• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.3
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• pp.346-354
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• 2021

Since 2015, Blockchain has experienced rapid development throughout the world, institutions including Central Banks, Government Departments, Commercial Banks, IT Giants are all accelerating their exploration on Blockchain, and investment on Blockchain related R&D departments and start-up companies also shows explosive growth. This paper studies the impact of blockchain technology on banks' conventional trade settlement methods and describes blockchain technology in term of its concepts, advantages, and disadvantages. It also studies the application processes of blockchain technology combined with conventional trade settlement methods (remittance, collection, and L/C), and analyzes the positive and negative impacts of blockchain technology on the conventional trade settlement methods. In addition, this paper lists the blockchain application cases, analyzes the technology development status and existing problems, and puts forward suggestions and measures for the development of blockchain finance in China based on the case analysis and impact research.

• Journal of the Korea Institute of Building Construction
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• v.21 no.3
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• pp.231-239
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• 2021

Entering the era of the fourth industrial revolution, information and communication technologies such as CCTV, home network systems and equipment are being used in the construction industry. In particular, in order to increase the autonomy of information and communication technologies in apartments, the government has announced an administrative revision of information and communication-related laws, and companies are focusing on developing technologies such as smart home services. In addition, most domestic and foreign studies on the information and communication work were mainly conducted on technology and management. However there is a lack of research on physical defects affecting the quality of ICT. Therefore, this study collected the defect data registered in the project management system of three domestic construction companies and classified them according to the standards of the Enforcement Decree of the Apartment House Management Act. According to the analysis of the frequency of defects work type, 88.10% of defects occurred in home network equipment work. In addition, analysis of defects type in the four detailed works showed the highest number of operation error. The cause was analyzed and prevention measures and countermeasures were presented in parts of design, construction, and maintenance. The results of this study will improve the quality of apartment housing and be used as basic data for future research on practical defect minimization and prevention measures.

• Journal of Digital Convergence
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• v.20 no.1
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• pp.55-69
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• 2022

The purpose of this study is to verify whether there is a mediating effect of self-efficacy in the relationship between self-leadership and organizational commitment, self-leadership and job satisfaction. Specifically, hypotheses were established and a research model was established that behavior-focused strategies, natural reward strategies, and constructive thought pattern strategies have a significant positive (+) effect on organizational commitment, self-efficacy among these variables. In order to analyze this, 239 copies of survey data were collected from general domestic companies and analyzed through SPSS 25.0. The mediating analysis was conducted in the manner proposed by Baron & Kenny(1986). As a result, among the sub-factors of self-leadership, behavior-focused strategies and natural reward strategies had a significant positive (+) effect on organizational commitment, but constructive thought pattern strategies had no causal relationship with organizational commitment. In addition, in the relationship between these variables, self-efficacy showed a partial mediating effect. On the other hand, in the relationship between self-leadership and job satisfaction, only the natural reward strategies among the sub-factors of self-leadership showed a significant positive (+) effect, and behavior-focused strategies and constructive thought pattern strategies showed no causal relationship. Among these variables, self-efficacy was found to show partial mediation. Details and implications of the analysis results were dealt with in the conclusions and discussions of the study.

• Economic Analysis
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• v.25 no.4
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• pp.58-108
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• 2019

This paper analyzes the determining factors in the unemployment rate among young people in their 20s by studying data from 30 OECD countries between 2000 and 2017. It identifies reasons why Korea has a higher youth unemployment rate than Japan, and assesses what implications Japan's youth unemployment measures could have on Korea. The study highlights the variables that have meaningful impacts on youth unemployment. They include the unemployment rate among the working-age population, the percentage of each age bracket in the overall population, the GDP growth rate, the percentage of wage laborers in each age group, the percentage of elderly people, and the percentage of part-time workers. This paper also finds that a decline in the youth population, especially among people in their 20s, does not help to address the issue of youth unemployment. Secondly, this paper explains the additional factors behind Korea's higher youth unemployment rates. One is Korea's disadvantageous employment environment, compared to that in Japan, in terms of wage earnings. Other factors include the existence of fewer decent corporate jobs than in Japan, and wide disparities in wages between large and small corporate jobs. Therefore, while making efforts to resolve long-term and structural problems, it is necessary to actively promote policy measures to solve short-term mismatch problems of youth employment by referring to Japanese policy examples.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.91-110
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• 2023

