• Korean Business Review
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• v.21 no.1
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• pp.91-117
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• 2008

Since the late 20th century, due to the globalization of markets and the rapid developments of information and communication technologies, the world has entered into the age of fierce competition. With their abundant low-cost labor, former communist countries such as China, Russia and East Europe and former low-developed countries such as India and Brazil have received foreign investments from major industrial companies. Thus, there are no other alternatives remained for Korea and OECD countries other than moving the industrial economies to the knowledge based innovation systems. In the knowledge based innovation systems, economic developments and employments of regions and nations depend solely on companies situated in their territories. In this article, after reviewing the current state of technological based innovation small firms in korea, we provide a theoretical framework to promote the level of technology based innovation and entrepreneurship. The theoretical framework for analysis and policy suggestions is based on the Schumpeter's value system model that consist of supply factors, demand factors, and supporting institutions. In order to create more innovative small businesses, we insist on the followings. First, more R&D and innovation activities related with promising new technologies should be performed. Second, more R&D funds for technological innovation small companies should be provided from governments. Third, more M&A transactions and technology transactions should be marketed freely and competitively. Fourth, more business incubating services and venture capital services should be provided. Fifth, entrepreneurship and innovation culture should be popular and institutionized among people.

• Journal of Korea Port Economic Association
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• v.24 no.3
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• pp.101-123
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• 2008

This paper is intended to suggest a development device for Gwangyang Free Economic Zone(GFEZ) to strengthen the competitiveness of its maritime port of Gwangyang and promote the regional development in Gwangyang Bay Area. This study will evaluate the background, processes, and targets for foreign investment, I examined historical treatises as they relate to inducing investments to Free Economic Zones. I will explore good operation and inducing investment methods GFEZ within the 4P(product, price, promotion, place) framework. I tried to suggest a new management methodology for the Free Economic Zones as suggested by the central government from the regional view point. The purpose of this paper is to investigate the marketing strategies; compare them to the 4P for deploying an actual more efficient governing structure body for the GFEZ. As proposals to promote and activate GFEZ, I recommended in this paper several benchmarks. First, GFEZ must construct good clusters related to regional strengths as they relate to products. Second; to give more flexible incentives to foreign companies as compared with China, England, Ireland, India, Malasia, Thailand and Vietnam using prices as a guideline. Third, it is required to cultivate expert manpower who can communicate with foreign clients relate to promotion. A proactive public information system is also required in addition to marketing strategies for inducing investment. Lastly, GFEZ needs to became independent and separate from the central government and even from regional province.

• Archives of design research
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• v.18 no.3 s.61
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• pp.55-62
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• 2005

Recently, the opening of the world economy and regional ization of national demand endless endeavors and investments to upbring design-industry for regional economic development. Our domestic commodities, declined in price competitiveness against China and Southeast Asian nations, need construction of brand image of commodities and consumers' response through expanded concernments in marketing spheres. The design-industry is a important means to strengthen competitiveness of the domestic industry through discrimination of commodities and creativity of high added value. Design-industry has some problems in circumstances; commodity development and marketing are below the level in the sphere of manufacture industry yet and businessmen are unwilling to invest demanded for development of new design. Establishment of the personal foundation, useful for accumulating competent designers' experiences and technologies, can not pass limit. In addition, a system and a policy to protect design-industry have a problem in effectiveness. Concrete strategies to activate design industry which meets social and cultural circumstances of Daegu and Gyeongbuk region are to establish policy for construction of specialists' infra, systematization of study & development of design, common ownership of information on design, and expansion of design company. The business to upbring design in the region should be operated to develop competitive commodities which is helpful to the regional economy and to increase value of the regional enterprises' brand. In addition, foundation of design should be expanded personal, material, and informational infra through design-centered so that they are helpful to activate economy of Daegu and Gyeongbuk through making design reformational cluster.

