• Journal of Internet of Things and Convergence
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• v.8 no.3
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• pp.11-20
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• 2022

Corporations operate within long term strategy. The Chief Executive Officer (CEO) makes decisions and has responsibility for all executive activities which affects the corporate performance. If the CEO makes strategic choices through reasonable decision making, it could affect corporate performance and corporate's rise and fall. So the CEO's decision making is very important. As rapid change in the digital technology environment happened, through digital transition, corporations have been working on increasing corporate performance by practical and academic methods. However prior research was restricted to CEO's affect on organization, innovation or innovative activities and there is a lack of research linking CEO's characteristics to digital transition and corporate performance. As the digital age is coming, research on how CEO's characteristics affect digital transition and corporate performance is direly needed. From the case of domestic Big Data corporation RSN Co. ltd's digital transition success, understanding characteristics of CEO, digital transition and corporate performance through prior researches, and developing research model and research proposition was set. Research was performed on RSN co. ltd's case analysis, and how characteristics of CEO's matter on digital transformation and corporate performance. As a result of the proposition, when the CEO conjugates digital technology, the corporation was able to successfully complete digital transition and it also affects corporate performance. Also, this research's other point is that CEO's may have limits on thoughtful decision making. It is judged that it is necessary to try an empirical study in the future.

• Annual Conference of KIPS
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• 2023.05a
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• pp.268-270
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• 2023

기존 Web2.0 시대의 플랫폼 기업은 서비스를 통해 생성된 개인 데이터로 다양한 비즈니스를 창출해왔다. 하지만 데이터 제공자인 개인은 해당 수익에서 제외되는 모순된 상황에 놓였다. 이에 개인이 자신의 데이터를 적극 관리·통제하면서 능동적으로 활용할 수 있는 개념인 마이데이터(MyData)가 등장했다. 국내에서는 '20.8월 데이터3법(개인정보보호법, 신용정보법, 정보통신망법)이 통과되면서 신용정보법에 근거해 금융 분야 마이데이터 서비스가 활성화되기 시작했다. 그러나 현존하는 마이데이터 플랫폼은 중앙화된 시스템으로 본래 취지와 다르게 개인의 데이터 소유권과 통제권을 보장하기에 부족하다. 이에 본 논문에서는 기존 마이데이터 플랫폼의 한계점을 분석하고, Web3.0 등 변화하는 환경에서 개인의 데이터 주권을 보장하고, 데이터 가치를 공정하게 분배받을 수 있는 DID 플랫폼 기반의 마이데이터 서비스 모델을 제안한다.

• KIPS Transactions on Software and Data Engineering
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• v.13 no.1
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• pp.1-16
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• 2024

Over recent years, 6 Sigma has become a key methodology in manufacturing for quality improvement and cost reduction. However, challenges have arisen due to the difficulty in analyzing large-scale data generated by smart factories and its traditional, formal application. To address these limitations, a big data-based 6 Sigma approach has been developed, integrating the strengths of 6 Sigma and big data analysis, including statistical verification, mathematical optimization, interpretability, and machine learning. Despite its potential, the practical impact of this big data-based 6 Sigma on manufacturing processes and management performance has not been adequately verified, leading to its limited reliability and underutilization in practice. This study investigates the efficiency impact of DX SS, a big data-based 6 Sigma, on manufacturing processes, and identifies key success policies for its effective introduction and implementation in enterprises. The study highlights the importance of involving all executives and employees and researching key success policies, as demonstrated by cases where methodology implementation failed due to incorrect policies. This research aims to assist manufacturing companies in achieving successful outcomes by actively adopting and utilizing the methodologies presented.

• Korea Trade Review
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• v.48 no.2
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• pp.71-94
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• 2023

The purpose of this study is to prove the effect of psychic distance between home country and host country on overseas foreign direct investment(OFDI) of Spanish companies through panel analysis. The panel data was based on cultural, institutional, economic, and geographical distance data over the past decade between Spain and Spain's OFDI countries. According to the Random Effect Model(REM) analysis, cultural distance(CULD) had a negative effect on OFDI, while institutional distance(INSD) had a positive effect. Among economic distances, income size distance(GDP) had a positive effect on OFDI, but export size distance(EXPO) had a negative effect. Geographic distance(PKM) had a negative impact. Meanwhile, according to the results of quantile regression analysis to prove the psychic distance effect by OFDI size, the effects of CULD and INSD in the quartile (75%) to which Korea belongs were the same as the REM analysis results. In addition, GDP and EXPO had a positive effect, and PKM had a negative effect but EXPO had a positive effect. Therefore, FDI host countries need to establish differentiated strategies through quantile analysis while making continuous efforts to improve the system.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.1
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• pp.29-36
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• 2021

The amount of data generated has been growing exponentially, and the complexity of data has been increasing owing to the advancement of information technology (IT). Big data analysts and engineers have therefore been actively conducting research to minimize the analysis targets for faster processing and analysis of big data. Hadoop, which is widely used as a big data platform, provides various processing and analysis functions, including minimization of analysis targets through Hive, which is a subproject of Hadoop. However, Hive uses a vast amount of memory for data deduplication because it is implemented without considering the complexity of data. Therefore, an efficient algorithm has been proposed for data deduplication of complex structures. The performance evaluation results demonstrated that the proposed algorithm reduces the memory usage and data deduplication time by approximately 79% and 0.677%, respectively, compared to Hive. In the future, performance evaluation based on a large number of data nodes is required for a realistic verification of the proposed algorithm.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.5
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• pp.13-24
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• 2019

