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 eISSN: 3078-5855
This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
Promoting Sustainable Consumption in Tourism Cities: How Consumer Value Drives Refillable Beauty Product Adoption Across Online and Offline Shoppers in Da Nang
The rapid growth of global consumption is intensifying environmental pressures, particularly plastic waste, which is projected to increase by 68% by 2050 without intervention. In Vietnam, about 1.8 million tons of plastic garbage are produced annually, with only 10–27% being recycled, while the cosmetics industry significantly contributes to single-use packaging and emissions. Refillable beauty products have emerged as a circular solution, reducing packaging waste while offering economic benefits to consumers. In Da Nang, an emerging eco-city and major tourism hub, local residents show growing environmental awareness, yet the adoption of refillable beauty products remains limited due to habit and convenience barriers. This study investigates the key determinants influencing consumers’ purchase decisions toward refillable beauty products by integrating both cognitive (perceived value) and affective (positive emotion) pathways. A quantitative method was applied utilizing a standardized questionnaire with a 5-point Likert scale. Data were collected through an online survey using convenience sampling, yielding 420 valid responses from Da Nang local residents. Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4 was utilized to examine the data. The findings show that both consumer value determinants and marketing signals influence the customer's purchasing decisions of refillable beauty products. Furthermore, the dual dimensions of perception and emotion serve as mediators, predominantly affecting the consumer's decision-making process. Furthermore, through MGA, the study identified differences in purchase behaviors (online versus offline). The research provided theoretical contributions and suggested managerial implications for marketers and policymakers within the cosmetics industry.
Copyright (c) 2026 Phuoc Nguyen Huy, Linh Dang Le Khanh, Phuc Nguyen Thi, Ngan Tran Thi Thuy https://creativecommons.org/licenses/by-nc-sa/4.0
Preserving and Promoting Traditional Culture associated with Sustainable Tourism Development: Challenges and Potential in Tua Chua, Vietnam
Tourism serves as a fundamental catalyst for socio-economic development, yet its rapid expansion often necessitates a balanced approach to safeguard traditional values. In the highland district of Tua Chua, Dien Bien Province, the cultural heritage of ethnic minorities like the Mong, Dao, and Kho Mu presents a unique paradigm where tourism and cultural preservation interact and influence each other. This study aims to investigate the potentials and challenges of preserving traditional cultural heritage within the framework of sustainable tourism development in this region. Adopting a qualitative research design, this study utilized purposive sampling to engage 12 key informants, including cultural experts, local authorities, and community representatives (artisans, village elders, and homestay owners). Data collection was conducted from January 2024 to August 2025 through field surveys, in-depth interviews, and focus group discussions. The gathered data underwent thematic analysis, integrated with an analytical framework based on UNESCO’s cultural conservation standards (2003) and the United Nations’ sustainable tourism principles (1987). The findings identify ten distinctive traditional cultural elements including festivals, handicrafts, indigenous knowledge, and vernacular architecture that hold significant potential for experiential and heritage tourism. However, the research also delineates six critical challenges: the gradual erosion of cultural authenticity due to "staged authenticity" and commercialization, severe topographical and infrastructural constraints, inadequacies in cultural management, low levels of local community participation, fragmented promotion strategies, and emerging environmental sanitation concerns. The results affirm that the preservation of traditional culture can only be effective when harmoniously integrated into a comprehensive sustainable tourism strategy. To mitigate the risk of cultural degradation and loss of authenticity, it is imperative to shift from state-dependent conservation to community-based management models. This research provides a strategic roadmap for local authorities to balance economic growth with cultural integrity, emphasizing that infrastructure improvement and human resource training are vital for the long-term viability of Tua Chua as a sustainable tourism destination.
Copyright (c) 2026 Pham Hong Long, Ngo Viet Anh, Lo Thi Tuyet, Bui Nhat Quynh, Nguyen Manh Ha https://creativecommons.org/licenses/by-nc-sa/4.0
PennyLane–Qiskit Plugin: A Protocol for Integrating Noisy, Fake, and Real Quantum Backends
Quantum computing frameworks like PennyLane, Qiskit, and others offer powerful tools for developing quantum algorithms. However, integrating their devices can be challenging for newcomers, especially given the frequent updates in frameworks like Qiskit, which can create significant barriers to learning and thus slow down the growth of quantum computing and quantum machine learning. The PennyLane–Qiskit plugin is a particularly interesting framework that allows researchers and practitioners interested in quantum computing and quantum machine learning to implement and simulate realistic models in a simple and efficient way. This work provides a practical method to use the PennyLane–Qiskit plugin, focusing on three main device types: the Aer simulator with noise models, IBM fake backends (with FakeManilaV2 as a representative example), and real IBM Quantum hardware accessed remotely (with ibm_torino used in this study). We demonstrate how to set up and use each of these devices within PennyLane using a simple example of a quantum machine learning regression task on simulated stock price data. Our goal is to simplify the process for all interested users, especially (but not only) beginners by clearly explaining and demonstrating how to use each of these device options while highlighting best practices for effectively leveraging PennyLane and Qiskit together via Pennylane–Qiskit plugin. This work aims to provide a turnkey solution enabling users with only minimal foundational knowledge to quickly start running meaningful simulations. The three devices implementation will be explained with the code here. The accompanying full code will be provided to ensure that all results can be easily reproduced, including with users’ own datasets.
