• Journal of Global Scholars of Marketing Science
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• v.18 no.4
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• pp.307-331
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• 2008

The CEO and the Corporate Image is considered very important in the aspect of marketing. The fact that CEO image itself influences the company or value of the product directly and indirectly has been verified through many cases. Recently, the differentiation of products and services between companies became difficult because the disparity in technique between companies retrenched. As a result, the rate of people who decide to purchase or invest their money based on the corporate image or reputation has been increased. Also in the knowledge society like today, the talented employees are the company's customer and the company's necessity for managing those brains of marketing perspective on how to satisfy and attract the customers is being embossed. The Fashion industry is one of the most value-added industry and in those value-added businesses, the most important factor is the human resources' knowledge power. However the study of the relationships among the CEO image, the corporate image and employment brand value in fashion industry has not been carried out yet. This research considers that dynamic relationship exists among the CEO image, corporate image and employment brand value that affects a company's main goal of pursuing benefits and intends to investigate the relationships of the three concepts. The specific purposes of this study were, 1) to analyze the impact of CEO image on a corporate image, 2) to analyze the impact of corporate image on employment brand value, 3) to analyze the impact of CEO image on employment brand value, 4) to analyze whether corporate image plays a mediating role in the relationship between CEO image and employment brand value or not. A survey design with a structured questionnaire was employed for this research. A convenience sample of 398 subjects was selected from two groups, which are university students majoring in fashion and practitioners working in fashion industry. For the data analysis, descriptive statistic (i.e., frequency, percentage), factor analysis, and multiple regression analysis were used by utilizing SPSS 12.0 for Windows program. The results for this research are as follows, first, the study of the impact of CEO image (i.e., Managerial Competence, Reliability/Leadership, Personal Attractiveness) on corporate image (i.e., Product Image, Corporate Social Responsibility Image, Corporate Cultural Image) brought conclusion that the CEO image generally affected the corporate image in fashion industry. Managerial Competence and Reliability/Leadership affected Product Image, Corporate Social Responsibility Image and Corporate Cultural Image. However, while CEO's Personal Attractiveness affected Product Image and Corporate Social Responsibility Image, it did not affect Corporate Cultural Image. Second, the study of the impact of corporate image on employment brand value brought conclusion that corporate image (i.e., Product Image, Corporate Social Responsibility Image, Corporate Cultural Image) affected employment brand value. Corporate Cultural Image affected employment brand value the most and then the Corporate Social Responsibility Image and Product Image. Third, the study of the impact of CEO image on employment brand value brought conclusion that CEO image (i.e., Managerial Competence, Reliability/Leadership, Personal Attractiveness) affected the employment brand value. CEO's Reliability/Leadership affected the employment brand value the most and then CEO's Personal Attractiveness and CEO's Managerial Competence. Forth, the study examined whether corporate image plays a mediating role in relationship of CEO image and employment brand value and concluded that it does. Corporate image played a full mediating role between CEO's Managerial Competence and employment brand value while it played a partial mediating role between CEO's Reliability/Leadership and CEO's Personal Attractiveness. This study is meaningful in a sense that it examines the relationship among the CEO image, corporate image and employment brand value which has not been carried out yet in fashion industry. It will ultimately contribute to the success of a fashion company by providing useful information of establishing strategies for managing proper the CEO and the corporate image to the fashion company and operating the talented employees.

• Asia pacific journal of information systems
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• v.20 no.4
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• pp.101-123
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• 2010

