• The Journal of Society for e-Business Studies
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• v.26 no.3
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• pp.55-66
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• 2021

The mobile application (APP) market is growing at an unprecedented speed. Amid such growth, the global platform providers are mandating exclusive in-app payments and charging 30% for platform commission fees. A serious tension has arisen between mobile global platform providers and local content providers. The present study attempts to analyze the domestic mobile game market and in-app payment commission fees. This study estimates the size of the domestic mobile game market and platform commission fees by directly using publicly available financial statements and footnote information of some representative listed mobile game firms. Also, the study analyzes the cost structures of the same sample firms and attempts to draw some implications on sustainable growths of the mobile game ecosystem. We estimated that, in 2019, the domestic mobile game market is around 4.9 trillion Won and the ensuing in-app payment commission fees market was 1.5 trillion Won. High market share firms display a proportional increase in in-app payment commission fees in relation to sales growth. This, in turn, makes the in-app payment commission fees a primary cost item far exceeding employee salaries and R&D expenses. During the same period, low market share firms generated a mere profit or experienced net loss. Analysis of the cost structure reveals that these firms are even more liable to higher in-app payment commission fee cost structure than high market share. Most constituents of the mobile game ecosystem are small business entrepreneurs. By employing a micro-level analysis, the study estimates that, in 2019, a representative median firm generates 530 million Won in sales. At the same time, it spends 190 million Won in employee salaries, 50 Won million in R&D and 190 million Won in in-app payment commission fees, respectively. In the absence of other cost items, these three cost items alone account for 73.8% of sales revenue. The results imply that a sustainable growth of the local mobile game market heavily depends upon the cost structure of such representative median firm, the in-app payment commission fees being the primary cost item of such firm.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.9 no.1
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• pp.119-132
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• 2014

This study analyzed the failure prediction model of the firms listed on the KOSDAQ by considering whether there are embezzlement, malpractice and the largest shareholder changes or not. This study composed a total of 166 firms by using two-paired sampling method. For sample of failed firm, 83 manufacturing firms which delisted on KOSDAQ market for 4 years from 2009 to 2012 are selected. For sample of normal firm, 83 firms (with same item or same business as failed firm) that are listed on KOSDAQ market and perform normal business activities during the same period (from 2009 to 2012) are selected. This study selected 80 financial ratios for 5 years immediately preceding from delisting of sample firm above and conducted T-test to derive 19 of them which emerged for five consecutive years among significant variables and used forward selection to estimate logistic regression model. While the precedent studies only analyzed the data of three years immediately preceding the delisting, this study analyzes data of five years immediately preceding the delisting. This study is distinct from existing previous studies that it researches which significant financial characteristic influences the insolvency from the initial phase of insolvent firm with time lag and it also empirically analyzes the usefulness of data by building a firm's fail prediction model which considered embezzlement/malpractice and the largest shareholder changes as dummy variable(non-financial characteristics). The accuracy of classification of the prediction model with dummy variable appeared 95.2% in year T-1, 88.0% in year T-2, 81.3% in year T-3, 79.5% in year T-4, and 74.7% in year T-5. It increased as year of delisting approaches and showed generally higher the accuracy of classification than the results of existing previous studies. This study expects to reduce the damage of not only the firm but also investors, financial institutions and other stakeholders by finding the firm with high potential to fail in advance.

