• Korean Journal of Occupational Health Nursing
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• v.6 no.1
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• pp.23-43
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• 1997

The work-related diseases due to continuous overwork are mainly cerebro- and cardio-vascular ones, which is commonly called 'Karoshi', death from overwork. Many factors are capable for Karoshi : occupational stress in relation to technological renovation and industrial rationalization, competitive social structure, and accumulated fatigue accured to long time or irregular working. And its occurence is on the rise. The World Labor Report 1993 released by ILO, pointed out the diseases related to overwork and stress as one of the most important occupational health problem. In Korea, social awareness of Karoshi is at an infant stage, and reliable statistics for its occurence are not compiled in a convenient manner. Despite the rising Karoshi, there are no reliable clauses in workmen's compensation enough to settle down the disputes. Therefore, it is not uncommon that the Labour Ministry and Civil Court find difficulties in reaching an agreement. This study was intended to provide proper compensation and prevention program for workers by suggesting reasonable compensation clauses for the death from overwork. This study consists of two comparative reviews on the compensaton clauses for the death from overwork. One is to review legal standards of Karoshi among three countries, such as Korea, Japan and Taiwan. The other is to investigate the cases of Karoshi in Korea, 121 cases identified at the Labor Welfare Corperation and the Labour Ministrial process of examination and reexamination, and 73 leading cases at the High Court of Justice. The main findings of the study are as follows : 1. Comparisons of comperative review on compensation clauses for the death from overwork among three countries. 1) All of three countries have the same kinds of disease for compensation, which were cerebro-and cardiao-vascular diseases, while for cardiac disease group, Korea has the smaller number of diseases for compensation than Japan. 2) As for the definition of overwork, the three countries share equally that overload for one week prior to collapse is considered as an important factor, but accumulated chronic fatigue is disregarded. 3) As the basis of overwork, in Japan, there is a tendency to move from the conditions of an ordinary healthy adult to those of the individual concerned in Japan, whereas there is no such concern yet in Korea. 4) All the three countries use a common standard of medical judgement in demonstrating causal relationship between a job and a disease. However, Korea is progressive in the sense that in the case of CVA at worksite, the worker himself has no obligation to prove the cause. 2. The results of a comparative review on excutive decisions by Labor Ministry and judicial decisions by the Court in Korea : A judicial decision is based on the legalistic probability, but a excutive decision is not. Therefore, excutive decisions have such restrictions that : 1) TIA (transitory ischemic cerebral attack) and myocarditis are excluded from compensation, and there is little consistency of decision in the case of cause-unknown death. 2) There is a tendency not to compensate for the death from overwork since the work terms such as repeated long-time working, shift work or night-shift work are not considered as overloading. 3) There is a tendency to regard the conditions of a ordinary healthy adult rather than those of the individual concerned(age, existing diseases, health state, etc.) as the comparative basis of overload. 4) There remains a tendency not to compensate for the death from overwork in the case of collapse occuring out of workplace, on the ground of 'on the course of working' and 'in the cause of accident'. Through the study, the fact manifests itself that Korea's compensation clauses for work-related diseases due to overwork are very restrictive. So, it is necessary to extend the Labor Ministry's clauses of compensation for the death from overwork following to the recent changes of other countries and internal judicial decisions. This is very important in the perspective of occupational health that aims at health promotion of workers including prevention of the Karoshi.

• Korean Business Review
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• v.17 no.2
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• pp.63-87
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• 2004

To create and maintain comparative supremacy as a strategic tool of business, many organizations have introduced informational technology and system. By using this system, Some companies got a beneficial value for achieving organizational goals but others could not obtain their effectiveness and efficiency. In particular, a lot of organizations that tried to make strategic supremacy with e-commercial trade are under hard condition because of poor profit. It implies that it is essential to identify and analyse the consumer who uses e-commercial trade. This paper, therefore, focusing on internet shopping malls between business and consumer as one of areas of e-commercial trades, shows the difference between consumer expectation and performance. The results of this study are as follows: First, as for the significant difference of influencing factors to consumer satisfactions according to the types of internet shopping malls, there is a meaningful difference in consumer anxiety and internet usefulness, but not in consumer service. Prior to verify the differences in detail on consumer's anxiety and internet usefulness, we examined that there is any difference between expectation and performance. T-test was used for the variants of consumer anxiety and internet usefulness, and its meaningful probability was 0.000, which means that both showed statistically significant difference. Based on the results, we also found that regardless of the types of internet shopping malls, consumer expectation was greater than performance. although the difference between expectation and performance was not equal according to the internet shopping malls. Second, a regression analysis was performed to understand the relation between consumer service, internet usefulness, consumer anxiety, and consumer satisfaction, it was found that consumer service, internet usefulness, consumer anxiety had significantly effected on consumer satisfaction. Third, To verify the relation between consumer satisfaction and repurchase-intentions, intentions to spread out, Pearson correlation analysis was used. it was found that consumer satisfaction had positive effect on both intentions. This study has some limitations because of the shorts of money and time. since the sample of this study was consumers who have ever bought one or more products via internet shopping mall, this sample was appropriate. but the major parts of sample were college students, and the sample size was so small. therefore this results should carefully be generalized. For further study, it is required to select more precise samples and to include more variables.