The importance of manuals is emphasized because chemical accidents require swift response and recovery, and often result in environmental pollution and casualties. In this regard, the OECD revised OECD Guidelines for the Prevention, Preparedness, and Response to Chemical Accidents (referred to as the OECD Guidelines), in June 2023. Moreover, while existing research primarily raises awareness about chemical accidents, highlighting the need for a system-wide response including laws, regulations, and manuals, it was difficult to find comparative research on the attributes of manuals. So, this paper aims to compare and analyze the second and third editions of the OECD Guidelines, in order to uncover the information attributes and implications of the revised version. Specifically, TF-IDF (Term Frequency-Inverse Document Frequency) was applied to understand which keywords have become more important, and Word2Vec was applied to identify keywords that were used similarly and those that were differentiated. Lastly, a 2×2 matrix was proposed, identifying the topics within each quadrant to provide a deeper comparison of the information attributes of the OECD Guidelines. This study offers a framework to help researchers understand information attributes. From a practical perspective, it appears valuable for the revision of standard manuals by domestic government agencies and corporations related to chemistry.

• Information Systems Review
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• v.24 no.2
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• pp.1-20
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• 2022

Researchers are making contradictory claims through the concept of superstars and long tails about how the development of IT technology affects demand distribution. Unlike previous studies that focused on changes in demand from a macro point of view, this study explored whether the relationship between a company's marketing activities and consumer response differs depending on the product location (i.e., superstar vs. long tail) from a micro point of view. Based on the marketing mix framework, hypotheses were developed based on the relevant literature. In the case of empirical analysis, 2,835 daily data from 63 Korean films were tested using the quantile regression method. As a result of the analysis, it was found that the influence of marketing mix factors on sales varies depending on the location of the product. Specifically, the appeal breadth of the film and the effect of owned media are enhanced in superstar products, and the effect of acquisition media in long-tail products is enhanced and the negative effects of competition are mitigated. Unlike previous studies that focused on macroscopic changes in demand distribution, this study suggested marketing activities suitable for practitioners through microscopic analysis.

• Information Systems Review
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• v.20 no.2
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• pp.139-161
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• 2018

As Korea became one of the oldest countries in the world, successful aging emerged as an important issue to individuals as well as to society. This study aims to determine not only the Korean seniors' happiness and unhappiness factors but also the means to enhance their happiness and deal with unhappiness. We collected news articles related to the happiness and unhappiness of seniors with nine keywords based on Alderfer's ERG Theory. We then applied a topic modeling technique, Latent Dirichlet Allocation, to examine the main issues underlying the seniors' happiness and unhappiness. According to the analysis, we investigated the conditions of happiness and unhappiness by inspecting the topics based on each keyword. We also conducted a detailed analysis based on the main factors from topic modeling. We proposed specific ways to increase and overcome the happiness and unhappiness of seniors, respectively, in terms of government, corporate, family, and other social welfare organizations. This study indicates the major factors that affect the happiness and unhappiness of seniors. Specific methods to boost happiness and relief unhappiness are suggested from the additional analysis.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.83-102
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• 2021