Copyright (c) 2026 Matthieu Hubert, Quang Nhat Le, Emmanuel Haven https://creativecommons.org/licenses/by-nc-sa/4.0
An Examination of Factors Shaping Students’ Acceptance of Generative AI
Recognizing the proliferation of generative artificial intelligence (AI) technologies in facilitating student learning, this study seeks to explore the key drivers shaping students’ intention to use this technology. Grounded in the Stimulus–Organism–Response (S–O–R) framework, we develop an integrative conceptual model and employ PLS-SEM to analyze data collected from 370 tourism students. The article reveals that peer and family influence exert a profound influence on the formation of usage-related expectations and trust. Meanwhile, anthropomorphism serves as an antecedent in shaping perceived usefulness and trust. Trust and perceived ease of use function as organism variables and significantly influence students’ intention. Importantly, trust emerges as a key driver in forming students’ intention to use generative AI. These findings provide implications for academic institutions in formulating AI adoption strategies to enhance teaching and learning practices.
Copyright (c) 2026 Hang Le Nguyen Tue, Linh Tran Thi My, Dieu Duong Thi Xuan https://creativecommons.org/licenses/by-nc-sa/4.0
Consumer Responses to AI Assistance across the Purchase Journey: A Cognitive – Affective Perspective
Artificial intelligence (AI) has swiftly transitioned from a science-fiction notion to a dominant influence on daily life and commercial operations. The rapid expansion of generative AI, as demonstrated by ChatGPT attaining 100 million users in just two months, underscores its increasing impact on consumer-technology dynamics. AI is anticipated to add as much as USD 15.7 trillion to global GDP by 2030, making its position in retail and digital marketing increasingly critical. AI tools already assist consumers across the purchasing process, from information retrieval to decision-making. This study analyzes the impact of AI tools on consumer reactions throughout the purchasing process by exploring the cognitive (satisfaction) and affective (arousal) mechanisms that determine continuance intention. Using a survey with a 5-point Likert scale, this study employed a quantitative methodology. The survey was conducted online utilizing a convenience sampling method. The sample comprised 421 respondents from Vietnam, aged 18 to 50 years. The respondents are individuals who have previously engaged in AI-assisted purchase transactions. Subsequent to screening, the data were evaluated with the Structural Equation Model (SEM), conducted with SmartPLS 3. The results revealed two simultaneous psychological paths: the cognitive path (satisfaction) and the affective path (arousal). Satisfaction serves as a complementary mediator linking functional attributes to continuation intention, whereas arousal fully mediates the effects of personalization and anthropomorphism. The MGA results indicated that gender differences (male – female) and product type differences (utilitarian – hedonic) influence the formation of cognitive and emotional responses, rather than their translation into behavioral intention.
Copyright (c) 2026 Phuoc H. Nguyen https://creativecommons.org/licenses/by-nc-sa/4.0
Internship as a Catalyst for Workforce Development: A Thematic Synthesis and the Case of “Passage to ASEAN”
Internships are widely promoted as a response to workforce skill gaps, yet they are often examined as isolated educational experiences rather than as components of regional workforce systems. This study addresses this gap by examining how internships can function as ecosystem-level mechanisms for workforce development. Drawing on a comprehensive literature review, the research proposes the Regional Internship Ecosystem Assessment Framework (RIEAF), which integrates institutional capacity, experiential design, and strategic outcomes through a continuous feedback loop. The framework is applied to a qualitative case study of Passage to ASEAN (P2A), using documentary analysis and 18 semi-structured interviews. The findings indicate that P2A supports professional identity formation, technological adaptability, and cross-border employability, demonstrating the potential of regional internship networks to generate meaningful workforce outcomes. However, the transition from student mobility to coordinated workforce deployment remains largely informal in the absence of coordinated institutional mechanisms. The study, therefore, highlights the need for system-level mechanisms that capture and validate internship project portfolios and graduate competencies, enabling coordinated talent deployment and long-term workforce sustainability.
