Hotel Booking Research Articles

Eco-Chic stays: The influence of green communication on guest booking intentions

In this paper, we examine how green communication impacts luxury hotel bookings using data from China. To determine whether green communication has an impact on guests’ decision-making processes, 384 questionnaires were distributed across four major Chinese cities, Beijing, Shanghai, Guangzhou, and Shenzhen. Among the mediators and moderators were perceived brand integrity, intrinsic green motivation, and environmental knowledge. Results indicate that the combinations of perceived brand integrity and green intrinsic motivation affect guests’ booking intentions along with environmental knowledge, according to the results of this study. Luxury hospitality managers can use this research to develop a theoretical framework for sustainable communication with green consumers.

How hotel VR photography previews enhance customers’ immediate booking intention through mental imagery and perceived value

Purpose The purpose of this study is to examine how a hotel virtual reality photography (VRP) preview enhances customers’ immediate booking intention compared to a picture preview. Design/methodology/approach Data were collected using a one-factor between-subject experimental design and time-based systematic sampling. Respondents were asked to read a scenario on an iPad and assess a hotel preview (VRPs vs pictures) before answering the questionnaire. Findings The results show that a hotel VRP preview generates more mental imagery than a picture preview in terms of quantity, vividness, modality and valence. These four dimensions of mental imagery indirectly influence customers’ immediate booking intentions through perceived utilitarian value and hedonic value, among which modality has the greatest influence on utilitarian value and valence has the greatest influence on hedonic value. Moreover, utilitarian value has a stronger impact on customers’ immediate booking intentions than does hedonic value. Originality/value The results obtained in this study contribute to hospitality research by using cognitive consistency theory as the overarching framework, combining mental imagery theory, media richness theory and perceived value theory to explain the mechanism of tourists’ hotel booking decision-making from hotel previews. Comparing this study’s results with previous studies shows the difference in the effects of 360° (head-mounted) videos, VRPs and pictures on evoking mental imagery across tourism and hotel products, updating our knowledge of VR technology in tourism marketing.

INFLUENCE OF ONLINE REVIEWS ON CONSUMERS' HOTEL BOOKING INTENTIONS IN THAILAND: TRUST AND VALUE AS MEDIATING FACTOR

Purpose: The objective of this study is to explore the influence of online reviews on hotel booking intentions among consumers in Thailand, considering the mediating effects of trust and perceived value. Theoretical Framework: The Online Reviews (OR) construct, adapted from Wen et al. (2021), and the Trust (TRT) construct, assessed with measures from Ladhari and Michaud (2015). The Trust scale included four slightly modified items aligned with study objectives. The Value (VAL) construct was evaluated using measures from S. Y. Kim et al. (2017). Lastly, Booking Intention (BI) was assessed using four aspects adopted from Chakraborty and Biswal (2019). Design/methodology/approach: This study investigates the impact of online reviews on hotel booking intentions, employing trust and value as mediating factors. Utilizing a quantitative design through a survey conducted in Thailand, purposive sampling was employed to ensure diversity, with a minimum sample size of 379 determined to achieve a 0.05% sampling error at 95% confidence. Statistical methods, including a 5-point Likert scale, were utilized for data analysis, measure validation, and model reliability assessment, employing SEM analysis with ADENCO software. Descriptive statistics offered insights, contributing to a comprehensive understanding of the factors influencing individuals in Thailand when initiating and managing social entrepreneurial ventures. The study's methodology ensures robustness, reliability, and addresses potential biases through thorough sampling, statistical techniques, and validation procedures. Findings: The results emphasize the substantial influence of online reviews on trust, value perception, and hotel booking intentions. Demographic insights reveal sample characteristics and response variations. These findings deepen our understanding of factors influencing consumers' decisions in hotel booking. Research, Practical & Social implications: The demographic analysis conducted in this study reveals a gender distribution of 27.5% male and 72.5% female participants, primarily aged over 55 (49.1%), with 67.5% holding bachelor's degrees and earning between 30,001-50,000 baht (35.4%). Notably, civil servants and state enterprise employees dominate the occupational landscape (44%). Employing structural equation modeling (SEM), the study confirms that trust and value perception mediate the relationship. It elucidates significant connections between online reviews and trust, online reviews and value perception, and trust and booking intentions. The model remarkably clarifies a substantial portion of the variance in the dependent variables, ranging from 53.5% to 65.7%, highlighting the influential role of online reviews in shaping trust, value perception, and booking intentions. This study delves into the impact of online reviews on hotel booking intentions, with a specific focus on trust and value perception as mediators. Key findings encompass: 1) Online reviews significantly influence consumers' hotel booking intentions, playing a pivotal role in decision-making. 2) Trust and value perception mediate the relationship between online reviews and booking intentions, underscoring their profound impact on consumer behavior. 3) Employing a quantitative research design and survey data collection, the study provides systematic insights from a diverse participant sample. 4) The research not only supports existing literature on online reviews, trust, value perception, and hotel booking intentions but also contributes novel insights to these domains. Originality/Value: This study uncovers essential roles of online reviews, trust, and value perception in hotel booking, enlightening practitioners and researchers on digital-era consumer behavior.