• Asia pacific journal of information systems
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• v.19 no.2
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• pp.139-155
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• 2009

For the last few decades, many studies have tried to explore and unveil venture companies' success factors and unique features in order to identify the sources of such companies' competitive advantages over their rivals. Such venture companies have shown tendency to give high returns for investors generally making the best use of information technology. For this reason, many venture companies are keen on attracting avid investors' attention. Investors generally make their investment decisions by carefully examining the evaluation criteria of the alternatives. To them, credit rating information provided by international rating agencies, such as Standard and Poor's, Moody's and Fitch is crucial source as to such pivotal concerns as companies stability, growth, and risk status. But these types of information are generated only for the companies issuing corporate bonds, not venture companies. Therefore, this study proposes a method for evaluating venture businesses by presenting our recent empirical results using financial data of Korean venture companies listed on KOSDAQ in Korea exchange. In addition, this paper used multi-class SVM for the prediction of DEA-based efficiency rating for venture businesses, which was derived from our proposed method. Our approach sheds light on ways to locate efficient companies generating high level of profits. Above all, in determining effective ways to evaluate a venture firm's efficiency, it is important to understand the major contributing factors of such efficiency. Therefore, this paper is constructed on the basis of following two ideas to classify which companies are more efficient venture companies: i) making DEA based multi-class rating for sample companies and ii) developing multi-class SVM-based efficiency prediction model for classifying all companies. First, the Data Envelopment Analysis(DEA) is a non-parametric multiple input-output efficiency technique that measures the relative efficiency of decision making units(DMUs) using a linear programming based model. It is non-parametric because it requires no assumption on the shape or parameters of the underlying production function. DEA has been already widely applied for evaluating the relative efficiency of DMUs. Recently, a number of DEA based studies have evaluated the efficiency of various types of companies, such as internet companies and venture companies. It has been also applied to corporate credit ratings. In this study we utilized DEA for sorting venture companies by efficiency based ratings. The Support Vector Machine(SVM), on the other hand, is a popular technique for solving data classification problems. In this paper, we employed SVM to classify the efficiency ratings in IT venture companies according to the results of DEA. The SVM method was first developed by Vapnik (1995). As one of many machine learning techniques, SVM is based on a statistical theory. Thus far, the method has shown good performances especially in generalizing capacity in classification tasks, resulting in numerous applications in many areas of business, SVM is basically the algorithm that finds the maximum margin hyperplane, which is the maximum separation between classes. According to this method, support vectors are the closest to the maximum margin hyperplane. If it is impossible to classify, we can use the kernel function. In the case of nonlinear class boundaries, we can transform the inputs into a high-dimensional feature space, This is the original input space and is mapped into a high-dimensional dot-product space. Many studies applied SVM to the prediction of bankruptcy, the forecast a financial time series, and the problem of estimating credit rating, In this study we employed SVM for developing data mining-based efficiency prediction model. We used the Gaussian radial function as a kernel function of SVM. In multi-class SVM, we adopted one-against-one approach between binary classification method and two all-together methods, proposed by Weston and Watkins(1999) and Crammer and Singer(2000), respectively. In this research, we used corporate information of 154 companies listed on KOSDAQ market in Korea exchange. We obtained companies' financial information of 2005 from the KIS(Korea Information Service, Inc.). Using this data, we made multi-class rating with DEA efficiency and built multi-class prediction model based data mining. Among three manners of multi-classification, the hit ratio of the Weston and Watkins method is the best in the test data set. In multi classification problems as efficiency ratings of venture business, it is very useful for investors to know the class with errors, one class difference, when it is difficult to find out the accurate class in the actual market. So we presented accuracy results within 1-class errors, and the Weston and Watkins method showed 85.7% accuracy in our test samples. We conclude that the DEA based multi-class approach in venture business generates more information than the binary classification problem, notwithstanding its efficiency level. We believe this model can help investors in decision making as it provides a reliably tool to evaluate venture companies in the financial domain. For the future research, we perceive the need to enhance such areas as the variable selection process, the parameter selection of kernel function, the generalization, and the sample size of multi-class.

