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"Tourism -- Data processing"
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Open tourism : open innovation, crowdsourcing and co-creation challenging the tourism industry
Examining the concepts of open innovation, crowdsourcing and co-creation from a holistic point of view, 'Open Tourism' analyses each concept and considers their suitability to the tourism industry.
Book
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
Artificial intelligence research in hospitality: a state-of-the-art review and future directions
Purpose
The purpose of this study is to analyze state-of-the-art knowledge of artificial intelligence (AI) research in hospitality.
Design/methodology/approach
This study adopts the theory-context-methods framework to systematically review 100 AI-related articles recently published (i.e. from 2021 to April 2023) in three top-tier hospitality journals, namely, the International Journal of Contemporary Hospitality Management, International Journal of Hospitality Management and Journal of Hospitality Marketing and Management.
Findings
Findings suggest that studies of AI applications in hospitality are mostly theory-driven, whereas most AI methods research adopts a data-driven approach. State-of-the-art AI applications research exhibits the most interest in service robots. In AI methods research, little attention was paid to the amid-service/experience.
Research limitations/implications
This study reveals inadequacies in theory, context and methods in contemporary AI research. More research from hospitality suppliers’ perspectives and research on generative AI applications are advocated in response to the unveiled research gaps and recent AI developments.
Originality/value
This study classifies the most recent AI research in hospitality into two main streams – AI applications research and AI methods research – and discusses the gaps in each research stream and latest AI developments. The paper then suggests future research directions to guide researchers in advancing AI research in hospitality.
Journal Article
Analytics in smart tourism design : concepts and methods
Presenting cutting edge research on the development of analytics in travel and tourism, this text introduces new conceptual frameworks and measurement tools, as well as applications and case studies for destination marketing and management.
Book
The future is yesterday: Use of AI-driven facial recognition to enhance value in the travel and tourism industry
This study aims to investigate the role of artificial intelligence (AI) driven facial recognition to enhance a value proposition by influencing different areas of services in the travel and tourism industry. We adopted semi-structured interviews to derive insights from 26 respondents. Thematic analysis reveals the development of four main themes (personalization, data-driven service offering, security and safety, and seamless payments). Further, we mapped the impact of AI- driven facial recognition to enhance value and experience for corporate guests. Findings indicate that AI-based facial recognition can facilitate the travel and tourism industry in understanding travelers’ needs, optimization of service offers, and value-based services, whereas data-driven services can be realized in the form of customized trip planning, email, and calendar integration, and quick bill summarization. This contributes to strengthening the tourism literature through the lens of organizational information processing theory.
Journal Article
Information and Communication Technologies for Sustainable Tourism
Sustainable development is a highly topical issue and is of critical importance to tourism as the environment is of utmost importance for the continued development and prosperity of the industry. There have been numerous texts written on sustainable tourism and the measures to mitigate and manage this but none which acknowledges Information and Communication Technologies (ICT) as a mechanism of doing so despite being an emerging area of research. ICT in this context refers to innovative tools which form an integrated system of software and networked equipment that facilitates data processing information sharing communication and the ability to search and select from an existing range of products and services for an organisation's benefits. Despite the symbiotic relationship, which exists between ICT and sustainable tourism, there has been little research, which has considered how the use of such technology can be used to make sustainable tourism development a more workable reality.
This opportune book is the first to provide a focus on the interrelationship of these two important topics demonstrating their synergies and providing insight into a new and innovative approach to managing sustainable tourism development. It considers the use of technology to reduce the negative impacts of tourism from both the demand and supply side perspectives. A critical review of a range of cutting edge technologies used by tourists and businesses to assess their usefulness in managing sustainable tourism development from the macro to the micro level is also discussed. It further integrates examples and practical applications to show how ICT can be an invaluable mechanism in the management of sustainable tourism development.
