Online Hotel Research Articles

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.

Navigating the Digital Landscape: An Analysis of Hotel Booking Websites and Their Impact on Consumer Preferences

In an era where travel has become an integral part of modern life, online hotel booking platforms have revolutionized the travel industry, offering travelers a convenient and efficient way to plan and book accommodations. These platforms have emerged as essential tools, providing a user-friendly and time-efficient alternative for travellers to plan and book accommodations. This study explores customer perspectives on OYO Rooms, Fab Hotels, and Treebo hotels chains. It conducts a comprehensive analysis of the intricate dynamics and functionalities of these hotel booking websites, scrutinizing their services, features, and pricing models. The study unravels the profound influence of these platforms on travel decision-making, highlighting the factors that shape customer preferences and choices.

Online Satisfaction and Hotel Booking Intentions: An Information System Success Model Perspective

Online travel agencies (OTAs) have increased their market share in online hotel booking as the internet has grown exponentially. The present study aims to apprehend travellers using the internet to search and book hotels on the OTA website. Moreover, regarding online satisfaction, this study also tries to find user satisfaction and intention to book the hotel by extending the information system success model. A partial least-square (PLS) structural equation modelling approach was used to assess a research model that included three variables: system quality, information quality, and service quality. A total of 225 usable responses were obtained and analyzed using SmartPLS software. The findings revealed that information satisfaction and service satisfaction significantly influence the online booking intentions. The findings give valuable insights to the OTA website and OTA app developers on how they should postulate complete and accurate information while considering the service quality aspects to influence hotel booking. This research has extended the body of knowledge on online satisfaction and hotel booking intention by implementing and expanding the information system success model.

The Influence of Timeliness and Volume on Online Hotel Booking Intention in Batam City

This study aims to determine the influence of the role of timeliness and volume on online hotel booking intention in Batam City. This study uses a descriptive quantitative approach. The population and sample of the research is people who live or reside in Batam City with a total of 415 respondents based on the Slovin Formula calculation. Data collection technique used involved distributing questionnaires via Google Form. Sampling was carried out by applying random sampling techniques. Data analysis used in this research uses multiple regression analysis methods using SPSS software. The research results show that timeliness and volume have a significantly influence on online booking intention. The timeliness and volume of information, comments and opinions left in large numbers as well as updating useful information can be taken into consideration by consumers in determining their intention to book hotels online. Consumers may consider the timeliness and total volume of information, messages and interactive comments from other consumers that can be found in a product or service review. Individuals looking for information will exhibit greater interest, potentially resulting in a perception of decreased risk among consumers, particularly in the case of tourism products, which are frequently intangible and distinctive.

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.

Comparative Analysis of Dynamic Pricing Strategies for Online Hotel Bookings: A Case Study of Luxury Hotels in Major Tourist Destinations

This study conducted a comparative analysis of pricing models and strategies employed by luxury hotels in major tourist destinations. The analysis focused on revenue generation, customer satisfaction, customer segmentation, demand patterns, flexibility, and adaptability to market conditions. The findings highlighted the effectiveness of various pricing models, including time-based pricing, demand-based pricing, competitor-based pricing, and segmentation-based pricing, in maximizing revenue and enhancing customer satisfaction. The implications of the findings on revenue management and pricing strategies in luxury hotels were discussed, emphasizing the importance of customer segmentation, advanced technology, and continuous monitoring. The study also identified limitations and suggested potential areas for further research.

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 adoption of the green label by SMEs in the hotel sector: a leverage for reassuring their customers

PurposeIn the era of new technological revolution, seeking to survive and guarantee business sustainability in their digital internationalization, enterprises choose to become environmentally oriented. The need for new green business models has become evident in recent years, and enterprises offer green services in creative and eco-friendly ways. However, does the display of a green label on hotels' websites really promote the eco-conscious tourists' online booking intention? This study aims to examine the impact of the perceived label on the online sustainable hotel booking intention of the eco-conscious tourists, using the foundations of signal theory.Design/methodology/approachThis study adopted a structural equation model to integrate several constructs with a sample of 349 validated responses.FindingsThe empirical results highlight, the importance of the green label perception on the eco-conscious tourists' booking intention of online sustainable hotel and the role that green trust and green perceived risk play as a mediating variable between the perception of the exposed label and the booking intention. Indeed, when booking a sustainable hotel online, the tourists may be sensitive to the exposure of a green label. Therefore, this signal decreases the perceived risk of unsustainability and ultimately increases the trust in hotel's sustainability.Research limitations/implicationsThe first limitation is related to the sample employed in this study. Given that most of the participants were residents of France, the results of this study may not be generalized to the entire population. Secondly, a range of other factors can affect the eco-conscious tourists' intentions to book online a hotel with green label, such as their attitude, social media influence, tourists' satisfaction, etc. Indeed, other variables and/or signals could be adopted to study online booking intention in the pandemic era.Practical implicationsIn light of these results, theoretical and managerial implications are discussed. The findings make an important contribution to SMEs sustainability and internationalization by exploring new ties. This study considers how SMEs and specifically hotels start following green practices (e.g. adoption of an eco-label) relevant to their international environment where they operate and in response to global pressures. SMEs can survive better in the highly competitive global environment where they need to employ more green practices, however, managers should consider how green trust and green perceived risk can affect customer behavior. It also adds to the existing literature by dealing with customer perceptions about the green label of sustainable hotels and its subsequent effect on booking intention.Originality/valueThis study had shown the importance of the display of green label on the eco-conscious tourist's online booking intention.

