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Despite the rise in solo travelers, differences in online review behaviors between solo and companioned travelers remain underexplored. The current research, using the Elaboration Likelihood Model, self-presentation theory, and group polarization theory, finds that solo travelers give higher review scores based on hotel star ratings and value-for-money, but they weigh hotel service quality less than group travelers even after accounting for hotel heterogeneity through multilevel modeling. Additionally, solo travelers' review scores are less polarized than companioned travelers. These findings offer valuable recommendations for hotel management and targeted marketing strategies aimed at solo travelers. [ABSTRACT FROM AUTHOR]

Sentiment analysis presents unique challenges in hotel reviews, particularly in languages like Thai, renowned for nuanced expressions. Understanding customer opinions is pivotal, especially in domains such as hotel reviews, where subjective expressions prevail. This study delves into methodologies to refine sentiment analysis accuracy in Thai hotel reviews by integrating emoji classification with Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and stacking models. Through comparing models with and without emoji inclusion, we meticulously evaluate their performance metrics encompassing accuracy, precision, recall, and F1-score. The pinnacle achievement of 92.4% accuracy underscores the efficacy of advanced stacking techniques complemented by emoji integration. Our findings underscore the superiority of models encompassing emojis, affirming the value of amalgamating textual and emoji data. Leveraging sophisticated deep learning techniques and stacking models, our approach adeptly captures the subtle nuances of sentiment expressed in Thai text, resulting in heightened accuracy in sentiment analysis. This research underscores the paramount importance of embracing diverse data sources and sophisticated modeling strategies to elevate sentiment analysis accuracy in Thai hotel reviews. [ABSTRACT FROM AUTHOR]

In the business context, effectively responding to negative reviews is critical for a hotel to maintain reputation and customer relations. To explore the linguistic devices employed in addressing guest complaints, a corpus-based study is conducted on the use of interactional metadiscourse and identity construction in responses to negative online reviews of Chinese and British Hotels. Drawing upon the statistical results of the usage of interactional metadiscourse and the analysis of discourse examples, this study delves into the frequency and similarities/differences in the employment of five subcategories of interactional metadiscourse across the respective corpora of 100 responses to negative reviews from hotels in Beijing and hotels in London. Furthermore, the study examines the characteristics and similarities/ differences of the identity construction of manager, communicator, doer and advisor with the use of interactional metadiscourse. The findings reveal that there are significant differences between the two sides in the use of self-mentions, boosters, hedges and positive attitude markers, while there is little difference in the use of engagement markers and negative attitude markers. The most constructed identity by both sides is the communicator, and the least is the adviser, with little difference. The identity of manager is significantly more prevalent in responses from hotels in Beijing, whereas hotel responders in London exhibit a notably greater tendency to construct the identity of doer. The similarities and variances of interactional metadiscourse use and identity construction indicate the two sides' distinctive priorities in interactions with guests and different cultural values, which provide valuable insights for hotels on the effective use of metadiscourse to construct multiple identities, revealing that the strategically crafted responses play a pivotal role in shaping favorable images, fostering harmonious relationships with customers and promoting sustainable development of the hotels. [ABSTRACT FROM AUTHOR]

Regardless of the diversity of travellers, meeting customer expectations is essential for the success of hotel business worldwide. How do hoteliers cope with these challenges to meet guest expectations before arrival in terms of different regions and travel purposes? This study analyses travellers' complaints retrieved from the hotel review website 'TripAdvisor' to gain insight. A total of 1,868 travellers' complaints were analysed using a Two-Way ANOVA factorial design to examine the differences among the home regions of travellers, travel types, and other hotel service attributes. The findings indicate that the home region of travellers has a higher main effect than traveller type on hotel service attributes regarding online complaint behaviour. Finally, family travellers are more likely to complain about cleanliness than couples. [ABSTRACT FROM AUTHOR]

The main purpose of the existing study is to present the mediating influence of employee performance on the relationship between transformational leadership style and green innovation. This study conducted in the hotel sector in Amman, Jordan, where exploratory studies related to the hotel sector are limited. A total of 20 five-star hotels in Amman were involved in the research sampling process, targeted respondents were employees with titles below supervisor level (rank-and-file) were those employees who provide the services and are in direct contact with customers, and a simple random sampling was utilized to gather the data, 359 employees participated in this study, and survey form was sent to heads of departments at the targeted hotels to help in the distribution process. The data was analyzed using Smart-PLS software. The results showed a partially positive mediation of employee performance among both constructs of individualized consideration and intellectual stimulation of green innovation in the hotel sector. Moreover, these results proved the power of the relationship between employee performance and the adoption of green innovation. The current study contributes positively to the area of hospitality industry studies and its related activities. It provides better insights for hotel leaders on improving the adopted leadership styles, leading to outstanding employee performance, and green innovation to sustain market competitiveness and provide exceptional and innovative services for a better hospitality industry. [ABSTRACT FROM AUTHOR]

