• Journal of the Korean association of regional geographers
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• v.3 no.2
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• pp.1-17
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• 1997

Geographic Information Systems(GIS) are very useful for spatial analysis and policy in local government administration. Recognizing the value of GIS, Chungnam province authorities put a spur on the introduction and development of it. But they have some difficulty in this process because of technical restraint, expertise shortage and budget limit. This study has surveyed current achievement and conditions for GIS development and presented general framework and subordinate tasks to build up GIS. First of all, there are a few prior conditions to guarantee the success of GIS: First, we should set up reasonable long-term plan and follow systematic procedures according to the plan. Second, it is essential to clarify what initiatively manage to whole business and so we should make up GIS-Board as an institutional center for this job. Third, we must research how to take advantage of already existing NGIS(National Geographic Information System), so that we may eliminate redundancy of investment. We can save a lot of finance and human resources through it. Fourth, we must focus on the importance of accurate mapping by utilizing new technology like GPS(Global Positioning System). Fifth, we should arrange efficient training program to constantly produce excellent human resources for GIS.

• Journal of Service Research and Studies
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• v.10 no.2
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• pp.43-57
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• 2020

This study aims to verify whether service characteristic factors of theme park, consisting of ease of access, convenience of service, diversity of events and safety of facilities have positive effect on customer satisfaction and, in turn, customer satisfaction on intention of revisit for customers. We conducted surveys on 317 customers having experiences of visiting domestic theme parks and obtained the results that three factors except ease of access have positive effect on customer satisfaction and customer satisfaction on intention of revisit. As the further analysis, we checked if customer satisfaction has positive moderating effect on the relationship between four service characteristic factors and intention of revisit. We found that diversity of events and safety of facilities have full moderating effect and convenience of service partial moderating effect, while ease of access has not that effect, which offer implication that theme parks have to make more effort and investments related to diversity of events and safety of facilities to increase possibility of customer revisit.

• Journal of Digital Convergence
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• v.15 no.4
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• pp.91-105
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• 2017

Korea is rapidly becoming a multicultural society in recent years, and the number of multicultural families in 2015 exceeds 3.5% and 800,000. Also, as international marriage rate exceeds 10% by 2016, the number of multicultural families is expected to steadily increase. This study is a design of a metadata application profile as part of the foundation for providing learning resources and content tailored to the needs and preferences of married immigrant women and multicultural family members who need to adapt to Korean society. In order to verify the necessity of the research, we conducted an in-depth interview by screening consumer groups, and analyzed the relevant international and Korean national standards as de-jure standards for the design of metadata standard profiles. Then, we analyzed the contents characteristics for multicultural members, and organized the necessary metadata elements into profiles. We defined the mandatory/optional conditions to reflect the needs of content providers. This study is meaningful in that the study analyzes the educational needs of married immigrant women and presents the necessary metadata standards to develop and service effective educational content, such as korean-to-korean conversion system, personalized learning contents recommendation service, and learning management system.

• Journal of Climate Change Research
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• v.1 no.2
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• pp.179-188
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• 2010

To cope with various issues in the aspect of climate change adaptation of UNFCCC, Korea began preparing a Five-year National Climate Change Adaptation Plan in 2010 to be implemented from 2011~2015, for the purposes of securing a concrete system to adapt to climate change. Compared with the policies and measurement tools of developed countries, Korea's climate change adaptation capabilities suffers from a number of limitations including insufficiencies of basic information, human resources for research on climate change, and technology in risk and vulnerability assessment. At the same time, Korea maintains superior information technology systems, and comparatively strong climate change adaptation technologies. Recently, with the establishment of the Korea Adaptation Center for Climate Change as a specialized research organization in climate change adaptation, Korea has upgraded its ability to adapt to climate change and to provide support to other Asian countries which are vulnerable to climate change. In consideration of the close relation between climate change adaptation policy and technology development with the environmental industry, Korea's pursuit of cooperation and technical support for developing countries in the Asia region can be seen as the commencement of a long term investment for the nation's future. International cooperation on climate change adaptation between countries in the region can build a mutually complementary and integrated partnership in business, research, education, and other areas. Furthermore, Korea can also participate in the exploration of common issues as landmark projects that can attract global interest with developing countries.

• Journal of the Korean Society of Floral Art and Design
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• no.44
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• pp.75-100
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• 2021

The purpose of this study is to explore ways in which the flower design industry can utilize podcasts that are rapidly growing in recent years. I selected foreign flower podcasts that are ranked on the global podcast chart, and examined the genre, content, components, show hosts and etc. By analyzing the characteristics of the podcast, the type of communication between the host and the audience, the audience interaction, the industry connection, and the media expansion strategy, I tried to derive the possibility of the flower podcast in Korea. As a result of analyzing foreign flower podcasts, podcasters built listener communities based on their rich experience and knowledge through podcasts and used them for education and marketing. They acted as leaders in the industry or led public opinion such as the sustainable flower industry. Podcast shows were repurposed as various content and used to spread flower design culture. In Korea, flower podcasts can be the basis for the formation of a community related to the flower design industry. Flower design experts can use podcasts as a source asset for various content. Listeners within the industry can get hands-on knowledge about the business from flower podcasts. The popular flower podcast will contribute to the vitalization of flower design culture and industry. Flower podcasts can be a starting point to actively cope with the era of personal media.

