EAI Endorsed Transactions on Tourism, Technology and Intelligence
EAI Endorsed Transactions on Tourism, Technology and Intelligence (TTI) is an interdisciplinary scholarly refereed research journal that aims to promote the theory and practice of tourism by linking tourism, technology, and intelligence disciplines. It addresses the issues involved in intelligent planning, development, and implementation of technological capabilities to shape and accomplish tourism's strategic and operational objectives. It encourages theoretical and practical, policy and empirical contributions in tourism, technology, and intelligence across social science, economy, education, and engineering. It welcomes new theories, techniques, concepts, algorithms, prototypes, and applications impacting the hospitality and tourism sectors. INDEXING: GoogleScholar, Crossref, Dimensions, Semantic Scholar, Lens This journal is founded, co-organised, and managed by Duy Tan University, Vietnam, in collaboration with Passage to ASEAN (P2A), an ASEAN Entity. It is an official refereed publication of Duy Tan University and the publishing services is provided by EAI
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Determinants of workplace pro-environmental behavior in the hotel industry: A case study of service employees at five-star hotels in Da Nang, Vietnam
In the context of growing global concern for sustainable development, employees’ pro-environmental behavior in the workplace has become a vital factor for organizational sustainability in the hospitality industry. This study examines the factors that influence workplace pro-environmental behavior among service employees in five-star hotels in Da Nang, Vietnam. Based on existing literature, the research develops a conceptual model where local environmental policy and green organizational culture act as independent variables, while employees’ environmental intention and environmental commitment serve as mediators affecting workplace pro-environmental behavior. A quantitative research approach was used, employing survey data collected from hotel employees. The data were analyzed through Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), and Structural Equation Modeling (SEM). The results show that both local environmental protection policies and green organizational cultures positively influence employees’ environmental intention and environmental commitment. However, environmental intention did not significantly predict workplace pro-environmental behavior, whereas employees' environmental commitment had a strong positive effect. The model explained 61.8% (R² = 0.618) of the variance in workplace pro-environmental behavior, and both local policies and green organizational culture exerted significant indirect effects through employees’ commitment. These findings highlight the crucial role of organizational culture and local policies in promoting sustainable practices within the hotel industry. The study enhances the theoretical understanding of workplace pro-environmental behavior in service organizations and offers practical insights for hotel managers and policymakers to promote green practices among employees.
Copyright (c) 2026 Minh Thu Nguyen Thi, Thanh Nga Ngo Thi, Thuy Linh Huynh Ly https://creativecommons.org/licenses/by-nc-sa/4.0
Empowering Rural Communities through Role Modeling: The Spread of Community-based Tourism in Lo Lo Chai Village
Community-based tourism (CBT) is an important strategy for sustainable rural development in Vietnam, leveraging local cultural and natural resources to foster community resilience and economic growth. This study examines how community empowerment developed in Lo Lo Chai Village, Ha Giang province, where a pioneering family’s adoption of CBT practices catalyzed broader community transformation. Guided by Community Empowerment Theory and the concept of Kankou Machizukuri, the research examines how role modeling by early adopters inspired other households to participate in tourism, thereby preserving traditional Lo Lo architecture, revitalizing cultural heritage, and improving livelihoods. Through qualitative case study methods, including in-depth interviews with 18 participants and participant observation, the findings reveal that collective decision-making, shared values, and local leadership were instrumental in building social capital and promoting sustainable tourism practices. The study highlights how a local role model, supported by an exogenous catalyst, drove grassroots empowerment without the need for external training programs. This research contributes to understanding how role-model-driven diffusion operates within CBT, offering practical insights for rural destinations seeking inclusive and sustainable development.