Copyright (c) 2026 Hang Le, Phuong L.A. Bui, Hai Phan-T. H. https://creativecommons.org/licenses/by-nc-sa/4.0
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
Sacred Storytelling in The Tourism Development of The Marble Mountains: Insights from Field Data
This article examines visitor experiences at the Marble Mountains in the context of a gradual decline in the site’s sacred depth. In recent years, field observations suggest that some visitor behaviors at the site appear visually oriented and time-limited, including rapid movement, short stops, and photography-focused interactions, while deeper engagement with cultural and spiritual meanings remains uneven. The study is based on a data collection process carried out continuously over a two-month period (from July to September 2025), with the main on-site fieldwork conducted on 13 September 2025 at nine locations on Thủy Sơn, using participant observation, on-site note-taking, and informal conversations with local stakeholders. Findings indicate that the system of caves, pagodas, and legends at the Marble Mountains contains rich layers of storytelling, while the local community-including monks, vendors, and site staff-naturally assumes the role of “community curators” who maintain and transmit sacred narratives. Based on these insights, the article proposes a sacred storytelling model grounded in living heritage approaches to enhance experiential depth and support sustainable cultural tourism development at the Marble Mountains. The findings also suggest future implications for technology-supported heritage interpretation, in which digital tools may support, rather than replace, community-based sacred storytelling.
Copyright (c) 2026 Ha Pham https://creativecommons.org/licenses/by-nc-sa/4.0
From Bits to Meaning: A Survey of Semantic Communications for 6G Networks
The advent of sixth-generation (6G) networks necessitates a paradigm shift from conventional bit communication to meaning-centric systems capable of semantic understanding, contextual inference, and task-oriented optimization. This survey explores the emerging domain of semantic communications (SemCom), which aims to transmit not just data, but relevant and actionable meaning aligned with user intent. Unlike Shannon’s theory that emphasizes bit-level fidelity, SemCom emphasizes the semantic and pragmatic utility of transmitted information. This introduces a unified taxonomy aligned with 6G system layers and highlights applications in latency-sensitive, mission-critical applications such as autonomous vehicles (AVs), extended reality (XR), digital twins, and remote healthcare. The study identifies open challenges in semantic reliability, multi-agent alignment, green SemCom, and integration with quantum and bio-inspired systems—positioning SemCom as a cornerstone for future 6G networks.
Copyright (c) 2026 James Adu Ansere, Quang Nhat Le, Tuan Thanh Nguyen https://creativecommons.org/licenses/by-nc-sa/4.0
Metrics, Impact, and the Future of Pro-Poor Tourism: Towards a Theory-Driven Evaluation Framework
Pro-poor tourism has long been promoted as a mechanism for poverty alleviation through the integration of marginalised communities into tourism value chains. However, despite extensive policy uptake and academic interest, significant gaps remain in how the impacts of pro-poor tourism are evaluated, particularly in relation to social outcomes, empowerment, and long-term community change. The current paper argues that dominant evaluation approaches, which privilege short-term economic indicators such as income generation and employment, are insufficient to determine whether pro-poor tourism genuinely benefits poorer communities or contributes to meaningful social transformation. Drawing on theories of social innovation, empowerment, social capital, and social impact measurement, the paper critiques existing evaluation practices and highlights their conceptual and methodological limitations. In response, it proposes a theory-driven, multidimensional framework for evaluating pro-poor tourism that moves beyond narrow economic metrics to incorporate wellbeing, agency, participation in decision-making, improved digital inclusion and community cohesion. By positioning pro-poor tourism as a form of social innovation, the paper reframes impact measurement as both an evaluative and empowering process, capable of capturing complex social change over time, supported where possible by integrative digital tools. The proposed framework offers scholars, policymakers, development agencies, and tourism practitioners a more holistic and ethically grounded approach to understanding pro-poor tourism’s outcomes, thereby strengthening its potential as a tool for equitable and sustainable tourism development.
Copyright (c) 2026 Richard Hazenberg, Phuong Bui Le Anh 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
Slow Food Tourism In Vietnam: Unlocking Opportunities And Overcoming Challenges
This research explores the concept of Slow Food Tourism (SFT) and examines the opportunities and challenges for its development in Vietnam. Grounded in the principles of slow philosophy and sustainability, the SFT emphasizes the active participation of tourists, farmers, and local communities. Drawing on a review of existing literature and case studies, this study identifies Vietnam's rich culinary heritage and traditional agriculture as key assets for SFT development. However, significant challenges, such as limited infrastructure, insufficient policy support, and a lack of awareness and training among stakeholders, hinder their potential. The paper concludes with recommendations for leveraging Vietnam's unique strengths, while addressing these barriers to promoting SFT as a sustainable tourism model.
Copyright (c) 2026 Ngoc Thu Vu, Long Pham Hong 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