Online Hotel Research Articles

Hotel service quality dimensions and attributes: An analysis of online hotel customer reviews

This study examines service quality dimensions and attributes of the hotel industry in a famous beach resort destination of Phuket based on 25,267 online reviews from the TripAdvisor website collected for 56 hotels. Machine learning analysis using the KNIME analytics platform was employed to analyze four datasets, namely the total dataset, the couple dataset, the family dataset, and the friend dataset. The results reveal six dimensions of guest service quality in the hotel industry: leisure activities, tangibles and surroundings, reliability, responsiveness, service process, and food, with specific attributes identified in each dimension. The study was able to verify the robustness of HOLSERV Plus model as the dimensions developed by topic modelling of online reviews are found to correspond to the dimensions of HOLSERV framework, with some adaptation required. It is also confirmed by the current study that the same set of service quality dimensions and attributes is not applicable to all groups of customers, instead each group has its own unique requirements and expectations. In addition, service process is revealed in this study as the most sensitive dimension that determines customer dissatisfaction.

Advancement of Online Hotel Reservation with High Efficiency

Advancement of Online Hotel Reservation with High Efficiency

Crisis-Resistant Tourists – A Study of Hotel Online Reviews in the Times of Covid-19

Abstract Introduction. Tourists traveling in the times of crisis have a higher tolerance for risk and can be called ‘crisis-resistant’. The fact that they are ready to travel even if it is burdened with additional stress and obstacles makes them a valuable market segment for hoteliers, which is worth striving for particularly in the current pandemic situation. The research aims at better understanding of contemporary ‘crisis-resistant’ tourists through in-depth analysis of electronic Word of Mouth (eWOM). Material and methods. A web scrapping method was applied to acquire eWOM content posted by tourists traveling in the times of Covid-19. A total of 1239 reviews from 455 hotels were analyzed. Six hypotheses were tested with the use of T Test and Chi Square test. Results. The type of travel and the month of travel did not influence the frequency of coronavirus mentions. Similarly, reviews relating to COVID-19 did not vary in score nor in frequency of managerial replies. However, mention of coronavirus influenced the length of the review and its helpfulness. Conclusions. Crisis-resistant tourists treat issues related to the pandemic (including some in-hotel regulations and restrictions) as any other aspects of the hotel performance and accept them. Interestingly, those aspects do not negatively influence the total evaluation of the hotel, which may suggest that such tourists have already adapted to all the pandemic-induced burdens. At the same time, hotel managers fail to respond timely and adequately to negative comments, which seems to be of the key importance in such a situation.

Aspect-based sentiment analysis for hotel reviews using an improved model of long short-term memory

Advances in information technology have given rise to online hotel reservation options. The user review feature is an important factor during the online booking of hotels. Generally, most online hotel booking service providers provide review and rating features for assessing hotels. However, not all service providers provide rating features or recap reviews for every aspect of the hotel services offered. Therefore, we propose a method to summarise reviews based on multiple aspects, including food, room, service, and location. This method uses long short-term memory (LSTM), together with hidden layers and automation of the optimal number of hidden neurons. The F1-measure value of 75.28% for the best model was based on the fact that (i) the size of the first hidden layer is 1,200 neurons with the tanh activation function, and (ii) the size of the second hidden layer is 600 neurons with the ReLU activation function. The proposed model outperforms the baseline model (also known as standard LSTM) by 10.16%. It is anticipated that the model developed through this study can be accessed by users of online hotel booking services to acquire a review recap on more specific aspects of services offered by hotels

How do extrinsic cues influence consumers' online hotel booking decisions? An event-related potential experiment.

Booking decision is a typical decision-making behavior in hospitality, while the neural processing of it is still unclear. To address this issue, with the help of event-related potential (ERP), this work uncovered the neural mechanism of the influence of two extrinsic cues, namely, brand familiarity (familiar vs. unfamiliar) and online reviews (positive vs. negative) on online hotel booking decisions. Behavioral results indicated that the booking rate under the condition of positive reviews was higher than that of negative reviews. In addition, the response time in the case of familiar brands was longer than that of unfamiliar brands. ERP results showed that the P200 amplitude of familiar brands was smaller than that of unfamiliar brands, while for the late positive potential amplitude, the opposite was the case. It is suggested that in the early stage of cognitive processing, unfamiliar brands evoke more automatic and unconscious attention while in the later stage, familiar brands attract more conscious attention. This study also found that the N400 amplitude of negative online reviews was larger than that of positive online reviews, indicating that negative stimuli can result in a larger emotional conflicts than that of positive stimuli. This study provides new insights into the neural mechanism of online booking decisions in the hospitality.

