Online Hotel Reviews Research Articles

Words meet photos: How visual content impact rating

The fit information of multi-modal user-generated content has its unique value but is understudied. This study investigated how the fit between photos and text within a hotel review influences tourist rating behavior. Employing deep learning technologies and econometric analysis shows that the fit between photos and text has an inverted U-shape effect on tourists’ ratings using online hotel review data from Qunar.com. The dispersion of ratings and the number of reviews can weaken the above effect; this is due to the varying level of cognitive resources tourists possess. Endogeneity bias from text and photos is excluded by conducting the two-stage residual analysis. The information uncertainty reduction mechanism is tested between photo-text fit and ratings. This paper provides valuable insights for practitioners regarding user-generated content management in hospitality industry.

ChatGPT and online service recovery: How potential customers react to managerial responses of negative reviews

This study investigates the efficacy of generative artificial intelligence in online service recovery; specifically, the use of ChatGPT (vs. human employees) in preparing managerial response(s) (MR or MRs) to online hotel reviews is considered. ChatGPT could be used to generate human-like MRs for online service recovery but this could backfire due to algorithm aversion when an individual discounts algorithm decisions relative to human-made decisions. Data collected via interviews, a modified Turing test and an online experiment provide empirical support for this. Findings reveal that potential customers could not clearly differentiate between the two types of MR and could not clearly identify the ‘better’ of the two. Yet, when informed of the MR source, ChatGPT MRs led to lower affective, cognitive and conative outcomes. Findings also unveiled perceived authenticity and uncanniness as significant parallel mediating pathways in this algorithm aversion. Theoretical and managerial implications are discussed.

Understanding Booking.com’s rating drop in the context of online hotel reviews

This study examines the new Booking.com rating system, which has suffered a significant drop in scores awarded to accommodation. We aim to determine the extent of these declines and identify the factors that make them more pronounced in some hotels than in others. Our findings reveal a consistent, much more significant drop in scores than reflected in recently published studies that minimized the effects of the changes. Contrary to the predictions made in other studies, the highest-rated hotels have also suffered drops in their scores. Machine learning models identified “facilities” as the item that plays the most relevant role in consumers’ global satisfaction and contributes to predicting the magnitude of drops in scores with the new system. Implications for both hoteliers and academics utilizing Booking.com’s score data are identified, particularly for studies comparing data from different periods.

Morality or hypocrisy: The effect of hotel sustainability labels varies across different online review valence

Amidst growing emphasis on sustainability in the hotel industry, hotels are increasingly adopting sustainable practices. This trend extends to online platforms, where hotels showcase their sustainability labels as a form of advanced Corporate Social Responsibility (CSR). Such CSR communication is crucially influenced by how customers perceive the consistency of a hotel’s CSR efforts. In the sphere of online hotel booking, customer reviews play a pivotal role as other external clues, illustrating how well hotels fulfill their fundamental economic responsibility. Our study aims to investigate the interactive effects between hotel sustainability labels and the valence of online reviews on customers’ booking intentions. Based on attribution theory and several experimental studies, this research reveals that in the context of negative hotel review valence, the presence (vs. absence) of sustainability labels decreases customers’ booking intention due to an increase in perceived hypocrisy towards the hotel; conversely, in the context of positive hotel review valence, the presence (vs. absence) of sustainability labels enhances customers’ booking intention due to an increase in perceived morality towards the hotel. These findings contribute to the literature exploring hotel sustainability labels, online hotel reviews, and perceptions of hotel hypocrisy.