In response to the rapid advancement in smart technology and the 2019–2020 pandemic, the hospitality industry has accelerated its adoption of innovative technologies. However, new technologies are often disruptive and may not be aligned with the organization's strategy, technological expertise, and employee and customer expectations. This negatively affects the perceived value of the new technology and its adoption and continuous use. The review of the relevant research presented in this paper indicates that despite the many potential points of intersection between technology acceptance and adoption theories and studies of entrepreneurship and change management, these connections have not been explored in sufficient depth in the hospitality context. Drawing on extant theories, this study proposes a novel conceptual framework for the management of technology adoption in hotel organizations. The four stages of the framework represent the process of technology adoption from initiation to institutionalization to new challenges, connecting the theoretical constructs with the tangible needs of the hotel organizations. The framework considers technology adoption as a process of change that involves capability and leadership building. It shows how managing technology adoption through an integrated change management and digital capability-building perspective can lead to sustainable digital innovation. Practitioners can use the framework to systematically evaluate the potential of new and emerging technologies and develop the required digital competencies while managing organizational culture shifts and user resistance to change. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to explore the attraction, influence and persuasive effect of advertising (with or without metaphor) across different types of hotels (luxury vs. budget hotels). Design/methodology/approach: Two independent variables – namely, the hotel category (luxury versus budget) and the use of rhetorical figures (text with metaphors versus text without metaphors) – serve as the basis for the investigation. The research design is a 2 × 2 within-subjects construct based on eye-tracking methodology complemented by questionnaire-based data collection. Participants were engaged with hotel advertisements under four specific conditions, each reflecting a distinct combination of the two independent variables. Findings: The findings reveal the augmented attention-grabbing prowess of advertisements that use metaphors compared to those that do not. Furthermore, the study findings reveal that the hotel category significantly influences the efficacy of advertising; advertisements promoting luxury hotels were found to stimulate superior impression recall, garner higher customer affinity and prompt a more potent intention to patronize the hotel. Finally, the study shows that neither congruent nor incongruent pairings of visual (budget or luxury hotel) and verbal elements (with or without metaphor) yield additional advertising benefits. Practical implications: This research addresses the limited empirical guidance available for hotel operators at various levels regarding the design of advertisements incorporating rhetorical devices. It highlights the potential benefits of using figurative rhetoric, especially metaphors, in advertising for both budget and luxury hotels. The study reveals a notable correlation between hotel category, particularly luxury offerings, and customer attention, retention, preference and intention to visit. Furthermore, it encourages hotel operators, regardless of their hotel's ranking, to incorporate metaphorical language into their advertising designs, underscoring the need for strategic integration in overall advertisement planning. Originality/value: Using high-resolution online eye-tracking technology, this study innovatively examines the influence of hotel category and rhetorical devices on the effectiveness and attention-grabbing potential of advertisements. Through this approach, the study seeks to offer valuable managerial and advertising insights for those involved in hotel marketing. [ABSTRACT FROM AUTHOR]

Since the traditional method may cause the loss of linguistic information, in this paper, we chose the probabilistic linguistic term set (PLTS) to describe the evaluation index for the decision problem, then we propose an improved VIKOR method to select the best hotel. However, the traditional VIKOR method has lost original information and experts’ preference information, so the PLTS-VIKOR method is proposed for tourists. In addition, the weaknesses and strengths of traditional weighting methods are analyzed; the BWM and the CRITIC method for PLTS are developed to determine the subject and object weights. Then, a comprehensive combined weighting method based on the minimum total deviation principle is proposed. Further, we integrate the PLTS with all weighting methods. Finally, a case example of 5 online hotels is performed to illustrate the application of the improved approach, and the superiority and effectiveness are proved by comparative analysis. [ABSTRACT FROM AUTHOR]

This study seeks to explore how Dark Triad of psychopathy, Machiavellianism, and narcissism interact with consumers’ regulatory focus (prevention-focus and promotion-focus) to influence online consumer reviews in the hotel industry. We conducted an empirical analysis of 1,185,940 hotel reviews from TripAdvisor.com, utilizing the textual content of online reviews and employing Language Inquiry and Word Count analytical tools. The research reveals a multifaceted link between regulatory focus and Dark Triad in online reviews. The results showed that individuals with a promotion focus tend to give higher ratings than those with a prevention focus. Dark Triad significantly influences how regulatory focus impacts online reviews. This study initiates a new academic dialogue on the interaction between regulatory focus and Dark Triad traits within online consumer behavior studies. It provides practical guidance for the hotel industry to refine their management and response strategies by acknowledging the impact of consumer personality traits on online reviews. [ABSTRACT FROM AUTHOR]

In this paper, we explore the approaches to the problem of cross-domain few-shot classification of sentiment aspects. By cross-domain few-shot, we mean a setting where the model is trained on large data in one domain (for example, hotel reviews) and is intended to perform on another (for example, restaurant reviews) with only a few labelled examples in the target domain. We start with pre-trained monolingual language models. Using the Polish language dataset AspectEmo, we compare model training using standard gradient-based learning to a zero-shot approach and two dedicated few-shot methods: ProtoNet and NNShot. We find both dedicated methods much superior to both gradient learning and zero-shot setup, with a small advantage held by NNShot. Overall, we find few-shot to be a compelling alternative, achieving a surprising amount of performance compared to gradient training on full-size data. [ABSTRACT FROM AUTHOR]

