Applications and issues of big data in tourism research

This paper explores the current applications and associated issues surrounding Big Data in tourism research. Following a brief introduction of Big Data, we explore common Big Data sources and their methodologies for tourism studies including search engine data, webpage and booking data, user generated content (UGC), and device data. We identify opportunities, challenges and recognized concerns Big Data brings to tourism research. Using Canadian Federal legislation as an example, we then explore broader challenges associated with access and use of Big Data in tourism, including issues of ethics and judicial challenges related to privacy laws and legislation. We conclude with speculation what possibilities Big Data will bring to tourism research going forward

The use of crowdsourcing as a strategic model in future hotels

The purpose of this paper is to deAne, analyze their types, and to do an in-depth study of the concept and the importance of crowdsourcing for the management and marketing of hospitality and tourism Arms. More concretely, the paper analyzes the impact of crowdsourcing related processes, together with the evolution of the new conceptions of marketing and management, in the transformation of hotels. Furthermore the paper forecast the future of hotels, by exploring and studying diverse uses and possibilities of crowdsourcing techniques for improving diverse processes in different organizational areas of hotel business. With the new marketing perspective, the paper provides also several examples of its use in hotels, and pretends to create an exploratory framework, and analyze the strengths of the use of crowdsourcing related techniques in the hotel arena, and also the negative consequences of some of these techniques for hotel Arms.Garrigós Simón, FJ.; Narangajavana-Kaosiri, Y. (2015). The use of crowdsourcing as a strategic model in future hotels. Tourism today (Nicosia). 15:105-120. http://hdl.handle.net/10251/78860S1051201

Harnessing the power of the general public for crowdsourced business intelligence: a survey

International audienceCrowdsourced business intelligence (CrowdBI), which leverages the crowdsourced user-generated data to extract useful knowledge about business and create marketing intelligence to excel in the business environment, has become a surging research topic in recent years. Compared with the traditional business intelligence that is based on the firm-owned data and survey data, CrowdBI faces numerous unique issues, such as customer behavior analysis, brand tracking, and product improvement, demand forecasting and trend analysis, competitive intelligence, business popularity analysis and site recommendation, and urban commercial analysis. This paper first characterizes the concept model and unique features and presents a generic framework for CrowdBI. It also investigates novel application areas as well as the key challenges and techniques of CrowdBI. Furthermore, we make discussions about the future research directions of CrowdBI

Geography matters in online hotel reviews

In resonance with the popularity of user-generated contents (UGC) and the volunteered geographic information (VGI), this study crowdsourced 77,098 hotel reviews of 220 hotels provided by U.S. reviewers in the city of San Francisco, 2002 to 2015. In this exploratory analysis, we have revealed that there is spatial dependence of customer satisfaction at different locations (of hotels), which violates the assumption that ordinary least-square (OLS) is the best linear unbiased estimator (BLUE); therefore, spatial model might be required for analysing any antecedents and consequences of such phenomena. These results have implications in marketing and management strategies

Using contextual information to understand searching and browsing behavior

There is great imbalance in the richness of information on the web and the succinctness and poverty of search requests of web users, making their queries only a partial description of the underlying complex information needs. Finding ways to better leverage contextual information and make search context-aware holds the promise to dramatically improve the search experience of users. We conducted a series of studies to discover, model and utilize contextual information in order to understand and improve users' searching and browsing behavior on the web. Our results capture important aspects of context under the realistic conditions of different online search services, aiming to ensure that our scientific insights and solutions transfer to the operational settings of real world applications

THE IDENTIFICATION OF NOTEWORTHY HOTEL REVIEWS FOR HOTEL MANAGEMENT

The rapid emergence of user-generated content (UGC) inspires knowledge sharing among Internet users. A good example is the well-known travel site TripAdvisor.com, which enables users to share their experiences and express their opinions on attractions, accommodations, restaurants, etc. The UGC about travel provide precious information to the users as well as staff in travel industry. In particular, how to identify reviews that are noteworthy for hotel management is critical to the success of hotels in the competitive travel industry. We have employed two hotel managers to conduct an examination on Taiwan’s hotel reviews in Tripadvisor.com and found that noteworthy reviews can be characterized by their content features, sentiments, and review qualities. Through the experiments using tripadvisor.com data, we find that all three types of features are important in identifying noteworthy hotel reviews. Specifically, content features are shown to have the most impact, followed by sentiments and review qualities. With respect to the various methods for representing content features, LDA method achieves comparable performance to TF-IDF method with higher recall and much fewer features

A Machine Learning Approach for Classifying Textual Data in Crowdsourcing

Crowdsourcing represents an innovative approach that allows companies to engage a diverse network of people over the internet and use their collective creativity, expertise, or workforce for completing tasks that have previously been performed by dedicated employees or contractors. However, the process of reviewing and filtering the large amount of solutions, ideas, or feedback submitted by a crowd is a latent challenge. Identifying valuable inputs and separating them from low quality contributions that cannot be used by the companies is time-consuming and cost-intensive. In this study, we build upon the principles of text mining and machine learning to partially automatize this process. Our results show that it is possible to explain and predict the quality of crowdsourced contributions based on a set of textual features. We use these textual features to train and evaluate a classification algorithm capable of automatically filtering textual contributions in crowdsourcing

TOURISM DESTINATIONS DEVELOPMENT TRENDS THROUGH THE SOCIAL MEDIA CONTENT PERSPECTIVE

The aim of this article is to underline the actual trends regarding tourism destinations, which are presented on social media channels by the most important international tourism organizations. Social media is nowadays one of the most popular way of communication due to the extremely large amount of users they have, the level of engagement they facilitate and, as well, due to lower costs they require in order to establish a proper marketing campaign. Moreover, being present and active on social networks can help an organization in reaching their audience and make it easy to spread information and to create awareness about new concepts and strategies concerning destination development. Furthermore, social networks help organizations to receive feedback and reactions on their themes and offer them a real framework for attracting attention about what they find most appealing.  Based on content analysis of the social media data, this article want to emphasize the hottest topics related with sustainability, tourism destinations development and destination governance, which are promoted by the international specialized organizations.</p

Density-based spatial clustering and ordering points approach for characterizations of tourist behaviour

Knowledge about the spots where tourist activity is undertaken, including which segments from the tourist market visit them, is valuable information for tourist service managers. Nowadays, crowdsourced smartphones applications are used as part of tourist surveys looking for knowledge about the tourist in all phases of their journey. However, the representativeness of this type of source, or how to validate the outcomes, are part of the issues that still need to be solved. In this research, a method to discover hotspots using clustering techniques and give to these hotspots a data-driven interpretation is proposed. The representativeness of the dataset and the validation of the results against existing statistics is assessed. The method was evaluated using 124,725 trips, which have been gathered by 1505 devices. The results show that the proposed approach successfully detects hotspots related with the most common activities developed by overnight tourists and repeat visitors in the region under study