Hotel Booking Research Articles

Deep click interest network for reranking hotels

Nowadays, e-commerce platforms of hotels have become a new trend to help people book hotels online. Interest modeling aims to automatically construct user interests that are critical for e-commerce platforms. Although interest modeling has achieved noticeable successes in product recommendations, it has not been explored in hotel search, especially in the reranking stage. This paper studies the interest modeling task for reranking hotels, and identifies two key challenges—high behavior sparsity and large interest gap. To address the challenges, we propose the deep click interest network (DCIN). High behavior sparsity keeps us from extracting rich semantics to characterize user interest preference. Accordingly, we propose the deep click concept to model the multi-view semantics of a click and then enrich the semantics of both a single click and a click sequence, so that the difficulty caused by the behavior sparsity can be addressed. To address the interest gap challenge, DCIN models user interests with two cascaded units: (i) Mutual-attention Interest Calibration Unit (ICU), which uses the candidate hotel to calibrate the embedding of every click, since the candidate hotel is selected by the ranking stage and can represent user current interests to some extent. So the gap between the calibrated embedding and user current interests can be diminished; (ii) Multi-attention Interest Aggregation Unit (IAU), which estimates the weight of a calibrated embedding from multiple perspectives, i.e., context, user feedback and interest consistency. So the calibrated embeddings, which are relevant to the current query context and important to the user, dominate user current interests, and thus the interest gap can be further diminished. Offline experiments over two large datasets and online A/B testing over the platform of Meituan-Hotel show that DCIN significantly outperforms the baselines and improved baselines. Notably, DCIN has been deployed in Meituan-Hotel, resulting in an increase of 2.40% in Click Through Conversion Rate (CTCVR) and that of 1.09% in Click Through Rate (CTR).

Hotel BookingIntentions: ALiterature Review

This study aims to review tourism and hospitality management research on online satisfaction, offline satisfaction and booking intentions published in numerous recognised tourism and hospitality journals from 2000 to 2023. This article studied and reviewed 62 published articles that emerged in the past 22 years in selected top 10 tourism, leisure and hospitality journals. Various variables influencing customer satisfaction, that is, online satisfaction and offline satisfaction leading to the intention to book a hotel online, were identified based on the previous studies. A conceptual model is proposed, which can be empirically tested while conducting future studies in the tourism and hospitality sector, specifically with respect to hotel booking intention. This study offers a conceptual framework for the antecedents used to measure online and offline satisfaction while booking a hotel online. The study ascertained information quality, system quality and service quality as independent variables to measure online satisfaction and perceived values like perceived functional value, emotional value, social value, and monetary value as independent variables to measure offline satisfaction. This study provides a detailed literature review on hotel booking intention and variables that influence overall satisfaction, that is, online and offline satisfaction published in specific hospitality and tourism journals over the past 22 years.

The influence of positive and negative electronic word of mouth review on hotel customer e-repurchase

Digital technology makes hotel bookings through online travel agents more attractive and more and more people want to make online travel booking agents create more attractive features for hotel bookings. The purpose of this study was to examine the effect of E-WoM on E-repurchase, especially in hotel bookings through Online Travel Booking Agent.com in Semarang City. The method used is an experimental research method using 56 participants using a different test-One Way Annova. The results of the study show that there are differences in the effect of E-WoM on e-repurchase in the form of positive and negative customer reviews from Online Travel Booking Agent XYZ customers. So with these positive reviews, consumers will make repeat purchases at the same e-commerce company. If the previous transaction was unsatisfactory, the customer who will make a repurchase will become less trusting, thereby reducing the intention to repurchase from the e-commerce company.

Guest interaction with hotel booking website information: scale development and validation of antecedents and consequences

ABSTRACT Hotel booking websites to collect information about hospitality products/services are becoming more popular in the hotel industry. As a result, the importance of these websites for tourism firms marketing their services and products through electronic word of mouth (eWOM) on these hotel booking websites continues to grow. Despite its importance, there has been little research on how guests interact with information to increase eWOM. This paper creates a new scale for assessing guest interaction with Meituan.com information to fill this void. To this end, a three-step mixed-methods approach using open-ended surveys and published relevant research is used. Data were gathered from 440 hotel guests in Taiwan, China, and Hong Kong. Using SmartPLS 4, confirmatory factor analysis revealed 33 final items for the new scale. This paper significantly contributes to potential guest behavior by creating and confirming this new scale in the hospitality context.