• 한국체육학회지인문사회과학편
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• v.57 no.6
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• pp.123-134
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• 2018

The purpose of this study is to analyze the economic efficiency of the policy implemented by Ministry of Culture, Sports and Tourism on leading company in sport industry. The leading companies in sport industry are those who have a certain amount of sales in sport industry and the ones with potential to become global companies. Supporting areas include business advancement, overseas market development, and overseas PR marketing integration support. The research is performed by developing the equilibrium model composed of supply as well as demand and applying input-output analysis. The economic efficiency is estimated to in the form of changes in the sales of corporations and the ripple effect of the national economy. The results of the study are as follows. First, it is estimated that the sales growth rate of the company due to the implementation of the policy is from 3.74% to 5.19%. Second, the increase in sales reaches to a maximum of KRW 4,081 billion with a minimum of KRW 1,573 million, depending on the size of the company. Third, it is estimated that the production inducement effect for the national economy is from KRW 36 billion to KRW 93.4 billion. Fourth, the induced value added for the national economy is estimated to be at least KRW 11.3 billion, up to KRW 29.2 billion.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.51-68
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• 2023

With the emergence of the internet and the increasing use of online communities for over 20 years, the age range of users has also been rising. This study explores the linguistic changes that have occurred as the user age in online communities has increased. To do this, data was collected and analyzed from an online community that has been actively operating, despite new member registrations being closed nine years ago. By comparing the posts over an 11-year period from 2012 to 2022, changes such as an increase in average comments, a decrease in interrogative sentences, and a decrease in imperative statements were observed. The study also proposed loneliness due to aging and a decline in curiosity and confidence as potential causes of these changes. In South Korea, which is rapidly entering an aging society unprecedentedly fast on a global scale, the increase in single-person households has evolved loneliness from a personal issue to a social problem, manifested in an increase in solitary deaths and reclusive individuals. This research sheds light on one aspect of these social phenomena through the analysis of data from a large online community.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.309-323
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• 2023

As the role of online reviews has become increasingly crucial, numerous studies have been conducted to utilize helpful reviews. Helpful reviews, perceived by customers, have been verified in various research studies to be influenced by factors such as ratings, review length, review content, and so on. The determination of a review's helpfulness is generally based on the number of 'helpful' votes from consumers, with more 'helpful' votes considered to have a more significant impact on consumers' purchasing decisions. However, recently written reviews that have not been exposed to many customers may have relatively few 'helpful' votes and may lack 'helpful' votes altogether due to a lack of participation. Therefore, rather than relying on the number of 'helpful' votes to assess the helpfulness of reviews, we aim to classify them based on review content. In addition, the text of the review emerges as the most influential factor in review helpfulness. This study employs text mining techniques, including topic modeling and sentiment analysis, to analyze the diverse impacts of content and emotions embedded in the review text. In this study, we propose a review helpfulness prediction model based on review content, utilizing movie reviews from IMDb, a global movie information site. We construct a review helpfulness prediction model by using an explainable Graph Neural Network (GNN), while addressing the interpretability limitations of the machine learning model. The explainable graph neural network is expected to provide more reliable information about helpful or non-helpful reviews as it can identify connections between reviews.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.177-193
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• 2014

Over the past decade, there has been a rapid diffusion of electronic commerce and a rising number of interconnected networks, resulting in an escalation of security threats and privacy concerns. Electronic commerce has a built-in trade-off between the necessity of providing at least some personal information to consummate an online transaction, and the risk of negative consequences from providing such information. More recently, the frequent disclosure of private information has raised concerns about privacy and its impacts. This has motivated researchers in various fields to explore information privacy issues to address these concerns. Accordingly, the necessity for information privacy policies and technologies for collecting and storing data, and information privacy research in various fields such as medicine, computer science, business, and statistics has increased. The occurrence of various information security accidents have made finding experts in the information security field an important issue. Objective measures for finding such experts are required, as it is currently rather subjective. Based on social network analysis, this paper focused on a framework to evaluate the process of finding experts in the information security field. We collected data from the National Discovery for Science Leaders (NDSL) database, initially collecting about 2000 papers covering the period between 2005 and 2013. Outliers and the data of irrelevant papers were dropped, leaving 784 papers to test the suggested hypotheses. The co-authorship network data for co-author relationship, publisher, affiliation, and so on were analyzed using social network measures including centrality and structural hole. The results of our model estimation are as follows. With the exception of Hypothesis 3, which deals with the relationship between eigenvector centrality and performance, all of our hypotheses were supported. In line with our hypothesis, degree centrality (H1) was supported with its positive influence on the researchers' publishing performance (p<0.001). This finding indicates that as the degree of cooperation increased, the more the publishing performance of researchers increased. In addition, closeness centrality (H2) was also positively associated with researchers' publishing performance (p<0.001), suggesting that, as the efficiency of information acquisition increased, the more the researchers' publishing performance increased. This paper identified the difference in publishing performance among researchers. The analysis can be used to identify core experts and evaluate their performance in the information privacy research field. The co-authorship network for information privacy can aid in understanding the deep relationships among researchers. In addition, extracting characteristics of publishers and affiliations, this paper suggested an understanding of the social network measures and their potential for finding experts in the information privacy field. Social concerns about securing the objectivity of experts have increased, because experts in the information privacy field frequently participate in political consultation, and business education support and evaluation. In terms of practical implications, this research suggests an objective framework for experts in the information privacy field, and is useful for people who are in charge of managing research human resources. This study has some limitations, providing opportunities and suggestions for future research. Presenting the difference in information diffusion according to media and proximity presents difficulties for the generalization of the theory due to the small sample size. Therefore, further studies could consider an increased sample size and media diversity, the difference in information diffusion according to the media type, and information proximity could be explored in more detail. Moreover, previous network research has commonly observed a causal relationship between the independent and dependent variable (Kadushin, 2012). In this study, degree centrality as an independent variable might have causal relationship with performance as a dependent variable. However, in the case of network analysis research, network indices could be computed after the network relationship is created. An annual analysis could help mitigate this limitation.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.1-19
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• 2019