• Journal of East Asia Management
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• v.4 no.1
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• pp.23-51
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• 2023

We investigate whether the association between management overconfident tone and the level of audit effort measured by audit fees and hours differs depending on the level of earnings management. Prior studies suggest that firms led by overconfident managers are likely to initiate risky investments, report low quality financial statements, and have material weaknesses in internal control system. These characteristics, combined together, result in higher audit risk. At the same time, auditors assess audit risk based on the quality of financial reporting, measured by level of earnings management. As a result, the assess audit risk is likely to reflect the combined effect of management overconfidence and the level of earnings management. In this paper, we investigate whether auditors differentiate the effects of real earnings management (REM) and accrual-based earnings management (AEM) when they assess the audit risk related management overconfident. Using the CEO's letter published in 2018, we measure the CEO's tone representing the degree of overconfidence (i.e., activity). Based on this measure, we find that the positive association between managerial overconfident tone and audit effort is more pronounced as the level of REM is higher. However, we find that the baseline association does not vary depending on the level of AEM. These results suggest that auditors consider the managerial overconfident severer when such characteristic accompany the higher level of REM, which can be outcome of aggressive business decisions possibly leading to the higher audit risks. We further find that these results are stronger for Big 4 auditors and continuing auditors. This paper contributes to the literature and practice as follows. First, we provide contextual evidence on how auditors reflect managerial characteristics in the audit process by documenting that auditors actively increase their audit efforts only when overconfident managerial characteristics are highly likely to lead to audit risk. This result suggests that auditors conduct external auditing considering both the efficiency and effectiveness of the audit process. Second, we suggest that auditors use information obtained from a wide range of sources to identify audit risks. Our results provide evidence of how the auditing standards, which do not provide detailed guidelines for audit risk assessment, are being applied in practice. Finally, our results also enhance the understanding of how audit fees are determined. Combined with the studies related to audit pricing, we provide the important reference for discussion between the auditor and the auditee about the audit fee that has created acute tension after the enforcement of the new External Audit Act.

• Journal of National Security and Military Science
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• s.9
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• pp.313-340
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• 2011

The National Defense Reformation bill of "National Defense Reformation 2020" which have been constantly disputed and reformed by the government went through various levels of complementary measures after the North Korean sinking on the Republic of Korea (ROK) Naval Vessel "Cheonan". The final outcome of this reform is also known as the 307 Plan and this was announced on the 8th March. The reformed National Defense Reformation is to reduce the number of units and military personnel under the military structure reformation. However, in order for us to undertake successful National Defense Reformation, the use of privatized civilian resources are essential. Therefore according to this theory, the ROK Ministry of National Defense (MND) have selected the usage of privatized resources as one of the main core agenda for the National Defense Reformation management procedures, and under this agenda the MND plans to further expand the usage of private Especially the MND plans to minimize the personnel resources applied in non-combat areas and in turn use these supplemented personnel with optimization. In order to do this, the MND have initiated necessary appropriate analysis over the whole national defense section by understanding various projects and acquisition requests required by each militaries and civilian research institutions. However for efficient management of privatized civilian resources, first of all, those possible efficient private resources which can achieve optimization will need to be identified, and secondly continuous systematic reinforcements will need to be made in private resource usage legislations. Furthermore, we would need to consider the possibility of labor disputes because of privatization expansion. Therefore, full legal and systematic complementary measures are required in all possible issue arising areas which can affect the combat readiness posture. There is another problem of huge increase in operational expenses as reduction of standby forces are only reducing the number of soldiers and filling these numbers with more cost expensive commissioned officers. However, to overcome this problem, we would need to reduce the number of positions available for active officers and fill these positions with military reserve personnel who previously had working experiences with the related positions (thereby guaranteeing active officers re-employment after completing active service). This would in tum maintain the standards of combat readiness posture and reduce necessary financial budgets which may newly arise. The area of maintenance, supply, transportation, training & education duties which are highly efficient when using privatized resources, will need to be transformed from military management based to civilian management based system. For maintenance, this can be processed by integrating National Maintenance Support System. In order for us to undertake this procedure, we would need to develop maintenance units which are possible to be privatized and this will in turn reduce the military personnel executing job duties, improve service quality and prevent duplicate investments etc. For supply area, we will need to establish Integrated Military Logistics Center in-connection with national and civilian logistics system. This will in turn reduce the logistics time frame as well as required personnel and equipments. In terms of transportation, we will need to further expand the renting and leasing system. This will need to be executed by integrating the National Defense Transportation Information System which will in turn reduce the required personnel and financial budgets. Finally for training and education, retired military personnel can be employed as training instructors and at the military academy, further expansion in the number of civilian professors can be employed in-connection with National Defense Reformation. In other words, more active privatized civilian resources will need to be managed and used for National Defense Reformation.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.237-262
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• 2022

