Smart Tourism Research Articles

Untangling the smart tourism technostress enigma: assessing the effects of smart technology-related stressors on smart tourism avoidance behaviors in senior tourists

ABSTRACT This study employs the Stressor-Strain-Outcome theory and investigates the influence of senior tourist’s technostress, travel stains, and their avoidance behaviors towards smart tourism technologies (STTs). Results indicated that four distinct types of smart tourism technostressors have significant impacts on the smart tourism avoidance behaviors of senior tourists, and these direct relationships were mediated by travel anxiety, travel fatigue, and travel technophobia. Furthermore, Smart device literacy moderates the construction of smart tourism avoidance behavior. These findings deepened the understanding of the detrimental effects of STTs among senior tourists, with practical implications for STTs acceptance, development, and sustainability.

Regulation mechanisms of smart tourism development in Kazakhstan

The process of digitalization within the realm of tourism is not merely a trend but rather a significant catalyst that is rapidly propelling the comprehensive transformation of the tourism industry into a new era of technological advancement. This intricate process fundamentally involves the seamless integration and application of cutting-edge digital technologies across various tourism-related activities and services. The advent of innovative solutions that harness the immense capabilities of artificial intelligence, the analytical power of big data, the security features of blockchain, and the interconnectedness provided by the Internet of Things primarily serves to enhance the overall quality of services offered, optimize pricing strategies to align with market demands, and improve risk management protocols within the industry. This paper methods uses 100 Scopus indexed papers about Smart Tourism Development in Kazakhstan. It is imperative to underscore the fact that the ongoing digitalization process, while offering numerous advantages, simultaneously imposes rigorous new requirements concerning the qualifications and competencies of staff members, as well as the paramount importance of data security measures and the protection of consumer rights in the digital environment. The effective management of this digital transformation necessitates a holistic and integrated approach that encompasses not only the development of robust infrastructure but also the enhancement of digital literacy among employees and the establishment of a dynamic and innovative ecosystem that encourages creativity and adaptability.

Content Analysis of Bibliometric Studies Prepared About Smart Tourism

Smart tourism has become one of the most researched topics in recent years. There has been an increase in studies examining the main trends, bibliometric structures and networks, and social, intellectual, and conceptual frameworks of the concept of smart tourism within different parameters. The aim of this study is to evaluate bibliometric analysis studies on smart tourism indexed in WoS and Scopus databases by content analysis. In this context, 48 documents were found in the WoS and Scopus databases under the subject headings of "smart tourism" and "bibliometric analysis". This is the first study using content analysis to analyze bibliometric articles on smart tourism in WoS and Scopus databases. These analyses mainly focus on authors' analysis, articles' distribution according to publication years, country collaboration network, co-citation networks of authors and journals, frequency of keywords, and co-occurrence map analysis. Bibliometric analysis studies on smart tourism have gained momentum, especially after 2019, when most studies were conducted in the field. When the keywords of the studies in WoS are analyzed, it is determined that the most frequently repeated keyword is "bibliometrics" in 14 studies, followed by "smart destination" and "smart tourism destination" in 7 studies each, and "smart tourism" in 3 studies. These results show that most of the bibliometric studies in WoS focus on smart destinations.

Green showcases as a model for the conceptual re-signification of smart tourist destinations applied to the Amazon

This article presents the main functionalities of an autonomous device named S.A.N.D.R.O., powered by solar energy and featuring minimalist computational technology based on the Raspberry Pi. This device was originally conceived to enable the continuity of distance education in regions devoid of connectivity, such as many riverside localities in the Amazon. It became central in equipping schools with the purpose of remotely providing educational resources in crisis situations, as occurred in the year 2020 with the COVID-19 pandemic. These resources can be accessed through smartphones or kiosks, given that they fully replicate the modus operandi of the Internet, offering users a familiar experience despite the lack of actual internet connectivity. Beyond these adverse periods, one of the possible uses for the artifact is to enhance the ecotourism experience in the region, thereby contributing to transforming it into a Smart Tourist Destination. By adopting an innovative approach that reduces costs for consumers, the device can also be classified as a form of frugal innovation. This classification stems from its minimalist design and cost-effective technology, making advanced services accessible to communities and tourists in remote areas without requiring significant financial investment.