Hotel recommendation mechanism based on online reviews considering multi-attribute cooperative and interactive characteristics

Online reviews of hotels provide important information to consumers. The process of extracting useful information from diverse online reviews is crucial for making the best final decisions. To explore the hidden intrinsic information behind online reviews, this paper optimizes information extraction by integrating multiple sources, and gives the recommendation alternative. First, to meet quantitative requirements, the probabilistic linguistic term set is introduced to demonstrate the massive number of comments crawled. Second, considering preference and fluctuation, the relative importance of multiple attributes is determined. Because multiple attributes typically have cooperative or mutually exclusive relationships, a novel model is presented by introducing such relationship to modify relative importance. Third, inspired by the 2-additive Choquet integral operator and the Mahalanobis-Taguchi System, a bi-objective optimization model is proposed to illustrate the interactive effect of comments and develop an attribute correlation network. The specific relationships between attributes are reflected, including the positive and negative interactions. The relative importance, interactive imporantce and subgroup utility can be obtained. Fourth, to guarantee the operability and interpretability of the recommendation results, this paper presents a new information fusion operator and an probabilistic linguistic three-way recommendation process. Finally, a case study is used to demonstrate the complete procedures, and the parameter and comparative analyses highlight the effectiveness of the new operator and recommendation method.

Framing negative eWOM via conjunctions: The language of customer communication in the luxury hotel sector on social media

In the digital age, social media has a significant impact on consumer perceptions. This business communication study examines the linguistic strategies used to express and repair negative electronic word of mouth (eWOM) in Hong Kong’s luxury hotel sector. The data, which were collected from travel forums, consist of 17,344 words from online hotel reviews (11,647 words) and responses (5,697 words) made by the visitors and senior hotel management of 18 five-star hotels. Using systemic functional linguistics as a framework, this study analyses the logical relations that exist between expectant and concessive conjunctions, as well as the evaluative judgment resources present in an appraisal system. The analysis focuses on the poor service standards framed in the complaints and how the hotel management responds to such situations differently, particularly with respect to repairing customer expectations. This study also examines how loyal fans and frequent travellers present their customer identity when dealing with service-related issues. By developing an understanding of how luxury hotels manage customer complaints, repair customer relationships, and protect their brand reputation through dynamic interactions, this study can provide valuable communication insights for the hotel sector.

Network Structure of Online Customer Reviews and Online Hotel Reviews: A Systematic Literature Review

This study conducts a bibliometric analysis of online customer and hotel review research, aiming to provide insights into where each field comes from, stands now and ought to go in the future. In particular, this study examines how the existing research on online customer reviews can benefit future hotel review research. Data collected from Web-of-Science and Scopus created an expanded network of 797 core articles and 19,374 citations to identify intellectual structures, developing trends, and future research gaps. This study offers a visual overview of journals, institutions, countries, research themes and authors to assess the overall directions hotels can take. It underscores the necessity for rigorous and relevant research amid the proliferation of online reviews and emphasises the imperative for academia to bridge the gap between theoretical insights and practical applications within the dynamic tourism industry. This study provides researchers and industry professionals with useful tools to understand and deal with the complexities of online reviews. It also highlights the important role these reviews play in shaping the future of tourism strategies.

How to strategically respond to online hotel reviews: A strategy-aware deep learning approach

Online reviews exert a considerable influence on consumer purchase behavior, yet there remains ambiguity about the most effective managerial response strategies for positive and negative reviews. Addressing this gap, our study introduces a Strategy-Aware, Deep Learning-Based Natural Language Processing (Sa-DLNLP) model designed to optimize firm responses. The proposed model underwent rigorous evaluation through a human-coded study and was subsequently validated by a separate user response study. Our findings reveal that active-constructive responses significantly enhance the impact of positive reviews, whereas passive-constructive strategies are more effective in mitigating the damage from negative reviews. Additionally, the study underscores the importance of concise, personalized, and prompt responses across the board. Interestingly, responses that are overly explanatory, excessively empathetic, or challenge customers were found to be counterproductive when dealing with negative reviews. This study not only demystifies the art of managing online reviews but also offers an advanced deep learning methodology that can directly benefit the disciplines of Information Systems and Management.