Augmented and virtual reality in hotels: Impact on tourist satisfaction and intention to stay and return

Augmented reality (AR) and virtual reality (VR) have emerged as transformative technologies, revolutionizing the way tourists engage with hospitality service providers. A prime application is facilitating hotel bookings. Employing quantitative methods, this study establishes and validates a conceptual model that deciphers tourists’ inclinations towards hotel reservations and returns via AR and VR. Findings indicate that the perceived ease of use, innovativeness, and usefulness of AR and VR positively influence tourists’ satisfaction, driving them to embrace these technologies for hotel bookings. While there might be underlying concerns about associated risks, these risks do not significantly deter repeat visits. Consequently, this study illuminates the immense potential of AR and VR in elevating tourist experiences and promoting revisits. Hoteliers and marketers are advised to leverage these findings, fine-tuning their strategies to synchronize with this tech evolution and cater to evolving tourist expectations.

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.

Influence of Online Reviews on Hotel Reservation Intentions: Insights from Bangladeshi Travellers

The primary objective of this research is to undertake an empirical investigation of the overall influence of online reviews on hotel booking intentions within the context of Bangladesh. The study involved the execution of semi-structured interviews with a sample of 25 individuals with a recent history of frequent travel and hotel stays. Subsequently, a thematic analysis of the responses was conducted with the goal of elucidating the outcomes and ascertaining the influence of online reviews on individuals' predispositions towards booking a hotel for their accommodation facility. The findings of the study revealed that positive reviews wield the most substantial influence among the various dimensions of tourists' reviews. Notably, the idea of "trust" appeared within the identified indicators as a mediating variable in the association between reviews and the intention to book a hotel. Moreover, this empirical study also showed that unfavorable online reviews significantly influence individuals ‘intents to reserve hotels, whereas positive reviews do not yield an equivalent impact. Considering these outcomes, the authors draw a series of management implications, including the need to use this information to promote stakeholder engagement and collaboration, to use these indicators for the management of hotels to adopt a more holistic approach to address the service gap in the hospitality industry, and to focus on resilient planning for sustainable smart tourism development.

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.

Effects of service providers’ self-disclosure on booking platforms on consumers’ trust, hesitation, and booking intentions: moderating role of social media influencers

ABSTRACT This study aims to examine the relationship between service providers’ self-disclosure on hotel booking platforms (i.e. Booking.com and TripAdvisor) and consumers’ trust, hesitation, and booking intentions. The study also investigates the mediating effects of trust and hesitation on booking intentions and the moderating role of social media influencers (SMIs) on these relationships. Data analysis of 418 respondents found that service providers’ self-disclosure significantly affects consumers’ trust and hesitation, which in turn impacts their booking intentions. The results also showed that trust and hesitation both mediate the relationship between self-disclosure and booking intentions, with trust decreasing hesitation. Additionally, the study found that SMIs significantly strengthen the effect on consumers’ trust and weaken hesitation through self-disclosure. These findings contribute to our understanding of trust, service management, and consumer behavior, and have implications for online booking platforms. The study also acknowledges its limitations and suggests areas for future research.

