• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.3
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• pp.183-200
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• 2022

This study is a study on the effect of a healing experience program on satisfaction in the field of healing agriculture. In the development of a rural experience program, what factors constituting the healing experience program affect satisfaction, and how much time and participation cost affect the satisfaction of the healing experience program from the marketing point of view of the healing experience program. I want to analyze By researching the effect of experience cost and experience time on satisfaction of the healing experience program, I would like to suggest the development direction of the healing experience program. To this end, by empirically analyzing the effect of a healing experience program using experience cost and experience time as parameters on satisfaction, we present a theoretical basis for priority considerations when developing a rural experience program. There are entertainment experience, educational experience, deviant experience, and aesthetic experience as sub-factors of the experience program, experience time and experience cost as parameters, and satisfaction as a dependent variable. In addition, the reliability of the research results was secured by setting the demographic variables of the survey subjects as control variables. The empirical analysis was conducted on 314 valid questionnaires from the unspecified majority who were interested in or aware of the healing experience program. SPSS v22.0 was used, and to test the mediating effect, the three-step verification method of Baron & Kenny(1986) and the SPSS PROCESS Macro Model No. of Andrew F. Hayes(2018). 4 The reliability of the mediating effect was secured by applying the verification method and comparing the analysis resul. As a result of the study, it was found that educational experience (𝛽=.134, t=1.759*) had a positive (+) effect on experience cost, and aesthetic experience (𝛽=.144 t=1.684*) had a positive (+) effect on experience time. +) was found to have an effect. Also, educational experience (𝛽=.239, t=4.112***) was found to have a positive (+) effect on satisfaction, and aesthetic experience (𝛽=.330 t=4.921***) had a positive effect on satisfaction. It has been shown to have a (+) effect. Experience time was found to have a negative (-) inconsistent mediating effect between aesthetic experience and satisfaction. That is, it is the total effect (𝛽=.330 t=4.921***), and the direct effect (𝛽=.349 t=5.241***) increased by 𝛽=.019 compared to the total effect when the experience time was input, while the indirect effect (𝛽=-.019), which was shown to exert a negative (-) mediating effect.

• The Korean Journal of Archival Studies
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• no.80
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• pp.43-88
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• 2024

The National Archives and Records Administration (NARA) is a relatively quiet latecomer to the traditional archives of the Western world. Although the United States lacks a long history of organized public records·archives management, it has developed a modern system optimized for the American historical context. This system focuses on the systematic management and preservation of the vast amount of modern records produced and collected during the tumultuous 20th century. As a result, NARA has established a modern archival system that is optimized for the American historical context. The U.S. public records·archives management system is based on the principle that records·archives are the property of the American people and belong to the public. This concept originated during the British colonial era when records were used to safeguard the rights of the colonies as self-governing citizens. For Americans, records and archives have long been a symbol of the nation's identity, serving as a means of protecting individual freedoms, rights, and democracy throughout the country's history. It is natural, therefore, that American life and history should be documented, and that the recorded past should be managed and preserved for the nation's present and future. The public records·archives management system in the United States is the result of a convergence of theories, practices, lessons learned, and ideas that have been shaped by the country's history, philosophies, and values about records, and its unique experience with records management. This paper traces the origins of records and archives in the United States in a historical context to understand the organic relationship between American life and records. It examines the process of forming a modern public records management system that is both uniquely American and universal to the American context without falling into the two forms of traditions that reflect the uniqueness of American history.

• Land and Housing Review
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• v.15 no.1
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• pp.1-22
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• 2024

This research explores the household and housing characteristics of young renters aged between 19 and 34 living independently in rental housing of non-Seoul Metropolitan Area (non-SMA) and to determine the factors of their housing affordability and residential environment qualities in two districts of non-SMA - metropolises and non-metropolises. Using the 2020 Korean Housing Survey (KHS), this study identified 1,191 unmarried young renters, and most were single adults in mid-twenties who were salaried workers with a bachelor's degree or higher. Also, many lived in single-room occupancy of non-APT housing for less than 2 years and rarely relied on social services. The findings showed that the distinction of local housing market between metropolises and non-metropolises forced the former to spend more housing expense (tenancy deposit and rental fees) than the latter. With regard to housing affordability indices (Schwabe index, housing expense ratio and rent to income ratio), most were housing cost-burdened and nearly one quarter were severely rent-burdened. The regression analysis indicated that housing affordability in both districts was positively affected by income increase and social services, and housing satisfaction in non-metropolises was added to its determinants. Further, residential environment qualities were largely divided into two groups of livelihood and urban infrastructure, and the two factors influenced residential assessment in both districts. Since young renters interdependently living had suffered with housing affordability, both income growth and housing assistance are critically required to enable them not just to reduce the burden but to ensure livability.