This study investigates the impact of hotel service encounters on guests' behavioral outcomes in 5 and 4-star hotels. It also examines the mediating effect of perceived value and satisfaction on the relationship between service encounters and guests' loyalty. In addition, it investigates the moderating effect of hotel star ratings on the relationship between the study constructs. The study reveals that hotel service encounters significantly impact perceived value but not satisfaction. The hotel staff rather than the atmospherics is an antecedent of loyalty. At the same time, the perceived value of a hotel is an antecedent of satisfaction and loyalty. Meanwhile, it confirms the mediation of perceived value and satisfaction and a hotel's star rating as a moderator. It finds that the hotel star rating moderates the impact of hotel staff on perceived value, satisfaction, and loyalty as it is more robust for 5-star hotels than for 4-star. The theoretical and managerial implications of these findings have also been discussed. [ABSTRACT FROM AUTHOR]

Nowadays, the COVID-19 pandemic has had many harmful effects on the hotel industry. This study aims to identify and prioritize the factors affecting SERVQUAL in the hotel industry and evaluates five-star hotels during the COVID-19 Pandemic. The present study prioritizes the influence factors of SERVQUAL using the stepwise weight assessment ratio analysis (SWARA) method and using the Additive Ratio Assessment (ARAS) method to identify the best hotel among the five hotels in Iran in 2022. In the present study, 22 influence sub-criteria in the hotel industry were studied, and the delivery of service promise sub-criterion was ranked first, which shows its importance among other sub-criteria. Among the hotels studied, A1 hotels ranked first among other hotels. The present study examines and prioritizes the influence factors of SERVQUAL in the hotel industry to help hotel managers to identify the most important influence factors of SERVQUAL in the hotel industry to implement these factors in their hotels and can improve their strengths. [ABSTRACT FROM AUTHOR]

The concept of all-inclusive (AI) hotel service is complex and multidimensional; grounded on the SERVQUAL model, this research aims to identify the main components of AI service that moderate online ratings. Aside from the dimensions comprised in the SERVQUAL model, this research discusses price, place, and experiences as essential components in AI hotel ratings. This research was applied in the Algarve, a mature sun and sea destination with more than thirty all-inclusive hotels in operation since 2008. A fuzzy set qualitative analysis (fsQCA) approach is used from 6742 validated reviews of four- and five-star all-inclusive hotels retrieved from TripAdvisor between August 2003 and April 2019. Single constructs in the AI ecosystem have no impact as a rating enhancer on TripAdvisor, with two or more constructs necessary to trigger the goal. This research also contributes by applying a fuzzy set approach to the extracted user-generated content (UGC) regarding AI hotels on TripAdvisor and revealing the necessary and sufficient conditions through the identified constructs that most contribute to high online ratings. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of Professional Business Review (JPBReview) is the property of Open Access Publications LLC and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: Engaging in webcare, i.e. responding to online reviews, can positively affect consumer attitudes, intentions and behavior. Research is often scarce or inconsistent regarding the effects of specific webcare strategies on business performance. Therefore, this study tests whether and how several webcare strategies affect hotel bookings. Design/methodology/approach: We apply machine learning classifiers to secondary data (webcare messages) to classify webcare variables to be included in a regression analysis looking at the effect of these strategies on hotel bookings while controlling for possible confounds such as seasonality and hotel-specific effects. Findings: The strategies that have a positive effect on bookings are directing reviewers to a private channel, being defensive, offering compensation and having managers sign the response. Webcare strategies to be avoided are apologies, merely asking for more information, inviting customers for another visit and adding informal non-verbal cues. Strategies that do not appear to affect future bookings are expressing gratitude, personalizing and having staff members (rather than managers) sign webcare. Practical implications: These findings help managers optimize their webcare strategy for better business results and develop automated webcare. Originality/value: We look into several commonly used and studied webcare strategies that affect actual business outcomes, being that most previous research studies are experimental or look into a very limited set of strategies. [ABSTRACT FROM AUTHOR]