Transforming Customer Digital Footprints into Decision Enablers in Hospitality

The proliferation of online hotel review platforms has prompted decision-makers in the hospitality sector to acknowledge the significance of extracting valuable information from this vast source. While contemporary research has primarily focused on extracting sentiment and discussion topics from online reviews, the transformative potential of such insights remains largely untapped. In this paper, we propose an approach that leverages Natural Language Processing (NLP) techniques to convert unstructured textual reviews into a quantifiable and structured representation of emotions and hotel aspects. Building upon this derived representation, we conducted a segmentation analysis to gauge distinct emotion and concern-based profiles of customers, as well as profiles of hotels with similar customer emotions using a self-organizing unsupervised algorithm. We demonstrated the practicality of our approach using 22,450 online reviews collected from 44 hotels. The insights garnered from emotion analysis and review segmentation facilitate the development of targeted customer management strategies and informed decision-making.

Clustering Analysis of Hotel Network Reviews Based on Text Mining Method

With the development of information technology, users use online platforms to post real-time online comments to express their preferences and opinions on goods or services. Online review information expresses users' behavioral habits and special preferences. In depth, analysis of hotel online reviews can improve the adaptability of hotel services to user needs. Effective mining of the vast user review data will provide value for the development of the tourism industry. Using text mining methods to process hotel review data, multiple clustering methods were compared and analyzed for positive and negative feature words from the perspective of user experience. It was found that the k-means++ algorithm had a better clustering effect on user network reviews and achieved better clustering and segmentation of user evaluation information. Unsupervised clustering analysis can be used to further classify online comment information into categories based on positive and negative reviews, providing intellectual support for improving the precision and personalized service quality of hotels.

Temporal Dimensions of Crisis and Online Hotel Reviews: A Social Support Perspective of Narcissists During the Pandemic

This study sought to investigate the impact of the COVID-19 crisis on the online reviewing behavior of individuals with narcissistic traits. We conducted a textual analysis of online reviews submitted to TripAdvisor.com for hotels in New York City, covering the period from August 1, 2019, to July 31, 2022. This time frame allowed us to examine the phases before the COVID-19 crisis, during the early stages of the pandemic, and in the later stages of the crisis. We observed that the negative correlation between narcissism and review ratings was less evident during the initial phase of the COVID-19 crisis but became more pronounced as the crisis progressed. Conversely, the positive correlation between narcissism and the perceived helpfulness of reviews was stronger in the early stages of the COVID-19 crisis compared with the later stages. This suggests that the behavior of posting online reviews may serve as a form of social support mechanism, particularly utilized by narcissistic individuals to address their vulnerabilities, which was more apparent during the early phase of the COVID-19 crisis. These findings open new avenues for research and have practical implications for professionals in the hospitality, tourism, and service industries, especially in understanding consumer behavior during crisis periods.

Hospitality Discourse on Social Media: Evaluating Online Complaints and Service Recovery for Luxury Hotels

The number of hotel visitors who post their personal experiences on online hotel review forums after their trips has increased significantly in the digital age. However, there is limited linguistic research that examines 1) how international travelers communicate online to share their negative hotel experiences and 2) how luxury hotel management resolves complaints and regains customer trust. This study examines electronic word-of mouth (eWOM) in the reviews and focuses on posted by international travelers who stayed at Hong Kong's 5-star luxury hotels and e-replies posted by hotel management groups. The data are based on authentic reviews collected from an online travel review forum. The present study employs appraisal analysis (Martin & White, 2005) and specifically the engagement system to investigate heteroglossic voice in 38 sets of complex e-complaints and responses totaling 17,344 words. The findings contribute to the advancement of professional discourse in hospitality service encounters.

Implication of Online Consumer Reviews on Hotel Bookings in the Kingdom of Saudi Arabia

Travellers are increasingly shifting to booking hotels online to save time, minimize costs and enjoy the convenience it brings. In this study, the implications of online consumer reviews were examined to understand how it affects hotel bookings in Saudi Arabia. The study’s constructs were trust, valence, volume, realism and hotel booking intentions. Structural equation modelling and descriptive statistics were used to analyze the results from 400 respondents. It was found that the Saudi Arabian's intentions to trust on online hotel reviews are influenced significantly by the valence and realism of the reviews. The hotel booking intentions are significantly influenced by trust, valence, and realism of the reviews. The volume of reviews did not have a statistically significant influence on trust on the online reviews or the hotel booking intentions. Therefore, the study concludes that the valence, realism, and trust on online reviews are significant in shaping hotel reservation intentions.