REVOLUTIONIZING LUXURY: THE ROLE OF AI AND MACHINE LEARNING IN ENHANCING MARKETING STRATEGIES WITHIN THE TOURISM AND HOSPITALITY LUXURY SECTORS

This investigation aims to explore the profound impact of these technologies on marketing strategies within the luxury tourism and hospitality sectors in Portugal and Spain and to provide a comprehensive analysis of their role in enhancing service delivery and molding consumer behavior. Additionally, this research delves into the geographical patterns of online luxury hotel bookings, uncovering how regional economic conditions and cultural factors influence consumer behavior. Through a detailed quantitative examination of 2,048 survey responses, this study investigates how AI and ML technologies are employed to optimize digital marketing strategies, focusing particularly on the effectiveness of remarketing and retargeting tactics and their influence on consumer purchasing decisions. By analyzing the distribution of bookings across various districts in Portugal and Spain, the study highlights the disparities in economic prosperity and their correlation with the frequency and nature of luxury hotel stays. This geographical analysis not only provides insights into the current state of the luxury hospitality market but also sheds light on the broader trends of e-commerce adoption in these regions. The study anticipates that the integration of even more immersive technologies such as augmented reality (AR) and virtual reality (VR) will further blur the lines between digital convenience and tangible luxury, suggesting a continued evolution of luxury tourism where technology enhances both the digital and physical aspects of consumer experiences. AI and ML applications, such as chatbots for 24/7 customer service and predictive analytics for tailoring travel recommendations, have greatly improved customer interaction and operational efficiencies. While the industry benefits from technological advancements, there are ongoing challenges such as concerns over data privacy and the need for constant updates to algorithms to keep pace with evolving market conditions.

Feature Selection Based on Artificial Bee Colony and Gradient Boosting Decision Tree for Hotel Reservation Cancellation Prediction Using Random Forest

This study focuses on predicting hotel booking cancellations using machine learning to improve accuracy and operational efficiency. The methods used include Random Forest, Artificial Bee Colony (ABC), and Gradient Boosting Decision Tree (GBDT). ABC, which excels in optimization but is prone to local optima, is combined with GBDT for feature selection. The dataset used is Hotel_Bookings from Kaggle, with 119,390 entries and 28 features. The data is processed through cleansing, normalization, and split into 75% for training and 25% for testing. Feature selection is performed using ABC and GBDT, and the prediction model is built using Random Forest. Model evaluation using confusion matrix and metrics like precision, recall, f1-score, and accuracy shows accuracies of 86.17% and 86.65% for ABC and GBDT, respectively. Increasing the number of trees and features generally improves model performance, with feature selection showing significant performance improvements compared to models without feature selection.

Exploring the impact of AI-translated hotel reviews: the roles of reviewer nationality, cosmopolitanism, and review dispersion

ABSTRACT This study investigates how AI-translated reviews affect consumer behavior in tourism, emphasizing the roles of national stereotypes, cosmopolitanism, and review dispersion. Utilizing three experiments, it demonstrates that reviewer nationality significantly impacts hotel evaluations and booking intentions, with cosmopolitanism moderating this effect. High review dispersion further amplifies the influence of nationality among less cosmopolitan individuals. The findings highlight the complex interplay of AI translation, cultural biases, and consumer psychology in online hotel bookings, offering insights for managing AI tools and consumer perceptions in the hospitality industry.

Digital Technology's Role in Influencing Hotel Booking Intention: The Direct Impact of Negative Review Valence and the Moderating Effects of Brand Image, Star Category, and Gender within Indonesian Tourism OTAs

Digital Technology's Role in Influencing Hotel Booking Intention: The Direct Impact of Negative Review Valence and the Moderating Effects of Brand Image, Star Category, and Gender within Indonesian Tourism OTAs

Evaluating the Impact of App Quality, Product Attributes, and Customer Relationships on Hotel-Booking App Purchases: Insights from Malaysian Users

