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"Hospitality industry Data processing."
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Robots, Artificial Intelligence, and Service Automation in Travel, Tourism and Hospitality
Using a combination of theoretical discussion and real-world case studies, this book focuses on current and future use of RAISA technologies in the tourism economy, including examples from the hotel, restaurant, travel agency, museum, and events industries.
eBook
Improving the service industry with hyper-connectivity: IoT in hospitality
Purpose
Internet of Things (IoT) adoption is a differentiating factor in the hospitality industry which facilitates the integration of the digital and real world. This paper aims to explore academic research and practical applications of IoT in the hospitality domain to help identify opportunities and challenges with implementing the technology for creating competitive advantages and service operations process improvements.
Design/methodology/approach
This paper uses previous works and exemplars to demonstrate the use of IoT in hospitality. Academic indexing websites such as Google Scholar and ScienceDirect are used to search for related terms. Whitepapers, IoT project websites of service providers and media coverage are accessed to collect information. Related work is investigated by classifying into major categories of hospitality.
Findings
Hospitality is one of the leading industries that has adopted IoT to create innovative services, but this topic has not been investigated deeply. A comprehensive study is needed to give guidance to decision-makers and helps to design better services by presenting practical and potential benefits.
Practical implications
The IoT will usher in great opportunities in hospitality by enabling novel applications for customization and personalization of the services. Operational processes will be redefined for efficiency and speed. It will alter the expectations and servicescape; thus, its integration will be vital in terms of competitiveness and success.
Originality/value
This study provides a comprehensive overview of IoT research and applications in the hospitality domain. It contributes to better understanding of recent trends and potentials. A holistic approach was used instead of focusing on a single sector which enables the consideration of all aspects of the topic. Theoretical support in addition to technical aspects, challenges and concerns are offered to the reader.
Journal Article
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.
Journal Article
Customer acceptance of cashless payment systems in the hospitality industry
Purpose
The purpose of this study is to propose and test an extended version of technology acceptance model (TAM) to examine consumers’ acceptance of radio frequency identification (RFID) cashless payment systems in the hospitality industry.
Design/methodology/approach
A comprehensive structural model was developed by adding two additional constructs to original TAM, namely, self-efficacy and perceived risk. A self-administered online questionnaire was used to collect the data of the study from 305 respondents in the USA. Confirmatory factor analysis was conducted to validate the measurement model and structural equation modeling analysis was performed to test the proposed model.
Findings
Study results indicated that self-efficacy was significantly related to perceived ease of use. In addition, perceived risk significantly negatively influenced perceived usefulness and perceived ease of use. Study results further illustrated that perceived ease of use had a significant impact on perceived usefulness and both perceived ease of use and perceived usefulness were significantly associated with intention to use.
Practical implications
Study findings provide significant practical implications for US hospitality operators and technology vendors for identifying factors affecting users’ acceptance of RFID cashless payment systems in the hospitality industry.
Originality/value
By extending TAM, this study is one of the first studies to investigate RFID cashless payment system acceptance in the hospitality industry.
Journal Article
Hotel online reviews: creating a multi-source aggregated index
Purpose
This paper aims to develop a model to predict online review ratings from multiple sources, which can be used to detect fraudulent reviews and create proprietary rating indexes, or which can be used as a measure of selection in recommender systems.
Design/methodology/approach
This study applies machine learning and natural language processing approaches to combine features derived from the qualitative component of a review with the corresponding quantitative component and, therefore, generate a richer review rating.
Findings
Experiments were performed over a collection of hotel online reviews – written in English, Spanish and Portuguese – which shows a significant improvement over the previously reported results, and it not only demonstrates the scientific value of the approach but also strengthens the value of review prediction applications in the business environment.
Originality/value
This study shows the importance of building predictive models for revenue management and the application of the index generated by the model. It also demonstrates that, although difficult and challenging, it is possible to achieve valuable results in the application of text analysis across multiple languages.
