• The Journal of Fisheries Business Administration
• /
• v.34 no.1
• /
• pp.157-183
• /
• 2003

유류오염 사고를 사전에 예방할 수 있는 정책 수단으로는 여러 가지가 있지만 주요한 것으로는 인센티브제의 활용을 들 수 있다. 유류오염 사고를 예방하고 관리하기 위한 인센티브는 유출 사고로 인해 발생한 해양자원의 피해에 대해 가해자에게 배상책임(liability for losses due to spills)을 부과함으로써 제공될 수 있다. 유류오염 사고로 인한 피해액을 실제 화폐단위로 계량화하는 작업은 배상책임 부과제도를 정책수단으로 활용하기 위해 해결해야 할 가장 어려운 과제이다. 따라서, 최근 미국과 캐나다를 중심으로 발전하고 있는 자연자원 피해에 대한 가치 평가법(Natural Resource Damage Assessment : NRDA)은 배상책임 부과제도를 정책적으로 보완할 수 있는 이론적 도구로 간주되고 있다. NRDA는 잠재적인 가해자들에게 그들이 자연환경을 보존해야 하는 사회적 의무를 이행하지 못하고 이를 훼손하게 될 때 이로 인해 발생하는 모든 사회적 비용을 직접 부담해야 한다는 명확한 재무적 인센티브(financial incentive)를 부여함으로써 가해자 보상 원칙 (polluter pays principle)을 실현 할 수 있게 한다. 본 연구는, 유류오염 사고로 인한 환경자원 피해의 경제적 가치를 추정하는 가장 중요한 이론적 모형으로 활용되고 있는 가상상황평가법(CVM)에 대한 기초 개념과 이론적 체계, 그리고 이를 실제 피해액 추정에 성공적으로 적용시키기 위해 해결해야 할 문제점 등을 다루었다. 이를 위해, 본 연구에서는 1988년 캐나다 북서부 연안에서 발생한 Nestucca 유류오염 사고를 사례연구의 대상으로 선정하고, 사고 당시 캐나다 연방정부와 British Columbia 주정부를 대신하여 해양오염에 의한 환경피해의 경제적 가치를 추정한 미국의 컨설팅 회사인 RCG/Hagler, Baily Inc.의 가상상황평가법(CVM) 적용 사례를 분석 검토하였다. Nestucca 사례연구에서는 이들 연구자들이 실제로 활용한 설문지 설계, 설문방법 및 표본설계 등을 분석하였으며, 또한 CVM이 본질적으로 갖고 있는 방법론적 문제점들을 연구자들이 어떻게 해결하려고 했는가를 고찰하였다. 그리고, WTP 추정을 위해 RCG 연구자들이 사용한 사전규제접근법(ex ante regulatory approach)으로 인해 야기될 수 있는 환경자원 피해액 추정 방법의 한계점도 함께 검토하였다. 캐나다 연방정부와 British Columbia 주정부는 Nestucca 유류오염 사고로 인한 자연 자원 피해에 대한 손해배상으로 $4.3 Million의 보상금을 지급 받게 된다. 캐나다 정부는 이 보상금으로 Nestucca Oil Spill Trust Fund를 설립하여 피해를 입은 자연자원의 원상회복(restoration)을 위한 다양한 연구 프로젝트에 자금을 지원하고 있다. Nestucca 유류오염 사고를 계기로 캐나다 정부와 학계는 해양자원의 피해에 대한 경제적 가치평가와 자원의 원상회복에 대한 체계적인 접근 방안을 처음으로 마련 시행하게 되었다는 점에서, Nestucca 유류오염 사고에 대한 사례연구는 캐나다의 해양환경 보존 정책을 연구하는 출발점으로 평가될 수 있을 것이다. 이에 비해, 우리나라에서 대표적인 유류오염사고로 알려져 있는 시프린스호 사고와 관련된 손해배상금은 주로 연안어민들의 어업피해 배상으로 이루어져 있으며, 간접피해에 대한 배상액 48억 5천만원도 대부분 치어방류, 여수대학교 종묘배양장건립 등 피해지역 연안어업 발전을 위한 사업에 투자되었다.

