• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.361-381
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• 2023

Research on corporate bankruptcy prediction has been focused on financial information. Since the company's financial information is updated quarterly, there is a problem that timeliness is insufficient in predicting the possibility of a company's business closure in real time. Evaluated companies that want to improve this need a method of judging the soundness of a company that uses information other than financial information to judge the soundness of a target company. To this end, as information technology has made it easier to collect non-financial information about companies, research has been conducted to apply additional variables and various methodologies other than financial information to predict corporate bankruptcy. It has become an important research task to determine whether it has an effect. In this study, we examined the impact of electronic payment-related information, which constitutes non-financial information, when predicting the closure of business operators using electronic payment service and examined the difference in closure prediction accuracy according to the combination of financial and non-financial information. Specifically, three research models consisting of a financial information model, a non-financial information model, and a combined model were designed, and the closure prediction accuracy was confirmed with six algorithms including the Multi Layer Perceptron (MLP) algorithm. The model combining financial and non-financial information showed the highest prediction accuracy, followed by the non-financial information model and the financial information model in order. As for the prediction accuracy of business closure by algorithm, XGBoost showed the highest prediction accuracy among the six algorithms. As a result of examining the relative importance of a total of 87 variables used to predict business closure, it was confirmed that more than 70% of the top 20 variables that had a significant impact on the prediction of business closure were non-financial information. Through this, it was confirmed that electronic payment-related information of non-financial information is an important variable in predicting business closure, and the possibility of using non-financial information as an alternative to financial information was also examined. Based on this study, the importance of collecting and utilizing non-financial information as information that can predict business closure is recognized, and a plan to utilize it for corporate decision-making is also proposed.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.57-76
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• 2017

Recently, consumer needs are diversifying as information technologies are evolving rapidly. A lot of IT devices such as smart phones and tablet PCs are launching following the trend of information technology. While IT devices focused on the technical advance and improvement a few years ago, the situation is changed now. There is no difference in functional aspects, so companies are trying to differentiate IT devices in terms of appearance design. Consumers also consider design as being a more important factor in the decision-making of smart phones. Smart phones have become a fashion items, revealing consumers' own characteristics and personality. As the design and appearance of the smartphone become important things, it is necessary to examine consumer values from the design and appearance of IT devices. Furthermore, it is crucial to clarify the mechanisms of consumers' design evaluation and develop the design evaluation model based on the mechanism. Since the influence of design gets continuously strong, various and many studies related to design were carried out. These studies can classify three main streams. The first stream focuses on the role of design from the perspective of marketing and communication. The second one is the studies to find out an effective and appealing design from the perspective of industrial design. The last one is to examine the consumer values created by a product design, which means consumers' perception or feeling when they look and feel it. These numerous studies somewhat have dealt with consumer values, but they do not include product attributes, or do not cover the whole process and mechanism from product attributes to consumer values. In this study, we try to develop the holistic design evaluation model based on consumer values based on three-step approach from product attributes, perceived attributes, to consumer values. Product attributes means the real and physical characteristics each smart phone has. They consist of bezel, length, width, thickness, weight and curvature. Perceived attributes are derived from consumers' perception on product attributes. We consider perceived size of device, perceived size of display, perceived thickness, perceived weight, perceived bezel (top - bottom / left - right side), perceived curvature of edge, perceived curvature of back side, gap of each part, perceived gloss and perceived screen ratio. They are factorized into six clusters named as 'Size,' 'Slimness,' 'No-Frame,' 'Roundness,' 'Screen Ratio,' and 'Looseness.' We conducted qualitative research to find out consumer values, which are categorized into two: look and feel values. We identified the values named as 'Silhouette,' 'Neatness,' 'Attractiveness,' 'Polishing,' 'Innovativeness,' 'Professionalism,' 'Intellectualness,' 'Individuality,' and 'Distinctiveness' in terms of look values. Also, we identifies 'Stability,' 'Comfortableness,' 'Grip,' 'Solidity,' 'Non-fragility,' and 'Smoothness' in terms of feel values. They are factorized into five key values: 'Sleek Value,' 'Professional Value,' 'Unique Value,' 'Comfortable Value,' and 'Solid Value.' Finally, we developed the holistic design evaluation model by analyzing each relationship from product attributes, perceived attributes, to consumer values. This study has several theoretical and practical contributions. First, we found consumer values in terms of design evaluation and implicit chain relationship from the objective and physical characteristics to the subjective and mental evaluation. That is, the model explains the mechanism of design evaluation in consumer minds. Second, we suggest a general design evaluation process from product attributes, perceived attributes to consumer values. It is an adaptable methodology not only smart phone but also other IT products. Practically, this model can support the decision-making when companies initiative new product development. It can help product designers focus on their capacities with limited resources. Moreover, if its model combined with machine learning collecting consumers' purchasing data, most preferred values, sales data, etc., it will be able to evolve intelligent design decision support system.