Copyright (c) 2026 Nam Vu, Thu Trang Le, Hoai Thu Le, Vu Maki Khue https://creativecommons.org/licenses/by-nc-sa/4.0
Overview of Quantum Machine Learning for 6G
The forthcoming sixth generation (6G) of wireless networks requires fundamental rethinking of network intelligence, driven by the transition toward ubiquitous cognitive networks and the unprecedented complexity beyond 5G. The optimization demands of 6G surpass the capabilities of classical heuristics and conventional Machine Learning (ML), which encounter significant limitations in addressing dimensionality challenges in ultra-massive multiple-input multiple-output/terahertz/reconfigurable intelligent surfaces-assisted systems, stringent sub-millisecond latency requirements, and severe energy bottlenecks at the edge. Motivated by these gaps, this review investigates Quantum Machine Learning (QML) as a transformative solution, merging quantum mechanics with data-driven intelligence. We propose a unified and forward-looking perspective on ML integration for 6G, bridging previously siloed research domains such as cross-layer optimization, semantic- and intent-driven communication, and quantum-inspired acceleration. Furthermore, this work systematically reviews quantum-enhanced optimization methods and analyzes QML’s role as an intelligence anchor, demonstrating its potential to provide context-aware, resilient, and sustainable network control across various layers. Ultimately, the paper outlines pathways for integrating QML to ensure timely, scalable, and secure decision-making in the volatile 6G landscape.
Copyright (c) 2025 Dung Thanh Tran, Dac-Binh Ha, James Adu Ansere https://creativecommons.org/licenses/by-nc-sa/4.0
Arousal – Passage To Impulsive Purchase - Exploring The Driving Factors Through TikTok Short Marketing Videos
In the age of swiftly evolving digital technology, social media platforms, particularly TikTok, significantly influence consumer behavior. TikTok's unique features - its highly personalized "For You" algorithm, rapid short-form content, and sound-on design - intensify users' emotional arousal and reduce cognitive processing time, thereby reinforcing the arousal-impulse pathway more effectively than platforms like Instagram Reels or YouTube Shorts. These attributes foster an entertainment-focused environment where emotional engagement rapidly translates into impulsive behavior, highlighting TikTok's distinctive design impact on impulse buying behaviors. A quantitative methodology was utilized for this research, including a survey featuring a 5-point Likert scale. The survey was conducted online utilizing a convenience sampling technique. The sample size consists of 350 respondents in Vietnam, aged 18 to 30. The respondents are TikTok users who participate in online buying using this platform. Subsequent to screening, the data were evaluated utilizing Structural Equation Modelling (SEM), conducted with SmartPLS 3. The findings demonstrated that elements like entertainment, perceived usefulness, and aesthetic design in TikTok short marketing videos have positive effects on young consumers’ impulsive purchase intentions. Notably, this research has demonstrated the major mediation role of the affective factor - perceived enjoyment - in the transition to impulsive purchasing.
Copyright (c) 2025 Phuoc Nguyen https://creativecommons.org/licenses/by-nc-sa/4.0
A Hybrid Ensemble Deep Learning and Reservoir Computing Approach for Exchange Rate Prediction
A nation's currency plays a vital role in stabilizing the economy and determining exchange rates in global markets. Keeping track of the influential currencies while comparing them with one's own currency becomes essential these days. During this study, we have specifically focused on the exchange rate prediction between the United States dollar (USD) and the Canadian dollar (CAD). This pair is one of the most active currency pairs with significant economic implications for both nations. This paper studies the use of machine learning models for this specific matter, which includes long short-term memory (LSTM), gated recurrent units (GRU), and reservoir computing (RC) echo state network models. They were evaluated not only as individual models but also in various hybrid combinations. A hybrid model which combines LSTM and RC yielded better performance in terms of Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R2). Moreover, the full potential of RC represents a promising direction for future researchers to incorporate into time series analysis. In addition, considering internal and external factors that influence the exchange rate during model development would also give more accurate predictions.
Copyright (c) 2025 Senthan Prasanth, Chau Phung https://creativecommons.org/licenses/by-nc-sa/4.0
Simultaneous Dual-Band Classification for WLAN Band Selection
Accurate classification of dual-band Wi-Fi signals is essential for improving adaptive band selection and maintaining quality of service in complex indoor wireless environments. Although several efforts have addressed propagation modeling, only few works simultaneously examined dual-band classification across both 2.4 GHz and 5 GHz frequencies in realistic scenarios. In this work, we use the measurements data conducted in the Deutsches Museum Bonn, which captures both line-of-sight (LoS) and non-LoS (NLoS) propagation conditions in a complex indoor environment. Ten statistical features are extracted from the received signal data, including mean, standard deviation, and skewness. To classify the signals, multiple machine learning models are evaluated, including k-nearest neighbors, support vector machines, and two deep learning architectures. Among these, model 3A, which is a fully connected neural network comprising three hidden layers using ReLU activation with 64, 32, and 16 neurons, respectively, and a softmax output layer, achieves the best performance. Trained with the Adam optimizer and categorical cross-entropy loss, model 3A attains an overall classification accuracy of 93 \% at the optimal window, thus outperforming the baseline models in terms of precision, recall, and F1-score across all classes. These results highlight the model’s robustness for simultaneous dual-band classification and its potential application in intelligent band selection for next generation Wi-Fi systems.