Electronic Ticketing System for Monuments and Museums

Being one of the largest networks of ticketing systems operating over 1,015,000 km and transporting over 22 million visitors daily, e-ticketing systems spend over CR 94,000 to operate efficiently. From the recent proposal for smart cities, ticketing systems are projecting to museums and cultural monuments with the current trend towards digitization, smart ticketing systems are the most of the hour. Of the 94,000 kr. is an exorbitant salary of 23,500 kr. spent on paper that can be subsidized using effective alternative techniques. The smartphone, which has a profound effect on people's daily routine, can be used for paperless ticketing. In today's digital era, the way we access and experience cultural heritage sites, such as museums and monuments, is evolving. Traditional paper tickets and long queues are fast becoming outdated, replaced by more efficient and user-friendly digital solutions. This project explores the development of a Mobile based ticketing system that simplifies the ticket purchase process for visitors, provides easy entry verification through QR codes, and enhances security with biometric authentication. Beyond improving the visitor experience, this system helps site administrators manage visitor flow, protect against fraud, and even reduce environmental impact by going paperless. The application’s aim is to create a seamless and enjoyable journey for visitors—from purchasing tickets to entering the site—while providing insightful data for site managers on visitor patterns and peak hours. This digital transformation reflects the larger trend of integrating technology into our everyday experiences, making heritage sites more accessible, sustainable, and enjoyable for everyone. Key Words: e-ticketing system, digital ticketing ,QR code verification, biometric authentication, visitor management, cross-platform application, flutter framework, paperless ticketing, mobile ticketing solution, cultural heritage sites, museum access control, visitor analytics, crowd management ,secure ticketing system, smart tourism technology

Demystifying the Use of Open-Access Data in Smart Heritage Implementations

Smart Heritage, a concept closely linked to Smart Cities and Smart Tourism, is an emerging field focused on enhancing heritage identity, visitor experience, and cultural sustainability. While initial frameworks have been developed, there is a gap in applying Smart Heritage at the precinct level, especially in large-scale heritage sites. This study addresses this gap by examining how open-access data can be utilised in a real-world case study of Chinatown Melbourne, a key urban heritage precinct. Data sources include archival maps, open-access databases, and 3D models provided by the local city council, covering resources such as on-street parking, pedestrian activity, microclimate, and dwelling functionalities. This study employed a structured methodology that transitions from global best practices to local applications, linking these data resources to Smart Heritage applications and identifying opportunities for improving urban management, heritage curation, and the tourism experience within the case study precinct. The findings offer practical insights for researchers and policymakers, demonstrating how data can support the development of culturally sustainable and technologically integrated heritage precincts. Future research should explore additional data types and case studies to further advance the field of Smart Heritage.

Overseas Smart City Short Course of Airlangga and International Community Service: The Role of Science and Technology Towards Smart City

The smart city concept emerged as an alternative solution to overcome the negative impacts of urbanization in big cities, based on the concept of sustainable development. A smart city is a city that can utilize human resources, social capital, and modern telecommunications infrastructure to achieve sustainable economic growth and high quality of life through wise management of resources by the government based on community participation. Jombang has not been managed optimally due to a lack of digital exposure and promotion. Based on these problems, the Faculty of Science and Technology, Airlangga University will organize an international community service program in Jombang to introduce culture and tourism in Jombang to the world through international students and transfer knowledge regarding the application of smart tourism to increase tourism promotion in Jombang. This community service program was carried out together with international students who will first be given an introduction and knowledge of the smart city concept and its application at Universitas Airlangga and the City of Surabaya through a short course. Then the students will be invited to visit the tourist and cultural village in Jombang to transfer knowledge to the local community regarding the application of the smart tourism concept to promote tourism potential in Jombang.

Construction of Smart Tourism System Integrating Tourist Needs and Scene Characteristics

The demand for travel is increasing as human living conditions rise. The paper presents a smart tourism system architecture that incorporates visitors' demands and scenario characteristics, and performs path planning using path search algorithms and selective tour path recommendation algorithms, in order to improve tourists' travelling experiences and save them time. The experimental data showed that the enhanced heuristic search algorithm visited 122 nodes, which is 62.9% and 52.3% less than the sparrow search algorithm and the improved genetic search strategy, respectively. The number of iterations required to reach convergence for the selective tour path recommendation algorithm, genetic algorithm, discrete particle swarm algorithm, and genetic particle swarm algorithm, respectively, was 39, 90, 85, and 63, indicating that the proposed selective tour path recommendation algorithm has the fastest computational speed. The accuracy, stability, user satisfaction, and overall rating of the smart tourism system that integrates tourists' needs and scenario characteristics are all higher than those of the three types of tourism systems, such as the iBeacon Smart Tourism System, indicating that this smart tourism system is the best to use, helping to enhance tourists' experiences and promote the robust development of the tourism industry.