• Journal of Korea Port Economic Association
• /
• v.35 no.3
• /
• pp.61-72
• /
• 2019

In order to enhance the competitiveness of the container shipping industry and promote its development, based on the empirical analyses using multivariate time series models, this study aims to suggest a few strategies related to the dynamics of the container shipping market. It uses the vector autoregressive (VAR) and vector error correction (VEC) models as analytical methodologies. Additionally, it uses the annual trade volumes, fleets, and freight rates as the dataset. According to the empirical results, we can infer that the most exogenous variable, the trade volume, exerted the highest influence on the total dynamics of the container shipping market. Based on these empirical results, this study suggests some implications for ship investment, freight rate forecasting, and the strategies of shipping firms. Concerning ship investment, since the exogenous trade volume variable contributes most to the uncertainty of freight rates, corporate finance can be considered more appropriate for container ship investment than project finance. Concerning the freight rate forecasting, the VAR and VEC models use the past information and the cointegrating regression model assumes future information, and hence the former models are found better than the latter model. Finally, concerning the strategies of shipping firms, this study recommends the use of cycle-linked repayment scheme and services contract.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.12 no.2
• /
• pp.665-674
• /
• 2011

This study examined how students' satisfaction with their major department influences their organizational commitment and intention to pursue a career in tourism-related fields among college students majoring in hospitality and tourism. The results indicated that their overall satisfaction with their department affected positively their organizational commitment but not the behavioral intention to pursue career in the tourism-related fields. Organizational commitment, on the other hands, exerted a positively significant influence behavioral intention to pursue tourism-related career. The results of this study underscored the needs for the educators to manage students' psychological attachment to the department they belong to and their major field of study. Continuous relationship-building efforts should be made for the students to pursue tourism-related career after graduation. Such efforts include efficient communication between faculty and students, extended supports from the school and alumni to the students, and last but not the least, sufficient supports from colleges.

• Journal of Internet Computing and Services
• /
• v.17 no.6
• /
• pp.123-131
• /
• 2016

Process mining in big data environment utilize a number of data were generated from the business process. It generates lots of knowledge and insights regarding implementation and improvement of the process through the event log of the company's enterprise resource planning (ERP) system. In recent years, various research activities engaged with the audit work of company organizations are trying actively by using the maximum strength of the mining process. However, domestic studies on applicable sales auditing system for the process mining are insufficient under big data environment. Therefore, we propose process-mining methods that can be optimally applied to online and traditional auditing system. In advance, we propose continuous monitoring information system that can early detect and prevent the risk under the big data environment by monitoring risk factors in the organizations of enterprise. The scope of the research of this paper is to design a pre-verification system for risk factor via practical examples in sales auditing. Furthermore, realizations of preventive audit, continuous monitoring for high risk, reduction of fraud, and timely action for violation of rules are enhanced by proposed sales auditing system. According to the simulation results, avoidance of financial risks, reduction of audit period, and improvement of audit quality are represented.

• Journal of Digital Convergence
• /
• v.17 no.3
• /
• pp.139-152
• /
• 2019

In the fourth industrial revolution and the era of convergence and integration, on the situation that the internal colleges are needed active change included in the improvement of educational quality, I tested it on the purpose of empirical analysis with SPSS v.18 how colleges' efforts on the first periodic Organization Evaluation And Accreditation System(OEAAS) affects on the Perceived Management Performances on the perspective of BSC. As the test result, the Degree of Awareness of Colleges' Efforts on the OEAAS affects on just Colleges' Learning and on Growth. The Degree Propriety of Preparation of the OEAAS affects on Customer Performance, on Internal Process Performance, and, on Finance Performance. And the Degree of Satisfaction of Internal Assessment affects on all of BSC 4 performances. The results of this research could be used on making the management idea of colleges' performance on the OEAAS. In the future, it would be needed advanced researches which are able to make relatedness to the expanse of management performance with the OEAAS.