• Journal of Korean Society of Transportation
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• v.34 no.1
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• pp.43-54
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• 2016

Although there are continuous demands for activating BSSs(Bicycle Sharing Systems) due to the convenience and positive health effects, it is difficult to make a decision to support the existing systems and build more systems because of the deficit resulting from the operation of BSSs. Consequently, this study estimated the economic effects(WTP; Willingness to Pay) of BSS and analyzed the impact factors of WTP to support the above decision making in Daejeon. For this, we conducted a survey and collected 668 samples from the users and non-users of TASHU that is the BSS operated in Daejeon. Also, we used CVM(Contingent Valuation Method) for the estimation of WTP. The results show that the number of bicycle uses is a determinant factor having a positive relationship with WTP and car ownership and age are also determinant factors having a negative relationship with WTP. On the other hand, income and sex have no significant statistical relationship with WTP. Also, the economic benefit of TASHU was estimated as much as 49.9 billion KRW to 63.6 billion KRW. Considering the operation cost of 2.5 billion KRW, it is quite big benefit. Based on the results, it needs to support TASHU from a user perspective for the efficient operation of the system.

• Journal of Food Hygiene and Safety
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• v.18 no.4
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• pp.202-210
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• 2003

This study aims at making a HACCP(Hazard Analysis Critical Control Point)plan to be applied to soybean curd and verifing its effectiveness. First, we develped a general model of HACCP according to the guidelines of Codex (FAO/WHO). And we applied the model to 4 soybean curd workshops for 3 months. The HACCP model is composed of these procedures; HACCP team organization, production description, work flow chart, hazatd analysis, CCP (critical control point) decision, CL (critical limit) establishment, monitoring method decision, correction, verification and documentation. CCP were selection procedure and refrigeration procedure in non-wrapped soybean curd. CCP were selection procedure, heat-sterilizing and refrigeration in wrapped soybean curd. The result of bacterial experiment after apling the model for 3 months, the bacterial numbers of soybean curd box, wrapper, and soybean curd production were lower after appling than before appling, the model. We could verify that the appications of the HACCP model were effective to the soybean curd workshops.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.6
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• pp.116-126
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• 2021

Ground Component Command (GCC) has been developing operational planning and execution systems to implement "Decisive Integrated Operations", which is the concept of ground operations execution, and achieved remarkable results. In particular, "Simultaneous Offense-Defense Integrated Operations" is developed mainly to neutralize enemies in deep areas and develop favorable conditions for the allies early by simultaneously attacking and defending from the beginning of the war. On the other hand, it is limited to providing scientific and reasonable support for the commander's decision-making process because analyzing the effects of the deep operation with existing M&S systems is impossible. This study developed a model for analyzing the effects of deep operations that can be used in the KJCCS. Previous research was conducted on the effects of surveillance, physical strike, and non-physical strike, which are components of deep operations to find the characteristics and limitations and suggest a research direction. A methodology for analyzing the effects of deep operations reflecting the interactions of components using data was then developed by the GCC, and input data for each field was calculated through combat experiments and a literature review. Finally, the Deep operations Effect CAlculating Model(DECAM) was developed and distributed to the GCC and Corps battle staff during the ROK-US Combined Exercise. Through this study, the effectiveness of the methodology and the developed model were confirmed and contribute to the development of the GCC and Corps' abilities to perform deep operations.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.347-364
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• 2022

Fraudulent companies or sellers strategically manipulate reviews to influence customers' purchase decisions; therefore, the reliability of reviews has become crucial for customer decision-making. Since customers increasingly rely on online reviews to search for more detailed information about products or services before purchasing, many researchers focus on detecting manipulated reviews. However, the main problem in detecting manipulated reviews is the difficulties with obtaining data with manipulated reviews to utilize machine learning techniques with sufficient data. Also, the number of manipulated reviews is insufficient compared with the number of non-manipulated reviews, so the class imbalance problem occurs. The class with fewer examples is under-represented and can hamper a model's accuracy, so machine learning methods suffer from the class imbalance problem and solving the class imbalance problem is important to build an accurate model for detecting manipulated reviews. Thus, we propose an OpenAI-based reviews generation model to solve the manipulated reviews imbalance problem, thereby enhancing the accuracy of manipulated reviews detection. In this research, we applied the novel autoregressive language model - GPT-3 to generate reviews based on manipulated reviews. Moreover, we found that applying GPT-3 model for oversampling manipulated reviews can recover a satisfactory portion of performance losses and shows better performance in classification (logit, decision tree, neural networks) than traditional oversampling models such as random oversampling and SMOTE.

