Special Issue "Online Reviews in Tourism and Hospitality: Different Methods and Applications"

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Applications".

Deadline for manuscript submissions: closed (31 July 2021) | Viewed by 1792

Special Issue Editors

Dr. Marisol B. Correia
E-Mail Website
Guest Editor
ESGHT, Universidade do Algarve & Centre for Tourism Research, Development and Innovation – CiTUR & Research Centre for Tourism, Sustainability and Well-being - CinTurs & CEG-IST, Instituto Superior Técnico, Universidade de Lisboa, Portugal
Interests: business intelligence; information systems; e-tourism; website evaluation; evolutionary computation; ICT
Dr. Nuno Antonio
E-Mail Website
Guest Editor
Nova IMS, ISCTE-IUL, ESGHT, Universidade do Algarve & Centre for Tourism Research, Development and Innovation – CiTUR & Research Centre for Tourism (colaborator), Portugal
Interests: data science; business intelligence; data mining; machine learning; hospitality; tourism
Dr. Filipa Perdigão
E-Mail Website
Guest Editor
ESGHT, Universidade do Algarve & Centre for Tourism Research, Development and Innovation – CiTUR; Portugal
Interests: communication; social media; discourse analysis; corpus linguistics; language practices in tourism; mediated representations of and about tourism

Special Issue Information

Dear Colleagues,

Over the past few years, the widespread use of web 2.0 platforms has been causing radical changes in the promotion of tourist destination, by following the clear strategy of incorporating user-generated content (UGC). Online reviews (ORs) are a form of UGC that reflects evaluations and comments about the visitor’s own experience, as well as about the destination itself. UGC can equate to electronic word of mouth (eWOM); both are crucial in shaping the image and reputation of destinations and are considered more reliable than official sources because they are regarded as genuine and not focused on business (De Ascaniis and Cantoni, 2017). Thus, ORs are increasingly recognized as an important component in the construction of a destination’s image (Yeoh, Othman, and Ahmad, 2013), and while consumer (traveller) empowerment has risen in terms of travel choices and destinations, the role of hospitality and tourism-related firms in influencing consumers’ travel decisions has diminished (O’Connor, 2010).

The analysis of ORs has been carried out for more than a decade and for various sectors of activity, but in particular for tourism and even more for hospitality. According to Schuckert, Liu, and Law (2015), over half of the academic articles related to online reviews in tourism and hospitality published in academic journals between 2004 and 2013 focused on hotels. Recent studies prove the potential of UGC to assist hoteliers, and more resources are recommended to be employed in research on ORs (Antonio, de Almeida, Nunes, Batista, and Ribeiro, 2018). For example, Kwok, Xie, and Richards (2017) point out that online hotel ratings create a rich resource of both quantitative and qualitative data whereby reviewers or commenters and review readers can consider their options as both tourists and consumers. On the other hand, managers can rely on data analytical techniques to consider outcomes in terms of consumer decision-making and business performance. Therefore, a consequence of the popularity of online hotel ratings is that ORs constitute a new and increasingly important element of the marketing communication mix and have growing implications for both theory and practice (Phillips, Antonio, Almeida, and Nunes, 2018).

Of the many online reviews platforms for tourism and hospitality, we can highlight TripAdvisor, Expedia, Booking.com, Yelp, and Yahoo!. Several studies that compare and examine these platforms have concluded that ORs vary considerably in terms of their linguistic characteristics, semantic features, sentiment, rating, and usefulness as well as in the relationships between these features (Xiang, Du, Ma, and Fan, 2017). In addition to the quantitative features of valence (rating of OR), volume (total number of ORs) ,and variance (level of inconsistency of reviewers’ opinion), consumers are also allowed to provide textual comments about their experience with a particular business (Kwok, Xie, and Richards, 2017). However, although ORs have two evaluation components, the quantitative ratings, and the qualitative text written by the user, most research on ORs has been focusing solely on the quantitative component, even though it would seem that the qualitative component has the potential to provide a richer overview of ORs (Duan, Yu, Cao, and Levy, 2016).

For the quantitative and qualitative research of ORs, different methods have been used in recent literature, such as data mining, association rules, natural language processing, text mining, naïve bayes, linear regression, deep learning, etc. (Ribeiro, Antonio, & Correia, 2020; Saura, Palos-Sanchez, and Grilo, 2019), but there are a wide range of methods and techniques that can be used and explored from various fields, such as data science and information technology, and in particular, from machine learning, statistics, and big data, among others. However, to focus on the qualitative component of ORs or even on both components—quantitative and qualitative—mixed-method approaches could be an interesting possibility. 

For this purpose, this Special Issue intends to provide a forum to discuss the methods and techniques that can be used for both quantitative and qualitative approaches to ORs analysis, as well as to identify new trends and developments in this area, including the possibility of exploring their applications in the hospitality and tourism industry.