• Land and Housing Review
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• v.14 no.1
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• pp.47-66
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• 2023

This study reviews prior studies on the residential environment characteristics, residential satisfaction, residential ownership consciousness and housing movement of MZ generation and analyze the structural equation models using the 2020 Korea Housing Survey data. Using 14 residential characteristics based on three classifications, we explore the effects on residential satisfaction, residential ownership consciousness, and housing movement. The empirical results are summarized as follows. First, based on factor analysis with Varimax of principal component analysis, parking facility items were excluded from the analysis by hindering validity, and as a result, KMO was 0.925 and Bartlett's test result showed a significant probability of less than 0.01. This indicates that the factor analysis model was suitable. Second, the results of the structural equation analysis for the MZ generation show that the surrounding environment, which is a potential variable of the residential environment characteristics, was statistically significant, but the accessibility and convenience were not statistically significant. Third, we find that the higher the satisfaction with the accessibility of commercial facilities, the more significant the sense of housing ownership appears. This suggests that the younger generation such as the MZ generation has a stronger desire for consumption. Fourth, the overall housing satisfaction of the MZ generation was significant for housing movement, but not for housing ownership. Compared to the industrialized generation, the baby boom generation, and the X generation, MZ generation shows distinct factors for housing satisfaction, housing ownership, and housing movement. Therefore, the residential environment characteristics of the residential survey should be improved and supplemented following the trend of the times. In addition, the government and local governments should prioritize actively participating in the housing market that suits the environment and characteristics of the target generation. Finally, our study provides implications regarding the need for housing-related research on how differ in special temporal situations such as COVID-19 in the future.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.5
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• pp.53-73
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• 2023

Although it is mandatory to wear a seat belt that can minimize human injury when traffic accident occurs, the number of traffic accident casualties not wearing seat belts still accounts for a significant proportion.The seat belt wearing detection system for all seats is a system that identifies whether all seat passengers wear a seat belt and encourages their usage, also it can be a useful technical countermeasure. Firstly, this study established the viability of system implementation by assessing the factors influencing the severity of injuries in traffic accidents through the development of an ordered probit model. Analysis results showed that the use of seat belts has statistically significant effects on the severity of traffic accidents, reducing the probability of death or serious injury by 0.054 times in the event of a traffic accident. Secondly, a meta-analysis was conducted based on prior research related to seat belts and injuries in traffic accidents to estimate the expected reduction in accident severity upon the implementation of the system.The analysis of the effect of accident severity reduction revealed that wearing seat belts would lead to a 63.3% decrease in fatal accidents, with the front seats showing a reduction of 75.7% and the rear seats showing a reduction of 58.1% in fatal accidents. Lastly, Using the results of the meta-analysis and traffic accident statistics, the expected decrease in the number of traffic accident casualties with the implementation of the system was derived to analyze the traffic safety effects of the proposed detection system. The analysis demonstrated that with an increase in the adoption rate of the system, the number of casualties in accidents where seat belts were not worn decreased. Specifically, at a system adoption rate of 60%, it is anticipated that the number of fatalities would decrease by more than three times compared to the current scenario. Based on the analysis results, operational strategies for the system were proposed to increase seat belt usage rates and reduce accident severity.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.5
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• pp.77-90
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• 2023