Artificial intelligence (AI) is one of the main driving forces leading the Fourth Industrial Revolution. The technologies associated with AI have already shown superior abilities that are equal to or better than people in many fields including image and speech recognition. Particularly, many efforts have been actively given to identify the current technology trends and analyze development directions of it, because AI technologies can be utilized in a wide range of fields including medical, financial, manufacturing, service, and education fields. Major platforms that can develop complex AI algorithms for learning, reasoning, and recognition have been open to the public as open source projects. As a result, technologies and services that utilize them have increased rapidly. It has been confirmed as one of the major reasons for the fast development of AI technologies. Additionally, the spread of the technology is greatly in debt to open source software, developed by major global companies, supporting natural language recognition, speech recognition, and image recognition. Therefore, this study aimed to identify the practical trend of AI technology development by analyzing OSS projects associated with AI, which have been developed by the online collaboration of many parties. This study searched and collected a list of major projects related to AI, which were generated from 2000 to July 2018 on Github. This study confirmed the development trends of major technologies in detail by applying text mining technique targeting topic information, which indicates the characteristics of the collected projects and technical fields. The results of the analysis showed that the number of software development projects by year was less than 100 projects per year until 2013. However, it increased to 229 projects in 2014 and 597 projects in 2015. Particularly, the number of open source projects related to AI increased rapidly in 2016 (2,559 OSS projects). It was confirmed that the number of projects initiated in 2017 was 14,213, which is almost four-folds of the number of total projects generated from 2009 to 2016 (3,555 projects). The number of projects initiated from Jan to Jul 2018 was 8,737. The development trend of AI-related technologies was evaluated by dividing the study period into three phases. The appearance frequency of topics indicate the technology trends of AI-related OSS projects. The results showed that the natural language processing technology has continued to be at the top in all years. It implied that OSS had been developed continuously. Until 2015, Python, C ++, and Java, programming languages, were listed as the top ten frequently appeared topics. However, after 2016, programming languages other than Python disappeared from the top ten topics. Instead of them, platforms supporting the development of AI algorithms, such as TensorFlow and Keras, are showing high appearance frequency. Additionally, reinforcement learning algorithms and convolutional neural networks, which have been used in various fields, were frequently appeared topics. The results of topic network analysis showed that the most important topics of degree centrality were similar to those of appearance frequency. The main difference was that visualization and medical imaging topics were found at the top of the list, although they were not in the top of the list from 2009 to 2012. The results indicated that OSS was developed in the medical field in order to utilize the AI technology. Moreover, although the computer vision was in the top 10 of the appearance frequency list from 2013 to 2015, they were not in the top 10 of the degree centrality. The topics at the top of the degree centrality list were similar to those at the top of the appearance frequency list. It was found that the ranks of the composite neural network and reinforcement learning were changed slightly. The trend of technology development was examined using the appearance frequency of topics and degree centrality. The results showed that machine learning revealed the highest frequency and the highest degree centrality in all years. Moreover, it is noteworthy that, although the deep learning topic showed a low frequency and a low degree centrality between 2009 and 2012, their ranks abruptly increased between 2013 and 2015. It was confirmed that in recent years both technologies had high appearance frequency and degree centrality. TensorFlow first appeared during the phase of 2013-2015, and the appearance frequency and degree centrality of it soared between 2016 and 2018 to be at the top of the lists after deep learning, python. Computer vision and reinforcement learning did not show an abrupt increase or decrease, and they had relatively low appearance frequency and degree centrality compared with the above-mentioned topics. Based on these analysis results, it is possible to identify the fields in which AI technologies are actively developed. The results of this study can be used as a baseline dataset for more empirical analysis on future technology trends that can be converged.