• Korean Journal of Labor Studies
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• v.23 no.1
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• pp.221-253
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• 2017

This study aims at analyzing the management strategy and performance of companies that have been pursuing Generational Diversity. The management strategies were examined in terms of production, organizational structure and skill development. Performance was then evaluated using Balanced Scorecard (BSC). We selected four Japanese companies that practice Generational Diversity between the younger(age less then 34) and older generation(age older then 65). Our findings suggest the following. The common management strategies of the four companies include 1) creating generation-diverse teams 2) ensuring flexible work arrangements and 3) providing skill training programs. These strategies have yield positive outcomes such as sales increase, cost reduction (financial perspective) and expansion of the market share (customer perspective). Non-financial performance includes improvement of product and service quality (internal business perspective) and skill improvement of both the young and the old workers (learning and growth perspective). This study provides practical implications to domestic companies for their successful management of generational diversity in workplace.

• The Journal of Economics, Marketing and Management
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• v.6 no.4
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• pp.51-64
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• 2018

Purpose - This study is intended to look into the influences of restaurant consumers' e-WOM information communication on product perception risk, benefit and WOM effect. Research design, data, and Methodology - To achieve this, a survey was empirically carried out to 426 restaurant consumers. Results - The findings are as follows. First, the influence of e-WOM on product perception risk showed that WOM information sender characteristics, WOM information recipient characteristics and online community had a statistically significant positive influence on product perception risk. Second, the influence of e-WOM on product risk benefit showed that WOM information sender characteristics, WOM information recipient characteristics and online communication had a statistically significant positive influence on product risk benefit. Third, WOM risk perception had a statistically significant positive influence on WOM acceptance. Fourth, WOM risk benefit had a statistically significant positive influence on WOM effect. Conclusions - As for the above-mentioned findings, the communication between e-WOM sender and recipient had a positive influence on the product evaluation and attitude change in the restaurant industry, and the WOM effect had an influence on the financial performance and non-financial performance. The communication attaches importance to a direct using and tasting experience due to the nature of restaurant industry when it is simultaneously performed as a positive mechanism between sender and recipient through each channel of these factors. But the e-WOM culture can lead to the WOM effect when both sender and recipient share the persuasive communicability in reality that diversifies communication methods, having a positive influence on the management performance.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.7 no.2
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• pp.113-127
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• 2012

From the end of 1st quarter of 2012, Korean mandatory firms had started releasing financial reports conforming to the K-IFRS(Korean adopted International Financial Reporting Standards). Major characteristics of IFRS, such as 'principles based' features, consolidated reporting, 'fair value' measurement, increased pressure for non-financial disclosures have resulted in brief and various disclosure practices regarding the main body of each statements and vast amount of note description requirements. Meanwhile, a host of previous studies on IFRS disclosures have incorporated regulatory and/or 'compete information' perspectives, mainly focusing on suggesting further enforcement of strengthened requirements and providing guidelines for specific treatments. Thus, as an extension of prior findings and suggestions this study had explored to conduct an integrative approach embracing views of the reporting entities and the users of financial information. In spite of all the state-driven efforts for faithful representation and comparability of corporate financial reports, an overhaul of disclosure practices of fiscal year 2010 and 2011 had revealed numerous cases of insufficiency and discordance in terms of mandatory norms and market expectations. As to the causes of such shortcomings, this study identified several factors from the corporate side and the users of the information; some inherent aspects of IFRS, industry/corporate-specific context, expenditures related to internalizing IFRS system, reduced time frame for presentation. lack of clarity and details to meet the quality of information - understandability, comparability etc. - commonly requested by the user group. In order to improve current disclosure practices, dual approach had been suggested; Firstly, to encourage and facilitate implementation, (1) further segmentation and differentiation of mandates among companies, (2) redefining the scope and depth of note descriptions, (3) diversification and coordination of reporting periods, (4) providing support for equipping disclosure systems and granting incentives for best practices had been discussed. Secondly, as for the hard measures, (5) regularizing active involvement of corporate and user group delegations in the establishment and amendment process of K-IFRS (6) enforcing detailed and standardized disclosure on reporting entities had been recommended.