Copyright (c) 2025 Nana Esi Nyarko, Quang Nhat Le https://creativecommons.org/licenses/by-nc-sa/4.0
Domestic Tourists’ Behavioral Intentions: Insights from a Review Study
Domestic tourism has become increasingly important for sustaining the industry, yet limited research has explored the behavioral intentions of local travelers. To address this gap, the paper develops an integrative framework that links motivational factors, perceived benefits, satisfaction, and behavioral intention. Unlike studies earlier that mostly focused on international contexts or emphasized direct relationships, the proposed framework highlights the mediating influence of perceived benefits and satisfaction, offering a more comprehensive perspective on tourist behavior. The originality of this work lies in advancing theoretical insights through a synthesized framework, providing a deeper understanding of the factors influencing domestic tourism and supporting the development of sustainable tourism strategies.
Copyright (c) 2025 Phan Thi Hong Hai, Phuong Bui L. A. https://creativecommons.org/licenses/by-nc-sa/4.0
From Village to Cloud: A Framework for the Emergence and Sustainability of Digital Social Enterprises (DSEs) in Rural Tourism
INTRODUCTION: Diversification into tourism in rural economies has emerged as a viable pathway to enhance resilience, with digitalization further expanding reach, enriching visitor experiences, and enabling inclusive participation. Digital Social Enterprises (DSEs) – hybrid ventures that integrate a social mission with entrepreneurial approaches and digital technologies – offer significant potential for advancing sustainable rural tourism. Despite this promise, there is a lack of an integrated framework explaining how DSEs in rural tourism emerge and, subsequently, achieve long-term sustainability. OBJECTIVES: This conceptual paper addresses that gap by synthesizing concepts on digital social entrepreneurship, entrepreneurial intention theory, and sustainable rural tourism. RESULTS: It proposes a two-phase framework - emergence and sustainability -linking antecedents such as empathy, moral obligation, prior experience, and perceived feasibility and desirability, with enabling conditions including community assets, digital infrastructure, and ecosystem support, as well as hybrid organizing and legitimacy CONCLUSION: The framework is accompanied by propositions outlining how these factors interact to influence both the creation and endurance of rural tourism DSEs. The study contributes theoretically by bridging disparate research streams
Copyright (c) 2025 Ari Margiono https://creativecommons.org/licenses/by-nc-sa/4.0
GS-MultiRC: Multi-step Reservoir Computing Leveraging Grid Search for Stock Indices Prediction
Stock market prediction plays a crucial role in investment decision-making, portfolio management, and risk assessment, significantly impacting financial stability and economic growth. Accurately forecasting stock prices, which are chaotic and nonlinear, has become a main point of financial research. Deep learning approaches, such as neural networks and long-short-term memory (LSTM) models, have been more reliable than traditional approaches such as the ARMA and ARIMA models. However, these methods require a lot of computational power, complex fine-tuning procedures, and often overfit, especially with limited or noisy data. Reservoir Computing (RC) has emerged as a potential alternative for financial time series prediction. It uses a fixed, randomly connected reservoir to capture patterns in data, requiring only the output layer to be trained. This design makes RC computationally efficient and simpler to use. However, RC models can struggle with overfitting when the reservoir is too large compared to the data or when the model can not adapt well to unseen data. To address these drawbacks, we propose a multi-step RC model, focusing on popular stock indices, including CSI300, FTSE100, S&P500, and SSE50. Our approach includes a retraining step where the reservoir evolves by forecasting some of the training data and simulating real-world testing conditions. These evolved internal states, affected by prediction errors, are used to retrain the output layer, making the model more robust and less likely to overfit. Our experiments show that our model performs more accurately and efficiently than conventional RC and LSTM models, making it a workable and trustworthy option for stock market prediction. This work contributes to utilizing RC-based approaches in terms of the financial forecasting domain.
