• Asia-Pacific Journal of Business
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• v.11 no.3
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• pp.261-271
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• 2020

Purpose - This study was to establish a unified tourism management class for university students based on Gangwon-do. Community based learning(CBL) was applied to provide a tangible and intangible resource of tourism resources the theoretical approaches and the actual experiences of the community. Design/methodology/approach - In order to design a unified tourism management module, this study applied qualitative research and quantitative research methods to collect information on the direction of the module. the study conducted in-depth interviews and then an online survey. Findings - According to the results of the study, the main parts should include necessity of unification, inter-Korean tourism, inter-Korean cooperation, inter-Korean economy, and international relations. Research implications or Originality - The overall composition of the unification tourism management class should be designed as the unification tourism management theory to acquire the subject knowledge, the field trip to the border area for experiential learning, and the assignment of the field study task to understand the community.

• Journal of Digital Convergence
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• v.13 no.9
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• pp.243-250
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• 2015

Mobile learning content implementing is IT tourism convergence study that IT convergence IT and tourism. Learning to increase the effectiveness of mobile learning content for each learning module, It proposed u-smart tourist information 2.0 systems. Mobile learning content, implementation is u-smart tourist information 2.0 can use the system. Convergence/integration of design patterns and XML is so interesting to students. This is the maximum benefit which is taught classes for each learning module divided into learning the Design Pattern NCS. As a result, the learner. In particular, attendance has come out better the effect of learning and improved. Another advantage is tourism, information content information quality mobile learning content for and construct a tourist information content that you can do in real time. Also, mobile learning content, implementation in the next NCS expected to use a lot of help in learning. This study is the result of increased learning the analysis of the lessons learned. Implement mobile learning content gives fun and interesting to the learner to ten design process using the u-Smart Tourist Information class 2.0.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2031-2048
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• 2021

Not only can air pollution reduce the overall competitiveness of tourist destinations, but also changes tourists' travel decisions, thereby affecting the tourism flows. The study presents a machine learning method to analyze how the haze pollution puts spatial effect on tourism flows in China from 2001 to 2018, and reveals the regional differences in heterogeneity among eastern, central, and western China. Our investigation reveals three interesting observations. First, the Environmental Kuznets Curve of the impact of haze pollution on tourism flows is not significant. In the eastern and western regions, the interaction between haze pollution and domestic tourism flows as well as inbound tourism flows shows an inverted U-shaped curve respectively. Second, there is an significantly positive spillover effect of tourism flows in all of the eastern, central, and western regions. As to the intensity of spillover, domestic tourism flows is higher than that of the inbound tourism flows. Both of the above figures are greatest in the eastern. Third, the Chinese haze pollution mainly reduces the inbound tourism flows, and only imposes significantly negative direct effects on the domestic tourism flows in the central region. In the central and eastern regions, significantly negative direct effects and spillover effects are exerted on inbound tourism.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1162-1181
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• 2023

Tourism flow is not only the manifestation of tourists' special displacement change, but also an important driving mode of regional connection. It has been considered as one of significantly topics in many applications. The existing research on tourism flow prediction based on tourist number or statistical model is not in-depth enough or ignores the nonlinearity and complexity of tourism flow. In this paper, taking Nanjing as an example, we propose a prediction method of urban tourism flow based on deep learning methods using travel diaries of domestic tourists. Our proposed method can extract the spatio-temporal dependence relationship of tourism flow and further forecast the tourism flow to attractions for every day of the year or for every time period of the day. Experimental results show that our proposed method is slightly better than other benchmark models in terms of prediction accuracy, especially in predicting seasonal trends. The proposed method has practical significance in preventing tourists unnecessary crowding and saving a lot of queuing time.

• Journal of Platform Technology
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• v.11 no.6
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• pp.21-27
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• 2023

In this paper, we introduce a reinforcement learning-based algorithm for personalized tourist path recommendations. The algorithm employs a reinforcement learning agent to explore tourist regions and identify optimal paths that are expected to enhance tourism experiences. The concept of tourism experience is defined through points of interest (POI) located along tourist paths within the tourist area. These metrics are quantified through aggregated evaluation scores derived from reviews submitted by past visitors. In the experimental setup, the foundational learning model used to find tour paths is the Deep Q-Network (DQN). Despite the limited availability of historical tourist behavior data, the agent adeptly learns travel paths by incorporating preference scores of tourist POIs and spatial information of the travel area.

