• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.2
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• pp.157-169
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• 2023

This study examined the effect of entrepreneurial orientation on creative behavior and the mediating effect of knowledge sharing behavior in the relationship between entrepreneurial orientation and creative behavior. In particular, this study examined the moderating effect of leader-member exchange (LMX) in the relationship between entrepreneurial orientation and creative behavior. In this study, after distributing 500 questionnaires to executives and employees working at small and medium-sized companies in Seoul and Gyeonggi-do, 259 questionnaires were used for hypothesis verification, excluding 38 unfaithful or missing responses. The analysis results are as follows. First, it was found that entrepreneurial orientation had a significant positive (+) effect on creative behavior. Second, it was found that entrepreneurial orientation had a significant positive (+) effect on knowledge sharing behavior. Third, knowledge sharing behavior was found to have a significant positive (+) effect on creative behavior. Fourth, knowledge sharing behavior was found to play a partial mediating role in the relationship between entrepreneurial orientation and creative behavior. Finally, it was found that LMX strengthened the positive (+) relationship between entrepreneurial orientation and creative behavior. The theoretical implications of this study are as follows. First, this study makes a theoretical contribution in that it revealed the mediating effect of knowledge-sharing behavior in the relationship between entrepreneurial orientation and creative behavior through empirical analysis of corporate members. Next, this study has theoretical implications in that it revealed that LMX strengthens the positive (+) relationship between entrepreneurial orientation and creative behavior. On the other hand, the practical implications of this study are as follows. First, companies need to find ways to strengthen the entrepreneurial orientation. Next, companies need to find ways to improve the quality of LMX between bosses and subordinates. Finally, this study discussed research limitations and future research directions.

• Knowledge Management Research
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• v.23 no.4
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• pp.315-338
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• 2022

In 1986, Korea established legal systems to support small and medium-sized start-ups, which becomes the main pillars of national development. The legal systems have stimulated start-up ecosystems to have more than 1 million new start-up companies founded every year during the past 30 years. To analyze the trend of Korea's start-up ecosystem, in this study, we collected 1.18 million news articles from 1991 to 2020. Then, we extracted news articles that have the keywords "start-up", "venture", and "start-up". We employed network analysis and topic modeling to analyze collected news articles. Our analysis can contribute to analyzing the government policy direction shown in the history of start-up support policy. Specifically, our analysis identifies the dynamic characteristics of government influenced by external environmental factors (e.g., society, economy, and culture). The results of our analysis suggest that the start-up ecosystems in Korea have changed and developed mainly by the government policies for corporation governance, industrial development planning, deregulation, and economic prosperity plan. Our frequency keyword analysis contributes to understanding entrepreneurial productivity attributed to activities among the networked components in industrial ecosystems. Our analyses and results provide practitioners and researchers with practical and academic implications that can help to establish dedicated support policies through forecast tasks of the economic environment surrounding the start-ups. Korean entrepreneurial productivity has been empowered by growing numbers of large companies in the mobile phone industry. The spectrum of large companies incorporates content startups, platform providers, online shopping malls, and youth-oriented start-ups. In addition, economic situational factors contribute to the growth of Korean entrepreneurial productivity the economic, which are related to the global expansions of the mobile industry, and government efforts to foster start-ups. Our research is methodologically implicative. We employ natural language processes for 30 years of media articles, which enables more rigorous analysis compared to the existing studies which only observe changes in government and policy based on a qualitative manner.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.3
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• pp.73-84
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• 2019