The purpose of this study is to provide a theoretical basis for future studies by systematically integrating existing empirical studies that examine the relationship between the entrepreneurs' competencies and business performance. As the results of empirical studies examining the relationship between the entrepreneurs' competencies and business performance are mixed, this study tries to derive different results from individual studies through meta-analysis into standardized statistics(effect sizes). To accomplish the purpose, this study developed hypotheses about the positive effects of the entrepreneurial competencies including opportunity recognition competency, managerial competency, technical-functional competency, strategic competency, and relationship competencies on the business performance through theoretical reviews of the literature. Also, this study conducted meta-analysis with 15 sample data using open source based statistical program 'R'. The results of this study are as follows. The opportunity recognition competency, managerial competency, technical-functional competency, strategic competency, and relationship competency were found to significantly affect financial business performance. Also, each of the factors had a moderate effect size on the performance. Among these competencies, the most effective factor was the strategic competency, followed by the opportunity recognition competency, technical-functional competency, and relationship competency. There is still no study using meta-analysis on the relationship between the entrepreneurs' competencies and business performance in South Korea. Therefore, the results of this study are expected to provide implications for developing the theoretical basis of future studies.

• Journal of the Korea Convergence Society
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• v.7 no.5
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• pp.1-6
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• 2016

Many studies have been carried out for the development of big data utilization and analysis technology recently. There is a tendency that government agencies and companies to introduce a Hadoop of a processing platform for analyzing big data is increasing gradually. Increased interest with respect to the processing and analysis of these big data collection technology of data has become a major issue in parallel to it. However, study of the collection technology as compared to the study of data analysis techniques, it is insignificant situation. Therefore, in this paper, to build on the Hadoop cluster is a big data analysis platform, through the Apache sqoop, stylized from relational databases, to collect the data. In addition, to provide a sensor through the Apache flume, a system to collect on the basis of the data file of the Web application, the non-structured data such as log files to stream. The collection of data through these convergence would be able to utilize as a basic material of big data analysis.

• Journal of the Korean Data and Information Science Society
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• v.28 no.3
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• pp.605-615
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• 2017

Recently, financial supervisory authority of Korea and international credit rating agencies have been concerned about a stand-alone rating that is calculated without incorporating guaranteed support of parent companies. Guaranteed by parent companies, most foreign subsidiaries keeps good credit rate in spite of weak financial status. However, what if the parent companies stop supporting the foreign subsidiaries, they could have a probability to go bankrupt. In this paper, we have validated a credit rating model through statistical measurers such as performance, calibration, and stability for Korean companies owning foreign subsidiaries.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.01a
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• pp.433-435
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• 2023

본 연구는 기업가정신 글로벌비즈니스 세미나 과목의 과제로써 기업가정신이 무엇인지 확립하고 기업가정신과 연관 지을 수 있는 주제를 선정하여 스스로 연구함으로써 그 의미를 구체화 하고 새로운 패러다임을 제시하고자 하는데 의미가 있다고 볼 수 있다. 저자들은 '문화'가 갖는 상대성에 주목하여 기업가 정신을 해석하였으며 재학 중 배웠던 '홉스테드의 문화 특성' 이론과 접목시켜 문화가 기업가 정신에 어떤 영향을 끼치는지 연구하였다. 글로벌 기업가정신 모니터(GEM)의 글로벌 기업가 지수 순위 조사 결과와, 홉스테드 인사이트(Hofstede-insight)의 6가지 차원에 따른 국가별 문화지수 파일을 접목시켜 수치화하고, 시각적 그래프 및 엑셀 데이터 다중 회귀분석을 통하여 결과를 도출하였다. 홉스테드의 문화 특성이 기업가 정신 순위에 영향을 미칠 것이라고 가정하고 분석한 결과, 홉스테드의 6가지 차원 중 하나인 불확실성 회피성향이 기업가 정신에 가장 높은 영향을 미친다는 사실을 발견하였다. 이번 연구를 통해기업가 정신을 계승하는 기업문화를 제고하고 경제학과 인류학 통합 연구의 필요성을 제시한다.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.241-265
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• 2023

A corporate insolvency prediction model serves as a vital tool for objectively monitoring the financial condition of companies. It enables timely warnings, facilitates responsive actions, and supports the formulation of effective management strategies to mitigate bankruptcy risks and enhance performance. Investors and financial institutions utilize default prediction models to minimize financial losses. As the interest in utilizing artificial intelligence (AI) technology for corporate insolvency prediction grows, extensive research has been conducted in this domain. However, there is an increasing demand for explainable AI models in corporate insolvency prediction, emphasizing interpretability and reliability. The SHAP (SHapley Additive exPlanations) technique has gained significant popularity and has demonstrated strong performance in various applications. Nonetheless, it has limitations such as computational cost, processing time, and scalability concerns based on the number of variables. This study introduces a novel approach to variable selection that reduces the number of variables by averaging SHAP values from bootstrapped data subsets instead of using the entire dataset. This technique aims to improve computational efficiency while maintaining excellent predictive performance. To obtain classification results, we aim to train random forest, XGBoost, and C5.0 models using carefully selected variables with high interpretability. The classification accuracy of the ensemble model, generated through soft voting as the goal of high-performance model design, is compared with the individual models. The study leverages data from 1,698 Korean light industrial companies and employs bootstrapping to create distinct data groups. Logistic Regression is employed to calculate SHAP values for each data group, and their averages are computed to derive the final SHAP values. The proposed model enhances interpretability and aims to achieve superior predictive performance.