There has been a growing interest in IPOs (Initial Public Offerings) due to the profitable returns that IPO stocks can offer to investors. However, IPOs can be speculative investments that may involve substantial risk as well because shares tend to be volatile, and the supply of IPO shares is often highly limited. Therefore, it is crucially important that IPO investors are well informed of the issuing firms and the market before deciding whether to invest or not. Unlike institutional investors, individual investors are at a disadvantage since there are few opportunities for individuals to obtain information on the IPOs. In this regard, the purpose of this study is to provide individual investors with the information they may consider when making an IPO investment decision. This study presents a model that uses machine learning and text analysis to predict whether an IPO stock price would move up or down after the first 5 trading days. Our sample includes 691 Korean IPOs from June 2009 to December 2020. The input variables for the prediction are three tone variables created from IPO prospectuses and quantitative variables that are either firm-specific, issue-specific, or market-specific. The three prospectus tone variables indicate the percentage of positive, neutral, and negative sentences in a prospectus, respectively. We considered only the sentences in the Risk Factors section of a prospectus for the tone analysis in this study. All sentences were classified into 'positive', 'neutral', and 'negative' via text analysis using TF-IDF (Term Frequency - Inverse Document Frequency). Measuring the tone of each sentence was conducted by machine learning instead of a lexicon-based approach due to the lack of sentiment dictionaries suitable for Korean text analysis in the context of finance. For this reason, the training set was created by randomly selecting 10% of the sentences from each prospectus, and the sentence classification task on the training set was performed after reading each sentence in person. Then, based on the training set, a Support Vector Machine model was utilized to predict the tone of sentences in the test set. Finally, the machine learning model calculated the percentages of positive, neutral, and negative sentences in each prospectus. To predict the price movement of an IPO stock, four different machine learning techniques were applied: Logistic Regression, Random Forest, Support Vector Machine, and Artificial Neural Network. According to the results, models that use quantitative variables using technical analysis and prospectus tone variables together show higher accuracy than models that use only quantitative variables. More specifically, the prediction accuracy was improved by 1.45% points in the Random Forest model, 4.34% points in the Artificial Neural Network model, and 5.07% points in the Support Vector Machine model. After testing the performance of these machine learning techniques, the Artificial Neural Network model using both quantitative variables and prospectus tone variables was the model with the highest prediction accuracy rate, which was 61.59%. The results indicate that the tone of a prospectus is a significant factor in predicting the price movement of an IPO stock. In addition, the McNemar test was used to verify the statistically significant difference between the models. The model using only quantitative variables and the model using both the quantitative variables and the prospectus tone variables were compared, and it was confirmed that the predictive performance improved significantly at a 1% significance level.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.287-316
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• 2023

As global energy crisis and the COVID-19 pandemic have emerged as social issues, there is a growing demand for companies to move away from profit-centric business models and embrace sustainable management that balances environmental, social, and governance (ESG) factors. ESG activities of companies vary across industries, and industry-specific weights are applied in ESG evaluations. Therefore, it is important to develop strategic management approaches that reflect the characteristics of each industry and the importance of each ESG factor. Additionally, with the stance of strengthened focus on ESG disclosures, specific guidelines are needed to identify and report on sustainable management activities of domestic companies. To understand corporate sustainability strategies, analyzing ESG reports and news articles by industry can help identify strategic characteristics in specific industries. However, each company has its own unique strategies and report structures, making it difficult to grasp detailed trends or action items. In our study, we analyzed ESG reports (2019-2021) and news articles (2019-2022) of six companies in the 'Finance,' 'Manufacturing,' and 'IT' sectors to examine the sustainability strategies of leading domestic ESG companies. Text mining techniques such as keyword frequency analysis and topic modeling were applied to identify industry-specific, ESG element-specific management strategies and issues. The analysis revealed that in the 'Finance' sector, customer-centric management strategies and efforts to promote an inclusive culture within and outside the company were prominent. Strategies addressing climate change, such as carbon neutrality and expanding green finance, were also emphasized. In the 'Manufacturing' sector, the focus was on creating sustainable communities through occupational health and safety issues, sustainable supply chain management, low-carbon technology development, and eco-friendly investments to achieve carbon neutrality. In the 'IT' sector, there was a tendency to focus on technological innovation and digital responsibility to enhance social value through technology. Furthermore, the key issues identified in the ESG factors were as follows: under the 'Environmental' element, issues such as greenhouse gas and carbon emission management, industry-specific eco-friendly activities, and green partnerships were identified. Under the 'Social' element, key issues included social contribution activities through stakeholder engagement, supporting the growth and coexistence of members and partner companies, and enhancing customer value through stable service provision. Under the 'Governance' element, key issues were identified as strengthening board independence through the appointment of outside directors, risk management and communication for sustainable growth, and establishing transparent governance structures. The exploration of the relationship between ESG disclosures in reports and ESG issues in news articles revealed that the sustainability strategies disclosed in reports were aligned with the issues related to ESG disclosed in news articles. However, there was a tendency to strengthen ESG activities for prevention and improvement after negative media coverage that could have a negative impact on corporate image. Additionally, environmental issues were mentioned more frequently in news articles compared to ESG reports, with environmental-related keywords being emphasized in the 'Finance' sector in the reports. Thus, ESG reports and news articles shared some similarities in content due to the sharing of information sources. However, the impact of media coverage influenced the emphasis on specific sustainability strategies, and the extent of mentioning environmental issues varied across documents. Based on our study, the following contributions were derived. From a practical perspective, companies need to consider their characteristics and establish sustainability strategies that align with their capabilities and situations. From an academic perspective, unlike previous studies on ESG strategies, we present a subdivided methodology through analysis considering the industry-specific characteristics of companies.