• Korea Journal of Hospital Management
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• v.23 no.1
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• pp.51-64
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• 2018

Purposes : In February 2014, the government said that the National Health Insurance Service (NHIS) will enforce plan for reducing the financial burden from two major non-covered services including physician surcharges and private room charges, the main causes to increase uninsured, by 2017. The purpose of this study is to analyze the policy effect that performed so far by comparing out-of-pocket payment rates of policy process Methodology: This study analyzed admission medical expenses that occurred from January 2013 to March 2016 at a upper grade general hospitals in Daejeon. Number of study subjects were 134,924 and the data were analyzed with SPSS 22.0 program by using frequency, percentage, mean, standard deviation, ANOVA. The effect of two major non-payment improvement plan on out-of-pocket rates was ascertained via generalized estimating equation. Findings: Out-of-pocket payment rates was statistically significantly declined 2.7 percent than enforcement ago. Also, out-of-pocket payment, physician surcharge, the proportion of out-of-pocket payment of hospital room charge to out-of-pocket payment was statistically significantly declined. However, a further analysis of the cause of the decline in total medical costs is needed. Practical Implications: Physician surcharges and private room charges improvement policy had a positive effect on the decline of out-of-pocket payment rate. The policy of physician surcharges was very effective after the first policy enforcement but it was less effective to medical aids and near poor that was a more greater coverage than national health insurance. Since the policy has not been finalized, we have to continue a research for the successful implementation of the policy.

• Korean Journal of Human Ecology
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• v.20 no.4
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• pp.819-829
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• 2011

The purpose of this study was to explore the influences of personality and the money attitude on the giving of money by university students. 275 questionnaires were used for the statistical analysis of this study. The questionnaire includes attitudes towards money and giving money, whether or not respondents had a NGO membership, the level of participation into volunteer service, and other demographic information. The results of the study show that, female, middle class students with no religious preference tended to be more positiveabout giving money. There was a positive relationship between citizenship and giving money attitudes. It means that more actively attended citizenship activities were by students, the more positive attitudes towards giving money they had. The compulsive & consumptive types were more sardonic than others. The managerial types perceived less negatively giving organization. Based on these results, the following have been suggested in order to expand personal giving attitudes, voluntary programs, and activities related to the civil society for students. There needs to be a financial management education program with a balance struck between expenditure categories of consumption and non-consumption, including giving money to others.

• Journal of Distribution Science
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• v.20 no.2
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• pp.101-108
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• 2022

Purpose: In this logistics disturbance period, this study conducts research of distribution and logistics firms in Korea. The purpose of this exploratory research is to analyze global competitive capability influence on business performance. And give managerial implications and contribute to academics. Research design, data, and methodology: This research empirically analyzes the relationship between global competitive capability and business performance. As for business performance this research considered non-financial performance and measured with business performance fulfillment. As for antecedent variables, this research measured three global competitive capability constructs; preparation, utilization, intensive capability. And each construct includes two capability concepts. This study used 2,316 executing direct export distribution and logistics industry firmsfrom KOTRA's GCL data. This research used frequency analysis, reliability analysis, correlation analysis, and step-wise regression analysis by SPSS26. Results: The result shows that all the variables except export infra showed statistically significant. As results show, mid/long strategy & global mind of preparation capability, both communication and marketing of utilization capability and market strategy and product/goods/service of intensive capability give a positive influence on business performance fulfillment. Conclusions: Based on the results, this research provide implication for practical management, contribution to academic, and suggestion for feature research.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.234-244
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• 2022

Tourism products involve the transfer of money that is flowing to countries with partners or borders, which do not possess any relations surrounding their business environment. Suitable platforms must be generated by the tourism industry, which are beneficial to users when their demands are satisfied based on financial, technology, knowledge, and industry matters. Intermediaries are applied to alleviate different problems that are related to the non-fulfilment of contracts of existing users and service providers who are offering their services and represent a reliable third party. However, it is significant that intermediaries must be reliable when charges are incurred for any possible commission. Cryptocurrencies rely on blockchain technology to provide smoothness in money interchange without the need for reliable third parties. This interchange allows an increasing number of different new forms, which are related to different customer-to-customer transactions. The study attempts to provide an appropriate answer to the main research question, which is: 'Will the widespread adoption of cryptocurrencies bring new types of customer-to-customer markets from a technological, organizational, and environmental perspective?'.

