• Title/Summary/Keyword: 기업의 재무성과

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Impact of Entrepreneurial Morality on Financial Performance and Social Performance through Entrepreneurship and Social Responsibility (기업가의 도덕성이 기업가정신 및 사회적 책임을 통한 재무적 성과와 사회적 성과에 미치는 영향)

  • Kim, Yeon Jong;Park, Sang Hyeok;Oh, Seung Hee
    • The Journal of Information Systems
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    • v.29 no.1
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    • pp.137-158
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    • 2020
  • Purpose This study aims to analyze how entrepreneurs' morality affects entrepreneurship and social responsibility. The purpose of this study is to analyze the differences of entrepreneurship and social responsibility on corporate financial performance and social performance by measuring entrepreneurial morality index. Design/methodology/approach The research model is based on the existing literature related to morality, entrepreneurship, social responsibility, and corporate performance. In order to verify the research model, empirical analysis was conducted. The collected data were analyzed by Smart-pls 2.0 based on the structural equation model based measurement model verification and the structural model verification two - step approach. Using the bootstrapping method of PLS, 500 samples were constructed and hypothesis verification was performed. Findings The results of this study are as follows. In the case of general manufacturing companies, business people are more focused on improving corporate performance than morality, and have a somewhat consistent effect with entrepreneurial spirit that does not have a space of morality. When entrepreneurship is strengthened, financial performance and social performance. Business entrepreneurs in social enterprises are more aware of social responsibility than entrepreneurship, so they achieve both financial performance and social performance at the same time. As a result of this study, it was found that there is a difference in perception depending on the morality of the business people, entrepreneurship, social bookkeeping, and management performance according to the type of company.

Risk-based Profit Prediction Model for International Construction Projects (해외건설공사의 리스크 분석에 기초한 수익성 예측모델에 관한 연구)

  • Han, Seung-Heon;Kim, Du-Yon
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.26 no.4D
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    • pp.635-647
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    • 2006
  • Korean construction companies first advanced to the international markets in 1960's and so far have brought more than 4,900 projects which account for 193 billion dollars approximately. With the large increase of national employment and income being followed by the achievement, Korea's construction industry has made an enormous contribution to the improvement of domestic economy for the last 40 years. However, recently the increased risk in international markets as well as the sharpening competition with foreign companies promising in terms of advanced technologies and low labor cost have been driving Korean construction away from the market shares. According to ENR (Engineering News Record, 1994~2003), it is revealed that 15.1% of top 225 global contractors are suffering from loss in international construction markets. This phenomenon is largely due to the highly uncertain characteristics of international projects, which are inherently exposed to various and complicated risky situations. Furthermore, especially for Korean construction companies, it is often the case that the failure in an international construction project cannot be offset by even a sufficient number of successful domestic achievements. Therefore, not only the selective screening among the nominated projects which have strong possibility of collapse but the systematic strategies for controlling potential risk factors are also considered indispensable in international construction portfolio management. The purpose of this study is to first analyze the causal relationships of the profit-influencing variables and the project success, and develop the profitability forecasting model in international construction projects.

Business Strategies for Korean Private Security-Guard Companies Utilizing Resource-based Theory and AHP Method (자원기반 이론과 AHP 방법을 활용한 민간 경호경비 기업의 전략 연구)