• Health Policy and Management
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• v.34 no.2
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• pp.196-210
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• 2024

Background: In January 2018, revised elderly fixed outpatient copayment for the elderly were implemented. When people ages 65 years and older receive outpatient treatment at clinic-level medical institutions (clinic, dental clinic, Korean medicine clinic), with medical expenses exceeding 15,000 won but not exceeding 25,000 won, their copayment rates have decreased differentially from 30%. This study aimed to examine the changes of health utilization of elderly after revised elderly fixed outpatient copayment. Methods: We used Korea health panel data from 2016 to 2018. The time period is divided into before and after the revised elderly fixed outpatient copayment. We conducted Poisson segmented regression to estimate the changes in outpatient utilization and inpatient utilization and conducted segmented regression to estimate the changes in medical expenses. Results: Immediately after the revised policy, the number of clinic and Korean medicine outpatient visits of medical expenses under 15,000 won decreased. But the number of clinic outpatient visits in the range of 15,000 to 20,000 won and Korean medicine clinic in the range of 20,000 to 25,000 won increased. Copayment in outpatient temporarily decreased. The inpatient admission rates and total medical expenses temporarily decreased but increased again. Conclusion: We confirmed the temporary increase in outpatient utilization in the medical expense segment with reduced copayment rates. And a temporary decrease in medical expenses followed by an increase again. To reduce the burden of medical expense among elderly in the long run, efforts to establish chronic disease management policies aimed at preventing disease occurrence and deterioration in advance need to continue.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.1-23
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• 2013

To discover significant social issues such as unemployment, economy crisis, social welfare etc. that are urgent issues to be solved in a modern society, in the existing approach, researchers usually collect opinions from professional experts and scholars through either online or offline surveys. However, such a method does not seem to be effective from time to time. As usual, due to the problem of expense, a large number of survey replies are seldom gathered. In some cases, it is also hard to find out professional persons dealing with specific social issues. Thus, the sample set is often small and may have some bias. Furthermore, regarding a social issue, several experts may make totally different conclusions because each expert has his subjective point of view and different background. In this case, it is considerably hard to figure out what current social issues are and which social issues are really important. To surmount the shortcomings of the current approach, in this paper, we develop a prototype system that semi-automatically detects social issue keywords representing social issues and problems from about 1.3 million news articles issued by about 10 major domestic presses in Korea from June 2009 until July 2012. Our proposed system consists of (1) collecting and extracting texts from the collected news articles, (2) identifying only news articles related to social issues, (3) analyzing the lexical items of Korean sentences, (4) finding a set of topics regarding social keywords over time based on probabilistic topic modeling, (5) matching relevant paragraphs to a given topic, and (6) visualizing social keywords for easy understanding. In particular, we propose a novel matching algorithm relying on generative models. The goal of our proposed matching algorithm is to best match paragraphs to each topic. Technically, using a topic model such as Latent Dirichlet Allocation (LDA), we can obtain a set of topics, each of which has relevant terms and their probability values. In our problem, given a set of text documents (e.g., news articles), LDA shows a set of topic clusters, and then each topic cluster is labeled by human annotators, where each topic label stands for a social keyword. For example, suppose there is a topic (e.g., Topic1 = {(unemployment, 0.4), (layoff, 0.3), (business, 0.3)}) and then a human annotator labels "Unemployment Problem" on Topic1. In this example, it is non-trivial to understand what happened to the unemployment problem in our society. In other words, taking a look at only social keywords, we have no idea of the detailed events occurring in our society. To tackle this matter, we develop the matching algorithm that computes the probability value of a paragraph given a topic, relying on (i) topic terms and (ii) their probability values. For instance, given a set of text documents, we segment each text document to paragraphs. In the meantime, using LDA, we can extract a set of topics from the text documents. Based on our matching process, each paragraph is assigned to a topic, indicating that the paragraph best matches the topic. Finally, each topic has several best matched paragraphs. Furthermore, assuming there are a topic (e.g., Unemployment Problem) and the best matched paragraph (e.g., Up to 300 workers lost their jobs in XXX company at Seoul). In this case, we can grasp the detailed information of the social keyword such as "300 workers", "unemployment", "XXX company", and "Seoul". In addition, our system visualizes social keywords over time. Therefore, through our matching process and keyword visualization, most researchers will be able to detect social issues easily and quickly. Through this prototype system, we have detected various social issues appearing in our society and also showed effectiveness of our proposed methods according to our experimental results. Note that you can also use our proof-of-concept system in http://dslab.snu.ac.kr/demo.html.