• Asia pacific journal of information systems
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• v.19 no.2
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• pp.157-178
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• 2009

Corporate credit rating is a very important factor in the market for corporate debt. Information concerning corporate operations is often disseminated to market participants through the changes in credit ratings that are published by professional rating agencies, such as Standard and Poor's (S&P) and Moody's Investor Service. Since these agencies generally require a large fee for the service, and the periodically provided ratings sometimes do not reflect the default risk of the company at the time, it may be advantageous for bond-market participants to be able to classify credit ratings before the agencies actually publish them. As a result, it is very important for companies (especially, financial companies) to develop a proper model of credit rating. From a technical perspective, the credit rating constitutes a typical, multiclass, classification problem because rating agencies generally have ten or more categories of ratings. For example, S&P's ratings range from AAA for the highest-quality bonds to D for the lowest-quality bonds. The professional rating agencies emphasize the importance of analysts' subjective judgments in the determination of credit ratings. However, in practice, a mathematical model that uses the financial variables of companies plays an important role in determining credit ratings, since it is convenient to apply and cost efficient. These financial variables include the ratios that represent a company's leverage status, liquidity status, and profitability status. Several statistical and artificial intelligence (AI) techniques have been applied as tools for predicting credit ratings. Among them, artificial neural networks are most prevalent in the area of finance because of their broad applicability to many business problems and their preeminent ability to adapt. However, artificial neural networks also have many defects, including the difficulty in determining the values of the control parameters and the number of processing elements in the layer as well as the risk of over-fitting. Of late, because of their robustness and high accuracy, support vector machines (SVMs) have become popular as a solution for problems with generating accurate prediction. An SVM's solution may be globally optimal because SVMs seek to minimize structural risk. On the other hand, artificial neural network models may tend to find locally optimal solutions because they seek to minimize empirical risk. In addition, no parameters need to be tuned in SVMs, barring the upper bound for non-separable cases in linear SVMs. Since SVMs were originally devised for binary classification, however they are not intrinsically geared for multiclass classifications as in credit ratings. Thus, researchers have tried to extend the original SVM to multiclass classification. Hitherto, a variety of techniques to extend standard SVMs to multiclass SVMs (MSVMs) has been proposed in the literature Only a few types of MSVM are, however, tested using prior studies that apply MSVMs to credit ratings studies. In this study, we examined six different techniques of MSVMs: (1) One-Against-One, (2) One-Against-AIL (3) DAGSVM, (4) ECOC, (5) Method of Weston and Watkins, and (6) Method of Crammer and Singer. In addition, we examined the prediction accuracy of some modified version of conventional MSVM techniques. To find the most appropriate technique of MSVMs for corporate bond rating, we applied all the techniques of MSVMs to a real-world case of credit rating in Korea. The best application is in corporate bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. For our study the research data were collected from National Information and Credit Evaluation, Inc., a major bond-rating company in Korea. The data set is comprised of the bond-ratings for the year 2002 and various financial variables for 1,295 companies from the manufacturing industry in Korea. We compared the results of these techniques with one another, and with those of traditional methods for credit ratings, such as multiple discriminant analysis (MDA), multinomial logistic regression (MLOGIT), and artificial neural networks (ANNs). As a result, we found that DAGSVM with an ordered list was the best approach for the prediction of bond rating. In addition, we found that the modified version of ECOC approach can yield higher prediction accuracy for the cases showing clear patterns.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.177-192
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• 2016

