• Korea Trade Review
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• v.41 no.4
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• pp.41-59
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• 2016

Given the increasing importance of entrepreneurship in a nation's economic growth, this study empirically examined the effect of entrepreneurial behavior and environment on economic growth based on country level data. While previous studies have centered on entrepreneurship as a dominant variable that impacts economic growth, this study has extends the discussion by empirically testing the effects of two entrepreneurial variables, which are entrepreneurial behavior and entrepreneurial environment, on economic growth. Furthermore, the study attempted to examine the moderating effects of trade and innovation on the relation between the independent variables and economic growth. According to the panel analysis using data from GEM and World Bank, both entrepreneurial behavior and environment affected national economic growth, completely or partly. Results showed that opportunity-driven entrepreneurial behavior has a significant effect on economic growth, suggesting that qualitative aspect of entrepreneurial activities are critical to economic growth. The results also indicated that both trade and innovation have significant moderating effects on the relationship between governmental support program and economic growth.

• Journal of Practical Engineering Education
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• v.16 no.1_spc
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• pp.79-97
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• 2024

This study confirmed the mediating effects of employee commitment and career satisfaction on the relationship between learning agility and adaptive performance. For this purpose, a survey was conducted online targeting employees of domestic companies and used for the final analysis of 329 pieces of data. Structural equations were used to analyze the relationships between variables. According the results of it was confirmed that learning agility had a statistically positive effect on employee commitment and adaptation performance, employee commitment had a positive effect on career satisfaction, and career satisfaction had a positive effect on adaptation performance. Additionally, employee commitment and career satisfaction had a significant mediating effect on the relationship between learning agility and adaptive performance. Based on these results, the academic and practical implications and future research directions for HRD are presented.

• Knowledge Management Research
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• v.24 no.4
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• pp.177-193
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• 2023

This study explores the role of organizational culture, job characteristics, and work values and orientation in shaping the competitiveness of a multinational company (MNC) based in Korea. The purpose of the study was to examine the impact of these variables on the competitiveness attributes of the organizational culture profile through MR-QAP analysis. Data were collected from 161 employees in 15 different teams at a Korean automotive company headquartered in Seoul. The results of the study revealed the impact of network characteristics associated with competitive organizational culture on competitiveness. 'found to have a negative effect on competitiveness. Among the organizational culture profiles, social responsibility, supportiveness, innovation, and performance orientation have a significant positive effect on competitive organizational culture, while emphasis on rewards and stability have no significant effect. These findings provide practical implications for understanding the complex dynamics of organizational culture and promoting strategic approaches to enhance organizational competitiveness.

• Information Systems Review
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• v.22 no.3
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• pp.157-172
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• 2020

The demand of information security consultants is expected to increase due to the emergence of ISMS-P incorporating ISMS and PIMS, the implementation of European Privacy Act (GDPR) and various security accidents. In this paper, we collected and analyzed advertisements of job advertisement sites that could identify firms' demand explicitly. We selected representative job advertisement sites in Korea and the United States and collected job advertisement details of information security consultants in 2014 and 2019. The collected data were visualized using text mining and analyzed using non-parametric methods to determine whether there was a change in the role of the information security consultant. The findings show that the requirements for information security consultants have changed very little. This means that the role does not change much over a five year time gap. The results of the study are expected to be helpful to policy makers related to information security consultants, those seeking to find employment as information security consultants, and those seeking information security consultants.

• Journal of Platform Technology
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• v.12 no.3
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• pp.27-40
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• 2024

Collaborative programs are tools designed to support multiple people working together, enhancing collaboration and communication efficiency, improving productivity, and overcoming the constraints of time and place. In the endemic era, many companies and individuals prefer using collaborative programs. These programs often handle sensitive information, such as work content, documents, and user data, which can cause significant damage if leaked. Exploiting this, various attack scenarios have emerged, including malware attacks disguised as collaborative programs, exploiting vulnerabilities within these programs, and stealing internal tokens. To prevent such attacks, it is essential to analyze and respond to potential threats proactively. This paper focuses on Notion, a widely used collaborative program, to collect and analyze artifacts related to user information and activities in both PC and Android environments. Based on the collected data, we categorize critical information, discuss potential threats, and propose countermeasures.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.4
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• pp.109-120
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• 2020