• Journal of Intelligence and Information Systems
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• v.27 no.2
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• pp.33-54
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• 2021

With the fourth industrial revolution and the arrival of the New Normal era due to Corona, the importance of Non-contact technologies such as artificial intelligence and big data research has been increasing. Convergent research is being conducted in earnest to keep up with these research trends, but not many studies have been conducted in the area of nuclear research using artificial intelligence and big data-related technologies such as natural language processing and text mining analysis. This study was conducted to confirm the applicability of data science analysis techniques to the field of nuclear research. Furthermore, the study of identifying trends in nuclear spent fuel recognition is critical in terms of being able to determine directions to nuclear industry policies and respond in advance to changes in industrial policies. For those reasons, this study conducted a media trend analysis of pyroprocessing, a spent nuclear fuel treatment technology. We objectively analyze changes in media perception of spent nuclear fuel dry treatment techniques by applying text mining analysis techniques. Text data specializing in Naver's web news articles, including the keywords "Pyroprocessing" and "Sodium Cooled Reactor," were collected through Python code to identify changes in perception over time. The analysis period was set from 2007 to 2020, when the first article was published, and detailed and multi-layered analysis of text data was carried out through analysis methods such as word cloud writing based on frequency analysis, TF-IDF and degree centrality calculation. Analysis of the frequency of the keyword showed that there was a change in media perception of spent nuclear fuel dry treatment technology in the mid-2010s, which was influenced by the Gyeongju earthquake in 2016 and the implementation of the new government's energy conversion policy in 2017. Therefore, trend analysis was conducted based on the corresponding time period, and word frequency analysis, TF-IDF, degree centrality values, and semantic network graphs were derived. Studies show that before the 2010s, media perception of spent nuclear fuel dry treatment technology was diplomatic and positive. However, over time, the frequency of keywords such as "safety", "reexamination", "disposal", and "disassembly" has increased, indicating that the sustainability of spent nuclear fuel dry treatment technology is being seriously considered. It was confirmed that social awareness also changed as spent nuclear fuel dry treatment technology, which was recognized as a political and diplomatic technology, became ambiguous due to changes in domestic policy. This means that domestic policy changes such as nuclear power policy have a greater impact on media perceptions than issues of "spent nuclear fuel processing technology" itself. This seems to be because nuclear policy is a socially more discussed and public-friendly topic than spent nuclear fuel. Therefore, in order to improve social awareness of spent nuclear fuel processing technology, it would be necessary to provide sufficient information about this, and linking it to nuclear policy issues would also be a good idea. In addition, the study highlighted the importance of social science research in nuclear power. It is necessary to apply the social sciences sector widely to the nuclear engineering sector, and considering national policy changes, we could confirm that the nuclear industry would be sustainable. However, this study has limitations that it has applied big data analysis methods only to detailed research areas such as "Pyroprocessing," a spent nuclear fuel dry processing technology. Furthermore, there was no clear basis for the cause of the change in social perception, and only news articles were analyzed to determine social perception. Considering future comments, it is expected that more reliable results will be produced and efficiently used in the field of nuclear policy research if a media trend analysis study on nuclear power is conducted. Recently, the development of uncontact-related technologies such as artificial intelligence and big data research is accelerating in the wake of the recent arrival of the New Normal era caused by corona. Convergence research is being conducted in earnest in various research fields to follow these research trends, but not many studies have been conducted in the nuclear field with artificial intelligence and big data-related technologies such as natural language processing and text mining analysis. The academic significance of this study is that it was possible to confirm the applicability of data science analysis technology in the field of nuclear research. Furthermore, due to the impact of current government energy policies such as nuclear power plant reductions, re-evaluation of spent fuel treatment technology research is undertaken, and key keyword analysis in the field can contribute to future research orientation. It is important to consider the views of others outside, not just the safety technology and engineering integrity of nuclear power, and further reconsider whether it is appropriate to discuss nuclear engineering technology internally. In addition, if multidisciplinary research on nuclear power is carried out, reasonable alternatives can be prepared to maintain the nuclear industry.