• Journal of Smart Tourism
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• v.1 no.2
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• pp.3-12
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• 2021

The interaction between technology and tourism has been a dynamic research area recently. This study aims to review the progress and development of technology in tourism research via a bibliometric analysis. We derive the source data from the Web of Science (WoS) core collection and use CiteSpace for bibliometric analysis, including countries, institutions, authors, categories, references, and keywords. The analysis results are as follows: i) The number of published articles on the role of technology in tourism has increased in recent years. ii) Technology-related articles in tourism are abundant in Tourism Management, Journal of Travel Research, and Annals of Tourism Research. iii) The countries with the most contributions are China, the US, and the UK. The most active institutions are the Hong Kong Polytechnic University, University of Central Florida, Bournemouth University, University of Queensland, and Kyung Hee University. iv) The reference analysis results identify eight extensively researched topics from the most cited papers, and the keyword burst analysis results present an emerging trend. This study identifies the effect and development of technology in tourism research. Our findings provide implications for researchers about the current research focus of technology and the future research trend of technology in the tourism field.

• Asia-Pacific Journal of Business
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• v.12 no.2
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• pp.161-174
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• 2021

Purpose - The purpose of this study was to examine the recent popular consumption trend, the hocance phenomenon, using social media big data. The study intended to present practical directions and marketing measures for the recovery and growth of the hotel industry after COVID-19 pandemic. Design/methodology/approach - Big data analysis has been used in various fields, and in this study, it was used to understand the hocance phenomenon. For three years from January 1, 2018 to December 31, 2020, we collected text data including the keyword 'hocance' from the blog and cafe of NAVER and Daum. TEXTOM and UCINET 6 were used to collect and analyze the data. Findings - According to the results of analysis, the words such as 'hocance', 'hotel', 'Seoul', 'travel', 'swimming pool', 'Incheon', 'breakfast', 'child' and 'friend' were identified with high frequency. The results of CONCOR analysis showed similar results in all three years. It has been confirmed that 'swimming pool', 'breakfast', 'child' and 'friend' are important when deciding on the hocance package. Research implications or Originality - The study was differentiated in that it used social media big data instead of traditional research methods. Furthermore, it reflected social phenomena as a consumption trend so there was practical value in establishing marketing strategies for the tourism and hotel industry.

• Journal of Smart Tourism
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• v.4 no.3
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• pp.9-21
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• 2024

Space tourism is a growing industry sector that faces challenges of cost, risk, environmental impact, and sustainability. However, few studies address space tourism in an Asian culture, particularly in the context of artificial intelligence (AI), which is an increasingly significant topic b oth in the tourism sector and in society overall. To address the research gap, this work establishes an analytical framework which contrasts t hree varieties of space tourism using partial least squares, multi-group analysis, and fuzzy-set Qualitative Comparative Analysis. It surveyed 1,000 prospective space travelers from South Korean who are eager to take part in space to urism to examine AI's role in enhancing sustainable space tourism. Findings indicate that recognizing AI benefits are crucial for sustainable on-Earth, suborbital, and orbital space tourism, particularly the latter. The study offers both conceptual and applied knowledge to enhance the sustainability of space tourism.0000

• Culinary science and hospitality research
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• v.23 no.7
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• pp.1-10
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• 2017

This study proposed SNS big data analysis method of food service industry in the 4th industrial revolution. This study analyzed the keyword of the fourth industrial revolution by using Google trend. Based on the data posted on the SNS from January 1, 2016 to September 5, 2017 (1 year and 8 months) utilizing the "Social Metrics". Through the social insights, the related words related to cooking were analyzed and visualized about attributes, products, hobbies and leisure. As a result of the analysis, keywords were found such as cooking, entrepreneurship, franchise, restaurant, job search, Twitter, family, friends, menu, reaction, video, etc. As a theoretical implication of this study, we proposed how to utilize big data produced from various online materials for research on restaurant business, interpret atypical data as meaningful data and suggest the basic direction of field application. In order to utilize positioning of customers of restaurant companies in the future, this study suggests more detailed and in-depth consumer sentiment as a basic resource for marketing data development through various menu development and customers' perception change. In addition, this study provides marketing implications for the foodservice industry and how to use big data for the cooking industry in preparation for the fourth industrial revolution.