Role of Hotel Servicescape in Satisfaction and Revisit Intention of Consumers: Familiarity as a Moderator

Globally the hospitality industry is seen immensely growing because of opening up of tourism in countries worldwide. One such growth of hospitality industry is specifically being witnessed in a middle eastern country namely Saudi Arabia. The expansion of leisure and religious tourism for diversifying the oil-based economy under the kingdom’s vision 2030 is rapidly fueling growth of hotels in Saudi Arabia. However, very little is known about what aspects of these hotels lead to consumer satisfaction and revisit intentions. Moreover, modern internet hotel booking technologies by providing 360-degree images, videos of hotels and hotel rooms are helping consumers to get well familiar with the hotels aforehand their visits. Very little is also known about the effects of this aforehand familiarity of consumers on the relationship of satisfaction with revisit intention. In marketing terminology, the place where the interaction between consumers and hotel service provider takes place is called hotel servicescape. The hotel servicescape has two important aspects namely physical and social. This study examines how these two aspects of hotel servicescape leads to consumer satisfaction and revisit intentions. The study also examines the moderating role of familiarity on the relationship of satisfaction with revisit intention. The study sample comprised of 390 frequent stayers in hotels of Saudi Arabia. The study sample was collected using snowball sampling method. All of the hypothesized relationships of the study were examined rigorously and simultaneously using structural equation modeling technique. The results of the study revealed that both physical and social aspects of hotel’s servicescape shared significant positive relationships with satisfaction. Significant positive relationship was also reported between satisfaction and revisit intention. Familiarity significantly moderated the relationship between satisfaction and revisit intention in this study. The study provided new findings on the role played by familiarity in strengthening the relationship between satisfaction and revisit intention. Some marketing implications, limitations and future research directions are also discussed in the study.

THE EFFECT OF BRAND IMAGE AND BRAND TRUST ON HOTEL BOOKING PURCHASE DECISIONS IN THE AGODA APPLICATION

The rapid development of technology and the increasingly diverse market demands drive business growth and innovation to meet market desires. The competitive market environment compels every company to establish consumer image and trust to maintain their presence in society. The study aimed to understand how Brand Image and Brand Trust affected purchasing decisions for hotel bookings on the Agoda Application. The research used a quantitative method involving a sample of 60 respondents, and the distribution of questionnaires was facilitated through Google Forms. The sampling technique used in this research was convenience sampling, with participants who had conducted transactions on the Agoda Application. Both primary and secondary data were used in this study. The research employed Partial Least Squares (PLS) analysis, including validity tests, reliability tests, and hypothesis testing. The research findings indicated that brand image and trust significantly and positively affected purchasing decisions

An Analysis of Hotel Booking Cancellations and Factors Affecting Revenue Generation

Abstract: This study examines the growing problem of can- celled hotel reservations in the hospitality sector, in particular in the City Hotel and Resort Hotel segments. In order to alleviate the issue and enhance revenue creation, it looks to identify the underlying causes and offers specific business guidance. It provides a comprehensive picture of the problem by taking into account both internal and external forces. The research demonstrates the similarities and differences between City and Resort Hotels in terms of cancellations, revenue impact, and issues through comparative analysis. In order to decrease cancellations and improve overall business efficiency, the paper suggests tactics spanning consumer involvement, booking policies, price, customer support, and marketing. In conclusion, this study offers helpful advice to business executives on how to handlecancellations of reservations and boost financial results.

The impact of partitioned pricing on hotel booking and the moderating effect of temporal distance

Utilizing the distinctive context of hotel bookings, where fees hold notable prominence, this study investigates the impact of partitioned pricing on consumer booking intentions. Our findings reveal that partitioned pricing in the hotel industry adversely influences booking intentions due to the perception of unfairness, rather than the perception of price, as observed in retail studies. Furthermore, this research confirms that temporal distance moderates the negative impact of partitioned pricing on booking intentions, while also uncovering asymmetrical consequences of payment timing across varying booking windows. These insights contribute to a deeper understanding of the conditional effects of partitioned pricing, thereby expanding the current pricing literature and offering valuable guidance for hotel industry professionals.