Reducing the Bias in Online Reviews Using Propensity Score Adjustment

Online hotel reviews on platforms like TripAdvisor are crucial in shaping customer choices and steering business strategies in the hospitality sector. However, the effectiveness of these platforms is partially hindered by the self-selection bias found in voluntary reviews. This bias can create false expectations and unsatisfactory experiences, mainly as the feedback generally comes from a non-representative group of self-motivated reviewers (SMRs). A common strategy to mitigate this bias is increasing the number of reviews through customer surveys, generating retailer-prompted reviews (RPRs). However, these RPRs, despite reducing selection bias, tend to lack the depth and insight of SMRs, resulting in a credibility gap and reduced representativeness. To address this, our study presents a novel approach using the propensity score adjustment (PSA) technique. This method leverages the distribution of RPRs to refine the accuracy of text data from SMRs, aiming to enhance the reliability and representativeness of online reviews. By combining the strengths of both RPRs and SMRs, we aim to create an online review environment that is both accurate and reliable. In conclusion, this research marks an important step toward improving online review platforms, aiming for a more transparent and trustworthy environment for reviews.

The role of user-generated content in tourism decision-making: an exemplary study of Andalusia, Spain

PurposeThis research proposes to organise and distil this massive amount of data, making it easier to understand. Using data mining, machine learning techniques and visual approaches, researchers and managers can extract valuable insights (on guests' preferences) and convert them into strategic thinking based on exploration and predictive analysis. Consequently, this research aims to assist hotel managers in making informed decisions, thus improving the overall guest experience and increasing competitiveness.Design/methodology/approachThis research employs natural language processing techniques, data visualisation proposals and machine learning methodologies to analyse unstructured guest service experience content. In particular, this research (1) applies data mining to evaluate the role and significance of critical terms and semantic structures in hotel assessments; (2) identifies salient tokens to depict guests' narratives based on term frequency and the information quantity they convey; and (3) tackles the challenge of managing extensive document repositories through automated identification of latent topics in reviews by using machine learning methods for semantic grouping and pattern visualisation.FindingsThis study’s findings (1) aim to identify critical features and topics that guests highlight during their hotel stays, (2) visually explore the relationships between these features and differences among diverse types of travellers through online hotel reviews and (3) determine predictive power. Their implications are crucial for the hospitality domain, as they provide real-time insights into guests' perceptions and business performance and are essential for making informed decisions and staying competitive.Originality/valueThis research seeks to minimise the cognitive processing costs of the enormous amount of content published by the user through a better organisation of hotel service reviews and their visualisation. Likewise, this research aims to propose a methodology and method available to tourism organisations to obtain truly useable knowledge in the design of the hotel offer and its value propositions.

Expressive illocutionary acts and politeness in managerial responses to online hotel reviews

This paper explores expressive illocutionary acts in responses to hotel guests' reviews posted on Tripadvisor and aims to compare their occurrences and realizations in 320 managerial responses in English from English and Serbian hotels, with manifestations of linguistic politeness a focus of analysis. Drawing largely on Leech's (2014) framework, it has attempted to determine whether the acts which tend to come in the form of formulaic, routinized expressions occurring in the responses written by hotel representatives with different backgrounds exhibit some differences as a reflection of culture-specific politeness values. The analysis has revealed considerable similarity between the two data sets in the lexico-grammatical realization of expressives and the use of resources for politeness. However, some noteworthy points of dissimilarity have emerged which correlate closely with the differences in cultural value preferences of the two countries in Hofstede et al.'s (2010) dimensions of power distance and of individualism.