Recently, firms are operating a number of teams to accomplish organizational performance. Especially, ad hoc teams like proposal preparation team are quite different from permanent teams like R&D team in the sense of how the team forms network structure and deals with organizational learning culture and knowledge management participation efforts. Moreover, depending on the team characteristics, individual creativity will differ from each other, which will lead to organizational performance eventually. Previous studies in the field of creativity are lacking in this issue. So main objectives of this study are organized as follows. First, the issue of how to improve individual creativity and organizational performance will be analyzed empirically. This issue will be performed depending on team characteristics such as ad hoc team and permanent team. Antecedents adopted for this research objective are cultural and knowledge factors such as organizational learning culture, and knowledge management participation. Second, the network structure such as degree centrality, and structural hole is used to analyze its influence on individual creativity and organizational performance. SI (System Integration) companies are facing severely tough requirements from clients to submit very creative proposals. Also, R&D teams are widely accepted as relatively creative teams because their responsibilities are focused on suggesting innovative techniques to make their companies remain competitive in the market. SI teams are usually ad hoc, while R&D teams are permanent on an average. By taking advantage of these characteristics of the two kinds of teams, we will prove the validity of the proposed research questions. To obtain the survey data, we accessed 7 SI teams (74 members), and 6 R&D teams (63 members), collecting 137 valid questionnaires. PLS technique was applied to analyze the survey data. Results are as follows. First, in case of SI teams, organizational learning culture affects individual creativity significantly. Meanwhile, knowledge management participation has a significant influence on Individual creativity for the permanent teams. Second, degree centrality Influences individual creativity significantly in case of SI teams. This is comparable with the fact that structural hole has a significant impact on individual creativity for the R&D teams. Practical implications can be summarized as follows: First, network structure of ad hoc team should be designed differently from one of permanent team. Ad hoc team is supposed to show a high creativity in a rather short period, implying that network density among team members should be improved, and those members with high degree centrality should be encouraged to show their Individual creativity and take a leading role by allowing them to get heavily engaged in knowledge sharing and diffusion. In contrast, permanent team should be designed to take advantage of structural hole instead of focusing on network density. Since structural hole can be utilized very effectively in the permanent team, strong arbitrators' merits in the permanent team will increase and therefore helps increase both network efficiency and effectiveness too. In this way, individual creativity in the permanent team is likely to lead to organizational creativity in a seamless way. Second, way of Increasing individual creativity should be sought from the perspective of organizational culture and knowledge management. Organization is supposed to provide a cultural atmosphere in which Innovative idea suggestions and active discussion among team members are encouraged. In this way, trust builds up among team members, facilitating the formation of organizational learning culture. Third, in the ad hoc team, organizational looming culture should be built such a way that individual creativity can grow up fast in a rather short period. Since time is tight, reasonable compensation policy, leader's Initiatives, and learning culture formation should be done In a short period so that mutual trust is built among members quickly, and necessary knowledge and information can be learnt rapidly. Fourth, in the permanent team, it should be kept in mind that the degree of participation in knowledge management determines level of Individual creativity. Therefore, the team ought to facilitate knowledge circulation process such as knowledge creation, storage, sharing, utilization, and learning among team members, which will lead to team performance. In this way, firms must control knowledge networks in permanent team and ad hoc team in a way mentioned above so that individual creativity as well as team performance can be maximized.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.127-139
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• 2013

With an advent of recent knowledge-based society, the interest in intellectual property has increased. Firms have tired to result in productive outcomes through continuous innovative activity. Especially, ICT firms which lead high-tech industry have tried to manage intellectual property more systematically. Firm's interest in the patent has increased in order to manage the innovative activity and Knowledge property. The patent involves not only simple information but also important values as information of technology, management and right. Moreover, as the patent has the detailed contents regarding technology development activity, it is regarded as valuable data. The patent which reflects technology spread and research outcomes and business performances are closely interrelated as the patent is considered as a significant the level of firm's innovation. As the patent information which represents companies' intellectual capital is accumulated continuously, it has become possible to do quantitative analysis. The advantages of patent in the related industry information and it's standardize information can be easily obtained. Through the patent, the flow of knowledge can be determined. The patent information can analyze in various levels from patent to nation. The patent information is used to analyze technical status and the effects on performance. The patent which has a high frequency of citation refers to having high technological values. Analyzing the patent information contains both citation index analysis using the number of citation and network analysis using citation relationship. Network analysis can provide the information on the flows of knowledge and technological changes, and it can show future research direction. Studies using the patent citation analysis vary academically and practically. For the citation index research, studies to analyze influential big patent has been conducted, and for the network analysis research, studies to find out the flows of technology in a certain industry has been conducted. Social network analysis is applied not only in the sociology, but also in a field of management consulting and company's knowledge management. Research of how the company's network position has an impact on business performances has been conducted from various aspects in a field of network analysis. Social network analysis can be based on the visual forms. Network indicators are available through the quantitative analysis. Social network analysis is used when analyzing outcomes in terms of the position of network. Social network analysis focuses largely on centrality and structural holes. Centrality indicates that actors having central positions among other actors have an advantage to exert stronger influence for exchange relationship. Degree centrality, betweenness centrality and closeness centrality are used for centrality analysis. Structural holes refer to an empty place in social structure and are defined as efficiency and constraints. This study stresses and analyzes firms' network in terms of the patent and how network characteristics have an influence on business performances. For the purpose of doing this, seventy-four ICT companies listed in S&P500 are chosen for the sample. UCINET6 is used to analyze the network structural characteristics such as outdegree centrality, betweenness centrality and efficiency. Then, regression analysis test is conducted to find out how these network characteristics are related to business performance. It is found that each network index has significant impacts on net income, i.e. business performance. However, it is found that efficiency is negatively associated with business performance. As the efficiency increases, net income decreases and it has a negative impact on business performances. Furthermore, it is shown that betweenness centrality solely has statistically significance for the multiple regression analysis with three network indexes. The patent citation network analysis shows the flows of knowledge between firms, and it can be expected to contribute to company's management strategies by analyzing company's network structural positions.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.2
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• pp.235-247
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• 2019