• KDI Journal of Economic Policy
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• v.12 no.2
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• pp.95-120
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• 1990

The underpricing of new shares of a firm that are offered to the public for the first time (initial offerings) is well known and has puzzled financial economists for a long time since it seems at odds with the optimal behavior of the owners of issuing firms. Past attempts by financial economists to explain this phenomenon have not been successful in the sense that the explanations given by them are either inconsistent with the equilibrium theory or implausible. Approaches by such authors as Welch or Allen and Faulhaber are no exceptions. In this paper, we develop a signalling model of capital investment to explain the underpricing phenomenon and also analyze the efficiency of investment. The model focuses on the information asymmetry between the owners of issuing firms and general investors. We consider a firm that has been owned and operated by a single owner and that has a profitable project but has no capital to develop it. The profit from the project depends on the capital invested in the project as well as a profitability parameter. The model also assumes that the financial market is represented by a single investor who maximizes the expected wealth. The owner has superior information as to the value of the firm to investors in the sense that it knows the true value of the parameter while investors have only a probability distribution about the parameter. The owner offers the representative investor a fraction of the ownership of the firm in return for a certain amount of investment in the firm. This offer condition is equivalent to the usual offer condition consisting of the number of issues to sell and the unit price of a share. Thus, the model is a signalling game. Using Kreps' criterion as the solution concept, we obtained an essentially unique separating equilibrium offer condition. Analysis of this separating equilibrium shows that the owner of the firm with high profitability chooses an offer condition that raises an amount of capital that is short of the amount that maximizes the potential profit from the project. It also reveals that the fraction of the ownership of the firm that the representative investor receives from the owner of the highly profitable firm in return for its investment has a value that exceeds the investment. In other words, the initial offering in the model is underpriced when the profitability of the firm is high. The source of underpricing and underinvestment is the signalling activity by the owner of the highly profitable firm who attempts to convince investors that his firm has a highly profitable project by choosing an offer condition that cannot be imitated by the owner of a firm with low profitability. Thus, we obtained two main results. First, underpricing is a result of a signalling activity by the owner of a firm with high profitability when there exists information asymmetry between the owner of the issuing firm and investors. Second, such information asymmetry also leads to underinvestment in a highly profitable project. Those results clearly show the underpricing entails underinvestment and that information asymmetry leads to a social cost as well as a private cost. The above results are quite general in the sense that they are based upon a neoclassical profit function and full rationality of economic agents. We believe that the results of this paper can be used as a basis for further research on the capital investment process. For instance, one can view the results of this paper as a subgame equilibrium in a larger game in which a firm chooses among diverse ways to raise capital. In addition, the method used in this paper can be used in analyzing a wide range of problems arising from information asymmetry that the Korean financial market faces.

• Information Systems Review
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• v.26 no.1
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• pp.269-288
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• 2024

The main objective of this study is to empirically explore how the organizational culture influences financial performance of companies. To achieve this, 58 companies included in the KOSPI 200 were selected from an online job platform in South Korea, JobPlanet. In order to understand the organizational culture of these companies, data was collected and analyzed from 81,067 reviews written by current and former members of these companies on JobPlanet over a period of 9 years from 2014 to 2022. To define the organizational culture of each company based on the review data, this study utilized well-known text analysis techniques, namely Word2Vec and FastText analysis methods. By modifying, supplementing, and extending the keywords associated with the five organizational culture values (Innovation, Integrity, Quality, Respect, and Teamwork) defined by Guiso et al. (2015), this study created a new Culture Dictionary. By using this dictionary, this study explored which cultural values-related keywords appear most often in the review data of each company, revealing the relative strength of specific cultural values within companies. Going a step further, the study also investigated which cultural values statistically impact financial performance. The results indicated that the organizational culture focusing on innovation and creativity (Innovation) and on customers and the market (Quality) positively influenced Tobin's Q, an indicator of a company's future value and growth. For the indicator of profitability, ROA, only the organizational culture emphasizing customers and the market (Quality) showed statistically significant impact. This study distinguishes itself from traditional surveys and case analysis-based research on organizational culture by analyzing large-scale text data to explore organizational culture.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.123-139
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• 2019