  • Kim, Heung-Ki;Lee, Jong-Won
    • Korean Security Journal
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    • no.36
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    • pp.177-200
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    • 2013
  • As we enter a high industrial society that widens the gap between the rich and poor, demand for the security services has grown explosively. With the growth in quantitative expansion of security services, people have also placed increased requirements on more sophisticated and diversified security services. Consequently, market outlook for private security services industry is positive. However, Korea's private security services companies are experiencing difficulties in finding a direction to capture this new market opportunity due to their small sizes and lack of management-strategic thinking skills. Therefore, we intend to offer a direction of development for our private security services industry using a management-strategy theory and the Analytic Hierarchy Process(AHP), a structured decision-making method. A resource-based theory is one of the important management strategy theories. It explains that a company's overall performance is primarily determined by its competitive resources. Using this theory, we could analyze a company's unique resources and core competencies and set a strategic direction for the company accordingly. The usefulness and validity of this theory has been demonstrated as it has often been subject to empirical verification since 1990s. Based on this theory, we outlined a set of basic procedures to establish a management strategy for the private security services companies. We also used the AHP method to identify competitive resources, core competencies, and strategies from private security services companies in contrast with public companies. The AHP method is a technique that can be used in the decision making process by quantifying experts' knowledge and unstructured problems. This is a verified method that has been used in the management decision making in the corporate environment as well as for the various academic studies. In order to perform this method, we gathered data from 11 experts from academic, industrial, and research sectors and drew distinctive resources, competencies, and strategic direction for private security services companies vis-a-vis public organizations. Through this process, we came to the conclusion that private security services companies generally have intangible resources as their distinctive resources compared with public organization. Among those intangible resources, relational resources, customer information, and technologies were analyzed as important. In contrast, tangible resources such as equipment, funds, distribution channels are found to be relatively scarce. We also found the competencies in sales and marketing and new product development as core competencies. We chose a concentration strategy focusing on a particular market segment as a strategic direction considering these resources and competencies of private security services companies. A concentration strategy is the right fit for smaller companies as a strategy to allow them to focus all of their efforts on target customers in a single segment. Thus, private security services companies would face the important tasks such as developing a new market and appropriate products for such market segment and continuing marketing activities to manage their customers. Additionally, continuous recruitment is required to facilitate the effective use of human resources in order to strengthen their marketing competency in a long term.

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The Impact of Social Capital and Laboratory Startup Team Diversity on Startup Performance Based on a Network Perspective: Focusing on the I-Corps Program (네트워크 관점에 기반한 사회적 자본 및 실험실 창업팀 다양성이창업 성과에 미치는 영향: I-Corps program을 중심으로)

  • Lee, Jai Ho;Sohn, Youngwoo;Han, Jung Wha;Lee, Sang-Myung
    • Asia-Pacific Journal of Business Venturing and Entrepreneurship
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    • v.18 no.6
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    • pp.173-189
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    • 2023
  • As supreme technologies continue to be developed, industries such as artificial intelligence, biotechnology, robots, aerospace, electric vehicles, and solar energy are created, and the macro business environment is rapidly changing. Due to these large-scale changes and increased complexity, it is necessary to pay attention to the effect of social capital, which can create new value by utilizing capital increasing the importance of relationships rather than technology or asset ownership itself at the level of start-up strategy. Social capital is a concept first proposed by Hanifan in 1916, and refers to the overall sum of capabilities or resources that are latent or available for use in mutual, continuous, organic relationships or accumulated human relationship networks between individuals or social members. In addition, the diversity of start-up teams with diverse backgrounds, characteristics, and capabilities, rather than one exceptional founder, has been emphasized. Founding team diversity refers to the diversity of in-depth factors such as demographic factors, beliefs, and values of the founding team. In addition, changes in the macro environment are emphasizing the importance of technology start-ups and laboratory start-ups that lead industrial innovation and create the nation's core growth engines. This study focused on the I-Corps' program. I-Corps, which means innovation corps, is a laboratory startup program launched by the National Research Foundation (NSF) in 2011 to encourage entrepreneurship and commercialization of research results. It focuses on forming a startup team involving professors, researchers and market discovery activities. Taking these characteristics into account, this study empirically verified the impact of social capital from a network perspective and founding team diversity on I-Corps start-up performance. As a result of the analysis, the educational diversity of the founding team had a negative (-) effect on the financial performance of the founding team. On the other side, the gender diversity and the cognitive dimension of social capital had a positive (+) effect on the financial performance of the founding team. This study is expected to provide more useful theoretical and practical implications regarding the diversity, social capital, and performance interpretation of the I-Corps Lab startup team.

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A Study on the Influence of the Founder's Self-Efficacy on the Sales of the Founding Company (창업자의 자기효능감이 창업기업의 매출에 미치는 영향에 관한 연구)