This study examines the Macau hotel industry's response to global shocks using vector autoregression analysis. According to the findings, the hotel occupancy rate reacts negatively to a one-standard-deviation shock in global geopolitical risk. However, this reaction decreases after the second period and completely loses its effect after the fourth period. Hotel occupancy rate responds weakly and short term negatively to the shock of global economic policy uncertainty. The hotel industry reacts short term, weakly, and positively to a shock in US monetary policy uncertainty. According to the variance decomposition results, at the end of the tenth term (long-term), 96.25% of the change in hotel occupancy rates is explained by their own dynamics. The rest is 3.00% geopolitical risk, 0.5% US monetary policy uncertainty, and 0.25% economic policy uncertainty. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Businesses rely heavily on review analysis of consumer feedback and reviews when making decisions. This research examines fifteen years' worth of hotel evaluations to learn more about the opinions of visitors and how they relate to review scores in various years and regions. After the dataset was cleansed to concentrate on reviews written in English, TextBlob was used to analyze sentiment and classify the reviews as positive, neutral, or negative. The results validate using sentiment analysis in conjunction with numerical ratings, which show a positive link between more excellent ratings and positive sentiment scores. A significant drop in sentiment scores following the 2008-2009 recession emphasizes how the global financial crisis affected the hospitality sector. Significant differences in guest feelings between cities, states, and nations are shown by geographic analysis, underscoring the necessity of region-specific approaches. Sentiment distribution and trends were revealed via intricate visualizations such as heat maps, box plots, scatter plots, and histograms. To improve guest happiness, the study uses an understanding of geographical variations. These results give hotel management a solid framework for raising customer satisfaction and loyalty, customizing marketing campaigns, and enhancing service quality. For deeper insights, future studies may investigate more sophisticated NLP techniques. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Recognizing the importance of affective fulfillment for contemporary customers, this study aims to investigate how the expressions of affective needs in online reviews impact review usefulness. Drawing on information processing theory, we proposed a framework based on big data analytics and econometrics to explore 469,202 online reviews. Empirical findings unveil that consumers’ expressions of affective needs involve Service, Room, Decoration, Enhancement facility, and Location; both the affective needs presence and intensity regarding the first four attributes positively influence review usefulness. The expressions of functional needs have bigger impacts than that of affective needs regarding Room but smaller than Service, Enhancement facility, and Decoration. Hotel type moderates this relationship: the influence of affective need expressions is amplified in luxury (vs. economy) hotels, indicating that their customers crave more disclosure for affective needs. This study contributes to research on review usefulness and hospitality market segmentation. Practical implications for multiple stakeholders are discussed. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of Professional Business Review (JPBReview) is the property of Open Access Publications LLC and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

One significant challenge in sentiment analysis is the presence of negation, which reverses the meanings of sentences, transforming positive statements into negative ones and impacting the sentiment conveyed in the text. This issue is particularly pronounced in Arabic, a language known for its complex morphology. Detecting negation is crucial for enhancing sentiment analysis performance and various natural language processing applications. This paper presents an approach for automatically detecting negation in user-generated Arabic hotel reviews through lexical and structural features. It comprises several stages: data collection, text pre-processing, feature extraction, supervised learning classification, and evaluation. The study employed multiple supervised classification techniques, including naïve Bayes, random forest, logistic regression, support vector machines, and deep learning, to analyse lexical and structural features extracted from the dataset. The results of the experiments yielded promising outcomes, demonstrating the feasibility of the approach for practical applications. The classifiers exhibited highly comparable performance in identifying negation, with only marginal deviations in their performance metrics. Notably, the deep learning classifier consistently emerged as the top performer, achieving an exceptionally high overall accuracy rate of 99.24 percent, surpassing established benchmarks in Arabic text processing and underscoring its potential for practical applications. These findings hold significant implications for advancing Arabic text processing, particularly in sentiment analysis and related NLP tasks. The high accuracy of 99.24 percent achieved by the deep learning classifier highlights its robustness in accurately detecting negation, a critical challenge in sentiment analysis. This classifier performance demonstrates the potential to be integrated into real-world applications, such as automated review systems and opinion mining tools, where accurate sentiment interpretation is essential. [ABSTRACT FROM AUTHOR]

Biophilic design made by incorporating nature into interior spaces have proven that biophilia has positive effects on people's psychological, and physical well-being through scientific studies. Although increasing number of people and rapid urbanization have led to changes in leisure activities and accommodation sector specifically after the COVID 19 world pandemic, it is observed that scientific research pool do not align with the number of biophilic hotels recently designed and occupied. With this study, it has been observed that biophilic design studies in the literature are increasing especially after the pandemic, however the number of studies in office and commercial spaces show an augmenting trend compared to the accommodation sector. Within the scope of this study, a comprehensive systematic literature review on biophilic hotel design was conducted. Gaps were identified in the literature on the use of biophilic design in hotels compared to the evaluation of biophilic design elements in other areas. As a result of the review, 14 relevant articles obtained from Web of Science Core Collection and Scopus databases were analyzed in detail. The methods and results led to the identification of the literature gap in this field. It has been observed that studies evaluating biophilic hotel design in the light of scientific data have increased after COVID-19. Most of the studies examined are focused on hotel lobby cases. Although lobbies are important places in terms of the first impression of visitors in hotels, there is no study focusing on hotel rooms. Future studies can also focus on the accommodation areas. Especially for the tourism sector, where customer satisfaction and psychology are at the forefront, it is important to investigate the scientific effects of biophilic design examined in the literature to draw the necessary lessons from existing studies in order to guide future designs. [ABSTRACT FROM AUTHOR]