Big Data in Tourism: A Bibliometric Analysis (2014-2024)

The emergence of big data and its related technologies has brought about novel economic models, industry phenomena, and relational networks, instigating revolutionary changes with significant value for tourism sustainability. This study conducts a bibliometric analysis of 212 articles (2014-2024) on big data in tourism from the Web of Science (WoS) Core Collection database, aiming to create a knowledge map based on big data in tourism. This study utilizes VOSviewer software to carry out citation analysis, co-citation analysis, co-authorship analysis, and keyword co-occurrence analysis, revealing trends in publications, national contributions, influential journals and authors, author collaborations, as well as the conceptual structure and research trends in the field of big data in tourism. The findings indicate a concentration of research in seven areas: machine learning, social network analysis, sustainability, tourism demand forecasting, artificial intelligence, smart tourism, and text mining techniques. The research has focused on emerging hot topics since 2022, including destination image, COVID-19, topic modeling, and urban tourism. This study maps the knowledge of big data in tourism, elucidates the academic evolution in this field, and offers future research directions for scholars in the domain.

The Influence of Smart Tourism Technologies on Tourist Engagement, Memorable Experience, and Destination Loyalty: The Moderating Role of Place Identity

Smart tourism technologies (STTs) are essential for destinations to be competitive. While this encourages researchers to understand how STTs impact important factors in destinations, there remain gaps in the existing research. Therefore, this study aims to examine how and when STTs influence destination loyalty, considering different roles of tourist engagement, memorable tourism experience (MTE), and place identity. The information provided by 393 tourists confirmed that STTs significantly influence tourist engagement, MTE, and destination loyalty, and two variables—tourist engagement and MTE—mediate the relationship between STTs and destination loyalty. Moreover, place identity moderates the relationship between STTs and tourist engagement.

Smart tourism transformation of the Şi̇le district: strategic aspects through a SWOT analysis

PurposeThis study aims to evaluate the smart destination potential of the Şile district by identifying its strengths, weaknesses, opportunities and threats related to smart city applications.Design/methodology/approachA comprehensive SWOT analysis was conducted using data from participants and relevant documentation to assess the feasibility of smart tourism initiatives in Şile.FindingsThe analysis reveals that Şile, due to its proximity to Istanbul, has significant potential to develop into a smart tourism destination. Key strengths include its natural attractions, while challenges revolve around infrastructure limitations.Research limitations/implicationsThe study is limited to the Şile district and may not be generalizable to other regions. Future research could expand the scope to include comparative studies with other potential smart tourism destinations.Originality/valueThis research contributes to the growing body of literature on smart tourism by offering strategic recommendations tailored to the unique context of Şile, a district with significant but underutilized potential.

Navigating overtourism destinations: Leveraging smart tourism solutions for sustainable travel experience

<p>This paper explores the integration of artificial intelligence (AI) and smart tourism technologies to address the multifaceted challenges of overtourism in tourist destinations. Overtourism leads to significant environmental degradation, strains local resources, and diminishes visitor satisfaction, ultimately threatening the sustainability of these destinations. Destinations can enhance sustainability and tailor travel experiences by utilizing cutting-edge technologies like the Internet of Things (IoT), augmented reality (AR), virtual reality (VR), and big data analytics. These technologies offer innovative solutions to optimize resource use, enhance visitor engagement, and promote responsible tourism practices. The study underscores the potential of smart tourism technologies to manage tourist flows, reduce environmental impacts, and improve the overall quality of life for local communities. Strategic planning, substantial investment, and collaboration among various stakeholders, including government agencies, local communities, and the private sector, are essential for the successful implementation of these solutions. The findings highlight the critical role of technological innovation in fostering a balanced and sustainable future for tourism, ensuring that destinations can thrive economically while preserving their cultural and natural heritage. This research provides valuable insights for policymakers and tourism managers aiming to mitigate the adverse effects of overtourism through the adoption of AI and smart tourism technologies.</p>

Research on Jiangxi Cultural Wisdom Tourism Industry Strategy Based on OHDSI General Data Model and Digital Art Empowerment

With the rapid development and wide application of a new round of information technologies, such as the global Internet of Things, the new generation of mobile Internet, cloud computing, etc., informatization has opened a new stage of smart tourism (ST), and ST has become the main trend of tourism development in China at present and for a long time to come. Tourism industry has become one of the important pillar industries in Jiangxi, and it plays an increasingly prominent role in stimulating and supporting economic and social development. Wisdom tourism is an important means to improve tourism service, tourism management and tourism marketing, and an important starting point to promote the upgrading of tourism industry in Jiangxi Province, and to achieve carry-over and leapfrog development and overtaking at corners. This paper is constantly improving and updating on the basis of data warehouse integration mechanism. Under the data warehouse mechanism, through the effective integration of spatial data, virtual reality data and dynamic data, a comprehensive data warehouse oriented to tourism promotion, scenic spot management and department management is formed. It aims to promote the rapid implementation of ST, bring tourists a comfortable tourism experience, save costs and improve efficiency for tourism service providers.