The government recently announced various policies for developing big-data and artificial intelligence fields to provide a great opportunity to the public with respect to disclosure of high-quality data within public institutions. KSURE(Korea Trade Insurance Corporation) is a major public institution for financial policy in Korea, and thus the company is strongly committed to backing export companies with various systems. Nevertheless, there are still fewer cases of realized business model based on big-data analyses. In this situation, this paper aims to develop a new business model which can be applied to an ex-ante prediction for the likelihood of the insurance accident of credit guarantee. We utilize internal data from KSURE which supports export companies in Korea and apply machine learning models. Then, we conduct performance comparison among the predictive models including Logistic Regression, Random Forest, XGBoost, LightGBM, and DNN(Deep Neural Network). For decades, many researchers have tried to find better models which can help to predict bankruptcy since the ex-ante prediction is crucial for corporate managers, investors, creditors, and other stakeholders. The development of the prediction for financial distress or bankruptcy was originated from Smith(1930), Fitzpatrick(1932), or Merwin(1942). One of the most famous models is the Altman's Z-score model(Altman, 1968) which was based on the multiple discriminant analysis. This model is widely used in both research and practice by this time. The author suggests the score model that utilizes five key financial ratios to predict the probability of bankruptcy in the next two years. Ohlson(1980) introduces logit model to complement some limitations of previous models. Furthermore, Elmer and Borowski(1988) develop and examine a rule-based, automated system which conducts the financial analysis of savings and loans. Since the 1980s, researchers in Korea have started to examine analyses on the prediction of financial distress or bankruptcy. Kim(1987) analyzes financial ratios and develops the prediction model. Also, Han et al.(1995, 1996, 1997, 2003, 2005, 2006) construct the prediction model using various techniques including artificial neural network. Yang(1996) introduces multiple discriminant analysis and logit model. Besides, Kim and Kim(2001) utilize artificial neural network techniques for ex-ante prediction of insolvent enterprises. After that, many scholars have been trying to predict financial distress or bankruptcy more precisely based on diverse models such as Random Forest or SVM. One major distinction of our research from the previous research is that we focus on examining the predicted probability of default for each sample case, not only on investigating the classification accuracy of each model for the entire sample. Most predictive models in this paper show that the level of the accuracy of classification is about 70% based on the entire sample. To be specific, LightGBM model shows the highest accuracy of 71.1% and Logit model indicates the lowest accuracy of 69%. However, we confirm that there are open to multiple interpretations. In the context of the business, we have to put more emphasis on efforts to minimize type 2 error which causes more harmful operating losses for the guaranty company. Thus, we also compare the classification accuracy by splitting predicted probability of the default into ten equal intervals. When we examine the classification accuracy for each interval, Logit model has the highest accuracy of 100% for 0~10% of the predicted probability of the default, however, Logit model has a relatively lower accuracy of 61.5% for 90~100% of the predicted probability of the default. On the other hand, Random Forest, XGBoost, LightGBM, and DNN indicate more desirable results since they indicate a higher level of accuracy for both 0~10% and 90~100% of the predicted probability of the default but have a lower level of accuracy around 50% of the predicted probability of the default. When it comes to the distribution of samples for each predicted probability of the default, both LightGBM and XGBoost models have a relatively large number of samples for both 0~10% and 90~100% of the predicted probability of the default. Although Random Forest model has an advantage with regard to the perspective of classification accuracy with small number of cases, LightGBM or XGBoost could become a more desirable model since they classify large number of cases into the two extreme intervals of the predicted probability of the default, even allowing for their relatively low classification accuracy. Considering the importance of type 2 error and total prediction accuracy, XGBoost and DNN show superior performance. Next, Random Forest and LightGBM show good results, but logistic regression shows the worst performance. However, each predictive model has a comparative advantage in terms of various evaluation standards. For instance, Random Forest model shows almost 100% accuracy for samples which are expected to have a high level of the probability of default. Collectively, we can construct more comprehensive ensemble models which contain multiple classification machine learning models and conduct majority voting for maximizing its overall performance.

• Journal of Intelligence and Information Systems
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• v.20 no.3
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• pp.93-108
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• 2014