Despite decades of work experience, workers at small- and medium-sized enterprises(SME) here have yet to make inroads into the self-employed sector that utilizes the job competency they have accumulated at work after retirement. Unlike large companies, SME do not have a proper system for improving the long-term job competency of their employees as they focus on their immediate performance. It is necessary to analyse the independent variables affecting the job competency of employees of SME to derive practical implications for the personnel of SME. In the preceding studies, there are independent variable analyses that affect job competency in specialized industries, such as health care, public officials and IT, but the analysis of workers at SME is insufficient. This study set the person-job fit and organizational justice based on the prior studies of the independent variables that affect the job competency of SME general workers as a dependent variable. The sub-variables of each variable derived knowledge, skills, experience, and desire for person-job fit, and distribution, procedural and deployment justice for organizational justice, respectively. The survey of employees of SME in Korea was conducted from February to March 2019 by Likert 5 scales, and the survey was retrieved from 323 people and analyzed in a demonstration using the SPSS and AMOS statistics package. Among the four sub-independent variables of person-job fit, knowledge, skills and experience were shown to have a significant impact on the job competency, and desire was not shown to be so. Among the three sub-independent variables of organizational justice, deployment justice has a significant impact on job competency, but distribution and procedural justices have not. Personnel managers of SME need to improve the job competency of their employees by appropriately utilizing independent variables such as knowledge, skills, experience and deployment at each stage, including recruitment, deployment, and promotion. Future job competency modeling studies are needed to overcome the limitations of this study, which fails to objectively measure job competency.

• Management & Information Systems Review
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• v.37 no.4
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• pp.1-20
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• 2018

This study aimed to examine earnings quality of firms selected as Global Champs project which has been promoted by the government since 2013 to support small and medium sized enterprises, for the screening year(t-1) and selected year(t). Earing quality is measured as the value of discretionary accruals estimated by Dechow et al.(1995) adjusted Jones model and Kothari et al.(2005) model, respectively. I analyze the differences of earning quality between the Global Champ firms and the paired firms selected through criteria of the similar total assets and the same industry in the screening year and the selected year. This study is motivated by the needs of measurement of the performance of the Project from the accounting transparent point of view. As the results of this study, major findings are summarized as follows. Firstly the earnings quality of the selected firms was lower than that of the paired firms. This can be explained as a result of motivation of earnings management by companies eager to meet the requirements to be selected for the Project. Secondly, in the selected year, the earnings quality was proved to improve, comparing to the screening year. This can be explained by the efforts of companies to reinforce management innovation and transparent management, which in turn led to positive effects on the earnings quality. These findings were found to be consistent in the additional analyses, where the earning quality of the reconstructed sample with only selected companies was compared for the screening year and the selected year, based on the year before the screening year(t-2).

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.1
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• pp.172-178
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• 2021

In the ship repair market, interest in maintenance and repair is steadily increasing due to the reinforcement of prevention of environmental pollution caused by ships and the reinforcement of safety standards for ship structures. By reflecting this effect, the number of requests for repairs by foreign shipping companies increases to repair shipbuilders in the Southwest Sea. However, because most of the repair shipbuilders in the southwestern area are small and medium-sized companies, it is difficult to lead to the integrated synergy effect of the repair shipbuilding companies. Moreover, the infrastructure is not integrated; hence, using the infrastructure jointly is a challenge, which acts as an obstacle to the activation of the repair shipbuilding industry. Floating docks are indispensable to operating the repair shipbuilding business; in addition, most of them are operated through renovation/repair after importing aging caisson docks from overseas. However, their service life is more than 30 years; additionally, there is no structure inspection standard. Therefore, it is vulnerable to the safety field. In this study, the finite element analysis program of ANSYS was used to evaluate the structural safety of the modified caisson dock and obtain additional structural reinforcement schemes to solve the derived problems. For the floating docks, there are classification regulations; however, concerning structural strength, the regulations are insufficient, and the applicability is inferior. These insufficient evaluation areas were supplemented through a detailed structural FE-analysis. The reinforcement plan was decided by reinforcing the pontoon deck and reinforcement of the side tank, considering the characteristics of the repair shipyard condition. The final plan was selected to reinforce the side wing tank through the structural analysis of the decision; in addition, the actual structure was fabricated to reflect the reinforcement plan. Our results can be used as reference data for improving the structural strength of similar facilities; we believe that the optimal solution can be found quickly if this method is used during renovation/repair.