Copyright (c) 2025 Quan Thanh Dao, Chau Bao Phung https://creativecommons.org/licenses/by-nc-sa/4.0
Higher Education and Student Short-Term Mobility in ASEAN Countries: Current Trends and Priorities - A Case of Passage to ASEAN
In the twenty-first century, international student mobility is among the most notable phenomena in Higher Education. While the current literature on its opportunities and challenges is still expanding, the majority of research recognizes its transformative potential along with a costly barrier to entry for most students. Long-term student mobility also takes priority in most scholarly works, despite the growing demand for short-term exchanges. Among the organizations seeking to alleviate said challenge and foster human capital is Passage to ASEAN (P2A) Network, a regional organization of Institutions of Higher Education (IHEs), businesses and government agencies in Southeast Asia. P2A has, since its inception, been dedicated to fostering academic exchanges, cultural understanding, collaboration prospects, and technological integration among ASEAN member IHEs. This study explores the P2A Network’s structure, its goals and impacts on regional education, especially tourism and technology, and its potential to further enhance collaborations in the region. This study employs a mixed-method approach, synthesizing document analysis and stakeholder interviews. P2A published material is closely examined and combined with emerging themes from a total of 12 interviews with individuals who have worked with P2A. This study highlights Passage to ASEAN’s role in expanding accessible Short-Term Mobility (STM) through cultural exchanges and virtual programs. By reducing cost barriers and leveraging online platforms, P2A has successfully increased participation and provided valuable educational benefits. These findings offer insights into effective STM models and serve as a reference for institutions aiming to enhance their mobility initiatives.
Copyright (c) 2025 Hoa D. Tran, Phuong Bui L. A. https://creativecommons.org/licenses/by-nc-sa/4.0
Efficient Machine Learning for Wi-Fi CSI-based Human Activity Recognition Using Fast Monte Carlo based Feature Extraction
High-dimensional doppler data extracted from Wi-Fi channel state information (CSI) offers distinctive velocity and time patterns that are useful for human activity recognition (HAR), but its scale poses significant challenges for real-time inference and deployment on resource-constrained devices. This work proposes an efficient, fast monte carlo (MC) feature selection framework based on the frieze-kannanvempala (FKV) algorithm and coefficient estimation to address this bottleneck. The CSI is preprocessed, and doppler traces are computed to encode the velocity and direction of distinct activities. Afterwards, we perform FKV to decompose the doppler data, and the coefficient of the resulting singular vectors is estimated. Using rejection sampling, the topmost features are selected on the basis of their weights, thereby reducing the size of our features. The method identifies a compact set of velocity-time features that preserve critical motion information while significantly reducing computational overhead. The experimental evaluations demonstrated that the decision tree classifier achieved the highest precision at 99.8%, followed by convolutional neural networks (CNN) 96%, the hybrid CNN-long-short-term memory (CNN-LSTM) achieved 87%, while the LSTM model lagged at 53%. These results demonstrated that the integration of fast MC-based feature selection significantly reduced computational overhead without sacrificing classification performance, making it suitable for scalable and real-time HAR applications.
Copyright (c) 2025 Emelia Logah https://creativecommons.org/licenses/by-nc-sa/4.0
The Tourism-Migration Nexus: Accessing Care and Support as a Retired British National in Spain
INTRODUCTION: The movement of older people from one country to another has been described in a multitude of ways, including ‘Residential Tourism’ and ‘International Retirement Migration’. Tourism is often the stepping stone to retirement migration and many older retirees live fluid lifestyles where home ownership, access to welfare and social networks are maintained across the home and host countries both physically and through technology. OBJECTIVES: This paper focuses on older British people in Spain, and explores the strategies employed to access support in later life. It draws on Grid-Group cultural theory to explore the social network configurations that include the individual, their local and transnational community, as well as the wider socio-cultural context within which they are located. METHODS: The paper draws on data from narrative interviews with 25 older British people in Spain. CONCLUSION: The paper exemplifies four different ‘types’ of social network organization and how these relate to help seeking behavior in later life.
Copyright (c) 2025 Kelly Hall https://creativecommons.org/licenses/by-nc-sa/4.0
The Relationship Between Perception and Actual Behavior of Students Regarding Responsible Tourism A Case Study of Hospitality and Tourism Institute - Duy Tan University
This study investigated the relationship between students’ perceptions of responsible tourism and their actual travel behaviors within the Hospitality and Tourism Institute - Duy Tan University. The primary objective was to determine whether students' understanding of responsible tourism influences their travel behaviors in practice. The study used quantitative methods with a survey instrument designed based on a 5-point Likert scale. A sample of 490 students participated in a structured questionnaire, followed by semi-structured interviews with a selected subset of participants. The results are expected to reveal a significant correlation between students' perceptions of responsible tourism and their actual travel behaviors. Additionally, the study aimed to identify gaps between knowledge and practice, highlighting the need for stronger educational programs to bridge this divide. Overall, the study underscores the importance of promoting deeper awareness to foster more responsible actions among future tourism professionals.