• Management & Information Systems Review
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• v.37 no.3
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• pp.83-113
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• 2018

The purpose of this study to empirically examine a smart work investment and job performance by change resistance. Firstly, There investigates mediating role of the communication between the smart work investment and the job performance. Secondly, It will identify the job productivity differences through a level of organizational change resistance that reduced smart work investment. The smart work is to provide the flexibility of time and location and is a working method to improve a work productivity of organization members. The introduction of smart work means the adoption of new organizational culture, institution and technology and requires a novel change of a custom and pattern on existing organization culture and institution because of transformation form of communication and collaboration. The method of this study adopts a structural equation model to test a mediating effect of communication and a moderating effect of change resistance level. This model confirms whether smart work investments provide a positive impact on communication and organizational productivity. In addition, I will classify a change resistance level of smart work by cluster analysis and then check a critical path difference of job productivity between each group. As a result, The organizational IT, institution and culture on the smart work investment appeared to important influencers in communication and also had a direct influence of individual performance. Also, The three independent variables of smart work investment have an indirect influence of individual and organizational performance through communication mediating variables. However, the organizational IT and institution as independent variables do not provide direct influence of organization performance. Nevertheless, two independent variables of organizational IT and institution have an indirect influence the organization performance through communication mediating variables. As a result of confirming a productivity of three groups on organization resistance, there was a difference the individual and organizational performance among groups. The low-level group of organizational resistance showed high coefficient value of performance compared to other groups. The group analysis implications, The smart work investment appeared significantly to revise the institution first, build culture secondly and advanced technology lastly. The theoretical implication from this study contributes an extension of social science theory through socio-technical systems, institution, culture, change resistance and job performance based on smart work. The practical implications explain the smart work success in step-by-step investment rather than radical investment as level management of change resistance. In future research, the smart work performance between private and public firms will analyze a difference of the organizational culture, institution, technology and performance.

• Korean Journal of Soil Science and Fertilizer
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• v.11 no.4
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• pp.283-295
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• 1979

A new Farmland Expansion and Development Promotion Law was enacted in 1975. This law authorizes the Government to undertake development within a declared "reclamation area", wherever the land owners are unable to do so. In order to give additional impetus to conversion of waste hilly land into productive farmland, these hilly land development projects were conducted as large scale scheme which include soil fertility improvements such as the application of lime and phosphate. Farmland Expansion and Development Promotion Corps has attempted to undertake annual farm surveys in order to obtain some information about hilly land agriculture and farming operations within the reclamation project areas since 1976. As survey data accumulates, more and more clear picture of hilly land farming come to appear and enable us to conduct in-depth study. Effects of such upland reclamation include converting of previously unproductive slopeland into cultivable farmland for lucrative and commercial farming or food production. Furthermore, idle or marginal resources such as farm labor, equipment and compost would be fully employed. Socio-economic effects would include increases in land value and attitude change of farmers. On the other hand the preservation of natural environments might be damaged to the some extend by the projects. As shown in Table 7, the average farm size increased from 3,156 pyeong($3.3m^2$) to 5,562 pyeong, a 76.2% increase. The proportion of small farms with less than I ha dropped from 59.8% to 34.4%, but that of the large farms over 2 ha rose from 13.1% to 32.0% (See Table 8). The survey results indicate that as the farming on reclaimed uplands become time-honored, the acreage devoted for food crop production decreases against the economic crop growing acreage (see Table 6). For example, in the case of uplands reclaimed in 1972, the ratio of food crop acreages decreased from 99.7% in 1972 to 62.5% in 1977, whereas that of economic crop acreages increased from 0.3% in 1972 to 37.5% in 1977. The government used to actively encourage the farmers to carry out food crop production in the reclaimed upland targting toward the realization of self-sufficiency in food grains. It is, however, apparent that the farmers did hardly take the government advises as far as their economic interest were concerned. Yield per 10a. of various crops from the reclaimed uplands by year were surveyed as seen in Table 12. On the average, barley production in the reclaimed areas achieved 83.3% of the average unit yield from the existing upland in its 5 th year. Soybean yields showed a modest increase from 64% in the first year to 95%, in the 5 th year. In contrast, economic crops such as red pepper, totacco and radish achieved their maximum target yields in 3 years from starting to cultivate on the reclaimed farms. In order to test the post economic viability, an economic analysis was performed for each of selected subprojects on the basis of the data obtained through survey. The average actual internal economic rate of return on upland reclamation investments was found to be 20.3% which exceeded other types of projects of land and water development such as tidal land reclamation, irrigation or paddy rearrangement. The actual IRRs of subcategories of upland reclamation projects varied from 17.9% to 21.4% depending upon the kinds of cropping system adopted in each reclaimed areas such as food, economic, fruit or forage crops.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.35-48
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• 2014

