• The Journal of Information Systems
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• v.26 no.3
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• pp.171-185
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• 2017

Purpose Recently, there has been an increase in attempts to analyze social phenomena, consumption trends, and consumption behavior through a vast amount of customer data such as web search traffic information and social buzz information in various fields such as flu prediction and real estate price prediction. Internet portal service providers such as google and naver are disclosing web search traffic information of online users as services such as google trends and naver trends. Academic and industry are paying attention to research on information search behavior and utilization of online users based on the web search traffic information. Although there are many studies predicting social phenomena, consumption trends, political polls, etc. based on web search traffic information, it is hard to find the research to explain and predict tourism demand and establish tourism policy using it. In this study, we try to use web search traffic information to explain the tourism demand for major cities in Gangwon-do, the representative tourist area in Korea, and to develop a nowcasting model for the demand. Design/methodology/approach In the first step, the literature review on travel demand and web search traffic was conducted in parallel in two directions. In the second stage, we conducted a qualitative research to confirm the information retrieval behavior of the traveler. In the next step, we extracted the representative tourist cities of Gangwon-do and confirmed which keywords were used for the search. In the fourth step, we collected tourist demand data to be used as a dependent variable and collected web search traffic information of each keyword to be used as an independent variable. In the fifth step, we set up a time series benchmark model, and added the web search traffic information to this model to confirm whether the prediction model improved. In the last stage, we analyze the prediction models that are finally selected as optimal and confirm whether the influence of the keywords on the prediction of travel demand. Findings This study has developed a tourism demand forecasting model of Gangwon-do, a representative tourist destination in Korea, by expanding and applying web search traffic information to tourism demand forecasting. We compared the existing time series model with the benchmarking model and confirmed the superiority of the proposed model. In addition, this study also confirms that web search traffic information has a positive correlation with travel demand and precedes it by one or two months, thereby asserting its suitability as a prediction model. Furthermore, by deriving search keywords that have a significant effect on tourism demand forecast for each city, representative characteristics of each region can be selected.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.21-41
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• 2019

Thanks to the rapid development of information technologies, the data available on Internet have grown rapidly. In this era of big data, many studies have attempted to offer insights and express the effects of data analysis. In the tourism and hospitality industry, many firms and studies in the era of big data have paid attention to online reviews on social media because of their large influence over customers. As tourism is an information-intensive industry, the effect of these information networks on social media platforms is more remarkable compared to any other types of media. However, there are some limitations to the improvements in service quality that can be made based on opinions on social media platforms. Users on social media platforms represent their opinions as text, images, and so on. Raw data sets from these reviews are unstructured. Moreover, these data sets are too big to extract new information and hidden knowledge by human competences. To use them for business intelligence and analytics applications, proper big data techniques like Natural Language Processing and data mining techniques are needed. This study suggests an analytical approach to directly yield insights from these reviews to improve the service quality of hotels. Our proposed approach consists of topic mining to extract topics contained in the reviews and the decision tree modeling to explain the relationship between topics and ratings. Topic mining refers to a method for finding a group of words from a collection of documents that represents a document. Among several topic mining methods, we adopted the Latent Dirichlet Allocation algorithm, which is considered as the most universal algorithm. However, LDA is not enough to find insights that can improve service quality because it cannot find the relationship between topics and ratings. To overcome this limitation, we also use the Classification and Regression Tree method, which is a kind of decision tree technique. Through the CART method, we can find what topics are related to positive or negative ratings of a hotel and visualize the results. Therefore, this study aims to investigate the representation of an analytical approach for the improvement of hotel service quality from unstructured review data sets. Through experiments for four hotels in Hong Kong, we can find the strengths and weaknesses of services for each hotel and suggest improvements to aid in customer satisfaction. Especially from positive reviews, we find what these hotels should maintain for service quality. For example, compared with the other hotels, a hotel has a good location and room condition which are extracted from positive reviews for it. In contrast, we also find what they should modify in their services from negative reviews. For example, a hotel should improve room condition related to soundproof. These results mean that our approach is useful in finding some insights for the service quality of hotels. That is, from the enormous size of review data, our approach can provide practical suggestions for hotel managers to improve their service quality. In the past, studies for improving service quality relied on surveys or interviews of customers. However, these methods are often costly and time consuming and the results may be biased by biased sampling or untrustworthy answers. The proposed approach directly obtains honest feedback from customers' online reviews and draws some insights through a type of big data analysis. So it will be a more useful tool to overcome the limitations of surveys or interviews. Moreover, our approach easily obtains the service quality information of other hotels or services in the tourism industry because it needs only open online reviews and ratings as input data. Furthermore, the performance of our approach will be better if other structured and unstructured data sources are added.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.4
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• pp.36-40
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• 2022