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

• The Journal of the Convergence on Culture Technology
• /
• v.9 no.4
• /
• pp.303-308
• /
• 2023

The purpose of this study is to provide a practical guide for establishing BSC that can be practically applied by SMEs. To this end, a case study was conducted to establish a performance evaluation system through a field-required Balanced Scorecard (BSC) for company J, a tent pole manufacturer, and to provide a management strategy system map. As a survey method, the requirements of the ordering organization were organized through a comparison of the BSC-related proposal requests in the first stage. The BSC establishment method was organized through the arrangement of the second stage result report. The 3rd stage BSC derived KPI indicators for SMEs for each of the 4 perspectives. A corporate vision was derived through a 4-step SWOT analysis. A strategy map was developed through 5-step field-required KPI, weight setting, and BSC. The 6-step final strategy system was also drawn up. As a result of the study, the four perspectives of the BSC were reconstructed by department. That is, the financial (financial) perspective is from the executives' perspective, the customer's perspective is from the sales department's perspective, the internal process perspective is from the design department/production quality department's perspective, and the learning/innovation perspective is from the management department's perspective. In addition, a total of 11 CSFs and a total of 49 KPIs of J company were derived. The limitation of the study is that the final strategy system through the company's BSC has only been carried out, and it needs to be linked with the company's compensation system in the future.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.26 no.4D
• /
• pp.635-647
• /
• 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.

• Information Systems Review
• /
• v.24 no.1
• /
• pp.105-123
• /
• 2022

Thanks to the remarkable success of Artificial Intelligence (A.I.) techniques, a new possibility for its application on the real-world problem has begun. One of the prominent applications is the bankruptcy prediction model as it is often used as a basic knowledge base for credit scoring models in the financial industry. As a result, there has been extensive research on how to improve the prediction accuracy of the model. However, despite its impressive performance, it is difficult to implement machine learning (ML)-based models due to its intrinsic trait of obscurity, especially when the field requires or values an explanation about the result obtained by the model. The financial domain is one of the areas where explanation matters to stakeholders such as domain experts and customers. In this paper, we propose a novel approach to incorporate financial domain knowledge into local rule generation to provide explanations for the bankruptcy prediction model at instance level. The result shows the proposed method successfully selects and classifies the extracted rules based on the feasibility and information they convey to the users.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.19 no.2
• /
• pp.1-11
• /
• 2024

In the midst of the current turbulent global economy, traditional investment metrics are undergoing a metamorphosis, signaling the onset of what's often referred to as an "Investment cold season". Early-stage startups, despite their boundless potential, grapple with immediate revenue constraints, intensifying their pursuit of critical investments. While financial indicators once took center stage in investment evaluations, a notable paradigm shift is underway. Organizational culture, once relegated to the sidelines, has now emerged as a linchpin in forecasting a startup's resilience and enduring trajectory. Our comprehensive research, integrating insights from CVF and OCAI, unveils the intricate relationship between organizational culture and its magnetic appeal to investors. The results indicate that startups with a pronounced external focus, expertly balanced with flexibility and stability, hold particular allure for investment consideration. Furthermore, the study underscores the pivotal role of adhocracy and market-driven mindsets in shaping investment desirability. A significant observation emerges from the study: startups, whether they secured investment or failed to do so, consistently display strong clan culture, highlighting the widespread importance of nurturing a positive employee environment. Leadership deeply anchored in market culture, combined with an unwavering commitment to innovation and harmonious organizational practices, emerges as a potent recipe for attracting investor attention. Our model, with an impressive 88.3% predictive accuracy, serves as a guiding light for startups and astute investors, illuminating the intricate interplay of culture and investment success in today's economic landscape.