The study investigates the factors influencing tourists' online booking intentions. This study employed structural equation modeling to evaluate the online booking intentions of tourists after the second wave of the COVID-19 outbreak in India, extending the application of reactance theory in tourism and hospitality services. Mediation and moderation analysis facilitated the unfurling of direct and indirect linkages among the constructs. The findings suggest that online hotel reviews, perceived scarcity, and perceived enjoyment aggrandize tourists' perceived value quotients, escalating their online booking intentions. Besides, visual presentations improve the strength of the linkage, while perceived pandemic threat weakens the linkage between tourists' perceived value and online booking intentions. The research demystifies critical facilitators of tourists' perceived value and online booking intentions that may assist hotel owners in engaging potential tourists and maximizing their hotels' bottom line. The hotel industry lacks research on perceived scarcity, visual presentations, and perceived pandemic threat. This research adds to the body of knowledge by combining the abovementioned factors through a coherent theoretical framework. It makes prospective tourists cautious about COVID-19's virulence and the devastating consequences. [ABSTRACT FROM AUTHOR]

This study aims to dynamically mine the demands of hotel consumers. A total of 378,270 online reviews in the cities of Beijing, Chengdu, and Guangzhou in China were crawled using Python. Natural language processing (e.g., opinion mining and the BERT model) and an improved Kano model (containing One-dimensional, Attractive, Indifferent, and Must-be) were utilised to analyse online hotel reviews. The results indicate that the hotel attributes that consumers care about (e.g., Clean, Breakfast, and Front Desk) are dynamically fluctuating, and the attention and satisfaction of corresponding attributes will also change. This study classified consumer demand into eight types across cities and found that it changes over time. In addition, we also found that hotel attributes, satisfaction and attention, and consumer demands vary among different cities. Existing studies of capturing consumer demand are usually time-consuming and static, and the results are subjective. This study compared and analysed the consumer demands of hotels in different cities via a dynamic perspective, and used hybrid methods to improve the granularity of the analysis, expanding the general applicability of the Kano model. Hotel managers can refer to the results of this article to allocate resources for improvement and create competitive hotel services. [ABSTRACT FROM AUTHOR]

In this digital age the use of social media has evolved rapidly. This has empowered customers to express their sentiments about products or services on online platforms, leading to the emergence of influential electronic Word-of-Mouth (eWM). Therefore, in various industries, webcare, which refers to customer care via online communications, is essential. In the hotel industry webcare has become an effective marketing tool and has received considerable critical attention in hospitality and business studies over the past two decades. In the field of linguistics, webcare has received increasing attention in recent years while going relatively unnoticed in the research literature of Southeast Asia. This study examines the apology strategies as webcare in rapport building in the responses to negative online reviews by hotels of three different star-ratings in Malaysia on TripAdvisor. The findings identified eight apology strategies. It was found that the strategy of offer of repair by the five-star hotels was significantly higher than the four- and three-star hotels. Compared to the four- and three-star hotels, the five-star hotels were least likely to use the strategy of explanation of situations. This study sheds light on cultural insights into webcare strategies in social media communications in the hotel industry in Malaysia. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this article is to present findings on the use of behavioural economics elements on the websites of Polish hotels. Design/methodology/approach: This article presents how Polish hotels use elements of behavioural economics to increase website effectiveness and encourage users to book a hotel service, and thus achieve the company's business goals. The research method used is an exploratory content analysis of the websites of selected hotels in Poland. Findings: Some Polish hotels use elements of behavioural economics, and thus attempt to increase the number of bookings through their own/indirect booking channels-including the website. However, there is an area to be developed-many sites have identified areas where popular cognitive biases can be further exploited and thus work to increase conversions from the website. It was noted that the ability to exploit some elements of influence depends on the software that hoteliers use (usually the booking system-booking engine). Research limitations/implications: It would be advisable to continue the consideration based on a study of the use of behavioural economics elements on hotel websites on a larger scale and to test the effectiveness of individual measures (for example through A/B tests on the websites). In addition, since cognitive errors are not only experienced by consumers, but also by managers, an area for investigation regarding the impact of cognitive errors on managerial decisions is also suggested. Specific theoretical constructs allow for a good description of the mechanisms under investigation, creating an innovative research space in the field of management science and quality. Practical implications: Identifying and analysing the behavioural economics elements used on hotel websites, can shape the research space for determining the relationship between the use of these elements and the effectiveness of the website as one of the channels that achieves the company's business objective. Subsequently, enable managers to create effective communication on websites to increase the number of accommodation bookings through this channel. Social implications: with knowledge of cognitive errors, a manager can create effective marketing strategies, make better decisions and better manage his or her team. Originality/value: The websites of Polish hotels were reviewed, identifying the most common elements using behavioural economics. It was noted that Polish hotels use these elements to a limited extent. Examples of cognitive biases that are not yet or rarely used by hoteliers on their websites and could help to increase direct bookings were identified. In addition to theoretical knowledge, the article draws on the authors' many years of experience in the topic of management, including in the hotel industry. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to examine the role of experiential familiarity in determining the competitiveness of hotel chains. It does so by comparing the attribute-performance perceptions of guests who had and had not previously stayed at a property belonging to a specific hotel chain. It also examines how far such perceptions shape word-of-mouth and future purchase intentions. Design/methodology/approach: Data were collected from 1,016 Finnish leisure tourists in 2021 using an online questionnaire, providing a representative sample of Finnish domestic leisure tourists. Findings: The results indicate that the competitiveness of different hotel chains depends on a small number of key attributes. Differentiation between hotel chains can be seen from the results. Previous guests rate hotel chain attributes more highly than non-previous guests. Behavioral intentions do not differ between previous and non-previous guests, but how many times a person has stayed in the hotel chain significantly influences behavioral intentions. The results provide strategic levers that hotel chains can use to enhance their competitiveness. Practical implications: Hotels should invest in attributes that have the biggest positive impact on customer behavior. These will be different for different hotel chains. By understanding these differences, it is possible to communicate relevant attributes to customers through marketing and develop hotel features that will drive revisit intention and word-of-mouth marketing. Originality/value: This study found that while certain hotel attributes had a significant shaping effect on guests' performance ratings, there were no decisive differences between those with or without experiential familiarity with the hotel chain. [ABSTRACT FROM AUTHOR]