• Journal of Environmental Impact Assessment
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• v.32 no.1
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• pp.29-40
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• 2023

The need for improvement is raised due to limitations with environmental impact assessment, and the importance for data-based environmental impact assessment is increasing. In this study, data demand was derived by analyzing Agreed Terms and Conditions in the Water Environment field (Water Quality, Hydraulic & Hydrologic Conditions, and Marine Environment) of environmental impact assessment. Agreed Terms and Conditions on environmental impact assessment in the water environment field were classified and categorized by environmental impact assessment stage (addition to status survey, impact prediction and evaluation, establishment of reduction measures, post-environmental impact survey), and data demand for each type of consultation opinion was linked. As a result of the categorization of Agreed Terms and Conditions, it was classified into 18 types in the water quality, 15 types in the hydraulic & hydrologic conditions, and 17 types in the marine environment. As a result of linking data demand, the total number of data demand was 236 in the water quality, 98 in the hydraulic & hydrologic conditions, and 73 in the marine environment. The highest number of Agreed Terms and Conditions and data demands were found in the water quality for the evaluation item and establishment of reduction measures, specifically establishment of non-point source pollution reduction measures, for the stage. The numbers were judged to be linked to the relative importance of the items and the primary purpose of environmental impact assessment. The derivation of data demand through the analysis of Agreed Terms and Conditions in the environmental impact assessment can contribute to the advancement of the preparation of environmental impact assessment reports and is expected to increase data utilization by various decision-makers by establishing a systematic database.

• Journal of East Asia Management
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• v.1 no.1
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• pp.1-27
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• 2020

Military experience has a great impact on a soldier ability to handle risks. Therefore, when those soldiers become managers, they may behave differently in making risky corporate decisions, especially in activities like the R&D investment. However, studies on how military experience affect R&D have been largely missing in the largest emerging economy, i.e. China, despite that the country hires a higher percentage of military managers than the US. In addition, it remains a question whether military managers affect the state-owned enterprises (SOEs) in China, as many of the corporate decisions are made by the government. This paper tries to address these questions. The imprinting theory and the upper echelon theory suggest that managers' personal experience can affect their behaviour, which in turn influences their corporate decisions. In this paper, we examine whether managers with military experience lead to higher R&D investment and whether such an effect exists in state-owned enterprises. Based on a sample of listed firms in China's A-share market over 2008-2017, we make two findings. First, companies with military managers have high R&D investment. By dividing managers' military positions into high and low rank, we find that companies tend to have higher (lower) R&D investment if their managers hold a high-rank (low-rank) position. Second, the effect of high-rank military managers on R&D investment is more pronounced if the manager is also the founder and the company is a non-state-owned enterprise. For low-ranking military managers, a stronger effect on R&D investment is also observed if they are also the founder, but whether their companies are state-owned or not has no impact on R&D investment. This study identifies managers' military experience as a contributing factors to corporate R&D investment in the largest emerging economy. This paper tests an implication of the imprinting theory and the upper echelon theory, i.e., managers' personal experience can affect their behaviour, which in turn influences their corporate decisions. Specifically, we focus on one aspect of personal experience - military experience - and look at whether it is beneficial to firms' technological innovation, therefore enriches the literature of managerial heterogeneity. Our findings on the influence of managers' military experience on firms' technological innovation can help us better understand the role of managers play in corporate decision making, and how managers' individual traits interact with the firm's characteristics.

• Journal of the Korean Geotechnical Society
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• v.23 no.6
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• pp.97-102
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• 2007

Though the stability analysis of soil slopes widely employs the limit equilibrium method, the study on the jointed rock slopes must consider the direction of joint and the characteristics of Joint at the same time. This study analyzes the result of the change in the factors which show the characteristics of discontinuity and the shape factor of rock slopes, and so on, in an attempt to validate the propriety as to the interpretation of jointed rock slope stability which uses the general finite element program. First, the difference depending on the flow rules was compared, and the factor effect study was conducted. The selected independent variables included the direction of joint which displays the mechanical characteristics of discontinuity, adhesive cohesion, friction angle, the inclination and height of rock slope which reveal the shape of slope and surcharge load. And the horizontal displacement was numerically interpreted at the 1/3 point below the slope, a dependent variable, to compare the relative degree of factor effects. The findings of study on factor effects led to the validation that the result of horizontal displacement for each factor satisfied various engineering characteristics, making it possible to be applied to stability interpretation of jointed rock slope. A modelling is possible, which considers the application of the result of real geotechnical surveys & laboratory studies and the non-linear characteristics when designing the rock slope. In addition, the stress change which may result from the natural disaster, such as precipitation, and the construction, can be expressed. Furthermore, as the complicated rock condition and the ground supporting effect can be considered through FEM, it is considered to be very useful in making an engineering decision on the cut-slope, reinforcement and so on.

• 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.