• The Journal of Industrial Distribution & Business
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• v.2 no.2
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• pp.39-45
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• 2011

The Korean distribution industry is gearing up for an endless competition. Greeting low growth era, less competitive parties will be challanged seriously for their survival. But for large discount stores, they have shown steady annual growth for years. However, because of the saturation for numbers of stores, the difficulty of gaining new sites, and the changes in the consumer's consumption behavior caused by the recession, now they are seeking for a new customers-based business formats. Accordingly, a large corporate comopanies made supermarkets which are belonged to affiliated companies of large corporate comopanies. They based on the strong buying power, focused on SSM(Super Super Market) ave been aggressively develop nationwide multi-stores. The point is that these stores are threatening at small and medium-sized, community-based private supermarkets. Private supermarkets and retailers, who are using existing old operation systems and their dilapidated facilities, are losing a competitive edge in business. Recent the social effects of large series of corporate supermarkets for traditional markets has been very controversial, and commercial media, academia, and industry associated with it have been held many seminars and public hearings. This may slow down the speed in accordance with the regulations, but will not be the crucial alternative. The reason for this recent surge of enterprise-class SSM up, one of the reasons is a stagnation in their offline discount mart, so they are finding new growth areas. Already in the form of large supermarkets across the country got most of the geographical centre point and is saturated with stages. Targeting small businesses that do not cover discount Mart, in order to expand business in the form of SSM is urgent. By contrast, private supermarkets are going to lose their competitiveness. The vulnerability of individual supermarkets, one of the vulnerabilities is price which economies of scale can not be realized so they are purchasing a small amount of products and difficult to get a quantity discount. The lack of organization and collaboration, and education which is not practical, caused the absencer of service-oriented situations. As a first solution, making specialty shops which are handling agricultures, fruits and vegetables and manufactured goods is recommended. Second, private supermarkets franchisees join the organization for the organization and collaboration is recomaned. It can be meet the scale of economy and can be formed a alternative business formats to a government. Third, the education is needed as a good service will get consumer's awareness. In addition, a psychological stores operating is also one way to stimulate consumer sentiment as SSM can't operate. Japan already has a better conditions of their lives through small chain expression. This study includes the vulnerabilities of private supermarkets, and suggests a competitiveness reinforcement strategies.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.67-88
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• 2018

Until recently, as we recognize the significance of sustainable growth and competitiveness of small-and-medium sized enterprises (SMEs), governmental support for tangible resources such as R&D, manpower, funds, etc. has been mainly provided. However, it is also true that the inefficiency of support systems such as underestimated or redundant support has been raised because there exist conflicting policies in terms of appropriateness, effectiveness and efficiency of business support. From the perspective of the government or a company, we believe that due to limited resources of SMEs technology development and capacity enhancement through collaboration with external sources is the basis for creating competitive advantage for companies, and also emphasize value creation activities for it. This is why value chain network analysis is necessary in order to analyze inter-company deal relationships from a series of value chains and visualize results through establishing knowledge ecosystems at the corporate level. There exist Technology Opportunity Discovery (TOD) system that provides information on relevant products or technology status of companies with patents through retrievals over patent, product, or company name, CRETOP and KISLINE which both allow to view company (financial) information and credit information, but there exists no online system that provides a list of similar (competitive) companies based on the analysis of value chain network or information on potential clients or demanders that can have business deals in future. Therefore, we focus on the "Value Chain Network System (VCNS)", a support partner for planning the corporate business strategy developed and managed by KISTI, and investigate the types of embedded network-based analysis modules, databases (D/Bs) to support them, and how to utilize the system efficiently. Further we explore the function of network visualization in intelligent value chain analysis system which becomes the core information to understand industrial structure ystem and to develop a company's new product development. In order for a company to have the competitive superiority over other companies, it is necessary to identify who are the competitors with patents or products currently being produced, and searching for similar companies or competitors by each type of industry is the key to securing competitiveness in the commercialization of the target company. In addition, transaction information, which becomes business activity between companies, plays an important role in providing information regarding potential customers when both parties enter similar fields together. Identifying a competitor at the enterprise or industry level by using a network map based on such inter-company sales information can be implemented as a core module of value chain analysis. The Value Chain Network System (VCNS) combines the concepts of value chain and industrial structure analysis with corporate information simply collected to date, so that it can grasp not only the market competition situation of individual companies but also the value chain relationship of a specific industry. Especially, it can be useful as an information analysis tool at the corporate level such as identification of industry structure, identification of competitor trends, analysis of competitors, locating suppliers (sellers) and demanders (buyers), industry trends by item, finding promising items, finding new entrants, finding core companies and items by value chain, and recognizing the patents with corresponding companies, etc. In addition, based on the objectivity and reliability of the analysis results from transaction deals information and financial data, it is expected that value chain network system will be utilized for various purposes such as information support for business evaluation, R&D decision support and mid-term or short-term demand forecasting, in particular to more than 15,000 member companies in Korea, employees in R&D service sectors government-funded research institutes and public organizations. In order to strengthen business competitiveness of companies, technology, patent and market information have been provided so far mainly by government agencies and private research-and-development service companies. This service has been presented in frames of patent analysis (mainly for rating, quantitative analysis) or market analysis (for market prediction and demand forecasting based on market reports). However, there was a limitation to solving the lack of information, which is one of the difficulties that firms in Korea often face in the stage of commercialization. In particular, it is much more difficult to obtain information about competitors and potential candidates. In this study, the real-time value chain analysis and visualization service module based on the proposed network map and the data in hands is compared with the expected market share, estimated sales volume, contact information (which implies potential suppliers for raw material / parts, and potential demanders for complete products / modules). In future research, we intend to carry out the in-depth research for further investigating the indices of competitive factors through participation of research subjects and newly developing competitive indices for competitors or substitute items, and to additively promoting with data mining techniques and algorithms for improving the performance of VCNS.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.105-129
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• 2020