Personalizing hotel recommendations for tourists is challenging because it needs to model a logical aggregation of multiple criteria in terms of the user's decision process and preferences. In this paper, we propose a hierarchical multi-criteria hotel evaluation model that includes important hotel properties as well as hotel ratings by previous customers. The recommender system proposed, called HR.LSP, applies different graded logic aggregation operators following the method of Logic Scoring of Preference (LSP). Asymmetric aggregation is used for some of the criteria, where the user can not only define the importance of inputs, but also indicate if they are mandatory or optional. This is the first recommender system based on graded logic and the first non-professional use of LSP. The application of graded logic operators significantly improves the decision-making process of hotel selection for each user. The case of Riohacha (an emerging tourist destination in Colombia) is studied. In addition to quality and location criteria, some security information is included in the model as it is of utmost importance to provide to travelers a secure experience at the destination. [ABSTRACT FROM AUTHOR]

This research note delineates the conflict of hotel rate parity and key clauses of competition laws in both Europe and the U.S. We trace the origin of hotel rate parity to the principle of most favored nation (MFN) in international trade agreements. We show that rate parity challenges two pillars of competition law. Under rate parity agreements, it is travel intermediaries—not hotels—that demand rate parity, which comes down to the dominance of travel intermediaries over small and independent hotels. The courts view MFN status as a hindrance to competition and therefore in violation of competition law. The trend and message in Europe are clear: the clause is most likely to be judged as not complying with EU competition law and its national equivalents. In the U.S. though, a lack of case decisions precludes us from reaching any conclusion about the fate of the MFN clause. [ABSTRACT FROM AUTHOR]

Predicting hotel ratings from reviews involves natural language processing techniques to extract sentiment and features from text data, then applying machine learning (ML) models like regression or classification to estimate the corresponding rating based on these features. This study proposes an ensemble learning approach for predicting hotel ratings from user reviews. By integrating multiple ML algorithms trained on various textual features, including linguistic, semantic, and sentiment-based attributes, our model achieves superior predictive accuracy. The primary aim of this study is to predict user experience ratings with high accuracy. To achieve this, this research utilizes an Ensemble learning approach known as majority voting to make these predictions effectively. In this investigation, initially, the dataset undergoes cleaning and is subsequently subjected to a series of pre-processing steps using Natural Language Processing (NLP) techniques. The research includes a comparative analysis of various classifiers along with different embedding methods. Seven different types of classifiers are used alongside three embedding techniques i.e., Term Frequency-Inverse Document Frequency (TF-IDF), Bag of Words (BoW), and Word2Vec. The classifiers include, Logistic Regression without Cross Validation (LR), LR with Cross Validation (LRCV), Decision Tree Classifier (DTC), Stochastic Gradient Descent Classifier (SGDC), Random Forest Classifier (RFC), Support Vector Classifier (SVC) and K-Nearest Neighbour (KNN). Our proposed methodology demonstrates higher accuracy and robust performance. Accuracy and frequency are utilized as performance metrics for assessing and validating both classifiers and embedding techniques. As per the simulation results, TF-IDF in combination with LRCV achieves an accuracy rate of 61%. [ABSTRACT FROM AUTHOR]