  • Lee, Joonsung;Song, Inam
    • Asia-Pacific Journal of Business Venturing and Entrepreneurship
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    • v.14 no.5
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    • pp.61-78
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    • 2019
  • This study is about the effect of the founder's self-efficacy on the sales of the founding company by focusing on the factors that are currently emphasized in the founding education. In particular, this paper starts from the consciousness of the problem that the education that is being implemented to achieve the purpose of successful start-up among various government-based start-up support projects is failing to produce many start-up failures. Entrepreneurs cannot be assessed by objective financial data, but there is a high degree of uncertainty that should be determined based on their personal and learning abilities. In addition, many previous studies, which are likely to be successful when there is a high self-efficacy in a specific field due to the influence of factors such as personal experience or learning, will answer the direction of support for start-up companies. This study focuses on the impact of the founder's self-efficacy on the sales of the founding firms, especially the sales that are the key to the survival of the founding firms. This study has six major studies. First, to analyze whether the self-efficacy of entrepreneurs with respect to entrepreneurship affects the sales of entrepreneurs. Second, to analyze whether the self-efficacy of entrepreneurs with respect to market orientation affects the sales of entrepreneurs. Analysis of whether the founder's self-efficacy affects the sales of the founding firms. Fourth, analysis of whether the founder's self-efficiency affects the sales of the founding firms' understanding of management environment changes. An analysis of whether efficacy affects the sales of a start-up company, and sixth, an analysis of whether the founder's self-efficacy of business model building ability affects the sales of a start-up company. As a result of the empirical analysis, this study found that the self-efficacy of entrepreneurs on product differentiation capability and business model building capacity had a positive influence on the sales of entrepreneurs. The self-efficacy had a positive effect on self-efficacy, and the customer orientation had a positive effect on self-efficacy on business model building capacity. Also, it was confirmed that a path exists between the components of self-efficacy and that self-efficacy through the path has a positive effect on the sales of the start-up company. Therefore, the results of this study suggest the implications of establishing such a path and strengthening self-efficacy to create the survival and start-up performance of a start-up company if the goal of the start-up company is to survive when implementing various support projects for the start-up company.

A Study on the Effects of Franchise's Factors and Performance : Analysis Disclosure Agreement (프랜차이즈 가맹본부의 특성과 가맹점 사업 성과간의 영향에 관한 연구 : 정보공개서를 중심으로)

  • Lee, Eun-Ji;Cho, Chul-Ho
    • The Korean Journal of Franchise Management
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    • v.3 no.2
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    • pp.20-38
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    • 2012
  • After being introduced into franchises industry, franchise has made a phenomenal growth in a short time and a substantial contribution to job creation and economic revitalization. Nevertheless, franchise business operators failed a business or low profit because of a lack of information and indiscriminate foundation. Therefore the first object of this study is characteristics of franchise's factors on disclosure agreement in franchise associate website. second is examinations about casual relationship between factor and franchise performance with using Excel and SPSS 18.0 versions. The findings of present study were as follows. First, franchises manage small business mostly(financial data, scale so on) and franchise's type focused the food service industry. Specially, a business district select unprotected contract. Second, in franchise's factors, we could find statistically significant effect on annual average sales and annual average net profit. However growth rate of franchise don't have statistically significant effect. Third, we could find statistically significant difference on analysis both franchises' factors and financial data. In conclusion, we must consider of franchise industry environment and success effect on performance in starting one's business. Furthermore franchises plan ways for their sustained growth and protection of rights and interests. Finally business operator draw up their information and upgrade continuously for franchises industry growth. Discussion and theoretical and managerial implications of the results were described along with future franchise research suggestions.

Development on Early Warning System about Technology Leakage of Small and Medium Enterprises (중소기업 기술 유출에 대한 조기경보시스템 개발에 대한 연구)