As such, we invite researchers to submit original papers that include but are not limited to the following topics of interest:

Methods for exploring and analyzing online reviews;

How mixed-methods approaches can improve online review analysis;

Benefits of using different methods for analyzing online reviews;

Key factors/determinants in influencing the usage of online reviews;

How user’s individual characteristics impact the relationships in online travel review adoption;

Heterogeneity of users’ behavior and intentions;

Online reviews’ role in data driven decision making;

Impact of online reviews and online reputation in tourism and hospitality businesses;

Different applications of online reviews in tourism and hospitality;

Recent and future directions of tourism and hospitality online reviews.

References

Antonio, N., Almeida, A. de, Nunes, L., Batista, F. & Ribeiro, R. (2018). Hotel online reviews: different languages, different opinions. Information Technology & Tourism, 18(1–4), 157–185.

De Ascaniis, S. & Cantoni L. (2017). Online visit opinions about attractions of the religious heritage: An argumentative approach, Church. Communication and Culture, 2(2), 179-202.

Duan, W., Yu, Y., Cao, Q. & Levy, S. (2016). Exploring the impact of social media on hotel service performance: A sentimental analysis approach. Cornell Hospitality Quarterly, 57(3), 282–296.

Kwok, L., Xie, K. L. & Richards, T. (2017). Thematic framework of online review research: a systematic analysis of contemporary literature on seven major hospitality and tourism journals. International Journal of Contemporary Hospitality Management, 29(1), 307–54.

O’Connor, P. (2010). Managing a hotel’s image on TripAdvisor. Journal of Hospitality Marketing & Management, 19(7), 754–772.

Phillips, P., Antonio, N., Almeida, A. & Nunes, L. (2020). The influence of geographic and psychic distance on online hotel ratings. Journal of Travel Research, 59(4), 722–741.

Ribeiro, F. P., Antonio, N. & Correia, M. B. (2020 in press).  Uma abordagem metodológica para a análise comparativa de comentários de viagens online de duas cidades património da UNESCO. In Henriques, César, Herédia, Moreira (orgs). Turismo & História: Perspetivas sobre o patrimônio da humanidade no espaço ibero-americano (pp. 304-331). Caxias do Sul, RS, Educs.

Schuckert, M., Liu, X. & Law, R. (2015). Hospitality and tourism online reviews: Recent trends and future directions. Journal of Travel & Tourism Marketing, 32(5), 608-621.

Saura, J. R., Palos-Sanchez, P. & Grilo, A. (2019). Detecting indicators for startup business success: Sentiment analysis using text data mining. Sustainability, 11, 917.

Yeoh, E., Othman, K. & Ahmad, H. (2013).Understanding medical tourists: Word-of-mouth and viral marketing as potent marketing tools. Tourism Management, 34,196–201.

Xiang, Z., Du, Q., Ma, Y. & Fan, W. (2017). A comparative analysis of major online review platforms: Implications for social media analytics in hospitality and tourism. Tourism Management, 58, 51-65.

Dr. Marisol B. Correia
Dr. Nuno Antonio
Dr. Filipa Perdigão
Guest Editors

Manuscript Submission Information

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Keywords

  • Online reviews
  • User-generated contend
  • Data science
  • Qualitative approach
  • Quantitative approach
  • Mixed methods

Published Papers (1 paper)

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Research

Article
Research on the Role of Influencing Factors on Hotel Customer Satisfaction Based on BP Neural Network and Text Mining
Information 2021, 12(3), 99; https://doi.org/10.3390/info12030099 - 25 Feb 2021
Cited by 9 | Viewed by 1356
Abstract
With the flourishing development of the hotel industry, the study of customer satisfaction based on online reviews and data has become a new model. In this paper, customer reviews and ratings on Ctrip.com are used, and TF-IDF and K-means algorithms are used to [...] Read more.
With the flourishing development of the hotel industry, the study of customer satisfaction based on online reviews and data has become a new model. In this paper, customer reviews and ratings on Ctrip.com are used, and TF-IDF and K-means algorithms are used to extract and cluster the keywords of reviews texts. Finally, 10 first-level influencing factors of hotel customer satisfaction are determined: epidemic prevention, consumption emotion, convenience, environment, facilities, catering, target group, perceived value, price, and service. Based on backpropagation neural network and weight matrix operation, an influencing factor analysis model of hotel customer satisfaction is constructed to explore the role of these factors. The results show that consumption emotion, perceived value, epidemic prevention, target group, and convenience would significantly affect customer satisfaction, among which epidemic prevention becomes a new factor affecting customer satisfaction. Environment, facilities, catering, and service have relatively little effect on customer satisfaction, while price has the least effect. This study provides a path and method for online reviews of hotel management to improve customer satisfaction and provides a theoretical basis for the study of online reviews of hotels. Full article
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