Identification of Influential Factors and Improvement of Hotel Online User-Generated Scores: A Prescriptive Analytics Approach

ABSTRACT The information obtained from customers’ feedback can help hotel managers improve their provided services in a targeted manner according to customers’ expectations. Besides, other customers consider online hotel scores an efficient tool for quickly evaluating the quality of hotels’ services. Therefore, a higher online score indicates customer satisfaction and would lead to more bookings, price acceptance, and higher financial performance. In this article, we extracted the shortcomings related to hotel attributes utilizing a novel methodology that comprises machine learning algorithms, text mining, and a combination of customers’ comments and scores. Then we examined the quantitative effect of fixing these problems on hotels’ online scores. Furthermore, considering the origin of the problems, the cost required for fixing them, and the quantitative effect of solving them on improving the hotels’ online scores, we provided some prescriptions for hotel managers as the last phase of business analytics. This model and its resulting prescriptions can be used to increase hotels’ online scores significantly by improving service quality at the lowest cost. Finally, to describe the most important attributes, we used The Nordic European School of thought and classified them based on the technical and functional dimensions of Grönroos’ service quality model.

The Determinants of Helpful Hotel Reviews: A Social Influence Perspective

Online hotel reviews regarding specific experiences and the sensibility of hotel reviewers are important sources of information for consumers who want to book hotels in the future. However, since thousands of reviews are written for one hotel, it is practically impossible for customers to read all the reviews. To alleviate this problem, OTAs (online travel agencies) provide a helpful vote of reviews, helping customers to quickly find helpful reviews without much effort. Therefore, in this study, a ZINB (zero-inflated negative binomial regression) was performed to investigate factors that influence the helpfulness of hotel reviews using the social influence theory. As a result of the analysis, it was found that location, service, and value of the hotel, alongside normative influencing factors and the length of the review (including informational influencing factors), affect the helpfulness of the review regardless of the city. The results of this study are expected to help hotel managers to take remedial action on negative reviews and strengthen the promotion of positive reviews, potentially helping to increase customer satisfaction.

Research on Hotel Management System

With rapid growth of economy and tourism, there is an intensified competition can be seen in the hotel industry in today. To be in the competition, they need to continuously improve their management techniques and procedures. “Online Hotel Management system” is software developed by focus on these factors. Through this system, it will be able to manage various functions including room and hall reservations, ordering food, and managing employees and suppliers. We intend to develop this web application using React JS, Express JS, Node JS, and Mongo DB. This system addresses hotel management issues while avoiding issues that arise when tasks are carried out manually. In terms, the main objective of this whole process is to automate the day today manual tasks of this system. Therefore, this online hotel management system is designed to find a more practical, well-organized, and quick way of processing the service from the hotel for both nearby and distant customers by giving more user friendly and more GUI oriented experience.

Online Booking Services Assisted by Technology to Improve Customer Loyalty in Jordanian Five-Star Hotels

Purpose: This study's goal was to examine how customer satisfaction at Jordanian five-star hotels served as a mediator between the effects of website quality and e-trust on customer loyalty. Design/methodology/approach: Questionnaires were the primary method used in the study for gathering data. The survey was created using data from earlier research and given to visitors staying at several five-star hotels in Amman, the country's capital. A convenience sample of 401 people received questionnaires from the researchers. The valid data was examined using Structural Equation Modeling (SEM) and SPSS V.25 (AMOS V.20). Findings: The study found a favorable relationship between website quality and customer loyalty as well as a positive correlation between e-trust and consumer satisfaction. Additionally, there is a direct correlation between customer satisfaction and loyalty. Research implications: Hotels should take steps to gain the public's trust if they want their websites to be regularly preferred by visitors. The website should also show a relationship between its target audience and the information and images about the hotel by consistently updating both. It has been demonstrated that users of online hotel booking websites exhibit higher levels of trust in websites. When a customer sees what to expect when staying at the hotel, their sense of risk reduces. Originality/value: In order to research the marketing function of hotel services, which today heavily rely on online hotel reservations, this study is a ground-breaking endeavor to improve the worldwide tourism industry. The results should spur Jordanian hotels to research the factors that influence customer loyalty for online reservations.

