Destination image analytics through traveller-generated content

The explosion of content generated by users, in parallel with the spectacular growth of social media and the proliferation of mobile devices, is causing a paradigm shift in research. Surveys or interviews are no longer necessary to obtain users' opinions, because researchers can get this information freely on social media. In the field of tourism, online travel reviews (OTRs) hosted on travel-related websites stand out. The objective of this article is to demonstrate the usefulness of OTRs to analyse the image of a tourist destination. For this, a theoretical and methodological framework is defined, as well as metrics that allow for measuring different aspects (designative, appraisive and prescriptive) of the tourist image. The model is applied to the region of Attica (Greece) through a random sample of 300,000 TripAdvisor OTRs about attractions, activities, restaurants and hotels written in English between 2013 and 2018. The results show trends, preferences, assessments, and opinions from the demand side, which can be useful for destination managers in optimising the distribution of available resources and promoting sustainability

Web 2.0 and destination marketing: current trends and future directions

Over the last decade, destination marketers and Destination Marketing Organizations (DMOs) have increasingly invested in Web 2.0 technologies as a cost-effective means of promoting destinations online, in the face of drastic marketing budgets cuts. Recent scholarly and industry research has emphasized that Web 2.0 plays an increasing role in destination marketing. However, no comprehensive appraisal of this research area has been conducted so far. To address this gap, this study conducts a quantitative literature review to examine the extent to which Web 2.0 features in destination marketing research that was published until December 2019, by identifying research topics, gaps and future directions, and designing a theory-driven agenda for future research. The study’s findings indicate an increase in scholarly literature revolving around the adoption and use of Web 2.0 for destination marketing purposes. However, the emerging research field is fragmented in scope and displays several gaps. Most of the studies are descriptive in nature and a strong overarching conceptual framework that might help identify critical destination marketing problems linked to Web 2.0 technologies is missing

From Blockbuster to Neighbourhood Buster: The Effect of Films on Barcelona

In recent years, cities such as Venice, Dubrovnik, Paris and Barcelona have experienced an exponential increase in visitor numbers leading to episodes of tourismphobia by anti-tourism movements, or even the decline of the destination. Among other solutions, some destinations see film-induced tourism as a possible way of diversifying tourism supply and demand. Through the analysis of the locations of six thematic film routes in Barcelona compared to the same locations on the largest online travel review platform, TripAdvisor, it is concluded that, far from spreading out tourist flows, fiction-induced tourism in Barcelona has concentrated tourism at the main attractions of the city. Only a few exceptions of films with minor audiences lead tourists off the beaten track. Overall, this paper provides a set of recommendations, strategies and challenges for destination managers to help alleviate overtourism and to offer more sustainable tourism away from spots that attract mass tourism.This research was funded by the Spanish Ministry of Economy, Industry and Competitiveness (grants ID ECO2017-88984-R, TIN2015-71799-C2-2-P, and HAR2016-77734-P), and the support of the Institute of Social Development and Territory INDEST of University of Lleida (call 2018CRINDESTABC). First author also acknowledges the support of the Spanish Education Ministry for the abroad mobility stay “José Castillejo” (Ref. Number CAS19/00362)

OntoTouTra: tourist traceability ontology based on big data analytics

Tourist traceability is the analysis of the set of actions, procedures, and technical measures that allows us to identify and record the space–time causality of the tourist’s touring, from the beginning to the end of the chain of the tourist product. Besides, the traceability of tourists has implications for infrastructure, transport, products, marketing, the commercial viability of the industry, and the management of the destination’s social, environmental, and cultural impact. To this end, a tourist traceability system requires a knowledge base for processing elements, such as functions, objects, events, and logical connectors among them. A knowledge base provides us with information on the preparation, planning, and implementation or operation stages. In this regard, unifying tourism terminology in a traceability system is a challenge because we need a central repository that promotes standards for tourists and suppliers in forming a formal body of knowledge representation. Some studies are related to the construction of ontologies in tourism, but none focus on tourist traceability systems. For the above, we propose OntoTouTra, an ontology that uses formal specifications to represent knowledge of tourist traceability systems. This paper outlines the development of the OntoTouTra ontology and how we gathered and processed data from ubiquitous computing using Big Data analysis techniquesThis research was financially supported by the Ministry of Science, Technology, and Innovation of Colombia (733-2015) and by the Universidad Santo Tomás Seccional Tunja

Cultural heritage appraisal by visitors to global cities: the use of social media and urban analytics in urban buzz research