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.

The Influence of Visual Presentations and Online Reviews on Hotel Booking Intention on the Tiket.Com Application (Case Study: Tiket.Com Application Users)

Tiket.com is an Online Travel Agent (OTA) that provides online hotel bookings. This study aims to analyze the effect of visual presentation and online reviews on hotel booking intention on the Tiket.com application. This research integrates visual presentation and online reviews with the Technology Acceptance Model (TAM). The variables tested consist of Visual presentation, Perceived Ease of Use, and Online review as independent variables, Perceived Usefulness as a moderating variable, and Booking Intention as the dependent variable. The type of research is quantitative with a cross-sectional design and the research subjects are users of the Tiket.com application. Data was collected by distributing questionnaires to 119 respondents. The research hypothesis was tested using the Partial Least Squares Structural Equation Model (PLS-SEM) analysis technique. Based on the results of the hypothesis, H1, H3, and H8 are not in line with previous thinking because they do not positively influence buying interest in the tiket.com application. tiket.com applications should update booking features, and more attractive colors, update the system, reduce addsens, and update a more modern look.

UI/UX Design in the Hotel Reservation Application Using the Design Thinking Method

In this modern era, it is important for hotel and lodging businesses to move towards digital marketing so that they can survive and develop in a time when the need for the internet in everyday life is very high. And for that the author makes an online hotel reservation application and hotel reservation application as an intermediary between users and hoteliers. The purpose of this application is to design applications that suit market needs to support the sustainability of the hotel and lodging business as well as to design user experience and user interfaces, so that these applications can later be accepted by users from all walks of life (Heliany, 2019). And this application also provides cash and non-cash payments depending on the user, the system in this application can also make reservations in advance without coming directly to the hotel you want to rent. Everything can be done just by going through this application and you don't need to move from your current place. With the existing system in this application, it is expected to increase the attractiveness and convenience of application users. And this application is also an alternative to additional income for hotel and lodging businesses

Good or excellent? Factors determining online hotel ratings. A spatial approach

ABSTRACT In a competitive environment, where hotel demand is lower than market supply, it is of interest to determine what factors explain how excellence differentiates certain hotels from others. Using spatial quantile regression, this research investigates the effect of locational factors as well as those related to the management of human capital investment in Spanish hotels rated on Booking.com. The study shows that human capital investment in hotels can significantly increase guest delight. As regards location, hotels in protected natural areas that are far from large cities obtain higher guest review scores. Additionally, a spatial spillover effect due to reputation transfer among nearby hotels with median scores is found. However, this effect is not observed for hotels with very high scores. These results can serve to rationalize the available resources and inform hotel owners and managers about the factors required to achieve high scores. 在酒店市场竞争激烈的环境下，其需求低于市场供给，确定哪些因素更影响酒店评级尤为重要。本文采用空间分位数回归方法，调查了Booking.com上评级的西班牙酒店的地理因素和人力资本投资管理相关的因素的影响。研究表明，酒店的人力资本投入可以显著增加客人的满意度。在地理位置方面，远离大城市的自然保护区内的酒店获得了更高的顾客评分。此外，由于得分居中的临近酒店之间有声誉转移现象，其空间溢出效应被观察到。然而，这种效应并没有在得分非常高的酒店上观察到。这些研究结果可以帮助可用资源合理化分配，并使酒店管理者知晓获得高评分所需的因素。

A fuzzy decision support model for online review-driven hotel selection by considering risk attitudes of customers

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.