The Korean NPL market was formed by the government and foreign capital shortly after the 1997 IMF crisis. However, this market is short-lived, as the bad debt has started to increase after the global financial crisis in 2009 due to the real economic recession. NPL has become a major investment in the market in recent years when the domestic capital market's investment capital began to enter the NPL market in earnest. Although the domestic NPL market has received considerable attention due to the overheating of the NPL market in recent years, research on the NPL market has been abrupt since the history of capital market investment in the domestic NPL market is short. In addition, decision-making through more scientific and systematic analysis is required due to the decline in profitability and the price fluctuation due to the fluctuation of the real estate business. In this study, we propose a prediction model that can determine the achievement of the benchmark yield by using the NPL market related data in accordance with the market demand. In order to build the model, we used Korean NPL data from December 2013 to December 2017 for about 4 years. The total number of things data was 2291. As independent variables, only the variables related to the dependent variable were selected for the 11 variables that indicate the characteristics of the real estate. In order to select the variables, one to one t-test and logistic regression stepwise and decision tree were performed. Seven independent variables (purchase year, SPC (Special Purpose Company), municipality, appraisal value, purchase cost, OPB (Outstanding Principle Balance), HP (Holding Period)). The dependent variable is a bivariate variable that indicates whether the benchmark rate is reached. This is because the accuracy of the model predicting the binomial variables is higher than the model predicting the continuous variables, and the accuracy of these models is directly related to the effectiveness of the model. In addition, in the case of a special purpose company, whether or not to purchase the property is the main concern. Therefore, whether or not to achieve a certain level of return is enough to make a decision. For the dependent variable, we constructed and compared the predictive model by calculating the dependent variable by adjusting the numerical value to ascertain whether 12%, which is the standard rate of return used in the industry, is a meaningful reference value. As a result, it was found that the hit ratio average of the predictive model constructed using the dependent variable calculated by the 12% standard rate of return was the best at 64.60%. In order to propose an optimal prediction model based on the determined dependent variables and 7 independent variables, we construct a prediction model by applying the five methodologies of discriminant analysis, logistic regression analysis, decision tree, artificial neural network, and genetic algorithm linear model we tried to compare them. To do this, 10 sets of training data and testing data were extracted using 10 fold validation method. After building the model using this data, the hit ratio of each set was averaged and the performance was compared. As a result, the hit ratio average of prediction models constructed by using discriminant analysis, logistic regression model, decision tree, artificial neural network, and genetic algorithm linear model were 64.40%, 65.12%, 63.54%, 67.40%, and 60.51%, respectively. It was confirmed that the model using the artificial neural network is the best. Through this study, it is proved that it is effective to utilize 7 independent variables and artificial neural network prediction model in the future NPL market. The proposed model predicts that the 12% return of new things will be achieved beforehand, which will help the special purpose companies make investment decisions. Furthermore, we anticipate that the NPL market will be liquidated as the transaction proceeds at an appropriate price.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.11 no.2
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• pp.221-232
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• 2016

The impact of the recent small business start-up competition in the market, has become overheated. It is effected by early retirement of a generation of youth employment. This study is a study on the impact of SEMAS(Small Enterprise and Market Service) system operating of the funding system, education support programs, and consulting support system on the business performance of small business owners. It has surveyed 272 business owners, in Gyeongsangnam-province. The study includes specific support system for usage frequency and satisfaction and conducted from January 2013 to September 2015. In addition, it analyzes characteristic that motivation, business model, item, owner's experience, sales and demographic by small business owner. Analysis results, the management performance of small business that uses financial support system and consulting support system is shown to be high. But education support system is the opposite effect. As a result, the management performance is related to industry experience. Therefore education support system need to be reorganized to the support depends on the development stage. This study was conducted to help small business owners entered the start-up market and a decision-making person with a policy decision.