How Artificial Intelligence (AI) Is Powering New Tourism Marketing and the Future Agenda for Smart Tourist Destinations

Artificial intelligence (AI) is a disruptive technology that is being used by smart tourist destinations (STDs) to develop new business models and marketing services to increase tourists’ experiences and sales, revenue, productivity, and efficiency and STDs. However, the adoption of AI applications and platforms requires a high economic budget for STDs that want to integrate this digital tool into their future agenda and tourism development plans, especially when they set them up for marketing plans and operational processes. This iterative technology needs regular maintenance as well, leading to recurring costs and specialised crews in advanced technologies and marketing activities. This study aims to show the impact of AI advancements on STDs’ tourism marketing to enhance the quality of services and illustrate their future agenda to improve tourists’ experiences. A comprehensive literature review on AI technology and STDs has been conducted to illustrate new tourism marketing in their future agenda. Moreover, this study presents real examples of AI technology in a tourism context to better understand the potential of this digital tool. The findings of the current study support the idea that AI is a multipurpose tool that helps manage, monitor, and analyse sales information; revenue management; minimise prediction errors; streamline operations; and develop better marketing strategies, optimising economic resources, reducing marketing costs, and responding dynamically to changing needs for tourists and residents in STDs. Furthermore, the investment in AI technologies by STDs helps enhance the quality of products and services, and attract new investments, which benefit the regional economies and population’s quality of life. This study is the first to address the use of AI to improve tourist marketing in STDs, which is its primary uniqueness. Also, this study identifies new opportunities and initiatives through AI that can be developed to help tourism marketing in STDs.

Exploring the impact of artificial intelligence technologies on tourists’ smart experiences: the moderating role of emotional arousal level

ABSTRACT This study aims to explore how Artificial Intelligence (AI) technologies affect tourists’ smart experiences and the role of emotional arousal level in this relationship. Drawing from the appraisal theory of emotion, a conceptual framework is proposed to test hypotheses about the connections between AI technologies, emotional arousal level, and tourists’ smart experiences. This study utilized an online survey to collect 301 questionnaires from tourists who experienced AI technologies in four Chinese smart tourism destinations. The results obtained through the structural equation modeling method suggest that four factors – interaction, personalization, co-creation, and privacy – positively impact tourists’ smart experiences. Additionally, the study finds that tourists’ emotional arousal level is a moderator between AI technologies and tourists’ smart experiences. The findings provide theoretical and practical implications for smart tourism destinations and researchers to use AI technologies to enhance tourists’ smart experiences at smart tourism destinations.

Conclusion: sustainability, smart tourism destinations and evolving visitor preferences

PurposeThe primary aim of this themed issue is to highlight the incorporation of sustainability principles in the progress of tourism and the monitoring of tourists' evolving preferences, all within the sustainability framework and smart tourism destinations. The strategic question underlying this theme issue is how can we interpret, relate and monitor the rapidly evolving visitor preferences within sustainability and smart tourism destinations?Design/methodology/approachThis issue’s critical outcomes as they relate to the strategic question are reviewed, with reference to the contributions made to knowledge, policy and practice and the implications for future applied research concerning sustainability, smart tourism destinations and evolving visitor preferences.FindingsArising from the analysis and synthesis of recommendations, the following themes were proposed as critical concentration points for rapidly evolving visitor preferences within sustainability and smart tourism destinations: as in all areas of life, sustainability will continue to be a prominent concept in the tourism sector. Smart solutions and the use of technology have also been identified as determining factors in creating sustainable destinations. On the other hand, changing and evolving tourist preferences are emerging as a driving force for smart destinations and sustainability. Tourism destinations must take steps toward sustainability by considering the changing visitor structure, evolving demands and needs. Some articles in this special issue also analyze tourist demands and sustainability efforts of destinations in specific destinations and provide invaluable insights.Originality/valueAs it is essential for all stakeholders of the tourism industry to achieve competitiveness while securing sustainability, this particular article suggests ways for the industry as a whole, its players, destination administrations, local and national policymakers and other major stakeholders of the tourism industry to address some issues faced by the industry. Factors such as changing customer structure, use of technology and increasing environmental awareness make it necessary for destination management and all players individually to take vital measures. This paper and the collection of articles in this thematic issue offer crucial insights for academics, professionals and decision-makers in creating a complete framework to assess, connect and regularly track changing visitor preferences. The papers include recommendations for creating robust tourism locations that effectively tackle challenges, promote inclusion and prepare for future advancements. This is achieved by examining the convergence of sustainability, smart tourism and changing tourist tastes.

