Understanding user experiences of online travel review websites for hotel booking behaviours: an investigation of a dual motivation theory

Hwang, Jooyoung; Park, Sang Won; Woo, Mina

doi:10.1080/10941665.2018.1444648

Cited by 80 publications

(23 citation statements)

References 62 publications

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“…However, the analysis of moderator variables has been used to explain this phenomenon. Elements such as flexibility, the relevance of the information, and convenience are relevant to influence tourism motivation on the Internet (Hwang et al, 2018;Martins et al, 2015). In fact, motivations are different in relation to the complexity level of the purchase travel product and tourists' skills.…”

Section: Discussionmentioning

confidence: 99%

“…The perception of autonomy during the use of virtual reality tools is positively correlated to behavioural intentions to take a trip. Hwang et al (2018) found evidence that extrinsic and intrinsic motivation on social media influences booking intentions. Elements such as relevance, flexibility, completeness, timeliness, and trustworthiness of the information received through social media positively influence travel behaviour and their satisfaction with online reviews and to purchase online.…”

Section: Pre-consumption Stagementioning

confidence: 99%

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Influence of Internet On Tourism Consumer Behaviour: A Systematic Review

Sánchez

Deegan²,

Ricardo³

2022

Advances in Hospitality and Tourism Research (AHTR)

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This paper aims at identifying the state of academic research on the influence of Internet on tourism consumer behaviour. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Methodology was used to select the data and the analysis was complemented with bibliometric indicators. This research identified 83 relevant articles related to the influence of Internet on tourism consumer behaviour. The systematic review of the academic literature demonstrated that the Internet influences tourism consumer behaviour during the entire travel cycle stages. During the pre-consumption stage, motivations and decisionmaking process are highly influenced by several online sources. However, in the case of expectations, limited research was found on this area. Value co-creation behaviour is influenced by Internet during the final two stages of the travel cycle stages. Finally, evidence suggests that loyalty is influenced during the consumption and post-consumption stages. These results provide a categorized analysis of the influence of Internet on the behavioural variables during each of the travel cycle stages for academicians and practitioners. The analysis was conducted on two major academic databases: Web of Science Core Collection and Scopus. The inclusion of other data sources and other types of secondary data could complement these results.

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Section: Discussionmentioning

confidence: 99%

Section: Pre-consumption Stagementioning

confidence: 99%

Influence of Internet On Tourism Consumer Behaviour: A Systematic Review

Sánchez

Deegan²,

Ricardo³

2022

Advances in Hospitality and Tourism Research (AHTR)

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“…An important characteristic of online reviews is that users can assign the valence of their experience, typically from one to five stars; and positive reviews are a powerful promotional tool (Barreda & Bilgihan, 2013;Hwang, Park, & Woo, 2018). By contrast, negative reviews, despite their usefulness to other users, could harm the business (Kusumasondjaja, Shanka, & Marchegiani, 2012;Tsaur, Huang, & Luoh, 2014).…”

Section: Research Contextmentioning

confidence: 99%

Responding to the voice of the markets: an analysis of Tripadvisor reviews of UK retail markets

Taecharungroj

Warnaby

Parker

2020

JPMD

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Purpose The purpose of this paper is to investigate the experience of visitors to UK markets by analysing their Tripadvisor reviews to identify perceived experiential dimensions with a view to informing actions by those responsible for market management to provide a better consumer experience. Design/methodology/approach This research analysed 41,071 Tripadvisor reviews of 61 UK markets. A latent Dirichlet allocation machine learning algorithm was conducted to identify the experience dimensions of visitors. A text analysis was performed to indicate salience and valence of commonly used words. Findings Five dimensions of experience are identified: atmosphere, merchandise, local variety, food and disappointment, together with the underlying factors that drive positive experience. Practical implications Place and market managers should assess and position their market informed by diverse experiential dimensions. They should also improve and enhance the experience of visitors according to the underlying factors of each dimension. Originality/value Retail markets have historically played an important role in the development of urban places. However, the ability to continue performing this role requires a greater understanding of how markets are perceived by those who use them. One way to achieve this is to use emergent technologies to inform decision-making by those responsible for their management. It demonstrates the potential of a new analytical technique using digital technologies to improve one of the oldest forms of retailing.

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“…Likewise, user-generated content, especially online hotel reviews, is a potentially rich source of customer information regarding opinions and sentiment [5]. From the decisionmaking perspective, the display of user-generated reviews improves both the awareness of the hotel and the attitudes towards the hotel when forming an opinion [6,7]. And the use of review text has been shown to improve the accuracy of rating prediction for users and items, especially when there are few ratings [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

A BERT-Based Multi-Criteria Recommender System for Hotel Promotion Management

Zhuang

Kim

2021

Sustainability

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Numerous reviews are posted every day on travel information sharing platforms and sites. Hotels want to develop a customer recommender system to quickly and effectively identify potential target customers. TripAdvisor, the travel website that provided the data used in this study, allows customers to rate the hotel based on six criteria: Value, Service, Location, Room, Cleanliness, and Sleep Quality. Existing studies classify reviews into positive, negative, and neutral by extracting sentiment terms through simple sentimental analysis. However, this method has limitations in that it does not consider various aspects of hotels well. Therefore, this study performs fine-tuning the BERT (Bidirectional Encoder Representations from Transformers) model using review data with rating labels on the TripAdvisor site. This study suggests a multi-criteria recommender system to recommend a suitable target customers for the hotel. As the rating values of six criteria of TripAdvisor are insufficient, the proposed recommender system uses fine-tuned BERT to predict six criteria ratings. Based on this predicted ratings, a multi-criteria recommender system recommends personalized Top-N customers for each hotel. The performance of the multi-criteria recommender system suggested in this study is better than that of the benchmark system, a single-criteria recommender system using overall ratings.

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Understanding user experiences of online travel review websites for hotel booking behaviours: an investigation of a dual motivation theory

Cited by 80 publications

References 62 publications

Influence of Internet On Tourism Consumer Behaviour: A Systematic Review

Influence of Internet On Tourism Consumer Behaviour: A Systematic Review

Responding to the voice of the markets: an analysis of Tripadvisor reviews of UK retail markets

A BERT-Based Multi-Criteria Recommender System for Hotel Promotion Management

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