• Journal of Industrial Convergence
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• v.19 no.1
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• pp.53-63
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• 2021

As tourists travel to other regions, they encounter numerous facts that conflict with their views. At that time, we change our view of coping with life. In this respect, tourism is a new way of learning. As a new learning method, tourism experience research is a new approach. In this study, the relationship between experience and learning experienced in tourist destinations was analyzed by applying the activity theory. The analysis units applied in the activity theory were set as subjects, goals, communities, roles, methods and rules, outcomes, and relevance to local communities. Based on this, in-depth interviews were conducted with commentators and guides who had a great influence on the tourism experience to analyze the learning process of tourists. As a result of the analysis, the experiences of tourists during the tour were interactive in various forms within the unit as well as the unit of the activity system of the commentator and guide. This interaction induces changes in the tourism experience activity system, enabling tourists to learn. The content is that the value of learning increases as the role of guide and commentator increases, that the social and cultural dimension of tourism experience is included in the learning effect, and the contradictions that arise from interactions within or between activity systems. The fact that they find the solution process themselves, and that tourism activity is not an isolated unit, but exists at the intersection of hierarchies and networks, is affected by the activities and environments of others.

• Journal of Information Science Theory and Practice
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• v.12 no.1
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• pp.21-38
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• 2024

Infographics are influential and valuable communication tools for providing information, and can be used for promoting cultural learning resources and cultural tourism destinations. Therefore, this article presents values of using infographics for promoting Patum's culture and tourism in Phrao District, Chiang Mai, Thailand as cultural learning resources and tourist attractions. Employing a research and development approach, this study utilized three distinct instruments: (1) an interview form engaging 40 locals to uncover insights on promoting Patum village, (2) an assessment form evaluated by three arts and design experts, and (3) questionnaires distributed to 92 participants to gauge perceptions and satisfaction. The findings showcased the high quality and appropriateness of the eight infographics. Audiences derived six key values, including acquiring knowledge, fostering the promotion of Patum's culture, festivals, religions and beliefs, deriving aesthetic enjoyment, encouraging cultural tourism, contributing to the education sector, and enhancing comprehension of Patum's history. Participants expressed high satisfaction ($\bar{x}$=4.46) with the infographic use. The developed infographics are usable and valuable information to help audiences recognize Patum cultural learning resources and tourist destinations. They might be further tailored to the recognition of Patum village in the near future, affecting the area's development by increasing local people's incomes through cultural learning resources and tourism activities.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.1
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• pp.55-60
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• 2020

Adaptive recommendation systems have been developed with big data processing as a system that provides services tailored to users based on user information and usage patterns. Deep learning can be used in these adaptive recommendation systems to handle big data, providing more efficient user-friendly recommendation services. In this paper, we propose a system that uses deep learning to categorize and recommend tourism types to suit the user's personality. The system was divided into three layers according to its core role to increase efficiency and facilitate maintenance. Each layer consists of the Service Provisioning Layer that real users encounter, the Recommendation Service Layer, which provides recommended services based on user information entered, and the Adaptive Definition Layer, which learns the types of tourism suitable for personality types. The proposed system is highly scalable because it provides services using deep learning, and the adaptive recommendation system connects the user's personality type and tourism type to deliver the data to the user in a flexible manner.

• Journal of Agricultural Extension & Community Development
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• v.22 no.3
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• pp.245-260
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• 2015

Since the Discussion on responsible tourism sector began in the 1980s, the interest in responsible tourism has increased. Responsible tourism aims to preserve the local culture and environment, and make the benefits return benefits to local stakeholders. This study aims to obtain an empirical understanding of the responsible tourism market by using a segmentation approach to provide better information for responsible tourism marketers in Korea. A self-administered survey was obtained from 471 tourists in seven responsible tourism sites. As for the motivations of responsible tourism, seven factors ('faimly togetherness', 'escape relaxation', 'personal growth', 'social interaction', 'various experience', 'learning' and 'natural experience') were extracted. Six distinct segments were identified based on the motivation: escape from daily life relaxation seekers (19.15%), overall low motivation (7.8%), family togetherness seekers (21.18%). various experience seekers (12.77%), noverlty learning seekers (22.46%) and want-it-all (16.55%). Socio-demographic characteristics and tourism behaviors of each segmentation were also analyzed. The findings should be of interest to practitioners of responsible tourism marketing and operation.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.1
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• pp.61-68
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• 2021

The development of the fourth industry has enabled users to quickly share a lot of data online. We can analyze big data on information about tourist attractions and users' experiences and opinions using artificial intelligence. It can also analyze the association between characteristics of users and types of tourism. This paper analyzes individual characteristics, recommends customized tourist sites and proposes a system to provide the sacred texts of recommended tourist sites as AR services. The system uses machine learning to analyze the relationship between personality type and tourism type preference. Based on this, it recommends tourist attractions according to the gender and personality types of users. When the user finishes selecting a tourist destination from the recommendation list, it visualizes the information of the selected tourist destination with AR.