• International journal of advanced smart convergence
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• v.13 no.3
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• pp.306-311
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• 2024

In today's globalized economy, franchise companies are strategically preparing to expand beyond domestic markets into international markets. When expanding overseas, it is crucial that the brand identity of a franchise company is well established. Through marketing activities, the brand's value must be enhanced to build a positive image of the brand, and all these activities are referred to as brand management. This study aimed to analyze the relationship between brand management and international expansion, utilizing big data analysis techniques with Textom. A total of 31,564 pieces of data were collected for the period from January 1, 2024, to May 1, 2024, and analyzed after undergoing a refinement process. The analysis results showed that brand management is an essential element in the strategic process of international expansion, and subsequent studies should focus on qualitative research

• International journal of advanced smart convergence
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• v.13 no.3
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• pp.117-124
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• 2024

ESG is currently a global topic, meaning environmental, social, and governance, which are three important measures of socially responsible management. It is also having a great influence on improving competitiveness in the global market and enhancing corporate image. In this study, ESG in Korea was analyzed through big data, and four central keywords of ESG management in China based on Chinese data were derived. These four keywords are environment, management, corporate event, and quality certification. In addition, we want to understand the ESG perspective of China by studying ESG cases in China. Through this, we will be able to compare and analyze the differences between ESG approaches and key points between Korea and China.

• The Journal of the Convergence on Culture Technology
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• v.6 no.1
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• pp.413-418
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• 2020

The travel pattern of tourists for the development of the tourist course is designed to collect and analyze tourist information based on the big data of the carrier to improve the quality of the tourist course. In particular, the analyzed data is used to derive empirical data that can estimate the effect of tourists' inflow into tourism, and to utilize the information as basic data for the development of the tourist course. In addition, the travel pattern of tourists for the development of regional tourism courses is to collect and analyze information on the route and duration of tourists' travel based on big data collected by telecom operators, credit card companies and other data to improve the quality of tourist course development, and to derive empirical data to estimate the effect of tourist inflow through the analyzed data, based on the characteristics of the tourism course and the data needed for the development of new tourist courses in the future.

• 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.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.2
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• pp.113-119
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• 2020

Numerous tourist-related data produced on the Internet contain not only simple tourist information but also diverse ideas and opinions from users. In order to derive meaningful information about tourist sites from such big data, the social network analysis of tourist keywords can identify the frequency of keywords and the relationship between keywords. Thus, it is possible to make recommendations more suitable for users by utilizing the clear recommendation criteria of tourist attractions and the relationship between tourist attractions. In this paper, a recommendation system was designed based on tourist site information through big data social network analysis. Based on user personality information, the types of tourism suitable for users are classified through deep learning and the network analysis among tourist keywords is conducted to identify the relationship between tourist attractions belonging to the type of tourism. Tour information for related tourist attractions shown on SNS and blogs will be recommended through tagging.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.2
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• pp.112-117
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• 2023

This study aims to find out what tourists' interests and perceptions are like through online big data. Big data for a total of five years from 2018 to 2022 were collected using the Textom program. Sentiment analysis was performed with the collected data. Sentiment analysis expresses the necessity and emotions of city tours in online reviews written by tourists using city tours. The purpose of this study is to extract and analyze keywords representing satisfaction. The sentiment analysis program provided by the big data analysis platform "TEXTOM" was used to study positives and negatives based on sentiment analysis of tourists' online reviews. Sentiment analysis was conducted by collecting reviews related to the city tour. The degree of positive and negative emotions for the city tour was investigated and what emotional words were analyzed for each item. As a result of big data sentiment analysis to examine the emotions and sentiments of tourists about the city tour, 93.8% positive and 6.2% negative, indicating that more than half of the tourists are positively aware. This paper collects tourists' opinions based on the analyzed sentiment analysis, understands the quality characteristics of city tours based on the analysis using the collected data, and sentiment analysis provides important information to the city tour platform for each region.