Are consumers influenced by the use of green practices in five-star hotels: an assessment of guest's revisit intentions, inclusive ratings and hotel performance

PurposeThis study aims to find out the influence of green practices used by the five-star hotels on the guest’s online hotel assessment and their revisit intentions. It also evaluated how the use of green practices by the hotels influenced the guest’s willingness to pay a premium price. Apart from the conventional hotel service attributes, this study also identified some new and innovative services offered by the hotels which have an overall effect on the guest’s revisit intentions.Design/methodology/approachThis study applied focus group interviews from 12 hotel managers and accessed the hotel’s internal database to identify the latest and innovative service attributes offered by the hotels. The information regarding the green practices offered by the hotels was collected through TripAdvisor and LEED-IGBC website. It identified 10 independent and four dependent variables based on previous literature. Guest’s revisit intentions were measured on a five-point Likert scale. Data was analysed using a multi-step hierarchical regression model.FindingsThe use of green practices by the hotels revealed a positive and significant influence on the guest’s revisit-intentions and their intention to pay a premium price. It was also found that the use of new and innovative green practices has a positive influence on the guest’s overall online evaluation of the hotel.Practical implicationsThis study suggests that the amalgamation of green practices along with the conventional service attributes may help in the incremental revisit and online hotel assessment intentions, which might be of use for the managers and hotel policymakers.Originality/valueAlthough previous studies have explored the conventional hotel service attributes in the context of Indian Hotel industry, this is one of the first studies which discussed the influence of recent and emerging hotel service attributes on the guest’s revisit and pay a premium price intention. Moreover, the influence of green practices on the guest’s overall online evaluation of the five-star hotels in Delhi was discussed in this study which was not performed before.

Hotel Green Implementation for Future

The online hotel management system was designed for the client hotel green, colombo, sri lanka. Currently, the client uses a manual system for management purposes where employees handle large amounts of data manually. The proposed system allows users to store and manipulated all the data in their database making their data handling process very effective. The proposed system is developed to cover the process of the hotel such as employee management, resource management, food management, rooms management, and user account management. The admin adds and updates the available rooms and foods, and customers can check the available rooms and order foods through the system. Admin adds, updates, and deletes employee details. Admin can mark the attendance of the employees. Admin can search relevant details. Admin can view details of all customers. The customers register and log in to the system. Customers can update and delete their accounts. A web application was developed to ensure that the customers of the hotel can make orders and reservations online. The system reduces data manipulation errors in the current process, resulting in considerable development in human resource utilization. It improves the client's relationship with their customers gaining profit for the business. The system is user-friendly, reliable, and efficient and its statistics all the customer requirements.

AN ETNOGRAPHY EXAMINATION OF TRIPADVISOR REVIEWS: A CASE STUDY OF THE ST REGIS HOTEL KUALA LUMPUR

There has been a growing reliance on consumer-generated content as a source of knowledge for hospitality product decision-making. People post their views, ratings, and reviews of products and services on such platforms, and companies and research organizations are interested in analyzing and extracting all of the comments. Hotels must take advantage of online customer review information to better understand their customers and enhance hotel efficiency. However, analyzing guest reviews is challenging as it contains abundant data. Using The St Regis Kuala Lumpur as a case study, the objective of the paper is to 1) profile the online hotel reviews using netnography approach, (2) identify the hotel attributes that guests comment on based on the hotel online reviews and (3) examine the guest satisfaction attitude based on the online hotel reviews. A total of 388 reviews ranging from TripAdvisor were collected. The finding revealed that most hotel guests who left comments were international tourists, couples and left an excellent rating for the hotel. The result shows nine attributes are frequently mentioned; amenities, location, price, service, cleanliness, food and beverages, hotel and room environment. The results also show that guests left positive and negative reviews in a few common categories: hotel cleanliness, hotel environment, and amenities. Among all the attributes, satisfied guests tend to leave positive comments in hotel cleanliness, hotel and room setting, while dissatisfied guests emphasise amenities. The findings from this study make several theoretical and managerial implications regarding identifying hotel attributes that generate guest satisfaction and understanding of satisfied and dissatisfied guests using comments left by guest. The paper concludes with limitations and suggests future research.

