of stay and travel party size are recurrent determinants of expenditures in micro level tourism studies. In most of this research it has been tacitly assumed that these variables have linear effects on tourism spending. This study, situated in a Norwegian setting, questions this approach by explicitly scrutinizing the possible non-linearities in the relationships between tourism expenditures on the one hand and length of stay and travel party size on the other. The empirical results suggest a positive but diminishing relationship between length of stay and tourism expenditures and a convex (i.e. U) relationship between travel party size and tourism expenditures. The study also considers how a number of other key independent variables affect tourism expenditures. Finally, some managerial and scholarly implications are discussed.     

Characterizing destination networks through mobility traces of international tourists — A case study using a nationwide mobile positioning dataset

This article demonstrates how large-scale tourist mobility data can be linked with network science approaches to better understand tourism destinations and their interactions. By analyzing a mobile positioning dataset that captures the nationality and movement patterns of foreign tourists to South Korea, we employ a few metrics to quantify the network properties of tourism destinations, aiming to reveal the collective dynamics of tourist movements and key differences across nationalities. According to the results, the number of inbound tourists to destinations follows a log-normal distribution, which indicates a notable heterogeneity of destination attractiveness. Although this finding holds across different nationalities, we find that tourists from different countries tended to visit different places in South Korea. A community detection algorithm partitions South Korea into several tourism regions, each covering a set of destinations that are closely connected by tourist flows. The implications for transportation development and regional tourism planning are discussed.     

Toward an integrated model of tourist expectation formation and gender difference

This study aims to build and test a theoretical model of tourist expectation formation and seeks to explore the gender differences regarding how tourists form their expectations toward a travel destination. Survey data were obtained from 774 outbound Chinese tourists to Macao, and structural equation modelling was used to test the model and proposed hypotheses. The results reveal that travel motivation, advertising, and word-of-mouth (WOM) recommendations influence travelers' cognitive image and cognitive image interacts with affective image to form individuals' expectations toward travel destinations. Notably, the impacts of travel motivation and advertising on cognitive image, and of cognitive image on tourist expectations were significantly stronger for males than females, while the effects of WOM on cognitive image and of affective image on tourist expectations were stronger for females than males. Both theoretical and practical implications of these important findings are discussed.     

Spatial and temporal modelling of tourist movements using Semi-Markov processes

Tourist movement is a complex process. It can be modelled from a number of different perspectives; for example, Tourism, Geography, Economics, Mathematics, Computer Sciences and Psychology. This paper aims to establish a sound methodology, using Semi-Markov processes to model the spatial and temporal movement of tourists. The objective is to understand, predict, control for, and optimise the decisions made by tourists in their choice of attractions. Semi-Markov processes have a Markov chain and a renewal process embedded within their structure, and as such, can be used to provide a wide variety of practical models. One of the outcomes of this approach is the introduction of a measure to assess the attractiveness of particular tourist attractions based on spatial and temporal interactions between the attractions. A case study conducted at Phillip Island Nature Park, Victoria, Australia is used to validate the model. The study’s results support the proposed model’s efficiency. The results are also practical and useful for assisting park managers and tourist operators with park planning and marketing decisions: for example, knowing which attractions are the most popular, how long tourists will spend at any one site; and what the likely routes are that they will follow.     

Factors influencing tourist food consumption

This study attempts to identify the salient factors affecting tourist food consumption. By reviewing available studies in the hospitality and tourism literature and synthesising insights from food consumption and sociological research, five socio-cultural and psychological factors influencing tourist food consumption are identified: cultural/religious influences, socio-demographic factors, food-related personality traits, exposure effect/past experience, and motivational factors. The findings further suggest that the motivational factors can be categorised into five main dimensions: symbolic, obligatory, contrast, extension, and pleasure. Given the lack of research in examining tourist food consumption systematically, the multidisciplinary approach adopted in this study allows a comprehensive understanding of the phenomenon which forms the basis for further research and conceptual elaboration.     

Exploring visitor movement patterns in natural recreational areas

GPS technology is widely used to produce detailed data on the movement of people. Analysing massive amounts of GPS data, however, can be cumbersome. We present a novel approach to processing such data to aid interpretation and understanding of the aggregated movement of visitors in natural recreational areas. It involves the combined analysis of two kinds of movement patterns: ‘Movement Suspension Patterns’ (MSPs) and ‘Generalized Sequential Patterns’ (GSPs). MSPs denote the suspension of movement when walkers stop at a place, and are used to discover places of interest to visitors. GSPs represent the generalized sequence in which the places are visited, regardless of the trajectory followed, and are used to uncover commonalities in the way that people visit the area. Both patterns were analysed in a geographical context to characterise the aggregated flow of people and provide insights into visitors’ preferences and their interactions with the environment. We demonstrate the application of the approach in the Dwingelderveld National Park (The Netherlands).     

Representing tourists’ heterogeneous choices of destination and travel party with an integrated latent class and nested logit model

Aiming at a better understanding of heterogeneous interdependencies between destination and travel party choices in tourism, this study attempts to simultaneously represent these two choices by integrating the nested logit model with the latent class modeling approach to accommodate both types of nested model structures together. Empirical analysis confirmed the effectiveness of the developed model, using a data collected from more than 2000 tourists in Japan. It was observed that on average the two types of nested model structures are almost equally shared by samples and the model structures could significantly vary with income level and gender. Influential factors related to choices of destination and travel party were also explored. Concretely speaking, travel time, attractiveness of destination and number of tourism spots were found to be important influential factors in destination choice, and gender, age, marital status have important effects on travel party choice.     

‘You will like it!’ using open data to predict tourists' response to a tourist attraction

The increasing amount of user-generated content spread via social networking services such as reviews, comments, and past experiences, has made a great deal of information available. Tourists can access this information to support their decision making process. This information is freely accessible online and generates so-called “open data”. While many studies have investigated the effect of online reviews on tourists' decisions, none have directly investigated the extent to which open data analyses might predict tourists' response to a certain destination. To this end, our study contributes to the process of predicting tourists' future preferences via Mathematica^TM, software that analyzes a large set of the open data (i.e. tourists’ reviews) that is freely available on tripadvisor. This is devised by generating the classification function and the best model for predicting the destination tourists would potentially select. The implications for the tourist industry are discussed in terms of research and practice.