Many parts of life have been changed due to the unprecedented coronavirus outbreak, and Noncontact has now become a general culture of society around the world. Also, many years later, after the Fourth Industrial Revolution, it is now deeply embedded in the human lifestyle. The purpose of this paper's research is to investigate the metaverse perception before and after Corona. It was confirmed that the number of metaverse, the central keyword, was 70971 before Corona, but 261767 after Corona, which was more than three times the frequency. In addition, it was confirmed that the number of COVID-19, the reference point of this study, increased significantly to 1,9236 during the pre-COVID-19 period. Through this, it can be inferred that the metaverse accelerated and developed significantly after the corona. Metaverse about Keywords such as cryptocurrency, cryptocurrency, coin, and exchange appeared before Corona, and the word frequency ranking for blockchain, which is an underlying technology, was high, but after Corona, the word frequency ranking fell significantly as mentioned above. As such, it was confirmed that keywords for metaverse were changing before and after Corona, and as such, Consumers' perceptions were also changing.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.861-863
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• 2016

대부분의 기업은 경영전략을 결정하는데 고객의 소리(VOC:Voice of Customer)를 매우 중요한 정보로 사용하고 있기 때문에 기업들은 다양한 방법으로 고객과의 관계증진을 위해 VOC 데이터를 이용하고 있다. 그러나 수집된 내부VOC 데이터에서 많은 정성적인 데이터를 포함하고 있으므로 분석하는 데는 한계가 있다. 본 논문에서는 최근 소셜 빅 데이터를 분석하는데 사용하고 있는 시스템을 이용하여 다른 업종에 비해 고객이 다양하고 서비스가 매우 중요한 여행사 내부 VOC를 분석한다. 적용 사례로서 국내 대표적인 여행사에 직접 적용하여 분석한 결과를 제시한다. 본 연구 결과 빅 데이터 분석 도구를 다른 서비스업종의 내부 VOC의 정성적인 데이터를 분석하는데 활용할 수 있는 가능성을 보여주었다고 사료된다.

• Culinary science and hospitality research
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• v.22 no.5
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• pp.64-81
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• 2016

The study, based on existing studies, explored influencing relationship, suggesting app service quality and user reviews as previous elements to affect use satisfaction about users' comments based on big data and reuse intention. The study includes a comparative analysis of existing studies. Based on such analysis results, the authors looked into app service quality elements perceived by gourmet restaurant app users and the role of user reviews, and suggested practical implications that can help the development and operation of gourmet restaurant app contents. The study subjects were male and female consumers who over 20 years old throughout Korea who had not a searched smartphone gourmet restaurant app in the three months preceding the survey. The subjects were selected from consumers who search the restaurantsby using restaurant apps like Mango plate, Dining code, Hot place, and selecting restaurants. Among them, consumers with experience using restaurants were finally selected for the survey. According to the results, reliability, informativity, and system capability, among service quality, had positive influences on app use satisfaction, while design and mobility had no effect. App use satisfaction had positive influences on app reuse intention. User comment informativity played a controlling role. The study explored the importance of app service quality and user review informativity as elements that affect continued use of gourmet restaurant apps by dining-out consumers.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.3
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• pp.193-204
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• 2022

The 2018 Seoul Zero Pay is a policy actively promoted by the government as an economic stimulus package for small business owners and the self-employed who are experiencing economic depression due to COVID-19. However, the controversy over the effectiveness of Zero Pay continues even after two years have passed since the implementation of the policy. Zero Pay is a joint QR code mobile payment service introduced by the government, Seoul city, financial companies, and private simple payment providers to reduce the burden of card merchant fees for small business owners and self-employed people who are experiencing economic difficulties due to the economic downturn., it was attempted in the direction of economic revitalization for the return of alleyways[1]. Therefore, this study intends to draw implications for improvement measures so that the ongoing zero-pay can be further activated and the economy can be settled normally. The analysis results of this study are as follows. First, it shows the effect of increasing the income of small business owners by inducing consumption in alleyways through the economic revitalization policy of Zero Pay. Second, the issuance and distribution of Zero Pay helps to revitalize the local economy and contribute to the establishment of a virtuous cycle system. Third, stable operation is being realized by the introduction of blockchain technology to the Zero Pay platform. In terms of academic significance, the direction of Zero Pay's policies and systems was able to identify changes in the use of Zero Pay through big data analysis. The implementation of the zero-pay policy is in its infancy, and there are limitations in factors for examining the consumer image perception of zero-pay as there are insufficient prior studies. Therefore, continuous follow-up research on Zero Pay should be conducted.