  • Seo, Bong-Goon;Park, Do-Hyung
    • Journal of Intelligence and Information Systems
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    • v.23 no.1
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    • pp.143-159
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    • 2017
  • Due to the rapid development of IT in recent years, not only personal information but also the key technologies and information leakage that companies have are becoming important issues. For the enterprise, the core technology that the company possesses is a very important part for the survival of the enterprise and for the continuous competitive advantage. Recently, there have been many cases of technical infringement. Technology leaks not only cause tremendous financial losses such as falling stock prices for companies, but they also have a negative impact on corporate reputation and delays in corporate development. In the case of SMEs, where core technology is an important part of the enterprise, compared to large corporations, the preparation for technological leakage can be seen as an indispensable factor in the existence of the enterprise. As the necessity and importance of Information Security Management (ISM) is emerging, it is necessary to check and prepare for the threat of technology infringement early in the enterprise. Nevertheless, previous studies have shown that the majority of policy alternatives are represented by about 90%. As a research method, literature analysis accounted for 76% and empirical and statistical analysis accounted for a relatively low rate of 16%. For this reason, it is necessary to study the management model and prediction model to prevent leakage of technology to meet the characteristics of SMEs. In this study, before analyzing the empirical analysis, we divided the technical characteristics from the technology value perspective and the organizational factor from the technology control point based on many previous researches related to the factors affecting the technology leakage. A total of 12 related variables were selected for the two factors, and the analysis was performed with these variables. In this study, we use three - year data of "Small and Medium Enterprise Technical Statistics Survey" conducted by the Small and Medium Business Administration. Analysis data includes 30 industries based on KSIC-based 2-digit classification, and the number of companies affected by technology leakage is 415 over 3 years. Through this data, we conducted a randomized sampling in the same industry based on the KSIC in the same year, and compared with the companies (n = 415) and the unaffected firms (n = 415) 1:1 Corresponding samples were prepared and analyzed. In this research, we will conduct an empirical analysis to search for factors influencing technology leakage, and propose an early warning system through data mining. Specifically, in this study, based on the questionnaire survey of SMEs conducted by the Small and Medium Business Administration (SME), we classified the factors that affect the technology leakage of SMEs into two factors(Technology Characteristics, Organization Characteristics). And we propose a model that informs the possibility of technical infringement by using Support Vector Machine(SVM) which is one of the various techniques of data mining based on the proven factors through statistical analysis. Unlike previous studies, this study focused on the cases of various industries in many years, and it can be pointed out that the artificial intelligence model was developed through this study. In addition, since the factors are derived empirically according to the actual leakage of SME technology leakage, it will be possible to suggest to policy makers which companies should be managed from the viewpoint of technology protection. Finally, it is expected that the early warning model on the possibility of technology leakage proposed in this study will provide an opportunity to prevent technology Leakage from the viewpoint of enterprise and government in advance.

Efficiency Analysis of the Securities Firms using a Combined BSC and DEA Model (BSC와 DEA 결합모델을 이용한 증권사 효율성 분석)

  • Kim, Youngjin;Jung, Goosang;Hwang, Jae-Joon;Lee, Hyun-Soo;Kim, Sun Ah;Kim, Tae-Sung
    • Journal of Digital Convergence
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    • v.11 no.5
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    • pp.159-168
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    • 2013
  • This study analyze the business efficiency of securities company based on the 2011 performance of 29 securities firms which engage in domestic investment brokerage by applying a combination model of BSC and DEA. And we evaluate business state focused on efficiency which is based on logical system of BSC as business innovation method. The analysis of result is that companies with high customer efficiency index appeared that business efficiency composite index tended to be higher and we identified that customer perspective have an important factor to calculate business efficiency composite index of korea security company. In addition, based on the results of the efficiency analysis we analyze correlation analysis between traditional financial ratio and business efficiency composite index. We confirmed that company of high business efficiency level in terms of BSC have a good record in terms of profitability. BSC-DEA combination model expect to be utilized in security industry sector as well as other industrial sectors as good business indicator to determine the business efficiency and to be used a model can be evaluated the integrated firm valuation of tangible and intangible assets.

A Study on the Determinants of Capital Structure of Agricultural Corporations (농업법인의 자본구조 결정요인 연구)

  • Byun, Ji-Yeon;Im, In-Seob
    • The Journal of the Korea Contents Association
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    • v.21 no.10
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    • pp.368-377
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    • 2021
  • This study analyzed the determinants of capital structure based on the financial statements of agricultural corporations disclosed on the DART(data analysis, retrieval and transfer system) of the Financial Supervisory Service since 2011, when the Korea international financial reporting standards (K-IFRS) was introduced. There have been many empirical studies on the capital structure so far, but there are no studies targeting agricultural corporations. The sample period of agricultural corporations was from 2015 to 2019, with the debt ratio as the dependent variable, and among the variables suggested as meaningful in existing empirical studies, ROA(profitability), SIZE(corporate size), LIQ(liquidity), TA(tangible asset ratio), FA(fixed long-term suitability ratio), and GROWTH(growth potential) were selected as independent variables and panel data analysis was performed. As a result of the analysis, it was found that the debt ratio decreased as the ROA and SIZE of agricultural corporations increased. This can be interpreted as supporting the pecking order theory rather than the static trade-off theory in the relationship between the ROA and SIZE of Korean agricultural corporations with the capital structure. In addition, it was found that the debt ratio increased as the FA increased. These results suggest that Korean agricultural corporations need to establish a financing policy in consideration of ROA, SIZE, and FA.

