Band 13, Heft 2 - Special Issue: Tourism and Big Data: Challenges and Opportunities

Big Data analysis has drawn considerable interest both in tourism research and practice. The authors argue that, although the potential impact is promising, implementation often fails due to insufficient access to the data needed: Although Big Data is there, it is often not available for practical application. The authors therefore call to action in generating and using not only big, but open data.

The article at hand is driven by a methodological interest in the opportunities and challenges of applying an automated text mining approach, particularly a sentiment analysis on various tourism blogs at the same time. The study aims to answer the question to what extent advanced computational methods can improve the data acquisition and analysis of unstructured data sets stemming from various blogs and forums. Furthermore, the authors intend to explore to what extent the sentiment analysis is able to objectify the qualitative results identified by an earlier analysis by the authors using content analysis done by thematic coding. For the purpose of the specific tourism research question in this paper a new approach is proposed, which consists of a combination of sentiment analyses, supervised learning, and dimensionality reduction in order to identify terms that strongly load on specific emotions. The contribution indicates on the one hand, that advanced computational methods have their own specific constraints, but on the other hand, are able to provide a richer and deeper analysis following a quantitative approach. Several issues have to be taken into account, such as data protection constraints, the need for data cleaning, such as word stemming, dimension reduction, such as removal of custom stop words, and the development of descent ontologies. On the other hand, the quantitative method also provides, due to its standardised procedure, a less subjective insight in the given content, but is not less time consuming than traditional content analysis.

In recent years, numerous studies have been conducted on the phenomenon of dark tourism. This study seeks to understand what motivates people to visit dark tourism sites such as the Chernobyl exclusion zone by applying an automated text analytics approach. Tripadvisor was chosen as a source for data collection as tourists are increasingly sharing their experiences and leave feedback online. Several natural language processing methods, such as topic modelling (LDA) and sentiment analysis, were applied to extract the primary motivators behind a visit to Chernobyl. Topic modelling results present five main topics discussed by tourists. Based on the results, the main motivational factors are discussed in detail. Furthermore, the total sentiment score shows a positive perception of the dark tourism destination. This study follows an interdisciplinary research approach applying innovative data analytics methods to investigate dark tourism through social media. By implementing NLP methods, this study reveals tourists’ perceptions from online reviews, which are not easy to discover by traditional approaches. Moreover, the results provide guidelines to tourism managers in monitoring new trends in tourism, understanding tourists’ needs and wishes, and evaluating the quality of products or services.

The use of large and diverse data in real time (called Big Data) affects many business processes and models. The tourism industry, characterized by manifold sub-sectors and players, provides a variety of starting points for Big Data usage. Examples are the optimization of transport offers using transaction data or a comprehensive analysis of destination trends based on social media posts. Big Data is a trending topic, however, the general discourse centres around potential ideas but fewer practical solutions. Based on a systematic literature analysis of initially 148 peer-reviewed journal articles, this article evaluates the current state of Big Data research within tourism. For this purpose, research articles centering around tourism-related Big Data were investigated according to the actual state of implementation of an IT solution, whether they truly grasp or represent Big Data in technological terms, and which added value they create for the tourism industry and research community. One key finding is that traditional data analysis is often wrongfully subsumed under the Big Data label. Further, the scientific literature predominantly discusses ideas or theoretical considerations, fewer tangible Big Data implementations, and fails to address and/or meet all requirements to be classified as Big Data. Only a minority of the presented solutions processes data in real time, whereas many rely on only one data source or structured data. Furthermore, most articles revolve around post-trip data analyses and are set to a destination context. In contrast, other tourism sectors as well as data interpretation and usage in pre-trip and on-trip phases are less represented. Lastly, this literature analysis provides an overview of true Big Data solutions already in operation and enables researchers to validly classify their own research activities in order to plan initiatives more specifically.

Zusammenfassung Zusätzlich zum Klassiker der alternativen Reiseformen, dem Backpacking, hat sich im Bereich des Individualtourismus im letzten Jahrzehnt ein neuer Reisetrend, der sogenannte Vantourismus entwickelt. Immer mehr Menschen entscheiden sich dazu, im „Zuhause auf vier Rädern“ für begrenzte Zeit zu verreisen und dabei, anders als klassische Campingtouristen, weitgehend auf klassische Campingplätze und vorbestimmte Reiserouten zu verzichten. Aufgrund des Mangels an empirischer Forschung zu diesem Thema untersucht dieser Beitrag in einer Online-Studie, inwieweit sich VantouristInnen von klassischen CampingtouristInnen unterscheiden. Die Analyse zeigt, dass zahlreiche Diskrepanzen in Hinblick auf Reisemerkmale, Reisemotivationen, Urlaubsbedürfnisse und Lebensstilmerkmale einen eigenständigen Typus des Alternativtourismus nahelegen. Außerdem zeigt die empirische Prüfung eines ganzheitlichen Erklärungsansatzes, dass neben dem Alter, dem Familienstand und der Komfortorientierung vor allem Bedürfnisse der Selbstaktualisierung, Authentizität und Naturverbundenheit ausschlaggebend sind, sich für Vantourismus zu entscheiden.

We set out to assess the social impacts of tourism in a Community Based Tourism (CBT) destination by asking the following questions; (1) from a community and individual perspective, what are the major issues faced in a CBT destination?, and (2) is there any pattern to, or similarity between, quality of life and liveable environment impacts in a CBT destination? The Sagarmatha (Mt Everest) National Park in Nepal is used for the study area and where tourism is clearly an important contributor to the local economy. Despite high levels of resident and visitor satisfaction with tourism, survey results indicate that the major issues faced by communities and individuals in the Sagarmatha National Park and Buffer Zone (SNPBZ) are related to uncontrolled tourism development. This is most evident in a lack of relevant skills and training, increasing time burden to cater for tourists, frustrations felt by residents during peak season, the impact of inbound migration, lack of community control and most importantly, the impact of waste and water pollution. Management of the SNPBZ is based on a multi-stakeholder system that includes local population participation, but it does not seem to be working very well. Our survey indicates that uncontrolled growth of tourism businesses is placing increasing pressures on traditional cultures and the environment, thus creating negative impacts on quality of live and liveable environment for residents. Without an effective management system that enhances the ability of communities and Park management to control the impact of tourism, the situation is very likely to worsen in the future.