In order to achieve the results that are appropriate for the purpose of the enterprise, it is important to comprehensively understand the behaviors within the roles of the members of the organization and actions outside the roles. However, there have been relatively few studies on corporate social responsibility (CSR) among the existing studies that have been conducted to date. In particular, organizational citizenship behavior, a voluntary commitment by organizational members, is perceived as a very effective way of enhancing corporate performance, but studies on organizational citizenship behavior based on corporate social responsibility have rarely been conducted. In recent years, domestic companies have recognized social responsibility as an activity rather than an additional activity. Therefore, it is very meaningful to look at the organizational performance by examining the factors that make up this social responsibility from the perspective of the company. It is considered a task. In order to demonstrate this, this study collected 303 data from a business incubator center operated by universities and public institutions nationwide and used a total of 303 samples. As a result of the verification, the wages received by the members of the organization did not affect the organizational commitment, and the working hours and the working environment affected the organizational commitment. In addition, corporate social responsibility has an effect on organizational trust in the marketing side. Organizational commitment and organizational trust were studied to positively affect organizational citizenship behavior. In addition, this study divides the degree of perception of social responsibility of the organizational members into the high recognition group and the low recognition group and analyzed whether there is a difference in the level of organizational citizenship behavior according to the employment type (regular and irregular workers) The results of this study are as follows.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.1
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• pp.71-94
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• 2020

Korea's innovation cluster policy has been implemented since 2005 with the goal of balanced regional development. The purpose of this study is to investigate the factors affecting the sustainability of innovative cluster tenants by using Pangyo Techno Valley as an example. Pangyo Techno Valley was established under the leadership of the local government (Gyeonggi-do) rather than the central government and it is called "Silicon Valley of Korea" and "Asia Silicon Valley" and is becoming more representative. The growing number of companies in Pangyo Techno Valley decreased in 2017 compared to 2016. This is because Pangyo Techno Valley's business ecosystem will change from 2019. In this paper, quantitative and qualitative studies were conducted to investigate the influencing factors. Quantitative research was conducted based on the survey and qualitative research was applied through interviews. The quantitative research examined the factors affecting the sustainability of Pangyo Techno Valley, and the qualitative research examined the specific reasons and additional factors for the quantitative research results. The quantitative results showed that factors affecting sustainability in terms of changes in corporate internal conditions, human and physical infrastructure, cooperation and synergy, and occupancy patterns. The specific reason for the impact appeared in the qualitative research process. The support category of local governments did not show any significant factors in quantitative research. In addition, qualitative research suggested 'Good image of Pangyo Techno Valley' as the category that has the greatest impact on sustainability. It is shown that companies are passive and expect the role of local governments in activating cooperation network in Pangyo Techno Valley. In this paper, based on the results of the study, Pangyo Techno Valley is presented with a realistic plan based on real estate issues and an ideal plan with a long-term perspective.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.5
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• pp.91-107
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• 2023

Lean startup is a concept that combines the words "lean," meaning an efficient way of running a business, and "startup," meaning a new business. It is often cited as a strategy for minimizing failure in early-stage businesses, especially in software-based startups. By scrutinizing the case of a startup L, this study suggests that lean startup methodology(LSM) can be useful for hardware and manufacturing companies and identifies ways for early startups to successfully implement LSM. To this end, the study explained the core of LSM including the concepts of hypothesis-driven approach, BML feedback loop, minimum viable product(MVP), and pivot. Five criteria to evaluate the successful implementation of LSM were derived from the core concepts and applied to evaluate the case of startup L . The early startup L pivoted its main business model from defecation alert system for patients with limited mobility to one for infants or toddlers, and finally to a smart bottle for infants. In developing the former two products, analyzed from LSM's perspective, company L neither established a specific customer value proposition for its startup idea and nor verified it through MVP experiment, thus failed to create a BML feedback loop. However, through two rounds of pivots, startup L discovered new target customers and customer needs, and was able to establish a successful business model by repeatedly experimenting with MVPs with minimal effort and time. In other words, Company L's case shows that it is essential to go through the customer-market validation stage at the beginning of the business, and that it should be done through an MVP method that does not waste the startup's time and resources. It also shows that it is necessary to abandon and pivot a product or service that customers do not want, even if it is technically superior and functionally complete. Lastly, the study proves that the lean startup methodology is not limited to the software industry, but can also be applied to technology-based hardware industry. The findings of this study can be used as guidelines and methodologies for early-stage companies to minimize failures and to accelerate the process of establishing a business model, scaling up, and going global.