Journal Article
An Ontology Proposal for Implementing Digital Twins in Hospitality: The Case of Front-End Services
The implementation of Digital Twins (DTs) in hospitality facilities represents a significant opportunity to optimize front-end services, enhancing guest experience and operational efficiency. This paper proposes an ontology-driven approach for DTs in hotel reception areas, focusing on integrating IoT devices, real-time data processing, and service optimization. By modeling interactions between guests, receptionists, and hotel management systems, DTs enhance resource allocation, predictive maintenance, and customer satisfaction. Simulations and historical data analysis enable forecasting demand fluctuations and optimizing check-in/check-out processes. This research provides a structured framework for DT applications in hospitality, validated through scenario-based simulations, showing significant improvements in check-in time and guest satisfaction. Validation was conducted through scenario-based simulations reflecting real-world operational challenges, such as guest surges, room assignment, and staff workload balancing. Metrics including check-in time, guest satisfaction index, task completion rates, and prediction accuracy were used to evaluate performance. Simulations were grounded in historical hotel data and modeled typical peak-period dynamics to ensure realism. Results demonstrated a 25–35% reduction in check-in time, a 20% improvement in staff efficiency, and significant enhancements in guest satisfaction, underscoring the practical value of the proposed framework in real hospitality settings.
Journal Article
From Data to Delight: Leveraging Social Customer Relationship Management to Elevate Customer Satisfaction and Market Effectiveness
The current study aims to investigate ways through which the data on social customer relationship management (SCRM) enhance customer satisfaction (CS) as well as market effectiveness (ME) in the hotel industry. Moreover, it examines the mediator role of customer involvement using social media data (CIUSM). The moderating role of customer information processing capability (CIPC) between social customer relationship management (SCRM) and customer involvement using social media (CIUSM) was examined. Therefore, following the suggestions from Dominant (S-D) Logic, Social Exchange Theory (SET), and Dynamic Capabilities Theory (DCT), this research explores the role of SCRM in co-creation and organizational performance through the social media data of customers. Using PLS-SEM through SmartPLS, data from 389 participants were analyzed. The findings proved that SCRM directly improves both customer satisfaction and the effectiveness of the market due to the indirect effect of CIUSM data as a mediator among them. Also, it showed that SCRM improves directly CIUSM. Moreover, it proved the direct effect of CIUSM on customer satisfaction and market effectiveness. Moreover, CIPC, as a moderator, enhances SCRM impacts by demonstrating how hotels leverage data from social media activity as a competitive advantage. Based on the findings of this study, the three integrated theories provide a single framework to delve deeply into the intricate association between social media customer involvement to enhance hotel performance. Also, it ensures that hospitality managers engage customers, continuously respond to their needs and requirements, and embrace efficient data processing to deploy SCRM effectively.
Journal Article
Deep Learning-Based Truthful and Deceptive Hotel Reviews
For sustainable hospitality and tourism, the validity of online evaluations is crucial at a time when they influence travelers’ choices. Understanding the facts and conducting a thorough investigation to distinguish between truthful and deceptive hotel reviews are crucial. The urgent need to discern between truthful and deceptive hotel reviews is addressed by the current study. This misleading “opinion spam” is common in the hospitality sector, misleading potential customers and harming the standing of hotel review websites. This data science project aims to create a reliable detection system that correctly recognizes and classifies hotel reviews as either true or misleading. When it comes to natural language processing, sentiment analysis is essential for determining the text’s emotional tone. With an 800-instance dataset comprising true and false reviews, this study investigates the sentiment analysis performance of three deep learning models: Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Recurrent Neural Network (RNN). Among the training, testing, and validation sets, the CNN model yielded the highest accuracy rates, measuring 98%, 77%, and 80%, respectively. Despite showing balanced precision and recall, the LSTM model was not as accurate as the CNN model, with an accuracy of 60%. There were difficulties in capturing sequential relationships, for which the RNN model further trailed, with accuracy rates of 57%, 57%, and 58%. A thorough assessment of every model’s performance was conducted using ROC curves and classification reports.
Journal Article