Purpose: This study involves analysing the online feedback from customers about The Royal Heritage Haveli, a heritage hotel located in Jaipur. The aim is to perform sentiment analysis on reviews through polarity classification to ascertain the overall sentiment conveyed in the collected reviews. Design/methodology/approach: A comprehensive collection of 1,268 consumer reviews regarding the hotel was gathered from the Trip Advisor platform. The Beautiful Soup library in Python was utilized for web scraping activities. The data was subjected to pre-processing and analysis using the Orange data mining program. The VADER sentiment analysis method is employed to evaluate the polarity and general sentiment of the reviews. The study classifies review sentiments into three distinct categories: positive, neutral, and negative. Findings: An examination of tone and topic of internet reviews of The Royal Heritage Haveli has revealed interesting information about the entire guest experience. After evaluating the thoughts expressed by reviewers, it is clear that a significant majority communicate a sense of optimism, as they express happiness and joy. The analysis of emotions indicates a favorable tendency towards many components of the historic hotel. Originality/value: This study represents a unique effort to analyze online customer reviews of The Royal Heritage Haveli by employing a text-mining methodology. The objective is to determine the sentiment scores of these reviews to ascertain levels of pleasure and dissatisfaction. [ABSTRACT FROM AUTHOR]

This study employs machine learning algorithms to explore the determinants of customer dissatisfaction in the hotel sector, focusing on the Seoul hospitality market. Utilizing a dataset comprising 800 online complaint reviews across 30 distinct hotel categories, we conducted a comprehensive analysis to identify the primary drivers of customer complaints. Our findings indicate that service-related issues are predominant, with a significant impact on luxury hotel ratings due to perceived service quality gaps. Additionally, hardware concerns are a secondary factor, highlighting the importance of infrastructure and amenities to customer satisfaction. We also observed that anomalies in service delivery and staff attitudes significantly influence overall satisfaction levels. Leveraging natural language processing techniques for text classification, we categorized complaints into actionable insights, enabling targeted recommendations for hotel management. These include strategies to enhance service quality, upgrade hardware facilities, streamline additional charges and disposable item services, and emphasize employee training in service excellence and cross-cultural communication skills. The proposed interventions aim to bolster the competitiveness of South Korean hotels, cater to the evolving needs of a global clientele, and foster sustainable growth within the industry. Our approach demonstrates the utility of data-driven methodologies in optimizing customer satisfaction and operational efficiency in the hospitality sector. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to evaluate and structure the basic heuristics consumers use in evaluating word-of-mouth (WOM) about luxury hotel brands while analyzing the impact of deception in online consumer reviews. Design/methodology/approach: The research used a two-study mixed-methods approach, using interpersonal deception theory and social proof theory as lenses to conduct our analysis. For the first study, a qualitative conceptual mapping analysis was conducted, examining online consumer reviews to identify key concepts and their relationships in the context of luxury hotel brands. In the second study, the themes were further examined using a fuzzy-set qualitative comparative analysis to analyze their causal complexity and association between variables to determine how they influence the perceived helpfulness of online reviews for luxury hotel brands. Findings: The results underline the importance of functional, objective variables, such as the number of reviews and stars, as social proof heuristics and other factors, including clout, authenticity and analytic tone, as interpersonal communication heuristics. Therefore, consumers use a combination of social and interpersonal communication heuristics to extract information from reviews and manage deception risk. Research limitations/implications: The paper contributes to the consumer–brand relationship literature by assessing the heuristics consumers use in evaluating online reviews and provides additional information for research in online reputation management. Practical implications: This study's results can help marketing practitioners and brand managers manage their online reputations better. It can also aid managers in improving their messaging on hotel websites to entice consumers to complete bookings. Heuristics play an essential role in such messaging and understanding them can help marketers appeal directly to their target market. Originality/value: This study contributes to the literature on consumer–brand relationships by providing a framework of heuristics that consumers use when evaluating luxury service brands and contributes to WOM and online reputation research by highlighting factors that may make online reviews more helpful. [ABSTRACT FROM AUTHOR]

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This study expects to examine the impact of vacationer visits, lodging inhabitance rates, and unfamiliar direct speculation (FDI) on the monetary development of Solo Raya from 2013 to 2022. Financial development implies changes in the economy of a district. The exploration uses auxiliary information from 7 locale/urban communities, specifically Surakarta City, Boyolali Rule, Sukoharjo Regime, Karanganyar Rule, Wonogiri Regime, Sragen Rule, and Klaten Regime, handled through board information investigation utilizing the Decent Impact Model (FEM) technique. The consequences of this study show that, to some extent, the quantity of vacationers has a positive and huge relationship with monetary development, lodging inhabitance rates likewise have a positive and critical relationship with financial development, and FDI has a positive and critical relationship with monetary development. Simultaneously, the number of tourist visits, hotel occupancy rates, and FDI collectively influence Economic Growth in the Java Island Province from 2013 to 2022. [ABSTRACT FROM AUTHOR]