This study uses corporate data from 2012 to 2018 when K-IFRS was applied in earnest to predict default risks. The data used in the analysis totaled 10,545 rows, consisting of 160 columns including 38 in the statement of financial position, 26 in the statement of comprehensive income, 11 in the statement of cash flows, and 76 in the index of financial ratios. Unlike most previous prior studies used the default event as the basis for learning about default risk, this study calculated default risk using the market capitalization and stock price volatility of each company based on the Merton model. Through this, it was able to solve the problem of data imbalance due to the scarcity of default events, which had been pointed out as the limitation of the existing methodology, and the problem of reflecting the difference in default risk that exists within ordinary companies. Because learning was conducted only by using corporate information available to unlisted companies, default risks of unlisted companies without stock price information can be appropriately derived. Through this, it can provide stable default risk assessment services to unlisted companies that are difficult to determine proper default risk with traditional credit rating models such as small and medium-sized companies and startups. Although there has been an active study of predicting corporate default risks using machine learning recently, model bias issues exist because most studies are making predictions based on a single model. Stable and reliable valuation methodology is required for the calculation of default risk, given that the entity's default risk information is very widely utilized in the market and the sensitivity to the difference in default risk is high. Also, Strict standards are also required for methods of calculation. The credit rating method stipulated by the Financial Services Commission in the Financial Investment Regulations calls for the preparation of evaluation methods, including verification of the adequacy of evaluation methods, in consideration of past statistical data and experiences on credit ratings and changes in future market conditions. This study allowed the reduction of individual models' bias by utilizing stacking ensemble techniques that synthesize various machine learning models. This allows us to capture complex nonlinear relationships between default risk and various corporate information and maximize the advantages of machine learning-based default risk prediction models that take less time to calculate. To calculate forecasts by sub model to be used as input data for the Stacking Ensemble model, training data were divided into seven pieces, and sub-models were trained in a divided set to produce forecasts. To compare the predictive power of the Stacking Ensemble model, Random Forest, MLP, and CNN models were trained with full training data, then the predictive power of each model was verified on the test set. The analysis showed that the Stacking Ensemble model exceeded the predictive power of the Random Forest model, which had the best performance on a single model. Next, to check for statistically significant differences between the Stacking Ensemble model and the forecasts for each individual model, the Pair between the Stacking Ensemble model and each individual model was constructed. Because the results of the Shapiro-wilk normality test also showed that all Pair did not follow normality, Using the nonparametric method wilcoxon rank sum test, we checked whether the two model forecasts that make up the Pair showed statistically significant differences. The analysis showed that the forecasts of the Staging Ensemble model showed statistically significant differences from those of the MLP model and CNN model. In addition, this study can provide a methodology that allows existing credit rating agencies to apply machine learning-based bankruptcy risk prediction methodologies, given that traditional credit rating models can also be reflected as sub-models to calculate the final default probability. Also, the Stacking Ensemble techniques proposed in this study can help design to meet the requirements of the Financial Investment Business Regulations through the combination of various sub-models. We hope that this research will be used as a resource to increase practical use by overcoming and improving the limitations of existing machine learning-based models.