Traditional hotel bookings have declined due to changes in travel patterns and evolving consumer preferences. Understanding factors driving the surge in booking-app usage is crucial for hotel businesses to adapt and capitalise on the growing demand for digital booking platforms. This study investigates the factors influencing consumer purchase decisions on these apps, utilising an online questionnaire as a primary method of data collection, which conveniently inviting Malaysian hotel-app users to respond. The findings indicate that app quality, product-related attributes, and customer relationships significantly impact customer purchase decisions. Inferentially, Consumer-Relationship Factor (CRF) shows a medium, positive correlation, indicating that Malaysians highly value customer relationships and past experiences within the app, which have significantly impacted their purchase decisions. Simultaneously, this study also indicates a strong influence of customer relationships on hotel-booking app usage. Hence, the study may suggest that hotel operators enhance customer relationships by conducting satisfaction surveys, providing employee training, and utilising customer-relationship management tools to foster business growth. This is because prioritising customer relationships can lead to increased loyalty, repeat business, and positive reviews, which contribute to success in the hospitality industry.

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

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

A effect of active senior customer experience value on subjective well-being, and customer behavior intention of hotel: moderating role of hotel booking method

A effect of active senior customer experience value on subjective well-being, and customer behavior intention of hotel: moderating role of hotel booking method

Applying the SOR Framework in Online Hotel Bookings: Literature Review

Applying the SOR Framework in Online Hotel Bookings: Literature Review

Hotel review analysis based on LDA model and KeyBert model

The popularity of Internet applications and the continuous advancement of technology have made it easier for people to choose hotels. More and more people not only tend to book hotels through travel websites or software but also leave online reviews during their stay or after leaving the hotel. Almost all online bookers will carefully refer to reviews from previous customers of different hotels before making an order and then making a choice. In the past few years, many language models in the fields of machine learning and artificial intelligence have been widely used to analyze various texts. For reviews in areas such as hotels, most studies focus on sentiment analysis, that is, analyzing whether the sentiment in the reviews is positive or negative. Most previous studies used the LDA model to divide the topic words in the reviews or the KeyBert model to extract keywords from the text and compare and analyze them with other models. Some of these studies also used the hotel dataset compiled by Tan Songbo for analysis. Based on the theories of previous studies, this study uses the LDA model to extract and analyze different topic words of hotel reviews based on the hotel review data set compiled by Professor Tan Songbo to find out the advantages and disadvantages of the hotel. This study also uses the KeyBert model to extract the number of occurrences of specific keywords estimate the number of repeat customers by analyzing the keywords in the positive reviews, and estimate the number of non-target customer groups based on the keywords in the negative reviews. The research results are of great significance to the management and marketing decisions of the hotel, and can also provide better help to the hotel’s booker.

What Has the Most Impact? An Exploration of Dual-Processing Systems in Hotel Booking Decisions

This research explores the role of dual-processing systems in evaluating complex information on online travel agency (OTA) websites, explicitly focusing on the transformative effects of multiple cues considering information sources. Two experimental studies were conducted to explore the processing modes of information using priming in assessing the impact of a controllable cue (brand in study 1 and scarcity in study 2 and an uncontrollable cue (customer ratings). The findings indicate that customer ratings function as a primary cue. With positive priming producing heuristics, the value of a brand offsets the negative impact of low ratings, while it is disregarded with high ratings. The perceived value of scarcity aligns consistently with customer ratings due to its inherent uncertainty with irrelevant priming triggering a systematic mode. This research advances the understanding of customers’ evaluation of complex information and suggests practical guidelines for implementing optimal marketing strategies in OTA settings.

Smart Contract for Hotel Booking through Ethereum using Blockchain

Smart Contract for Hotel Booking through Ethereum using Blockchain

Explainable profit-driven hotel booking cancellation prediction based on heterogeneous stacking-based ensemble classification

The goal of hotel booking cancellation prediction in the hospitality industry is to identify potential cancellations from a large customer base and improve the efficiency of customer retention and capacity management efforts. Whilst prior research has shown that the predictive performance of hotel booking cancellation prediction can be further enhanced by integrating multiple classifiers, the explainability of such models is limited due to low interpretability and limited alignment with company goals. To address this limitation, we propose a novel heterogeneous linear stacking ensemble classifier for profit-driven hotel booking cancellation prediction. It enhances classifier explainability by (1) making models more accountable by axing model training towards profitability and (2) complementing models by global post-hoc model interpretation strategies. Through experiments based on real-world datasets, our proposed classification framework is demonstrated to lead to greater profits than other profit-oriented predictive models. Moreover, an in-depth interpretability analysis demonstrates the framework's ability to identify critical factors significantly impacting hotel cancellations, providing valuable insights for retention campaigns.