• Journal of the Korean Data and Information Science Society
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• v.25 no.4
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• pp.769-777
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• 2014

Recently many countries have established open government data platforms to disclose government or government controlled entities-owned data that can be freely used, reused and redistributed by anyone. Open government data can help you to make better decisions in your own life, or enable you to be more active in society. Open data is also making government more effective and transparent, which ultimately also reduces costs. This paper explains the open data concepts and circumstances in Korea, and also suggests detailed invigorating strategies such as data quality policy, data unification and standardization policy, open data service platform, and integrated support plan of big data and open government data.

• Journal of Smart Tourism
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• v.3 no.2
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• pp.23-36
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• 2023

Due to the phenomenon of aging, a new consumer segment known as the "new-silver generation" is emerging. Unlike the previous silver generation, this generation possesses significant economic power and consuming willingness, attracting attention from consumer goods companies. However, both the new-silver generation and the elderly face challenges in adopting contactless or self-service technologies such as self-order kiosks, resulting in negative reactions. Therefore, this study aims to investigate the attitude and response of the newsilver generation towards kiosks, as well as the factors influencing their resistance to such technology. By applying theoretical perspectives from the innovation resistance model, technostress theory, and the value-based model, this study identifies influencing factors for innovation resistance among the new-silver generation when using contactless technologies implemented in fast-food restaurants. The findings indicate that a lower awareness of new technologies and services corresponds to decreased adoption resistance, while a higher perceived value leads to more positive behaviors and attitudes among the new-silver generation utilizing kiosks at fast-food restaurants.

• Journal of Korean Society of Rural Planning
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• v.27 no.4
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• pp.13-24
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• 2021

This study used big data to analyze visitors' experiences in Fishing Experience Recreation Village. Through the portal site posting data for the past six years, the experience of visiting Fishing Experience Villages in Baekmi and Susan was analyzed. The analysis method used Text mining and Social Network Analysis which are Big data analysis techniques. Data was collected using Textom, and experience keywords were extracted by analyzing the frequency and importance of experience texts. Afterwards, the characteristics of the experience of visiting the Fishing Experience Village were identified through the analysis of the interaction between the experience keywords using 'U cinet 6.0' and 'NetDraw'. First, through TF and TF-IDF values, keywords such as "Gungpyeong Port", "Susan Port", and "Yacht Marina" that refer to the name of the port and the port facilities appeared at the top. This is interpreted as the name of the port has the greatest impact on the recognition of the Fishing Experience Villages, and visitors showed a lot of interest in the port facilities. Second, focusing on the unique elements of port facilities and fishing villages such as "mud flat experience", "fishing village experience", "Gungpyeong port", "Susan port", "yacht marina", and "beach" through the values of degree, closeness, and betweenness centrality interpreted as having an interaction with various experiences. Third, through the CONCOR analysis, it was confirmed that the visitor's experience was focused on the dynamic behavior, the experience program had the greatest influence on the experience of the visitor, and that the experience of the static and the dynamic behavior was relatively balanced. In conclusion, the experience of visitors in the Fishing Experience Villages is most affected by the environment of the fishing village such as the tidal flats and the coast and the fishing village experience program conducted at the fishing port facilities. In particular, it was found that fishing port facilities such as ports and marinas had a high influence on the awareness of the Fishing Experience Villages. Therefore, it is important to actively utilize the scenery and environment unique to fishing villages in order to revitalize the Fishing Experience Villages experience and improve the quality of the visitor experience. This study is significant in that it studied visitors' experiences in fishing village recreation villages using big data and derived the connection between fishing village and fishing village infrastructure in fishing village experience tourism.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.9
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• pp.1179-1184
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• 2018

Korean travel agencies that have been utilizing the Internet passively feel threatened as global online travel agencies are quickly permeating the Korean travel market. Under these circumstances, this study conducted a focused analysis on how ICT affects the business environment of travel agencies. Based on the analyzed data, it also presents coping strategies for the Korean travel agencies. First, it is imperative to accelerate platform development to counteract the distribution structure of global tourism products. Second, it is essential that travel agencies actively utilize big data, the new paradigm of technology where data are generated at high speed, high volume, and for numerous purposes. Third, it is necessary to actively utilize a travel blog marketing strategy. As a communication tool for travel agencies, the continuing development of the Internet highlights the usefulness of marketing activities using blogs. Finally, it is essential to provide each customer more specialized travel consultancy.