• Management & Information Systems Review
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• v.33 no.3
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• pp.129-154
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• 2014

We are making products and services that we didn't easily think or even imagine when young and living using them. Consumer needs become more diversified, markets and technologies are being constantly changed, and corporate global competitiveness is getting intensified. Now, companies that don't change nor create new values cannot survive. The cycle of new products is faster, and therefore corporations should improve previous products continuously or create new values according to such a trend. This era requires 'creativity' for companies to overcome uncertainty and survive. Corporate creativity is not easily activated as financial and physical compensation unlike the work ability or performance. When there is an environment that people with various abilities, experiences and backgrounds can express their opinions freely, it becomes possible. Despite the importance, the studies have focused on creativity education so far instead of the viewpoint of corporate business administration. In addition, most studies have dealt with job characteristics and employees' personality factors rather than the process that is connected to actual performance. In other words, the studies on environmental factors that can lead motivation as situational characteristics of the employee's company in a current non-monetary and non-physical way are very insufficient. Therefore, this study aims to comprehensively examine the process of creating creative performance in the environment inside the company that the employee could approach most closely from the corporate deinstitutional perspective. As the major variables, job characteristics and supervisor's ethical leadership were selected, and the process that is connected to the creative performance was explained by means of motivation necessary to understand creativity of the organization and creative process engagement(CPE). The summary of the verification results is following. First, job characteristics and supervisor's attitude(ethical leadership) had positive effects on intrinsic motivation and creativity process engagement. Second, intrinsic motivation and creativity process engagement had positive effects on creative performance. Third, intrinsic motivation and creativity process engagement had mediated the relationship between job characteristics, supervisor's attitude and intrinsic motivation, creativity process engagement

• Information Systems Review
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• v.14 no.3
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• pp.53-74
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• 2012

IT outsourcing (ITO) is an integration of two firms-external vendor(s) and a client firm-IT resources by contract. According to resource-based view(RBV), three different resources-ITO vendor's resource, client firm's resource, and the relationship resource between two firms- may have an impact on ITO performance. However, there have been few previous studies considering all three IT resources simultaneously. There have been also few empirical studies in ITO context, which test Bharadwaj (2000)'s findings: 1) IT resources can be divided into tangible IT asset and intangible IT capability, and 2) only IT capability has an impact on the IT performance. Therefore we examined whether, in ITO context, all three different resources have a significant impact on ITO performance. Adopting the findings of previous IT studies, we also divided IT resource into IT asset and IT capability. To achieve this research objective, we analyzed 62 ITO cases of 45 companies being listed in Korean top 100 companies for recent 3 years. Also, we analyzed the data with the Partial Least Squares method. The results of this research lead to the following conclusions: First, only when partnership is high, ITO vendors' resource can have an influence on ITO performance. Second, only client firm's IT capability, not IT asset, is directly related to the ITO success. Third, a firm's IT capability can increase the partnership. Therefore, we concluded that 1) RBV is also an useful theory in ITO context, 2) Bharadwaj(2000)'s suggestion is valid in ITO context as well, and 3) the relationship resource is also important in ITO.

• Journal of Global Scholars of Marketing Science
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• v.20 no.2
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• pp.110-122
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• 2010