Ensemble Learning with Support Vector Machines for Bond Rating (회사채 신용등급 예측을 위한 SVM 앙상블학습)

  • Kim, Myoung-Jong
    • Journal of Intelligence and Information Systems
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    • v.18 no.2
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    • pp.29-45
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    • 2012
  • Bond rating is regarded as an important event for measuring financial risk of companies and for determining the investment returns of investors. As a result, it has been a popular research topic for researchers to predict companies' credit ratings by applying statistical and machine learning techniques. The statistical techniques, including multiple regression, multiple discriminant analysis (MDA), logistic models (LOGIT), and probit analysis, have been traditionally used in bond rating. However, one major drawback is that it should be based on strict assumptions. Such strict assumptions include linearity, normality, independence among predictor variables and pre-existing functional forms relating the criterion variablesand the predictor variables. Those strict assumptions of traditional statistics have limited their application to the real world. Machine learning techniques also used in bond rating prediction models include decision trees (DT), neural networks (NN), and Support Vector Machine (SVM). Especially, SVM is recognized as a new and promising classification and regression analysis method. SVM learns a separating hyperplane that can maximize the margin between two categories. SVM is simple enough to be analyzed mathematical, and leads to high performance in practical applications. SVM implements the structuralrisk minimization principle and searches to minimize an upper bound of the generalization error. In addition, the solution of SVM may be a global optimum and thus, overfitting is unlikely to occur with SVM. In addition, SVM does not require too many data sample for training since it builds prediction models by only using some representative sample near the boundaries called support vectors. A number of experimental researches have indicated that SVM has been successfully applied in a variety of pattern recognition fields. However, there are three major drawbacks that can be potential causes for degrading SVM's performance. First, SVM is originally proposed for solving binary-class classification problems. Methods for combining SVMs for multi-class classification such as One-Against-One, One-Against-All have been proposed, but they do not improve the performance in multi-class classification problem as much as SVM for binary-class classification. Second, approximation algorithms (e.g. decomposition methods, sequential minimal optimization algorithm) could be used for effective multi-class computation to reduce computation time, but it could deteriorate classification performance. Third, the difficulty in multi-class prediction problems is in data imbalance problem that can occur when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed boundary and thus the reduction in the classification accuracy of such a classifier. SVM ensemble learning is one of machine learning methods to cope with the above drawbacks. Ensemble learning is a method for improving the performance of classification and prediction algorithms. AdaBoost is one of the widely used ensemble learning techniques. It constructs a composite classifier by sequentially training classifiers while increasing weight on the misclassified observations through iterations. The observations that are incorrectly predicted by previous classifiers are chosen more often than examples that are correctly predicted. Thus Boosting attempts to produce new classifiers that are better able to predict examples for which the current ensemble's performance is poor. In this way, it can reinforce the training of the misclassified observations of the minority class. This paper proposes a multiclass Geometric Mean-based Boosting (MGM-Boost) to resolve multiclass prediction problem. Since MGM-Boost introduces the notion of geometric mean into AdaBoost, it can perform learning process considering the geometric mean-based accuracy and errors of multiclass. This study applies MGM-Boost to the real-world bond rating case for Korean companies to examine the feasibility of MGM-Boost. 10-fold cross validations for threetimes with different random seeds are performed in order to ensure that the comparison among three different classifiers does not happen by chance. For each of 10-fold cross validation, the entire data set is first partitioned into tenequal-sized sets, and then each set is in turn used as the test set while the classifier trains on the other nine sets. That is, cross-validated folds have been tested independently of each algorithm. Through these steps, we have obtained the results for classifiers on each of the 30 experiments. In the comparison of arithmetic mean-based prediction accuracy between individual classifiers, MGM-Boost (52.95%) shows higher prediction accuracy than both AdaBoost (51.69%) and SVM (49.47%). MGM-Boost (28.12%) also shows the higher prediction accuracy than AdaBoost (24.65%) and SVM (15.42%)in terms of geometric mean-based prediction accuracy. T-test is used to examine whether the performance of each classifiers for 30 folds is significantly different. The results indicate that performance of MGM-Boost is significantly different from AdaBoost and SVM classifiers at 1% level. These results mean that MGM-Boost can provide robust and stable solutions to multi-classproblems such as bond rating.