Machine learning is a field of artificial intelligence. It refers to an area of computer science related to providing machines the ability to perform their own data analysis, decision making and forecasting. For example, one of the representative machine learning models is artificial neural network, which is a statistical learning algorithm inspired by the neural network structure of biology. In addition, there are other machine learning models such as decision tree model, naive bayes model and SVM(support vector machine) model. Among the machine learning models, we use SVM model in this study because it is mainly used for classification and regression analysis that fits well to our study. The core principle of SVM is to find a reasonable hyperplane that distinguishes different group in the data space. Given information about the data in any two groups, the SVM model judges to which group the new data belongs based on the hyperplane obtained from the given data set. Thus, the more the amount of meaningful data, the better the machine learning ability. In recent years, many financial experts have focused on machine learning, seeing the possibility of combining with machine learning and the financial field where vast amounts of financial data exist. Machine learning techniques have been proved to be powerful in describing the non-stationary and chaotic stock price dynamics. A lot of researches have been successfully conducted on forecasting of stock prices using machine learning algorithms. Recently, financial companies have begun to provide Robo-Advisor service, a compound word of Robot and Advisor, which can perform various financial tasks through advanced algorithms using rapidly changing huge amount of data. Robo-Adviser's main task is to advise the investors about the investor's personal investment propensity and to provide the service to manage the portfolio automatically. In this study, we propose a method of forecasting the Korean volatility index, VKOSPI, using the SVM model, which is one of the machine learning methods, and applying it to real option trading to increase the trading performance. VKOSPI is a measure of the future volatility of the KOSPI 200 index based on KOSPI 200 index option prices. VKOSPI is similar to the VIX index, which is based on S&P 500 option price in the United States. The Korea Exchange(KRX) calculates and announce the real-time VKOSPI index. VKOSPI is the same as the usual volatility and affects the option prices. The direction of VKOSPI and option prices show positive relation regardless of the option type (call and put options with various striking prices). If the volatility increases, all of the call and put option premium increases because the probability of the option's exercise possibility increases. The investor can know the rising value of the option price with respect to the volatility rising value in real time through Vega, a Black-Scholes's measurement index of an option's sensitivity to changes in the volatility. Therefore, accurate forecasting of VKOSPI movements is one of the important factors that can generate profit in option trading. In this study, we verified through real option data that the accurate forecast of VKOSPI is able to make a big profit in real option trading. To the best of our knowledge, there have been no studies on the idea of predicting the direction of VKOSPI based on machine learning and introducing the idea of applying it to actual option trading. In this study predicted daily VKOSPI changes through SVM model and then made intraday option strangle position, which gives profit as option prices reduce, only when VKOSPI is expected to decline during daytime. We analyzed the results and tested whether it is applicable to real option trading based on SVM's prediction. The results showed the prediction accuracy of VKOSPI was 57.83% on average, and the number of position entry times was 43.2 times, which is less than half of the benchmark (100 times). A small number of trading is an indicator of trading efficiency. In addition, the experiment proved that the trading performance was significantly higher than the benchmark.

• Journal of Korea Entertainment Industry Association
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• v.14 no.7
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• pp.397-408
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• 2020

This study conducted a focus group interview with 15 college freshman from J college to find out their experiences in non-face-to-face lectures with COVID-19. The contents of the interview were recorded and conducted, and the meaning was analyzed according to the focus group interview procedure through repeated listening. Components were 'Operation of non-face-to-face lectures in unprepared situations', 'Loss of orientation in lectures and departure from learning', 'One way listening', 'The convenience of taking a lectures'. The experience of 'Operating non-face-to-face lectures in unprepared situations' included the start of mixed non-face-to-face lectures, cumbersome and inconvenient online systems, and the demand for tuition refunds. The experience of 'Loss of orientation in lectures and departure from learning' has experienced difficulty in concentrating on lectures, Deficiency in the degree of recognition of learning content, and burden of assignments and exams. The experience of 'One way listening' has experienced lack of interaction between professors and learners and non reflection of liveliness in the field. Finally, participants experienced satisfaction with being able to lectures and repeat lectures at anytime and anywhere they wanted with the convenience of taking lectures. Based on this study, participants called for improvements in the quality lecture contents and interaction between professors and learners, and it is thought that universities will need administrative and financial support and education design and system construction to construct high-quality lecture contents.