This study aims to investigate the potential of alternative credit assessment through Social Networking Sites (SNS) as a complementary tool to conventional loan review processes. It seeks to discern the impact of SNS usage characteristics and loan product attributes on credit loan repayment. To achieve this objective, we conducted a binomial logistic regression analysis examining the influence of SNS usage patterns, loan characteristics, and personal attributes on credit loan conditions, utilizing data from Company A's credit loan program, which integrates SNS data into its actual loan review processes. Our findings reveal several noteworthy insights. Firstly, with respect to profile photos that reflect users' personalities and individual characteristics, individuals who choose to upload photos directly connected to their personal lives, such as images of themselves, their private circles (e.g., family and friends), and photos depicting social activities like hobbies, which tend to be favored by individuals with extroverted tendencies, as well as character and humor-themed photos, which are typically favored by individuals with conscientious traits, demonstrate a higher propensity for diligently repaying credit loans. Conversely, the utilization of photos like landscapes or images concealing one's identity did not exhibit a statistically significant causal relationship with loan repayment. Furthermore, a positive correlation was observed between the extent of SNS usage and the likelihood of loan repayment. However, the level of SNS interaction did not exert a significant effect on the probability of loan repayment. This observation may be attributed to the passive nature of the interaction variable, which primarily involves expressing sympathy for other users' comments rather than generating original content. The study also unveiled the statistical significance of loan duration and the number of loans, representing key characteristics of loan portfolios, in influencing credit loan repayment. This underscores the importance of considering loan duration and the quantity of loans as crucial determinants in the design of microcredit products. Among the personal characteristic variables examined, only gender emerged as a significant factor. This implies that the loan program scrutinized in this analysis does not exhibit substantial discrimination based on age and credit scores, as its customer base predominantly consists of individuals in their twenties and thirties with low credit scores, who encounter challenges in securing loans from traditional financial institutions. This research stands out from prior studies by empirically exploring the relationship between SNS usage and credit loan repayment while incorporating variables not typically addressed in existing credit rating research, such as profile pictures. It underscores the significance of harnessing subjective, unstructured information from SNS for loan screening, offering the potential to mitigate the financial disadvantages faced by borrowers with low credit scores or those ensnared in short-term liquidity constraints due to limited credit history a group often referred to as "thin filers." By utilizing such information, these individuals can potentially reduce their credit costs, whereas they are supposed to accrue a more substantial financial history through credit transactions under conventional credit assessment system.

• Asia pacific journal of information systems
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• v.21 no.1
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• pp.103-122
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• 2011

Recently, numerous documents have been made available electronically. Internet search engines and digital libraries commonly return query results containing hundreds or even thousands of documents. In this situation, it is virtually impossible for users to examine complete documents to determine whether they might be useful for them. For this reason, some on-line documents are accompanied by a list of keywords specified by the authors in an effort to guide the users by facilitating the filtering process. In this way, a set of keywords is often considered a condensed version of the whole document and therefore plays an important role for document retrieval, Web page retrieval, document clustering, summarization, text mining, and so on. Since many academic journals ask the authors to provide a list of five or six keywords on the first page of an article, keywords are most familiar in the context of journal articles. However, many other types of documents could not benefit from the use of keywords, including Web pages, email messages, news reports, magazine articles, and business papers. Although the potential benefit is large, the implementation itself is the obstacle; manually assigning keywords to all documents is a daunting task, or even impractical in that it is extremely tedious and time-consuming requiring a certain level of domain knowledge. Therefore, it is highly desirable to automate the keyword generation process. There are mainly two approaches to achieving this aim: keyword assignment approach and keyword extraction approach. Both approaches use machine learning methods and require, for training purposes, a set of documents with keywords already attached. In the former approach, there is a given set of vocabulary, and the aim is to match them to the texts. In other words, the keywords assignment approach seeks to select the words from a controlled vocabulary that best describes a document. Although this approach is domain dependent and is not easy to transfer and expand, it can generate implicit keywords that do not appear in a document. On the other hand, in the latter approach, the aim is to extract keywords with respect to their relevance in the text without prior vocabulary. In this approach, automatic keyword generation is treated as a classification task, and keywords are commonly extracted based on supervised learning techniques. Thus, keyword extraction algorithms classify candidate keywords in a document into positive or negative examples. Several systems such as Extractor and Kea were developed using keyword extraction approach. Most indicative words in a document are selected as keywords for that document and as a result, keywords extraction is limited to terms that appear in the document. Therefore, keywords extraction cannot generate implicit keywords that are not included in a document. According to the experiment results of Turney, about 64% to 90% of keywords assigned by the authors can be found in the full text of an article. Inversely, it also means that 10% to 36% of the keywords assigned by the authors do not appear in the article, which cannot be generated through keyword extraction algorithms. Our preliminary experiment result also shows that 37% of keywords assigned by the authors are not included in the full text. This is the reason why we have decided to adopt the keyword assignment approach. In this paper, we propose a new approach for automatic keyword assignment namely IVSM(Inverse Vector Space Model). The model is based on a vector space model. which is a conventional information retrieval model that represents documents and queries by vectors in a multidimensional space. IVSM generates an appropriate keyword set for a specific document by measuring the distance between the document and the keyword sets. The keyword assignment process of IVSM is as follows: (1) calculating the vector length of each keyword set based on each keyword weight; (2) preprocessing and parsing a target document that does not have keywords; (3) calculating the vector length of the target document based on the term frequency; (4) measuring the cosine similarity between each keyword set and the target document; and (5) generating keywords that have high similarity scores. Two keyword generation systems were implemented applying IVSM: IVSM system for Web-based community service and stand-alone IVSM system. Firstly, the IVSM system is implemented in a community service for sharing knowledge and opinions on current trends such as fashion, movies, social problems, and health information. The stand-alone IVSM system is dedicated to generating keywords for academic papers, and, indeed, it has been tested through a number of academic papers including those published by the Korean Association of Shipping and Logistics, the Korea Research Academy of Distribution Information, the Korea Logistics Society, the Korea Logistics Research Association, and the Korea Port Economic Association. We measured the performance of IVSM by the number of matches between the IVSM-generated keywords and the author-assigned keywords. According to our experiment, the precisions of IVSM applied to Web-based community service and academic journals were 0.75 and 0.71, respectively. The performance of both systems is much better than that of baseline systems that generate keywords based on simple probability. Also, IVSM shows comparable performance to Extractor that is a representative system of keyword extraction approach developed by Turney. As electronic documents increase, we expect that IVSM proposed in this paper can be applied to many electronic documents in Web-based community and digital library.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.105-129
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• 2020