Electronic word‐of‐mouth and Indian solo female travellers’ hotel booking intention: Moderating role of perceived risk

Electronic word‐of‐mouth and Indian solo female travellers’ hotel booking intention: Moderating role of perceived risk

Unveiling the web of interactions: Analyzing dynamic customer engagements across multiple websites

Online customers exhibit unique dynamic patterns during their journey. In the early phases, customers gather different information from multiple websites. Collectively, the information serves as references, helping customers to make informed decisions. Further, inferior options are eliminated until the journey converges on a single website for conversion. In this study, we examine the dynamic interactions among online customers and websites. To capture the dynamics, we propose a metric, momentum of information-building and incorporate the measure into a dynamically and mutually exciting marked point process model. We apply the proposed model to clickstream data for hotel bookings and find strong evidence of dynamic behavior. Also, we show that ignoring the dynamics may lead to bias in estimating the carryover and spillover effects and diminish the prediction accuracy of website visits and conversions. Furthermore, we offer recommendations for effective advertising strategies focusing on key aspects such as advertising density, timing, and targeting.

The Influence of Social Commerce Dynamics on Sustainable Hotel Brand Image, Customer Engagement, and Booking Intentions

Social commerce plays a significant role in various aspects of the hotel industry. By using social commerce platforms such as Facebook, Instagram, and hotel booking websites, hotels can enhance their brand visibility, engage more effectively with guests, and stimulate hotel bookings. Nonetheless, prior research reported a lack of studies in this area, namely social commerce in the context of domestic tourism. Drawing on Stimuli–Organism–Response (SOR) theory as being a well-established framework in social commerce research, this study seeks to examine the impact of social commerce on hotel booking intentions, both directly and indirectly, by considering the mediating effects of both brand image and customer engagement. This research used an online survey questionnaire as a tool for collecting primary data from domestic tourists in Egypt during the period from January to April 2024. The study sample encompassed 315 participants who were recruited using the convenience sampling technique. Using Smart PLS 4.0 software, Structural Equation Modeling was performed to examine the proposed model and hypotheses. The results highlighted the significant impact of social commerce on both hotel brand image and customer engagement, which in turn encouraged booking intentions among potential domestic guests. The results also supported the significant mediating effect of hotel brand image and customer engagement in the relationship between social commerce and hotel booking intentions. This study provides some theoretical contributions to the literature of hospitality management by addressing notable gaps in knowledge. This study also suggests some practical implications for industry executives that support their social commerce techniques and boost booking intentions and behavior among domestic tourists such as creating visually attractive content, sponsoring virtual mega events, actively interacting with social media followers, and incorporating booking functionalities in social media platforms. In so doing, hotels can boost their sustainable marketing practices to target a promising market segment by exploiting Web 2.0 capabilities.

Construction of the tourist sentiment dictionary for hotels to mining tourist demands: Based on Macao’s hotel reviews from Agoda

<p><span lang="AZ-LATIN">Tourist hotels (or tourist accommodations) are located near tourist attractions, primarily serving tourists. In recent years, with the gradual improvement of people’s living standards around the globe, tourists’ demands and standards for tourist hotel construction have been rising accordingly. In the context of technologization and informatization, various hotel booking platforms (Agoda, Booking, Trip, etc.) cover a large amount of review data in evaluating systems to reflect tourists’ demands. Meanwhile, identifying demand-oriented reviews and extracting core consumer demands from them is crucial for optimizing hotel services and enhancing tourist satisfaction. Therefore, this study explores the demands of tourists in tourist hotels from the perspective of text sentiment analysis and takes Macao, a famous tourist destination, as an example, based on reviews of tourist hotels on the Agoda site platform. Specifics are as follows: (1) Based on pointwise mutual information (PMI) and information entropy (IE), it realizes the identification of sentiment words in the field of tourist hotels and constructs a sentiment dictionary to address the problem of poor relevance between word segmentation results; (2) It summarizes the five types of reviews containing tourist demands (positive, negative, suggestion, demand, and comparison) and their characteristics to solve the ambiguity of texts and further accurately reveal the main demands of tourists; (3) It classifies tourist demands and group similar tourist demands into the same categories to address the problem of multiple expressions for the same demand. The present study provides empirical experiences from Macao’s hotels and contributes to the literature on text sentiment analysis in tourist hotels. Furthermore, the study results could enhance the mining accuracy and provide a detailed summarization of consumer demands and directions for the sustainable optimization improvement of tourist services.</span></p>

ANALYZING HOTEL DATA-DRIVEN SYSTEM BY USING DATA SCIENCE TECHNIQUES

In the past few years, both the City Hotel and Resort Hotel have experienced significant increases in their cancellation rates. As a result, both hotels are currently facing a range of challenges, such as reduced revenue and underutilized hotel rooms. Therefore, the top priority for both hotels is to reduce their cancellation rates, which will enhance their efficiency in generating revenue. This report focuses on the analysis of hotel booking cancellations and other factors that do not directly impact their business and annual revenue generation.