• Journal of Korea Port Economic Association
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• v.34 no.4
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• pp.141-160
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• 2018

In this paper, to analyze the perception and response strategy of Korean ship owners on Global Sulphur Cap 2020, examined the IMO environmental regulation status focusing on MARPOL Annex VI regulation about air pollution prevention, technological measures to reduce SOx emission, shipping industry and management status of Korean ship owners. First of all, the questionnaire was conducted for Korean ship owners after selecting the evaluation factors. The purpose of this study was to investigate the difference of the perception and response strategy of Korean ship owners by corporation size and main vessel type using frequency and cross analysis. It is confirmed that various researches on SOx emission reduction have been carried out from various points of view at home and abroad. In this study, existing studies related to technical factors for regulatory response and economics analysis were examined and evaluation factors were selected. As a result of analysis, it is found that large-sized shipping companies are more prepared for regulatory response than small and medium-sized bulk carrier owners. There were similar perception and the direction of response strategy about the impacts by corporation size and main vessel type. In about two years to be implemented in 2020, It is necessary to find an appropriate response strategy based on the support policy of the government and related organizations and the systematic analysis of the ship owners. Through this study, although the difference between the perception and response strategy of the ship owners by corporation size and main vessel type was understood, it was found that there were limitations on specific response strategy and corporate data collection. In future research, we should overcome the limitations of this study and conduct an in-depth study.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.69-88
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• 2022

One of the characteristics of South Korea's economic structure is that it is highly dependent on exports. Thus, many businesses are closely related to the global economy and diplomatic situation. In addition, small and medium-sized enterprises(SMEs) specialized in exporting are struggling due to the spread of COVID-19. Therefore, this study aimed to develop a model to forecast exports for next year to support SMEs' export strategy and decision making. Also, this study proposed a strategy to recommend promising export countries of each item based on the forecasting model. We analyzed important variables used in previous studies such as country-specific, item-specific, and macro-economic variables and collected those variables to train our prediction model. Next, through the exploratory data analysis(EDA) it was found that exports, which is a target variable, have a highly skewed distribution. To deal with this issue and improve predictive performance, we suggest a separated learning method. In a separated learning method, the whole dataset is divided into homogeneous subgroups and a prediction algorithm is applied to each group. Thus, characteristics of each group can be more precisely trained using different input variables and algorithms. In this study, we divided the dataset into five subgroups based on the exports to decrease skewness of the target variable. After the separation, we found that each group has different characteristics in countries and goods. For example, In Group 1, most of the exporting countries are developing countries and the majority of exporting goods are low value products such as glass and prints. On the other hand, major exporting countries of South Korea such as China, USA, and Vietnam are included in Group 4 and Group 5 and most exporting goods in these groups are high value products. Then we used LightGBM(LGBM) and Exponential Moving Average(EMA) for prediction. Considering the characteristics of each group, models were built using LGBM for Group 1 to 4 and EMA for Group 5. To evaluate the performance of the model, we compare different model structures and algorithms. As a result, it was found that the separated learning model had best performance compared to other models. After the model was built, we also provided variable importance of each group using SHAP-value to add explainability of our model. Based on the prediction model, we proposed a second-stage recommendation strategy for potential export countries. In the first phase, BCG matrix was used to find Star and Question Mark markets that are expected to grow rapidly. In the second phase, we calculated scores for each country and recommendations were made according to ranking. Using this recommendation framework, potential export countries were selected and information about those countries for each item was presented. There are several implications of this study. First of all, most of the preceding studies have conducted research on the specific situation or country. However, this study use various variables and develops a machine learning model for a wide range of countries and items. Second, as to our knowledge, it is the first attempt to adopt a separated learning method for exports prediction. By separating the dataset into 5 homogeneous subgroups, we could enhance the predictive performance of the model. Also, more detailed explanation of models by group is provided using SHAP values. Lastly, this study has several practical implications. There are some platforms which serve trade information including KOTRA, but most of them are based on past data. Therefore, it is not easy for companies to predict future trends. By utilizing the model and recommendation strategy in this research, trade related services in each platform can be improved so that companies including SMEs can fully utilize the service when making strategies and decisions for exports.