This cutting-edge volume provides a wealth of information on an important yet neglected subject. This book will be invaluable reading for students, researchers, academics and members of the tourism industry looking for new and
eBook
Exploring the impact of landscape environments on tourists’ emotional fluctuations in Fujian’s Coastal National Parks using machine learning
In recent years, the impact of landscape environments on tourists’ emotions has increasingly become a significant topic in sustainable tourism and urban planning research. However, studies on the relationship between multidimensional environmental features of Coastal National Parks and tourists’ emotions remain relatively limited. This study integrates machine learning and multi-source data to systematically explore how the landscape environments of Fujian’s Coastal National Parks influence tourists’ emotional fluctuations. Using natural language processing (NLP) techniques, sentiment indices were calculated from social media textual data, while semantic segmentation models and image analysis were employed to extract environmental feature data. The Light Gradient Boosting Machine (LightGBM) model and SHapley Additive exPlanations (SHAP) method were used to evaluate the relative importance of different environmental variables on tourists’ emotions, with the findings visualized using ArcMap. The results indicate: (1) Over the past five years, 87.06% of emotions were positive, with the highest sentiment indices observed in the Fuyao Islands, Changle, and Xiamen. (2) Greenness (0.0–0.2) and aquatic rate (0.1–0.15) had the most significant positive impacts on emotions, whereas transportation proportion and paving degree had relatively minor effects. This study provides a theoretical basis for the sustainable development of Coastal National Parks and offers practical insights for optimizing landscape planning to enhance tourists’ emotional experiences.
Journal Article
Urban tourism management based on artificial neural networks analysis and data mining
In the last several decades, cities all over the globe have seen the arrival of millions of tourists. Due to the rapid increase, innovative approaches are required to control tourist demand, generate precise forecasts, and offer tailored suggestions. This study presents the Urban Tourism Evaluation System (UTES), a novel hybrid method that combines collaborative filtering with cutting-edge techniques such as LSTM neural networks, fuzzy logic, big data analysis, and K-means clustering. The UTES system processes and analyzes large tourist datasets via its interconnected parts. At its core is a robust big data storage system that absorbs large amounts of tourist data. Tourists are categorized using K-means clustering algorithms according to their preferences and actions. Unclear inputs can be correctly deciphered by fuzzy logic. Long short-term memory (LSTM) networks allow for accurate demand forecasting in tourism by recording complex relationships between actual demand and predictive variables. Planners can use these projections to monitor and manage the number of tourists visiting cities. Simultaneously, the collaborative filtering tool analyzes traveller preferences using data mining, enabling highly personalized recommendations for locations, attractions, and activities. With its integrated components for visitor flow management and personalized recommendations, UTES is a comprehensive tourism management system that can evaluate data in real-time and respond with judgments. UTES revolutionizes the industry with data mining, neural networks, recommendation algorithms, big data analytics, and urban tourism management. Ultimately, UTES empowers data-driven decision-making, optimal visitor flow management, and highly personalized tourist experiences, meeting the diverse needs of modern urban travellers. The experimental results show that UTES achieves a demand forecasting accuracy of 93.4% and a recommendation accuracy of 96.7%, outperforming existing models. Additionally, UTES demonstrated lower MAE (78.2%) and RMSE (71.8%), superior real-time adaptability, clustering efficiency, and robust data uncertainty handling using fuzzy logic. These advantages make UTES a highly effective solution for optimizing urban tourism management.
Journal Article
Holy or Unholy? Interview with Open AI's ChatGPT
In this paper, OpenAI's ChatGPT (Generative Pre-trained Transformer), also known as GPT-3, a machine-learning model that has the ability to generate human-like text, was employed as an interviewee instead of a human subject. The scope of the interview was the impacts of OpenAI's GPT on higher education and academic publishing. Particularly, several questions about the impacts of OpenAI's ChatGPT and other AI-based machine learning models on the hospitality and tourism industry and education were asked. The originality of this paper derives from having the ChatGPT as an interviewee. ChatGPT stated that its use helps instructors delegate monotonous tasks such as grading and focus on more intellectual tasks, and students may utilize ChatGPT to brainstorm ideas. ChatGPT confesses the risk of diminishing critical thinking for students in the case of over-reliance on ChatGPT as well as educational inequalities. For academic work, ChatGPT addressed it cannot be a substitute for human creativity and intellectuality because originality and novelty lack in outputs generated by ChatGPT. The tourism and hospitality industry can benefit from ChatGPT for certain things such as personalized services, content creation, and many more.
Journal Article