Antecedents the adoption of tour itineraries from smart travel apps: an integration of experiential consumption theory and innovation resistance theory

Purpose The purpose of this study investigate the antecedents the adoption of tour itineraries from smart travel apps. Travelers are progressively expanding their smart travel planning applications to organize their trip-related activities. With the help of these apps, users achieve their favorite tour itineraries and choose their preferred destinations. Design/methodology/approach This study aimed to examine the results of smart tour itineraries on travelers and elucidate the motivations for their continual use and why travel experts are increasingly using smart tour itineraries. Innovation resistance and experiential consumption theories were used in this study. SmartPLS 3.2.8 was used to consider 682 valid samples using structural equation modeling (SEM). Findings This analysis identified the following crucial factors: usage, value, risk and traditional barriers. Moreover, utilitarian and hedonic values significantly affected barriers. Finally, theoretical and practical suggestions are presented along with future research directions. Originality/value This study encompasses the tender of innovation resistance theory to travel itineraries by integrating experiential consumption theory in the context of smart tourism apps.

Spatial layout optimization model integrating layered attention mechanism in the development of smart tourism management

Tourism demand projection is paramount for both corporate operations and destination management, facilitating tourists in crafting bespoke, multifaceted itineraries and enriching their vacation experiences. This study proposes a multi-layer self attention mechanism recommendation algorithm based on dynamic spatial perception, with the aim of refining the analysis of tourists’ emotional inclinations and providing precise estimates of tourism demand. Initially, the model is constructed upon a foundation of multi-layer attention modules, enabling the semantic discovery of proximate entities to the focal scenic locale and employing attention layers to consolidate akin positions, epitomizing them through contiguous vectors. Subsequently, leveraging tourist preferences, the model forecasts the likelihood of analogous attractions as a cornerstone for the recommendation system. Furthermore, an attention mechanism is employed to refine the spatial layout, utilizing the forecasted passenger flow grid to infer tourism demand across multiple scenic locales in forthcoming periods. Ultimately, through scrutiny of data pertaining to renowned tourist destinations in Beijing, the model exhibits an average MAPE of 8.11%, markedly surpassing benchmarks set by alternative deep learning models, thereby underscoring its precision and efficacy. The spatial layout optimization methodology predicated on a multi-layer attention mechanism propounded herein confers substantive benefits to tourism demand prognostication and recommendation systems, promising to elevate the operational standards and customer contentment within the tourism sector.

Enabling personalized smart tourism with location-based social networks

With the rapid advance of mobile internet, communication technology and the Internet of Things (IoT), the tourism industry is undergoing unprecedented transformation. Smart tourism offers users personalized and customized services for travel planning and recommendations. Location-based social networks (LBSNs) play a crucial role in smart tourism industry by providing abundant data sources through their social networking attributes. However, applying LBSNs to smart tourism is a challenge due to the need to deal with complex multi-source information modeling and tourism data sparsity. In this article, to fully harness the potential of LBSNs using deep learning technologies, we propose an knowledge-driven personalized recommendation method for smart tourism. Representation learning techniques can effectively modeling the contextual information (e.g., time, space, and semantics) in LBSNs, while the data augmentation strategy of contrastive learning techniques can explore user personalized travel behaviors and alleviate data sparsity. To demonstrate the effectiveness of the proposed approach, we conducted a case study on trip recommendation. Furthermore, the patterns of human mobility are revealed by exploring the effect of contextual data and tourist potential preferences.

Design and Optimization of Tourism Information Management System Based on Artificial Intelligence

This study aims to implement a tourism information management system based on the concept of Smart Tourism. As the people's living conditions have improved, the traditional model of tourism management failed to match the customers' demands, because it has some flaws such as low efficiency. Integrating AI with tourism management helps to meet clients' needs with smarter and more comfortable accommodations before, during, and after their trip. This study optimizes the artificial neural network (ANN) by utilizing long short-term memory (LSTM) to address its inherent faults, such as delayed convergence, easy oscillation, and falling into local minima. The experimental findings demonstrate that this algorithm achieves a recall rate of 99.6% and a prediction accuracy of 98.2%. This algorithm offers some useful recommendations for tourist management while avoiding the pitfalls of more conventional algorithms. In addition, this study extends the idea of "smart tourism" to particular forms of tourist informatization, which has the potential to boost the tourism industry's overall development level by facilitating structural upgrades and transformations.