To support business decision making, interests and efforts to analyze and use transaction data in different perspectives are increasing. Such efforts are not only limited to customer management or marketing, but also used for monitoring and detecting fraud transactions. Fraud transactions are evolving into various patterns by taking advantage of information technology. To reflect the evolution of fraud transactions, there are many efforts on fraud detection methods and advanced application systems in order to improve the accuracy and ease of fraud detection. As a case of fraud detection, this study aims to provide effective fraud detection methods for auction exception agricultural products in the largest Korean agricultural wholesale market. Auction exception products policy exists to complement auction-based trades in agricultural wholesale market. That is, most trades on agricultural products are performed by auction; however, specific products are assigned as auction exception products when total volumes of products are relatively small, the number of wholesalers is small, or there are difficulties for wholesalers to purchase the products. However, auction exception products policy makes several problems on fairness and transparency of transaction, which requires help of fraud detection. In this study, to generate fraud detection rules, real huge agricultural products trade transaction data from 2008 to 2010 in the market are analyzed, which increase more than 1 million transactions and 1 billion US dollar in transaction volume. Agricultural transaction data has unique characteristics such as frequent changes in supply volumes and turbulent time-dependent changes in price. Since this was the first trial to identify fraud transactions in this domain, there was no training data set for supervised learning. So, fraud detection rules are generated using outlier detection approach. We assume that outlier transactions have more possibility of fraud transactions than normal transactions. The outlier transactions are identified to compare daily average unit price, weekly average unit price, and quarterly average unit price of product items. Also quarterly averages unit price of product items of the specific wholesalers are used to identify outlier transactions. The reliability of generated fraud detection rules are confirmed by domain experts. To determine whether a transaction is fraudulent or not, normal distribution and normalized Z-value concept are applied. That is, a unit price of a transaction is transformed to Z-value to calculate the occurrence probability when we approximate the distribution of unit prices to normal distribution. The modified Z-value of the unit price in the transaction is used rather than using the original Z-value of it. The reason is that in the case of auction exception agricultural products, Z-values are influenced by outlier fraud transactions themselves because the number of wholesalers is small. The modified Z-values are called Self-Eliminated Z-scores because they are calculated excluding the unit price of the specific transaction which is subject to check whether it is fraud transaction or not. To show the usefulness of the proposed approach, a prototype of fraud transaction detection system is developed using Delphi. The system consists of five main menus and related submenus. First functionalities of the system is to import transaction databases. Next important functions are to set up fraud detection parameters. By changing fraud detection parameters, system users can control the number of potential fraud transactions. Execution functions provide fraud detection results which are found based on fraud detection parameters. The potential fraud transactions can be viewed on screen or exported as files. The study is an initial trial to identify fraud transactions in Auction Exception Agricultural Products. There are still many remained research topics of the issue. First, the scope of analysis data was limited due to the availability of data. It is necessary to include more data on transactions, wholesalers, and producers to detect fraud transactions more accurately. Next, we need to extend the scope of fraud transaction detection to fishery products. Also there are many possibilities to apply different data mining techniques for fraud detection. For example, time series approach is a potential technique to apply the problem. Even though outlier transactions are detected based on unit prices of transactions, however it is possible to derive fraud detection rules based on transaction volumes.

• The Journal of Society for e-Business Studies
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• v.25 no.4
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• pp.33-60
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• 2020

Along with the rapid advance in internet technologies, ubiquitous mobile device usage has enabled consumers to access real-time information and increased interaction with others through various social media. Consumers can now get information more easily when making purchase decisions, and these changes are affecting the brand landscape. In a digitally connected world, brand image is not communicated to the consumers one-sidedly. Rather, with consumers' growing influence, it is a result of co-creation where consumers have an active role in building brand image. This explains a reality where people no longer purchase products just because they know the brand or because it is a famous brand. However, there has been little discussion on the matter, and many practitioners still rely on the traditional measures of brand indicators. The goal of this research is to present the limitations of traditional definition and measurement of brand and brand image, and propose a more direct and adequate measure that reflects the nature of a connected world. Inspired by the proverb, "A man is known by the company he keeps," the proposed measurement offers insight to the position of brand (or brand image) through co-purchased product networks. This paper suggests a framework of network analysis that clusters brands of cosmetics by the frequency of other products purchased together. This is done by analyzing product networks of a brand extracted from actual purchase data on Amazon.com. This is a more direct approach, compared to past measures where consumers' intention or cognitive aspects are examined through survey. The practical implication is that our research attempts to close the gap between brand indicators and actual purchase behavior. From a theoretical standpoint, this paper extends the traditional conceptualization of brand image to a network perspective that reflects the nature of a digitally connected society.