• Health Policy and Management
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• v.7 no.1
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• pp.55-72
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• 1997

The purpose of this study is to analyze the consumers' demand pattern and the feature of the market for nursing homes, the number of which is tending upwards. The survey data were obtained from the interview of 500 elderly people living in Seoul and Kyung-Ki provincial area. All respondents were 60 years of age and above. The main findings were summarized as follows: 1. The respondents who are less aged, highly educated comparatively, and living with spouse show positive response for the use of nursing homes. The aged living independently and the aged living with unmarried children show higher demand for this facility. Also, the respondents who prefer independent living away from their childrenn, urban areas as their residence and flat-type housing show more interest for the facility. The respondents who are self- supportive, who has no financial supporter, no caretaker, and no domestic helper demonstrate strong inclination to the use of the facility. The respondents who are interested in this kind of facility, acknowledge the necessity of it show strong intention of moving into it. 2. Logistic regression analysis was conducted to understand factors related to the intention of moving into the nursing homes. The group who wish to live separated from their children in the future give 1.78 times more favorable response than the opposite. The group who have an interest in the facility for elderly has 2.02 times higher intention of moving than the opposite. The group who have an intention of using the facility for elderly it is 7.34 times more likely to move into it. 3. The respondents who are the potential consumers for nursing homes can be subdivided. Within the positive group, it could be divided into the group of living independently with the preference of flat-type housing, the group living independently with the preference of separate housing, and the group wishing to live with their children. Within the negative group, the factor of the division is their concern to the facility. Following this study, it can be said that old age people, who have been regarded as one homogeneous group so far, should be recognized as one characteristic individual. This study also shows that the demand aspect yet in its initial stage shold be researched in anticipation of rapid increase. The understanding of diciding factors, the segmentation of potential market will help work out proper strategy, which will contribute to providers' benefit.

• Korean journal of communication and information
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• v.78
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• pp.177-205
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• 2016

This study set out to investigate the reality of local press and changes to the occupational identity of reporters through the job mobility of reporters at local newspapers. The study examined what reasons the reporters had when they retired from one of three paper newspapers in Daejeon, where they moved to, and how their career mobility was. Some of them remained in the field of journalism including paper newspapers of the same kind and Internet newspapers, and others moved to various areas including politics, administration, academy, economy, and culture and art. The biggest number of them said they left their old paper newspapers because of poor wages and welfare benefits and absence of future visions. Their decision of leaving their old paper newspapers was also influenced by restructuring, restrictions to coverage and reporting, and great workload. Before the IMF foreign currency crisis in 1997, the press labor market was a typical internal labor market with the practitioners joining a newspaper in open recruitment and climbing up the promotion ladder from a common reporter through Deputy Head and Head of a department to Director of a bureau. The emergence of new media and the financial difficulties of newspaper corporations were currently making the internal labor market worse. Reporters made active use of social capital such as regionalism, alumni ties, and news beats rather than changing jobs by increasing their professionalism through self-development, thus causing side effects including the weakened supervision and criticism functions of local newspapers and damaging their occupational identity as reporters.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.6
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• pp.110-118
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• 2017

This study investigated empirically one of the controversial subjects in modern finance, in that there is an optimal level of capital structure for KOSPI-listed firms in the Korean capital market. Given the major theories on the capital structure, such as Myers' pecking order, trade-off, and agency cost ones, this study applied an analysis of covariance models in parametric and non-parametric statistical methods. In particular, two covariates to control for the possible effects of trade-off and agency cost, were employed separately in each corresponding model, while the other proxy for pecking order rationale was adopted in previous research [1] to conduct inter- and intra-industry analyses. Based on the outcomes obtained from the study, it was demonstrated empirically that there are optimal capital structures for firms in the sample industries at the inter-industry level, whereas statistical differences indicating non-existence of an optimal point, were revealed within the industry. Accordingly, these findings suggest a new vision to potential investors that firms in the domestic market may have financial opportunities to increase their value by gradually adjusting the leverage ratios in terms of the intra-industry perspective.