This study uses corporate data from 2012 to 2018 when K-IFRS was applied in earnest to predict default risks. The data used in the analysis totaled 10,545 rows, consisting of 160 columns including 38 in the statement of financial position, 26 in the statement of comprehensive income, 11 in the statement of cash flows, and 76 in the index of financial ratios. Unlike most previous prior studies used the default event as the basis for learning about default risk, this study calculated default risk using the market capitalization and stock price volatility of each company based on the Merton model. Through this, it was able to solve the problem of data imbalance due to the scarcity of default events, which had been pointed out as the limitation of the existing methodology, and the problem of reflecting the difference in default risk that exists within ordinary companies. Because learning was conducted only by using corporate information available to unlisted companies, default risks of unlisted companies without stock price information can be appropriately derived. Through this, it can provide stable default risk assessment services to unlisted companies that are difficult to determine proper default risk with traditional credit rating models such as small and medium-sized companies and startups. Although there has been an active study of predicting corporate default risks using machine learning recently, model bias issues exist because most studies are making predictions based on a single model. Stable and reliable valuation methodology is required for the calculation of default risk, given that the entity's default risk information is very widely utilized in the market and the sensitivity to the difference in default risk is high. Also, Strict standards are also required for methods of calculation. The credit rating method stipulated by the Financial Services Commission in the Financial Investment Regulations calls for the preparation of evaluation methods, including verification of the adequacy of evaluation methods, in consideration of past statistical data and experiences on credit ratings and changes in future market conditions. This study allowed the reduction of individual models' bias by utilizing stacking ensemble techniques that synthesize various machine learning models. This allows us to capture complex nonlinear relationships between default risk and various corporate information and maximize the advantages of machine learning-based default risk prediction models that take less time to calculate. To calculate forecasts by sub model to be used as input data for the Stacking Ensemble model, training data were divided into seven pieces, and sub-models were trained in a divided set to produce forecasts. To compare the predictive power of the Stacking Ensemble model, Random Forest, MLP, and CNN models were trained with full training data, then the predictive power of each model was verified on the test set. The analysis showed that the Stacking Ensemble model exceeded the predictive power of the Random Forest model, which had the best performance on a single model. Next, to check for statistically significant differences between the Stacking Ensemble model and the forecasts for each individual model, the Pair between the Stacking Ensemble model and each individual model was constructed. Because the results of the Shapiro-wilk normality test also showed that all Pair did not follow normality, Using the nonparametric method wilcoxon rank sum test, we checked whether the two model forecasts that make up the Pair showed statistically significant differences. The analysis showed that the forecasts of the Staging Ensemble model showed statistically significant differences from those of the MLP model and CNN model. In addition, this study can provide a methodology that allows existing credit rating agencies to apply machine learning-based bankruptcy risk prediction methodologies, given that traditional credit rating models can also be reflected as sub-models to calculate the final default probability. Also, the Stacking Ensemble techniques proposed in this study can help design to meet the requirements of the Financial Investment Business Regulations through the combination of various sub-models. We hope that this research will be used as a resource to increase practical use by overcoming and improving the limitations of existing machine learning-based models.