This study examines how the government's fairness in entrepreneurship support affects satisfaction and management performance, and examines whether these influences are different for each growth stage of start-up companies. For this study, data were collected for start-up companies that received government support for start-up within the past 5 years. Total 611 copies of the data were used in this study. The collected data were analyzed using SPSS and AMOS. The fairness used in this study was divided into three types, procedural fairness, interactive fairness, and distributed fairness. The effect of the three fairness on the satisfaction of start-up support project was analyzed. In addition, the effect of business support satisfaction on business performance was analyzed, where the business performance was evaluated by the questioner's satisfaction with their business. The start-up phase was divided into the start phase, early growth phase, stagnant phase, and high-level growth phase, and the moderating effect between the fairness and satisfaction of the government-supported projects by start-up phase was analyzed. As a result, it was found that every concept of fairness had a positive (+) effect on the satisfaction of the entrepreneurship support project, and the satisfaction of the entrepreneurship support project had a positive (+) effect on the management performance. The concept of procedural fairness in the start phase, procedural fairness in the early growth phase, interactive fairness, and the concept of all fairness in the stagnant phase influenced the satisfaction of the start-up support project. In this study, the fairness and effect of government-supported projects affecting the management performance of start-ups were identified by growth phase of start-ups. The results of these studies will help build a systematic system for entrepreneurship support and for start-ups, it will also greatly contribute to finding differentiated growth plans by growth stages of start-up companies.

• Journal of Convergence for Information Technology
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• v.10 no.9
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• pp.1-7
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• 2020

In order to implement a smart factory for manufacturing innovation, more digital twins will be developed and applied gradually. In particular, simulation and optimization of digital twins makes it possible to support critical decision-making like a predictive maintenance of the equipment for manufacturing. In terms of a user perspective, this study suggests the conceptual framework of Digital Twin Management System (DTMS) for supporting the analytical and managerial activities for Digital Twins. We integrate the methods and structure of the area like Manufacturing Engineering, Decision Support Systems, and Optimization for developing the DTMS. The framework suggested in this study shows a typical DSS which consists of dialog management system, model management system and data management system. It also includes Analytical Digital Twins and simulations & optimization module. The framework is being applied in one of the most competitive and complex industrial sector. Also this study is meaningful to suggest a new direction of research.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.4
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• pp.50-55
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• 2019

Recently Ransomware attacks are continuously increasing, and new Ransomware, which is difficult to detect just with a basic vaccine, continuously has its upward trend. Various solutions for Ransomware have been developed and applied. However, due to the disadvantages and limitations of existing solutions, damage caused by Ransomware has not been reduced. Ransomware is attacking various platforms no matter what platform it is, such as Windows, Linux, servers, IoT devices, and block chains. However, most existing solutions for Ransomware are difficult to apply to various platforms, and there is a limit that they are dependent on only some specific platforms while operating. This study analyzes the problems of existing Ransomware detection solutions and proposes the onboard module based Ransomware detection system; after the system defines the function of necessary elements through analyzing requirements that can actually reduce the damage caused by the Ransomware from the viewpoint of users, it supports various OS without pre-installation and is able to restore data even after being infected. We checked the feasibility of each function of the proposed system through the analysis of the existing technology and verified the suitability of the proposed techniques to meet the user's requirements through the questionnaire survey of a total of 264 users of personal and corporate PC users. As a result of statistical analysis of the questionnaire results, it was found that the score of intent to introduce the system was at 6.3 or more which appeared to be good, and the score of intent to change from existing solution to the proposed system was at 6.0 which appeared to be very high.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.1-19
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• 2018