Copyright (c) 2025 Ly T. Thuong, Thuy Vo https://creativecommons.org/licenses/by-nc-sa/4.0
Usage Of Dıgıtal Twın Technologıes In Aırports: The Case Of Istanbul Aırport
This study investigates the application potential of digital twin technology in airport management through the case of Istanbul Airport. Digital twin is an innovative technology that aims to enhance operational efficiency and strengthen decision-making processes by creating digital representations of physical systems. Adopting a qualitative research approach, the study collected data through literature review, document analysis, and semi-structured interviews. The findings indicate that digital twin applications at Istanbul Airport are primarily utilized in infrastructure monitoring, maintenance planning, and operational simulation. However, structural challenges such as data compatibility, human resources, and cybersecurity emerge during the integration process. The study identifies that digital twin systems contribute to airport management particularly in areas such as scenario analysis and predictive maintenance. It concludes that digital twin technology can be considered a strategic tool in airport management and offers practical recommendations for its implementation in the aviation sector.
Copyright (c) 2025 Rümeysa Durmaz, F.Didem Göçmen https://creativecommons.org/licenses/by-nc-sa/4.0
The role of metaverse in training and educational context: Potentialities, use-cases, and research directions
The rapid rise of smart devices and advancements in mobile computing, machine learning, and artificial intelligence have set the stage for the metaverse—a shared, immersive virtual world where people can interact through dynamic digital environments and avatars. This innovation is poised to transform various sectors, with education standing out as a key area of impact. In education, the metaverse promises to revolutionize learning by enabling students and instructors to engage in immersive virtual environments. Students can explore historical events, conduct experiments in virtual labs, or develop real-world skills in risk-free simulations. Educators can deliver adaptive and interactive lessons tailored to individual needs, creating more engaging and effective experiences. However, realizing the metaverse’s potential requires overcoming significant challenges, such as improving technology scalability, ensuring seamless user experiences, and addressing data privacy concerns. This paper examines the metaverse’s potential in education, highlights enabling technologies, and outlines key research directions to overcome current barriers.
Copyright (c) 2025 Antonino Masaracchia, Tinh T. Bui https://creativecommons.org/licenses/by-nc-sa/4.0
Pro-Poor Tourism as a Mechanism of Social Innovation and Empowerment
Pro-poor Tourism is a concept that emerged in the early 2000’s as a means of fostering the development of sustainable tourism and as a means of ensuring the benefits of this were felt by local populations. Whilst the efficacy of pro-poor tourism and the impact that it makes for communities has been questioned over the last two decades, with research focus on how its impacts on communities and how it is operationalised, there has been less of a focus on the underpinnings of what pro-poor tourism is (or should be) from a theoretical perspective. In particular, there remains a lack of understanding as to what constitutes pro-poor tourism from a sociological perspective. In this review paper, the concept of social innovation is used to theoretically explain the mechanisms for successful implementation of pro-poor tourism initiatives, as a means of empowerment. The paper illustrates how pro-poor tourism can act as a means for empowering the poor and achieving sustainable development agendas, by utilising ‘strong tradition’ social innovation approaches in order to enable social action that can refigure societal power structures.
Copyright (c) 2025 Richard Hazenberg, Phuong Bui L. A., Tran, T. M. Linh https://creativecommons.org/licenses/by-nc-sa/4.0
Eco and responsible tourism and effective community engagement: Learnings and considerations from the social enterprise sector in Vietnam
INTRODUCTION: In the eco and responsible tourism sector, social enterprises (organisations which fund their social mission through market activities) can meaningfully engage with local communities in their pursuit of social impact. OBJECTIVES: This paper explores the trade-offs social enterprises make to balance market viability and social mission within the eco and responsible tourism context, focusing on how these trade-offs intersect and impact organisational strategies. METHODS: The research adopts a thematic analysis of data collected from nine social enterprise organisations who partook in semi-structured interviews. RESULTS: Two intersecting axes of trade-offs are presented: how beneficiaries are included in the development of market outputs, and the second on how its social value is dispersed. CONCLUSION: A framework is developed to enable self-reflection and evaluation of the trade-offs and risks involved to social mission and market viability.