An attractive cultural heritage is an important magnet for visitors to many cities nowadays. The present paper aims to trace the constituents of the destination attractiveness of 40 global cities from the perspective of historical-cultural amenities, based on a merger of extensive systematic databases on these cities. The concept of cultural heritage buzz is introduced to highlight: (i) the importance of a varied collection of urban cultural amenities; (ii) the influence of urban cultural magnetism on foreign visitors, residents and artists; and (iii) the appreciation for a large set of local historical-cultural amenities by travelers collected from a systematic big data set (emerging from the global TripAdvisor platform). A multivariate and econometric analysis is undertaken to validate and test the quantitative picture of the above conceptual framework, with a view to assess the significance of historical-cultural assets and socio-cultural diversity in large urban agglomerations in the world as attraction factors for visitors. The results confirm our proposition on the significance of urban cultural heritage as a gravity factor for destination choices in international tourism in relation to a high appreciation for historical-cultural amenities.info:eu-repo/semantics/publishedVersio

Word of Mouth, the Importance of Reviews and Ratings in Tourism Marketing

The Internet and social media have given place to what is commonly known as the democratization of content and this phenomenon is changing the way that consumers and companies interact. Business strategies are shifting from influencing consumers directly and induce sales to mediating the influence that Internet users have on each other. A consumer review is “a mixture of fact and opinion, impression and sentiment, found and unfound tidbits, experiences, and even rumor” (Blackshaw & Nazarro, 2006). Consumers' comments are seen as honest and transparent, but it is their subjective perception what shapes the behavior of other potential consumers. With the emergence of the Internet, tourists search for information and reviews of destinations, hotels or services. Several studies have highlighted the great influence of online reputation through reviews and ratings and how it affects purchasing decisions by others (Schuckert, Liu, & Law, 2015). These reviews are seen as unbiased and trustworthy, and considered to reduce uncertainty and perceived risks (Gretzel & Yoo, 2008; Park & Nicolau, 2015). Before choosing a destination, tourists are likely to spend a significant amount of time searching for information including reviews of other tourists posted on the Internet. The average traveler browses 38 websites prior to purchasing vacation packages (Schaal, 2013), which may include tourism forums, online reviews in booking sites and other generic social media websites such as Facebook and Twitter.Peer reviewedFinal Accepted Versio

Business intelligence and big data in hospitality and tourism: a systematic literature review

Purpose This paper aims to examine the extent to which Business Intelligence and Big Data feature within academic research in hospitality and tourism published until 2016, by identifying research gaps and future developments and designing an agenda for future research. Design/methodology/approach The study consists of a systematic quantitative literature review of academic articles indexed on the Scopus and Web of Science databases. The articles were reviewed based on the following features: research topic; conceptual and theoretical characterization; sources of data; type of data and size; data collection methods; data analysis techniques; and data reporting and visualization. Findings Findings indicate an increase in hospitality and tourism management literature applying analytical techniques to large quantities of data. However, this research field is fairly fragmented in scope and limited in methodologies and displays several gaps. A conceptual framework that helps to identify critical business problems and links the domains of business intelligence and big data to tourism and hospitality management and development is missing. Moreover, epistemological dilemmas and consequences for theory development of big data-driven knowledge are still a terra incognita. Last, despite calls for more integration of management and data science, cross-disciplinary collaborations with computer and data scientists are rather episodic and related to specific types of work and research. Research limitations/implications This work is based on academic articles published before 2017; hence, scientific outputs published after the moment of writing have not been included. A rich research agenda is designed. Originality/value This study contributes to explore in depth and systematically to what extent hospitality and tourism scholars are aware of and working intendedly on business intelligence and big data. To the best of the authors’ knowledge, it is the first systematic literature review within hospitality and tourism research dealing with business intelligence and big data

Using sentiment analysis in tourism research: A systematic, bibliometric, and integrative review

Purpose: Sentiment analysis is built from the information provided through text (reviews) to help understand the social sentiment toward their brand, product, or service. The main purpose of this paper is to draw an overview of the topics and the use of the sentiment analysis approach in tourism research. Methods: The study is a bibliometric analysis (VOSviewer), with a systematic and integrative review. The search occurred in March 2021 (Scopus) applying the search terms "sentiment analysis" and "tourism" in the title, abstract, or keywords, resulting in a final sample of 111 papers. Results: This analysis pointed out that China (35) and the United States (24) are the leading countries studying sentiment analysis with tourism. The first paper using sentiment analysis was published in 2012; there is a growing interest in this topic, presenting qualitative and quantitative approaches. The main results present four clusters to understand this subject. Cluster 1 discusses sentiment analysis and its application in tourism research, searching how online reviews can impact decision-making. Cluster 2 examines the resources used to make sentiment analysis, such as social media. Cluster 3 argues about methodological approaches in sentiment analysis and tourism, such as deep learning and sentiment classification, to understand the user-generated content. Cluster 4 highlights questions relating to the internet and tourism. Implications: The use of sentiment analysis in tourism research shows that government and entrepreneurship can draw and enhance communication strategies, reduce cost, and time, and mainly contribute to the decision-making process and understand consumer behavior