The Effects of Website Quality, Review Ratings and Cewebrity Reviews on Online Booking Intention: A Serial Mediation Model

Social media and online reviews have had a tremendous impact on the hotel accommodation industry. Online reviews are a highly appreciated source of information for travellers making decisions about booking a hotel room through online travel agencies (OTA). This encourages researchers to investigate the direct and serial mediation effects of review ratings (RR) and cewebrity reviews (CR) on the relationship between booking website quality (BWQ) and online booking intentions (OBI). The stimulus-organism-response (S-O-R) framework was used to validate the hypotheses. Data were obtained from 665 users who experienced booking through OTA in India’s cities. SPSS and partial least squares structural equation modelling (PLS-SEM) were used to analyze the data. Researchers have used importance-performance map analysis (IPMA) to map the attributes and priorities of OBI. These findings suggest that BWQ positively affects OBI. Serial mediation of RR and CR in the relationship between BWQ and OBI was statistically significant. The findings revealed that when guests became more concerned about reviews, the relationship between BWQ and OBI became stronger. This research contributes to online reviews in the field of hospitality services as well as to stakeholders involved in the hotel booking process.

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.

TRAVEL AND TOURISM MANAGEMENT SYSTEM USING CHATBOT RECOMMENDER

Chatbots have gained increasing importance for research and practice with a lot of applications available today including Amazon’s Alexa or Apple’s Siri. In this paper, we present the underlying methods and technologies behind a Chatbot for e-tourism that allows people textually communicate with the purpose of booking hotels, planning trips, and asking for interesting sights worth being visit. In particular, we show how model- based reasoning can be used for enhancing user experience during a chat, e.g., in cases where too many possible selections are available or where user preferences are too restricted causing inconsistencies and as a consequence not possible answers to be provided. Besides the underlying foundations, we provide a use case from the intended tourism domain to show how such a model-based chatbot effectively can be used in practice. Key Words: travel recommendation, time sensitivity, recency effect, personalization, social media

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.

Predicting hotel booking cancelation with machine learning techniques

研究目的本研究旨在开发一个能够准确预测酒店客房取消的模型, 并进一步确定主要的取消因素。研究方法采用预测建模, 具体来说是机器学习方法, 来预测客房取消, 并识别主要的取消因素。研究发现通过使用三种不同的分类算法, 本研究表明使用XGBoost以及涉及支持向量机、随机森林和XGBoost的集成方法可以准确预测酒店客房取消。研究创新本研究尝试通过应用相对较新的方法, 即机器学习, 来预测酒店客房取消。通过实施预测建模, 这是目前新兴和创新的研究方法之一, 本研究最终为酒店管理运营在各个方面和层面提供了预测建议。

Effects of Booking Demand on the Selection Between the Resort Hotel and the City Hotel in Portugal

The tourism industry supports the economy of Portugal. The study on hospitality in Portugal is essential due to its correlation with tourism. The analysis of the hotel booking demand in Portugal is consequently quite significant. This paper conducts comparative analysis, regression analysis and factor analysis to compare the influence of hotel booking demand on the customers choice between the resort hotel and the city hotel in Portugal, thus providing recommendations for both city hotels and resort hotels in Portugal.

Customer Loyalty Prediction for Hotel Industry Using Machine Learning Approach

Today, machine learning is utilized in several industries, including tourism, hospitality, and the hotel industry. This project uses machine learning approaches such as classification to predict hotel customers’ loyalty and develop viable strategies for managing and structuring customer relationships. The research is conducted using the CRISP-DM technique, and the three chosen classification algorithms are random forest, logistic regression, and decision tree. This study investigated key characteristics of merchants’ customers’ behavior, interest, and preference using a real-world case study with a hotel booking dataset from the C3 Rewards and C3 Merchant systems. Following a comprehensive investigation of prospective preferences in the pre-processing phase, the best machine learning algorithms are identified and assessed for forecasting customer loyalty in the hotel business. The study's outcome was recorded and examined further before hotel operators utilized it as a reference. The chosen algorithms are developed utilizing Python programming language, and the analysis result is evaluated using the Confusion Matrix, specifically in terms of precision, recall, and F1-score. At the end of the experiment, the accuracy values generated by the logistic regression, decision tree, and random forest algorithms were 57.83%, 71.44%, and 69.91%, respectively. To overcome the limits of this study method, additional datasets or upgraded algorithms might be utilized better to understand each algorithm's benefits and limitations and achieve further advancement.Â