The emergence of the field of organizational buying behavior in the mid-1960’s with the publication of Industrial Buying and Creative Marketing (1967) set the stage for a new paradigm of thinking about how business was conducted in markets other than those serving ultimate consumers. Whether it is "industrial marketing" or "business-to-business marketing" (B-to-B), organizational buying behavior remains the core differentiating characteristic of this domain of marketing. This paper explores the impact of several dynamic factors that have influenced how organizations relate to one another in a rapidly increasing interdependence, which in turn can impact organizational buying behavior. The paper also raises the question of whether or not the major conceptual models of organizational buying behavior in an interdependent world are still relevant to guide research and managerial thinking, in this dynamic business environment. The paper is structured to explore three questions related to organizational interdependencies: 1. What are the factors and trends driving the emergence of organizational interdependencies? 2. Will the major conceptual models of organizational buying behavior that have developed over the past half century be applicable in a world of interdependent organizations? 3. What are the implications of organizational interdependencies on the research and practice of organizational buying behavior? Consideration of the factors and trends driving organizational interdependencies revealed five critical drivers in the relationships among organizations that can impact their purchasing behavior: Accelerating Globalization, Flattening Networks of Organizations, Disrupting Value Chains, Intensifying Government Involvement, and Continuously Fragmenting Customer Needs. These five interlinked drivers of interdependency and their underlying technological advances can alter the relationships within and among organizations that buy products and services to remain competitive in their markets. Viewed in the context of a customer driven marketing strategy, these forces affect three levels of strategy development: (1) evolving customer needs, (2) the resulting product/service/solution offerings to meet these needs, and (3) the organization competencies and processes required to develop and implement the offerings to meet needs. The five drivers of interdependency among organizations do not necessarily operate independently in their impact on how organizations buy. They can interact with each other and become even more potent in their impact on organizational buying behavior. For example, accelerating globalization may influence the emergence of additional networks that further disrupt traditional value chain relationships, thereby changing how organizations purchase products and services. Increased government involvement in business operations in one country may increase costs of doing business and therefore drive firms to seek low cost sources in emerging markets in other countries. This can reduce employment opportunitiesn one country and increase them in another, further accelerating the pace of globalization. The second major question in the paper is what impact these drivers of interdependencies have had on the core conceptual models of organizational buying behavior. Consider the three enduring conceptual models developed in the Industrial Buying and Creative Marketing and Organizational Buying Behavior books: the organizational buying process, the buying center, and the buying situation. A review of these core models of organizational buying behavior, as originally conceptualized, shows they are still valid and not likely to change with the increasingly intense drivers of interdependency among organizations. What will change however is the way in which buyers and sellers interact under conditions of interdependency. For example, increased interdependencies can lead to increased opportunities for collaboration as well as conflict between buying and selling organizations, thereby changing aspects of the buying process. In addition, the importance of communication processes between and among organizations will increase as the role of trust becomes an important criterion for a successful buying relationship. The third question in the paper explored consequences and implications of these interdependencies on organizational buying behavior for practice and research. The following are considered in the paper: the need to increase understanding of network influences on organizational buying behavior, the need to increase understanding of the role of trust and value among organizational participants, the need to improve understanding of how to manage organizational buying in networked environments, the need to increase understanding of customer needs in the value network, and the need to increase understanding of the impact of emerging new business models on organizational buying behavior. In many ways, these needs deriving from increased organizational interdependencies are an extension of the conceptual tradition in organizational buying behavior. In 1977, Nicosia and Wind suggested a focus on inter-organizational over intra-organizational perspectives, a trend that has received considerable momentum since the 1990's. Likewise for managers to survive in an increasingly interdependent world, they will need to better understand the complexities of how organizations relate to one another. The transition from an inter-organizational to an interdependent perspective has begun, and must continue so as to develop an improved understanding of these important relationships. A shift to such an interdependent network perspective may require many academicians and practitioners to fundamentally challenge and change the mental models underlying their business and organizational buying behavior models. The focus can no longer be only on the dyadic relations of the buying organization and the selling organization but should involve all the related members of the network, including the network of customers, developers, and other suppliers and intermediaries. Consider for example the numerous partner networks initiated by SAP which involves over 9000 companies and over a million participants. This evolving, complex, and uncertain reality of interdependencies and dynamic networks requires reconsideration of how purchase decisions are made; as a result they should be the focus of the next phase of research and theory building among academics and the focus of practical models and experiments undertaken by practitioners. The hope is that such research will take place, not in the isolation of the ivory tower, nor in the confines of the business world, but rather, by increased collaboration of academics and practitioners. In conclusion, the consideration of increased interdependence among organizations revealed the continued relevance of the fundamental models of organizational buying behavior. However to increase the value of these models in an interdependent world, academics and practitioners should improve their understanding of (1) network influences, (2) how to better manage these influences, (3) the role of trust and value among organizational participants, (4) the evolution of customer needs in the value network, and (5) the impact of emerging new business models on organizational buying behavior. To accomplish this, greater collaboration between industry and academia is needed to advance our understanding of organizational buying behavior in an interdependent world.

• 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.