According to the 2013 construction market outlook report, the liquidation of construction companies is expected to continue due to the ongoing residential construction recession. Bankruptcies of construction companies have a greater social impact compared to other industries. However, due to the different nature of the capital structure and debt-to-equity ratio, it is more difficult to forecast construction companies' bankruptcies than that of companies in other industries. The construction industry operates on greater leverage, with high debt-to-equity ratios, and project cash flow focused on the second half. The economic cycle greatly influences construction companies. Therefore, downturns tend to rapidly increase the bankruptcy rates of construction companies. High leverage, coupled with increased bankruptcy rates, could lead to greater burdens on banks providing loans to construction companies. Nevertheless, the bankruptcy prediction model concentrated mainly on financial institutions, with rare construction-specific studies. The bankruptcy prediction model based on corporate finance data has been studied for some time in various ways. However, the model is intended for all companies in general, and it may not be appropriate for forecasting bankruptcies of construction companies, who typically have high liquidity risks. The construction industry is capital-intensive, operates on long timelines with large-scale investment projects, and has comparatively longer payback periods than in other industries. With its unique capital structure, it can be difficult to apply a model used to judge the financial risk of companies in general to those in the construction industry. Diverse studies of bankruptcy forecasting models based on a company's financial statements have been conducted for many years. The subjects of the model, however, were general firms, and the models may not be proper for accurately forecasting companies with disproportionately large liquidity risks, such as construction companies. The construction industry is capital-intensive, requiring significant investments in long-term projects, therefore to realize returns from the investment. The unique capital structure means that the same criteria used for other industries cannot be applied to effectively evaluate financial risk for construction firms. Altman Z-score was first published in 1968, and is commonly used as a bankruptcy forecasting model. It forecasts the likelihood of a company going bankrupt by using a simple formula, classifying the results into three categories, and evaluating the corporate status as dangerous, moderate, or safe. When a company falls into the "dangerous" category, it has a high likelihood of bankruptcy within two years, while those in the "safe" category have a low likelihood of bankruptcy. For companies in the "moderate" category, it is difficult to forecast the risk. Many of the construction firm cases in this study fell in the "moderate" category, which made it difficult to forecast their risk. Along with the development of machine learning using computers, recent studies of corporate bankruptcy forecasting have used this technology. Pattern recognition, a representative application area in machine learning, is applied to forecasting corporate bankruptcy, with patterns analyzed based on a company's financial information, and then judged as to whether the pattern belongs to the bankruptcy risk group or the safe group. The representative machine learning models previously used in bankruptcy forecasting are Artificial Neural Networks, Adaptive Boosting (AdaBoost) and, the Support Vector Machine (SVM). There are also many hybrid studies combining these models. Existing studies using the traditional Z-Score technique or bankruptcy prediction using machine learning focus on companies in non-specific industries. Therefore, the industry-specific characteristics of companies are not considered. In this paper, we confirm that adaptive boosting (AdaBoost) is the most appropriate forecasting model for construction companies by based on company size. We classified construction companies into three groups - large, medium, and small based on the company's capital. We analyzed the predictive ability of AdaBoost for each group of companies. The experimental results showed that AdaBoost has more predictive ability than the other models, especially for the group of large companies with capital of more than 50 billion won.