Clustering using ordered weighted averaging operator and 2-tuple linguistic model for hotel segmentation: The case of TripAdvisor

With the growth of online tourism, it is important to analyze the reviews left by numerous customers on social networks to improve the hotel's online reputation. Hotel segmentation based on online reviews has attracted an increasing interest from many academics. The problem is that many hotel segmentation models overlook the fact that some customers focus on positive reviews when choosing a hotel, while others focus on negative ones. To address this shortcoming, this paper develops a novel approach to classify hotels using the ordered weighted averaging (OWA) operator, the 2-tuple linguistic model, and K-means clustering. The proposed approach has been evaluated with a real dataset from TripAdvisor, which contains more than 50 million customer online reviews on eight aspects of the hotel. The results show that the proposed model can produce denser and more separated clusters than the model without linguistic quantifiers. From a business point of view, this model enables hotels to distinguish customers' perceptions (from the less demanding to the most demanding) about their eight aspects, allowing them to specify which of them need to be improved and develop strategies more quickly. At the same time, it introduces a new way of ranking hotels online, allowing customers to create personalized rankings of hotels based on their degree of demand for various hotel aspects (better location, cleaner rooms, etc.) rather than the average ratings, so that they can select the most suitable hotels more quickly.

Predicting Conversion Rates in Online Hotel Bookings with Customer Reviews

E-commerce in the hospitality and tourism field has already ranked No. 2 among all online shopping categories worldwide. However, customers’ visits to a hotel booking website cannot guarantee the generation of sales, while the conversion rate is regarded as the indicator that effectively assesses the e-commerce website performance. This study aimed to investigate the influential factors of conversion rates from both affective content and the communication style of customer’s online reviews. The affective content was evaluated with eight emotional dimensions (i.e., joy, sadness, anger, fear, trust, disgust, anticipation, and surprise) in Plutchik’s emotion wheel, and the communication style perspective was assessed with linguistic style matching (LSM). In total, 111,926 customer reviews from 641 hotels in five cities in the U.S. were collected for the analysis. Results indicated that LSM and four emotions have significant impacts on hotel conversion rates. This research contributes to the knowledge body of customers’ conversion behaviors on hotel booking websites and offers pertinent practical implications.

"The Room Was Quite Small by American Standards": Are Online Hotel Reviews Culture Specific?

Hotel bookings are increasingly made online, and many travelers rely on eWOM in the form of peer hotel reviews. These reviews potentially contain information of great relevance to the tourism industry and offer a unique and ever-expanding corpus of unsolicited data. If these data are investigated systematically, it may provide insights that would enable hotel managers to be proactive in their marketing. The present study focuses on the underresearched area of the potential impact of nationality on the reviews. Using a corpus of authentic American and German hotel reviews and the qualitative, phenomenologically inspired method of systematic text condensation, this study investigates the impact of national culture on review comments in order to establish whether nationality makes a difference for the themes and attitudes expressed. The data indicate that Americans are more likely to focus on old-world charm, romance, physical comfort, personal service/relations, and problemsolving than are Germans. The overall results of this qualitative study allow us to conclude that there are indeed differences between the German and the American reviews to a degree that is worth pursuing in future mixed-methods research and that may have practice implications for hotel managers.

Customer preference identification from hotel online reviews: A neural network based fine-grained sentiment analysis

As a kind of user-generated information, online reviews contain customers’ preferences for different aspects of hotels, which not only influence customers’ booking decisions but also help hotel managers to improve service quality of hotels timely. The key of deriving customers’ preferences from hotels’ online reviews is to identify fine-grained sentiment towards hotel attributes. In general, fine-grained sentiment analysis involves multiple fundamental tasks such as sentiment element extraction, aspect-opinion pair (i.e., AOP) identification and sentiment orientation analysis. However, existing fine-grained sentiment analysis methods cannot efficiently identify AOPs, especially when dealing with Chinese reviews. To this end, we construct an improved convolutional neural network (i.e., CNN) model, which can comprehensively utilize unstructured features and structured features, to improve the performance of AOP identification. We further propose a refined fine-grained sentiment analysis methodology to calculate accurate customer sentiment intensity value for each evaluated aspect rather than positive or negative sentiment, integrated with aspect term clustering algorithm, to identify customers’ specific preferences for different hotel attributes. Finally, to illustrate the reasonability and advantages of the proposed methodology, we conduct an empirical study with hotels’ online reviews crawled from Ctrip.com. Empirical results indicate that our proposed method can indeed improve the performance of AOP identification, and can effectively identify customer preferences from hotels’ online reviews. Furthermore, we find that customers show different preferences for different hotel attributes, and these vary across the types of customers.