Since stock movements forecasting is an important issue both academically and practically, studies related to stock price prediction have been actively conducted. The stock price forecasting research is classified into structured data and unstructured data, and it is divided into technical analysis, fundamental analysis and media effect analysis in detail. In the big data era, research on stock price prediction combining big data is actively underway. Based on a large number of data, stock prediction research mainly focuses on machine learning techniques. Especially, research methods that combine the effects of media are attracting attention recently, among which researches that analyze online news and utilize online news to forecast stock prices are becoming main. Previous studies predicting stock prices through online news are mostly sentiment analysis of news, making different corpus for each company, and making a dictionary that predicts stock prices by recording responses according to the past stock price. Therefore, existing studies have examined the impact of online news on individual companies. For example, stock movements of Samsung Electronics are predicted with only online news of Samsung Electronics. In addition, a method of considering influences among highly relevant companies has also been studied recently. For example, stock movements of Samsung Electronics are predicted with news of Samsung Electronics and a highly related company like LG Electronics.These previous studies examine the effects of news of industrial sector with homogeneity on the individual company. In the previous studies, homogeneous industries are classified according to the Global Industrial Classification Standard. In other words, the existing studies were analyzed under the assumption that industries divided into Global Industrial Classification Standard have homogeneity. However, existing studies have limitations in that they do not take into account influential companies with high relevance or reflect the existence of heterogeneity within the same Global Industrial Classification Standard sectors. As a result of our examining the various sectors, it can be seen that there are sectors that show the industrial sectors are not a homogeneous group. To overcome these limitations of existing studies that do not reflect heterogeneity, our study suggests a methodology that reflects the heterogeneous effects of the industrial sector that affect the stock price by applying k-means clustering. Multiple Kernel Learning is mainly used to integrate data with various characteristics. Multiple Kernel Learning has several kernels, each of which receives and predicts different data. To incorporate effects of target firm and its relevant firms simultaneously, we used Multiple Kernel Learning. Each kernel was assigned to predict stock prices with variables of financial news of the industrial group divided by the target firm, K-means cluster analysis. In order to prove that the suggested methodology is appropriate, experiments were conducted through three years of online news and stock prices. The results of this study are as follows. (1) We confirmed that the information of the industrial sectors related to target company also contains meaningful information to predict stock movements of target company and confirmed that machine learning algorithm has better predictive power when considering the news of the relevant companies and target company's news together. (2) It is important to predict stock movements with varying number of clusters according to the level of homogeneity in the industrial sector. In other words, when stock prices are homogeneous in industrial sectors, it is important to use relational effect at the level of industry group without analyzing clusters or to use it in small number of clusters. When the stock price is heterogeneous in industry group, it is important to cluster them into groups. This study has a contribution that we testified firms classified as Global Industrial Classification Standard have heterogeneity and suggested it is necessary to define the relevance through machine learning and statistical analysis methodology rather than simply defining it in the Global Industrial Classification Standard. It has also contribution that we proved the efficiency of the prediction model reflecting heterogeneity.

• Journal of Venture Innovation
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• v.7 no.1
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• pp.91-110
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• 2024

In this study, we compared and analyzed the influence of self-leadership on innovative behavior and the mediating effect of job satisfaction among R&D practitioners in manufacturing and information communication technology (ICT) industries. To accomplish this, we conducted an online survey using random sampling methods and collected data from 209 respondents. We employed exploratory factor analysis, reliability analysis, correlation analysis, multiple regression analysis, and mediation analysis using SPSS 20.0 software to analyze the data and to compare differences between the manufacturing and ICT sectors. The research findings are as follows: Firstly, both in manufacturing and ICT sectors, self-leadership showed significant positive correlations with job satisfaction and innovative behavior. Secondly, in the analysis of the impact of self-leadership on innovative behavior, in the manufacturing sector, only natural reward strategy and constructive thought strategy showed significant positive effects, while in the ICT sector, behavioral-oriented strategy, natural reward strategy, and constructive thought strategy all showed significant positive effects. Thirdly, in the analysis of the impact of self-leadership on job satisfaction, in the manufacturing sector, only natural reward strategy and constructive thought strategy showed significant positive effects, while in the ICT sector, behavioral-oriented strategy and natural reward strategy showed significant positive effects. Fourthly, in the analysis of the impact of job satisfaction on innovative behavior, significant positive effects were observed in both manufacturing and ICT sectors, with manufacturing sector having relatively greater impact than ICT sector. Lastly, the results of the analysis on the mediating effect of job satisfaction indicate that in the manufacturing sector, only a constructive thinking strategy significantly influences, showing partial mediating effects. However, in the ICT sector, no mediating effects of job satisfaction were observed for any sub-factors of self-leadership. These research findings highlight differences in the mechanisms of action of self-leadership on innovative behavior and its mediating effects between the manufacturing and ICT sectors. Furthermore, the results suggest the importance of improving organizational strategies and culture towards promoting leadership, job design, and job satisfaction, considering the characteristics of each industry and research and development organization.