Predicting Critical Factors Impacting Hotel Online Ratings: A Comparison of Religious and Commercial Destinations in Saudi Arabia

User-generated online ratings have become a prominent tool for hotels to enhance overall customer satisfaction. Prior research on online ratings has mainly considered commercial destinations, whereas research on religious destinations is limited. This study uses the IPA technique and beta regression analysis to investigate the destination’s (commercial and religious) varying effects on the relationship between hotel service quality attributes and customer satisfaction. In total, data from 338 hotels representing 82,704 customer reviews in two Saudi Arabian cities (Alkhobar and Makkah) were collected from Booking.com and analyzed. Makkah was selected as a key religious heritage destination, whereas Alkhobar was chosen as a commercial destination as it hosts major sea resorts, recreational parks, and shopping destinations. The results indicate that commercial and religious destination tourists do not have similar perceptions of the selected eight hotel service quality attributes. While comfort, facilities, and value-for-money service quality attributes were considered important for commercial destination tourists, religious destination tourists viewed location, cleanliness, and breakfast as important. Since effective management of customer satisfaction is essential for hotels’ profitability and sustainability, therefore it is imperative to identify and prioritize service quality attributes related to each group of customers. This will enable the efficient application of limited resources.

The Effect of Price Discrimination on Fairness Perception and Online Hotel Reservation Intention

In light of the development of online travel agencies (OTAs), the rules of the entire tourism industry have changed. Due to the ease of finding information and comparing products, consumers can choose a hotel not only by room type, but also by rate, according to their preferences. The purpose of this study was to explore the effect of price discrimination on the fairness perception toward reservation intentions. The interaction effects of the brand familiarity and the type of consumers on the fairness perception were also examined. The study used an experimental design, with 2 price discriminations × 2 brand familiarities × 2 regulatory focuses, on a total of 320 valid subjects. The results showed that advantaged-price discriminations had higher fairness perceptions than equal-price discriminations, and that higher fairness perceptions had higher reservation intentions. The interaction effect of brand familiarity showed no significant impact on the fairness perceptions, while the regulatory focus had a mitigating effect on the price discrimination and on the fairness perceptions. This study provides insights into the relationship between online price discrimination and tourism, and it contributes to the literature on hospitality. It also provides the managerial implications for online hotels in developing pricing strategies.

Research on the Influencing Factors of Cancellation of Hotel Reservations

Booking hotels online is now a very common way for people to travel and stay, but a large number of cancellations due to itinerary changes and other factors can have a big impact on hotels, such as losing customers who really need a certain room type and losing them to other hotels. In order to reduce hotel losses, this paper uses the data of two hotels through data published on Kaggle's official website, identifies the factors that have the greatest impact on hotel cancellations through EDA visualization, and gives improvement measures. Machine learning algorithms are then used to guess whether the customer will cancel the booking. Each algorithm has its own area of expertise, so this article makes a comparison to the performance of decision trees, logistic regression, random forests. The result is that random forests have the highest accuracy and hotel managers can use the model to predict and change business strategies to increase profits.

Did COVID-19 change preferences for hygiene-related service attributes as satisfiers and dissatisfiers? An analysis of textual content of online hotel reviews

This study is a preliminary attempt to explore the impacts of COVID-19 on the changing roles of satisfiers and dissatisfiers in hotel service attributes. We analyzed online reviews posted between August 1st, 2019, and July 31st, 2022, for New York City hotels. Our results confirm that as a result of COVID-19, hygiene-related services are more likely to be perceived as satisfiers and less likely as dissatisfiers. COVID-19 was found to have an immediate negative effect on the occurrence of hygiene-related words in the negative aspects of online reviews, but this impact diminishes during the later phase. COVID-19 was found to have a steady positive effect on the occurrence of hygiene-related words in the positive aspects of online reviews.

The Importance of Green Certification Labels/Badges in Online Hotel Booking Choice: A Conjoint Investigation of Consumers’ Preferences Pre- and Post-COVID-19

In light of travelers’ growing preference for sustainable hotel accommodation, this study investigated the relative importance of green certification labels/badges in online hotel selection/booking choice. A conjoint analysis was performed on seven online hotel attributes (including green certification labels/badges) in two specific scenarios (imagining they were booking in a pre- and post-COVID-19 setting) using 270 U.S. subjects surveyed in April 2020. The results revealed that green certification labels/badges do impact travelers’ online hotel booking choice, but not as much as cancellation policies, hotel rating, price, and location. Price in particular exerted a greater influence on travelers’ booking preferences in the post-COVID-19 scenario. Yet, a significant percentage of travelers (around 40% in both scenarios) were willing to pay more for a hotel with green certification. These results contribute to our theoretical and practical understanding of the factors that influence online hotel booking, as well as the power of green certification labels/badges in driving online hotel bookings in the pre-and post-COVID-19 contexts.

Deciphering Predictors of Tourists’ Value and Intention amid COVID-19: The Interplay of Scarcity, Enjoyment, Visual Presentations, and Pandemic Threat

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.