Copyright (c) 2024 Michael Maher, Claire Paterson-Young https://creativecommons.org/licenses/by-nc-sa/4.0
A Hybrid Approach to On-Grid and Off-Grid Solar Energy with Optimum Investment: A Case Study in Vietnam
Vietnam’s demand for energy has grown significantly due to the country’s economy’s fast development and expansion over the last ten years. The majority of energy in the future will have to come from renewable sources due to the issue of greenhouse gas emissions, which will force a significant transition away from fossil fuels as the primary energy source. In the Power Development Plan VIII with a vision to 2050, Vietnam’s power sector has labored to develop new resources such as solar and wind energies in an effort to build and enhance the nation’s power system. A technical and financial model for the construction of a hybrid grid-connected photovoltaic plant with a battery energy storage system under practical optimization problems is presented in this work. Certain business models link the analysis to hourly irradiation by taking into account the hourly energy demand profile for planned working activities. The suggested hybrid system’s investment, needed power, and capacity are evaluated and optimized using a variety of scenarios. These findings demonstrate the system’s economic viability and technological viability in our suggested approaches.
Copyright (c) 2024 Long D. Nguyen, Phan Le Tuan Tu, Tran Minh Tri, Nguyen Kim Hoang Nam https://creativecommons.org/licenses/by-nc-sa/4.0
The Role of Machine Learning in Smart Education: Taxonomy, Challenges, and Use Cases
Education is a powerful domain of any country where the changes happened in this domain will reflect all other domains as well. The technical advancement should start with education domain or else there is no strength to that particular advancement. After COVID-19 cause severe upheaval to almost all the industries. In education, the adaptation were significantly impact the development of smart education. Even the developing countries were in the position to adapt the technological advancement through this pandemic. Machine learning plays pivotal role in the technological improvement. The intrusion of smart education fosters an abundance of electronic data and solutions. Machine learning techniques are used to implement models to analyse these larger datasets. In recent years, there have been plenty of studies which address the changes in education and model solutions using various machine learning techniques, such as Supervised, Unsupervised, Semi-supervised, Deep learning and Reinforcement learning techniques. This paper provides an overview, challenges and future directions of research on machine learning techniques applied in education with different levels.
Copyright (c) 2024 Premisha Premananthan, M. Fahim https://creativecommons.org/licenses/by-nc-sa/4.0
Improving State Management on Sustainable Tourism Accommodation Development in Vietnam: From a Business Perspective
This study examines the role of state management in promoting sustainable tourism accommodation development in Vietnam, focusing on balancing economic growth, environmental conservation, and social equity. The objectives are to assess the current state of sustainable tourism accommodation, identify challenges and opportunities in implementing sustainable practices, evaluate the effectiveness of existing government policies, and propose strategic recommendations for enhancing state management. Using quantitative surveys and qualitative interviews with stakeholders, the research reveals a growing commitment to sustainability among high-end accommodations but also highlights challenges such as high certification costs, inadequate regulatory frameworks, and limited stakeholder engagement. The findings underscore the critical role of government policies in fostering sustainable practices and recommend strengthening policy frameworks, providing financial incentives, enhancing stakeholder collaboration, and offering training programs to improve the skills and knowledge of tourism professionals. These steps are essential for Vietnam to achieve its sustainability goals and enhance the competitiveness of its tourism sector.
Copyright (c) 2025 Nam Vu, Ha Giang Chu https://creativecommons.org/licenses/by-nc-sa/4.0
Impact of 6G Space-Air-Ground Integrated Networks on Hard-to-Reach Areas: Tourism, Agriculture, Education, and Indigenous Communities
Due to low population density, difficult terrain, and insufficient infrastructure, the deployment of wireless communication in hard-to-reach areas has long been a challenge in the perspective of leadership corporations, companies, and governments despite the high demand from many local communities. The birth of space-air-ground integrated networks (SAGINs) which are designed to provide ubiquitous, seamless, and high-throughput connectivity is a promising solution to these challenges. In this paper, we investigate the unique difficulties faced by rural and remote areas without wireless communication in the information era. To address these problems, a general architecture of SAGINs is described with the aim to apply in these regions, following the unprecedented benefits in four key sectors including tourism, agriculture, education, and indigenous communities. Although SAGINs have been proposed recently, they are still in their fancy with a focus on the application in remote areas. Therefore, important open research topics are crucial to be investigated at the beginning of the design process to ensure that their full potential is leveraged to enhance the well-being of local populations and create sustainable development.