How Hotel Brand Website Contributes to Online Hotel Reservation on Consumer Review Website?

While past research only attempted to investigate the impact of consumer review website characteristics on the hotel reservation behavior, this study incorporated both characteristics of hotel brand website and consumer review website into consideration. Impacts of the two types of websites on reservation behaviors in consumer review websites were examined. The moderating effects of consumer involvement and risk aversion factors were also considered. Three hundred and two respondents from the questionnaire survey were chosen from Bangkok metropolitan population aged 25-60, who experienced with online hotel reservations. The findings show that apart from the review website’s ease of use, information usefulness and price offered, brand website’s information usefulness also has positive impact on consumers’ decisions on the review website. However, brand website’s ease of use discourages the usage of the review website. High-risk aversion consumers tend to use the review websites, while high-involvement consumers pay less intention to the review websites’ information usefulness and are unlikely to use them. These findings will help managers effectively manage and design decision algorithm for their multi-channels of hotel e-commerce. The results explain dynamic, search sequence and interrelationship of current traveler’s behaviors. The source of their hotel search is both consumer review website and hotel brand website.

Modelling the significance of website quality and online reviews to predict the intention and usage of online hotel booking platforms

Innovative technologies are paving the way for the online purchase of products and services. The COVID-19 also harness online shopping and hotel booking in the post-COVID-19 era. Online hotel booking is becoming the norm among young consumers. The six determinants of the intention to use online hotel booking platforms (OHBPs) are information quality, integrity, perceived risks, perceived benefits, system quality, and service quality was utilized in the study. Cross-sectional data were collected through the online survey, and 884 valid responses were utilised for the data analysis. The analysis results showed that the perceived benefits, system quality, and service quality significantly predicted the intention to use the OHBPs. Meanwhile, the usefulness and quantity of online reviews and the intention to use OHBPs have a positive and significant effect on the usage of the OHBPs. The study results confirmed the insignificant moderating role of the usefulness and quantity of online hotel booking reviews in the relationship between the intention to use and the usage of OHBPs. The intention to use OHBPs had low predictive power, while the usage of OHBPs had high predictive power. This study’s findings have offered valuable theoretical and managerial contributions. The management of OHBPs needs to concentrate on information quality and integrity to help manage the associated risks of online hotel booking. Previous customers' reviews are vital to encourage the usage of OHBPs. Finally, social media usage and promoting the OHBPs by sharing satisfied users' experiences improve the intention and usage of the OHBPs.

Brand first? The effect of hotel online word-of-mouth on consumer brand sensitivity.

With the e-commerce development and changing of hotels' booking channels, the online word-of-mouth, as a new signal of quality, is becoming to attract more attention of consumers. Using the scenario experiment, this study explores the effect of online word-of-mouth on brand sensitivity of consumers during the decision making for hotel booking. The results show that if the information about hotels obtained is limited in the decision-making process, consumers would have a higher sensitivity to the hotel brand. Increasing information about the online word-of-mouth can effectively reduce consumers' brand sensitivity to hotels. Besides, the moderating effect of the hotel grade on the relationship between the online word-of-mouth and brand sensitivity is affected by the scale of the negative differences of word-of-mouth.

Exploring the effects of official-structured managerial responses on hotel online popularity

As a burgeoning mode of service management, online managerial response has become a critical aspect driving consumer online engagement and hotel performance improvement. Nevertheless, managerial response strategy, especially presentation style, is rarely studied. With a mixed method approach that includes the use of secondary data and experimental analyses, we investigate how the official-structured presentation style of managerial response affects the volume of online reviews and online ratings, which represent hotel online popularity, and the moderating effect of managerial response speed. Results indicate that 1) responding to consumers in the official-structured text increases the volume of online reviews, 2) responding to consumers in an official-structured style increases online ratings, and 3) response speed as an indicator of management effort enhances the effect of managerial response’s official-structured style on hotel popularity. Aside from expanding the literature on managerial response strategy and hospitality marketing, our findings also provide important implications for hospitality administrators and marketers.