Imbalanced sentiment classification of online reviews based on SimBERT

The purpose of sentiment classification is to accomplish automatic judssssgment of the sentiment tendency of text. In the sentiment classification task of online reviews, traditional models focus on the optimization of algorithm performance, but ignore the imbalanced distribution of the number of sentiment classifications of online reviews, which causes serious degradation in the classification performance of the model in practical applications. The experiment was divided into two stages in the overall context. The first stage trains SimBERT using online review data so that SimBERT can fully learn the semantic features of online reviews. The second stage uses the trained SimBERT model to generate fake minority samples and mix them with the original samples to obtain a distributed balanced dataset. Then the mixed data set is input into the deep learning model to complete the sentiment classification task. Experimental results show that this method has excellent classification performance in the sentiment classification task of hotel online reviews compared with traditional deep learning models and models based on other imbalanced processing methods.

Research on a Hotel Collaborative Filtering Recommendation Algorithm Based on the Probabilistic Language Term Set

In the face of problems such as information overload and the information cocoon resulting from big data, it is a key point of current research to solve the problem of semantic fuzziness of online reviews and improve the accuracy of personalized recommendation algorithms by using online reviews. Based on the advantage of the probabilistic language term set to deal with fuzzy information and the historical data of online hotel reviews, this paper proposes a collaborative filtering recommendation algorithm for hotels. Firstly, the text data of hotel online reviews are crawled by a crawler and processed by jieba and TF-IDF tools. Secondly, the hotel evaluation attribute set is constructed, and the sentiment analysis of the review statements is carried out with the help of the HowNet sentiment dictionary and manual annotation method. The probabilistic language term set is used to classify the data and derive statistics, and the maximum deviation method is used to determine the weight of each attribute. Then, the cosine similarity formula is fused with the modified cosine similarity formula to calculate the similarity and construct the decision matrix. Finally, combined with the historical data of the user’s hotel selection, the hotel recommendation results are generated. This paper collected review data from 10 hotels in Macau from the official “Ctrip” website. The proposed recommendation algorithm model was then applied to process and analyze the data, resulting in the generation of a ranked list of hotel recommendations. To validate the accuracy and effectiveness of this research, the recommendation results were compared with those produced by other algorithms.

The impact of service robots on customer satisfaction online ratings: The moderating effects of rapport and contextual review factors

AbstractRecent research has established a positive relationship between the use of service robots powered by artificial intelligence in hospitality firms and customer satisfaction online ratings, a particularly important form of electronic word of mouth. However, it is not clear if and how this relationship is augmented or diminished by moderating factors. In this study, we examined four potential moderators by using machine learning and natural language processing techniques to analyze 20,166 online reviews of hotels that had implemented service robots. We had four key findings. First, a positive service robot‐satisfaction rating relationship was further enhanced by improved customer‐service robot rapport during the service encounter. Second, higher customer effort focused on service robots in a review reduced the service robot‐satisfaction rating relationship. Third, posting reviews using a mobile device (vs. other devices) showed higher satisfaction ratings. Finally, customers' prior experience in writing online reviews was unrelated to the service robot‐satisfaction rating relationship. Taken together, these results suggest that service robots should be designed to be interactive and encourage customers to build rapport, for example, by service robots engaging in conversational flows. Moreover, customers should be nudged to use their mobile devices to post timely reviews on their positive human–robot interactions.

MANILAS’ LUXURY HOTELS USING SERVQUAL FRAMEWORK: COMPREHENSIVE ASSESSMENT FROM ONLINE TRAVEL AGENCIES’ REPORTS

Online reviews are essential in the travel and hotel businesses. Because there are various types of hotels, customers frequently read online hotel reviews before making a reservation. The ability to distinguish between positive and negative reviews at luxury hotels according to SERVQUAL attributes of tangibility, reliability, empathy, responsiveness, and assurance could help hotels identify problems, prioritize solutions, and offer better services to Luxury hotels. The study outcomes will assist hotel management in gaining complete knowledge of client complaints regarding service difficulties and promoting service enhancements. This study was conducted to examine and analyze the content of positive and negative customer reviews from Booking.com, Expedia, and TripAdvisor in five-star hotels in Manila. SERVQUAL dimensions were used to classify positive and negative reviews from the selected OTAs in five-star hotels in Manila. Findings revealed that the responsiveness dimension in the recreational services area received the highest number of customer reviews than any other service area of the hotels gathered from the selected OTAs of the study.