Copyright (c) 2024 Tinh T. Bui, Antonino Masaracchia, Vishal Sharma, Octavia Dobre, Trung Q. Duong https://creativecommons.org/licenses/by-nc-sa/4.0
Machine Learning Applications in Smart Tourism: Overview, Research Challenges, and the Road Ahead
Tourism plays a vital role in stimulating economic growth, creating employment opportunities and facilitating cultural exchange and mutual understanding between people of different backgrounds. However, as an industry facing increasing competition on a global scale, tourism needs to embrace technologies to adapt quickly to changes. The emergence of smart tourism offers a route to achieve a competitive edge. To this end, machine learning techniques are used to analyze the abundant data available, forecast trends and and provide best solutions. This paper provides an overview, challenges and future directions of research on machine learning techniques applied in smart tourism.
Copyright (c) 2024 Premisha Premananthan, Hang Le, Minh-Hien T. Nguyen, Trung Q. Duong, M. Fahim https://creativecommons.org/licenses/by-nc-sa/4.0
The night economy: a novel approach to illuminating city tourism in Vietnam
Danang, a famous coastal city in Vietnam, is considered a global, attractive destination and a relatively safe place, with an annually increasing number of tourists. However, there has been a lack of leisure activities at night for visitors, which limits the city from exploiting its full potential to the degree it could. Policymakers have launched the 3-stages of Night-time economy (3-s NTE) development in 2021-2025 to stimulate tourism in Da Nang in the post pandemic period. Research introduced numerous features to assure the Danang 3-s NTE's smooth functioning from policymakers’ perspectives and to understand citizens' viewpoints on how it affects their daily lives. This study applied an inductive qualitative research method through interviews, sentiment analysis, and text data analysis to understand policymakers’ and citizens' positive and negative viewpoints to provide feedback on ensuring the NTE’s smooth operation. Twelve experts and twelve citizens were invited to the discussion and answered several questions related to Night-time economy elements oriented by city leaders. The data gathered were examined and deeply analyzed to explore interviewees’ reactions to the Night-time economy operation. In a post-pandemic world, issues related to fragility, sensitivity, and responsibility are interesting to Danang policymakers and citizens. The study outcomes explore the Da Nang government and citizens in relation to "landscapes" and "security," followed by the themes "development capability", "efficiency", "infrastructure" and "attractiveness". The results will contribute to managerial and practical implications for city leaders in considering how effective the Night-time economy is during the first phase of the operation process.
Copyright (c) 2024 Bui-Kim Luan, Phuong Bui https://creativecommons.org/licenses/by-nc-sa/4.0
Understanding Visitor Perceptions and Attitudes Towards UNESCO WHS Designation: A Case Study of Hoi An City
UNESCO is dedicated to identifying, protecting, and preserving global heritage. The designation of a site as a World Heritage Site, recognized as a prestigious brand, significantly enhances its appeal, drawing an increasing number of visitors to these locations. The study sought to determine visitors' perceptions and attitudes of World Heritage emblem, in sync with UNESCO's overall objectives. Concentrating on Hoi An, Vietnam, as a case study, unstructured interviews were conducted with over 30 travelers. The interview sampling was non-probability, using the snowball sampling method to examine awareness amongst visitors’ awareness of the Hoi An WHS emblem and designation, with the goal of providing essential insights for both locals and tourists to help preserve the genuine spirit endorsed by UNESCO regulations. The findings revealed that visitors' impressions were more aligned with the World Heritage Site (WHS) designation than with the emblem itself. Only a small percentage of visitors were aware of Hoi An’s WHS status before their visit and were able to recognize the logo and its association with UNESCO. The concept of a World Heritage site evoked numerous positive product attributes, such as "night, street, friendly people," and some negative associations, such as "river, garbage. crowded". This research idea offers valuable insights for professionals and policymakers in their future applications.
Copyright (c) 2024 Phuong Bui https://creativecommons.org/licenses/by-nc-sa/4.0