Online hotel reviews provide a vital information source for customers to select an optimal hotel, but a large amount of vague and unstructured information increases the difficulty of decision-making. From the perspective of customers' risk attitudes, this paper proposes a novel fuzzy decision support model for hotel selection based on online reviews. Firstly, the useful information from online reviews is extracted by attribute extraction and sentiment analysis, and then this information is aggregated into the Probabilistic Linguistic Term Set (PLTS) by considering the weight of each review. Secondly, the improved linguistic scale functions are constructed from the perspective of customers' risk attitude to convert PLTS into quantitative information. Thirdly, an integrated attribute weighting method is presented based on objective weights of the statistical measure and subjective weights of the Analytic Hierarchy Process (AHP) technique. Fourthly, an extended Combinative Distance-based Assessment (CODAS) method is developed to evaluate the performances of hotels. The effectiveness of the proposed model is verified by the practical case from TripAdvisor.com and the comparative analysis with the existing methods. [ABSTRACT FROM AUTHOR]

Purpose: This 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/approach: This 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. Findings: This 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/value: This 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. [ABSTRACT FROM AUTHOR]

Sustainability has become a prominent goal in tourism and hospitality. While green practices can benefit the environment and society, they can also lead to customer dissatisfaction. Therefore, the current study aims to identify different hotel green practices and examine their impact on customer satisfaction and dissatisfaction. This study uses a mixed method of business analytics and regression analysis on a dataset of 813,791 online reviews of 450 hotels in New York City derived from TripAdvisor.com. The research findings guide hotel operators to build strategies for green practices while reducing customers' discomfort and improving the overall customer experience. [ABSTRACT FROM AUTHOR]

Customers' hotel location evaluations are critical to hotel location decisions, future investment, visitor experience and urban tourism development. In this paper, spatial autocorrelation analysis, GeoDetection and geographically weighted models are used to explore the characteristics of the location satisfaction pattern of 1126 hotels in Wuhan and its causes.The findings suggest that there are obvious spatial clustering distribution characteristics of hotel location satisfaction in Wuhan. Distance from highway entrances/exit, scenic spot accessibility, and free parking services have the strongest positive influence on the spatial differentiation pattern of hotel location satisfaction, while hotel transportation services, the percentage of water area, and the number of universities have significant spatial differentiation characteristics on the influence of hotel location satisfaction. This study contributes to a better understanding of hotel location satisfaction pattern and its causes through multi-source data and spatial analysis techniques. The results of the study are of practical significance to the planning and development of urban hotels in Wuhan, as well as to other cities similar to Wuhan in terms of city scale, spatial layout of hotels, and tourism development. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Hedonic pricing (HP) models are widely used to capture the price heterogeneity of hotel attributes and complementary services. This study uses a broad-ranging database with information about 11 hotels, located in the same Majorcan (Spain) coastal town, to identify the main attributes, complementary services and guest characteristics that affect hotel pricing, hotel revenue (RevPAR) and repeat stays. The results found that, aside from the main sources of price variability, such as accommodation type, location and season, other hotel attributes/services were found to influence these dependent variables: refurbishments, room type, occupancy level, number of stars, clients' online ratings and the specific hotel. Attributes/services did not have a homogeneous effect on the three types of models estimated, as some attributes increased prices without increasing RevPAR or number of repeat stays. [ABSTRACT FROM AUTHOR]

This study aims to identify the critical hotel guest technology that enhances the hotel customer experience. By applying the two-factor theory, the study discovers the asymmetry impact of hotel guest technology on customer satisfaction and dissatisfaction. This study uses an integrated approach of big data analytics and impact asymmetry analysis on a dataset of 520,757 online reviews of 435 hotels in New York City derived from TripAdvisor.com. Big data analytics is implemented to identify significant attributes of hotel guest technology. Then, the five unique roles of hotel guest technology in customer satisfaction and/or dissatisfaction are identified through impact asymmetry analysis. The integrated approach reconciles inconsistent findings from prior research and guides hotel operators to prioritize hotel guest technology to increase customer experience. [ABSTRACT FROM AUTHOR]

TripAdvisor reviews and comparable data sources play an important role in many tasks in Natural Language Processing (NLP), providing a data basis for the identification and classification of subjective judgments, such as hotel or restaurant reviews, into positive or negative polarities. This study explores three important factors influencing variation in crowdsourced polarity judgments, focusing on TripAdvisor reviews in Spanish. Three hypotheses are tested: the role of Part Of Speech (POS), the impact of sentiment words such as "tasty", and the influence of neutral words like "ok" on judgment variation. The study's methodology employs one-word titles, demonstrating their efficacy in studying polarity variation of words. Statistical tests on mean equality are performed on word groups of our interest. The results of this study reveal that adjectives in one-word titles tend to result in lower judgment variation compared to other word types or POS. Sentiment words contribute to lower judgment variation as well, emphasizing the significance of sentiment words in research on polarity judgments, and neutral words are associated with higher judgment variation as expected. However, these effects cannot be always reproduced in longer titles, which suggests that longer titles do not represent the best data source for testing the ambiguity of single words due to the influence on word polarity by other words like negation in longer titles. This empirical investigation contributes valuable insights into the factors influencing polarity variation of words, providing a foundation for NLP practitioners that aim to capture and predict polarity judgments in Spanish and for researchers that aim to understand factors influencing judgment variation. [ABSTRACT FROM AUTHOR]