• Journal of the Korea Safety Management & Science
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• v.16 no.3
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• pp.451-469
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• 2014

The purpose of this study is to implement and develop the integrated Economic Value Added (EVA) and Time-Driven Activity-Based Costing (TDABC) model to seek both improvement of Net Operating Profit Less Adjusted Tax (NOPLAT) and reduction of Capital Charge (CC). Net Operating Profit Less Adjusted Tax (NOPLAT) can be maximized by reducing the indirect cost of an unused resource capacity increased by Cost Capacity Ratio (CCR) of TDABC. On the other hand, Capital Charge (CC) can be minimized by improving the efficiency of Invested Capital (IC) considered by Weighted Average Cost of Capital (WACC) of EVA. In addition, the integrated system of TDABC using Balance Scorecard (BSC) and EVA is developed by linking between the lagging indicators and the three leading indicators. The three leading indicators include customer, internal process and growth and learning perspectives whereas the lagging indicator includes NOPLAT and CC in terms of financial perspective. When the Critical Success Factor (CSF) of BSC is cascading as a cause and an effect relationship, time driver of TDABC and capital driver of EVA can be used efficiently as Key Performance Indicator (KPI) of BSC. For a better understanding of the proposed EVA/TDABC model and BSC/EVA/TDABC model, numerical examples are derived from this paper. From the proposed model, the time driver of TDABC and the capital driver of EVA are known to lessen indirect cost from comprehensive income statement when increasing the efficiency of operating IC from the statement of financial position with unified KPI cascading of aligned BSC CSFs.

• Journal of the Korea Safety Management & Science
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• v.16 no.1
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• pp.239-250
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• 2014

The purpose of this research is to implement and develop the Economic Cost Driver Size(ECDS) extended model to determine the optimal cash driver size with measurement complexity cost and allocation fail cost. ECDS model can be used to seek both measurement accuracy and time efficiency of the Activity-Base Costing (ABC). The study also develops Activity Priority Number (APN) to evaluate the importance of nonvalue-added activities improvement and to determine the representative cost driver of value-added activities when applying ECDS model. APN consists of Severity Priority Number (SPN), Undetectablitiy Priority Number (UPN) and Occurrence Priority Number (OPN). APN can be obtained from lower-stream activity, current activity, upper-stream activity in terms of hierarchical dependency of SIPOC (Supplier, Input, Process, Output, and Customer). In order to seek both efficiency of invested capital and reduction of overhead cost, the paper proposes the integrated ABC and Economic Value Added (EVA) model using redesigned ABC-based statement of comprehensive income and EVA-based statement of financial position. For a better understanding of the proposed ABC-EVA integrated model, numerical examples are demonstrated in this paper. Cost drivers of ABC and capital drivers of EVA in the proposed model can be used to reduce activity overhead cost from ABC-based statement of comprehensive income and to lessen activity capital charge from EVA-based statement of financial position.

• Korea Trade Review
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• v.45 no.2
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• pp.31-49
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• 2020

The purpose of this study is to investigate the effects of knowledge-based resource and ambidextrous capability on their export performance of small and medium-sized enterprises (SMEs). Drawing on an extensive review of the literature on Knowledge-based resource and ambidextrous capability, hypotheses are developed and tested using a sample of 284 SMEs in South Korea. Structural equation modeling was applied. As a result of analysis, first, international entrepreneurship has a significant effect on ambidextrous capability. Second, human capital has significant influence on ambidextrous capability. Third, ambidextrous capability has a significant effect on export performance. The results indicate that their knowledge-based resources ware significant driver of their ambidextrous capability. and that their ambidextrous capability was significant driver of their export performance. In the final conclusion section, implications and limitations of research results and suggestions for future research are discussed.

• Journal of Contemporary Eastern Asia
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• v.22 no.1
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• pp.1-25
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• 2023

The aim of this study is to answer the straightforward question of whether the implementation of IC has fueled non-interest incomes of banks or not. By utilizing the data of 26 domestic banks in Vietnam and employing the value-added intellectual coefficient model (VAIC) as the measure of IC efficiency, our empirical evidence manifests that IC plays a vital role in fostering non-interest incomes of banks. When dividing VAIC into different components, we find that structure capital employed (SCE) is the most important component to enhance the expansion of these incomes compared with other components including capital employed efficiency (CEE), human capital efficiency (HCE). These findings remain unchanged through some robustness tests performed. While the main driver of IC and SCE, CEE component becomes a substantial advantage to increase non-interest incomes in large banks. Meanwhile, the degree of impact of SCE is higher in small banks compared with large ones. Overall, this study would provide a deep insight into the role of IC in the transformation into non-interest income activities of banks in an emerging country, and therefore our findings would be useful for both scholars and policy-makers in Vietnam, where has undergone the period of major reforms in banking system.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.12
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• pp.583-591
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• 2020

Employability has recently become the first target of the national higher education. Its model has been updated to catch the new trend of Industry 4.0. This paper aims at analyzing and ranking the determinants of undergraduate employability, focusing on business and economics majors in Ho Chi Minh City, Vietnam. In-depth interviews with content analysis have been primarily conducted to reach an agreement on a key group of factors: human capital, social capital, and identity. The Stochastic Fractal Search Algorithm (SFSA) is then applied to rank the sub-factors. Human capital is composed of three major elements: attitude, skill, and knowledge. Social capital is approached at both structural and cognitive aspects with three typical types: bonding, bridging, and linking. The analysis has confirmed the change of priority in employability determinants. Human capital is still a driver but the priority of attitude has been confirmed in the contemporary context. Then, social capital with the important order of linking, bridging, and bonding is emphasized. Skill, knowledge, and identity share the least weight in the model. It is noted that identity is newly proposed in the model but a certain role has been found. The findings are crucial for education strategies to enhance university graduate employability.

• Journal of the Korean Institute of Gas
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• v.20 no.1
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• pp.7-12
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• 2016

Natural gas liquefaction process is essential to transport natural gas for long distances. Lots of compressors in this process are needed and the energy for these compressors can be supplied by drivers. Total driver cost can be changed by selecting various drivers. This study focused on the minimization of the driver cost to provide the energy to the compressors. Moreover, scenarios, extracting velocity is changed during whole operating period, are set with considering gas well capacity. The mathematical model was established by considering trade off relationship between the capital cost and the operating cost of the turbines. The model also considers the life time of the driver equipments. As the result, the driver cost of the optimized case was reduced by 6.4% than the base case.

• Journal of Digital Convergence
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• v.19 no.4
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• pp.25-31
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• 2021

Moving toward an aged society, traffic accidents involving elderly drivers have also attracted broader public attention. A rapid increase of senior involvement in crashes calls for developing appropriate crash-severity prediction models specific to senior drivers. In that regard, this study leverages machine learning (ML) algorithms so as to predict the severity of vehicle-pedestrian collisions induced by elderly drivers. Specifically, four ML algorithms (i.e., Logistic model, K-nearest Neighbor (KNN), Random Forest (RF), and Support Vector Machine (SVM)) have been developed and compared. Our results show that Logistic model and SVM have outperformed their rivals in terms of the overall prediction accuracy, while precision measure exhibits in favor of RF. We also clarify that driver education and technology development would be effective countermeasures against severity risks of senior driver-induced collisions. These allow us to support informed decision making for policymakers to enhance public safety.

• Journal of Korean Society of Forest Science
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• v.104 no.4
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• pp.677-684
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• 2015

This study analyzed a long-run growth trend of the forestry sector in the Republic of Korea, and forecasted the potential growth in the future after investigating main drivers of growth in the forestry sector through growth accounting analysis. Based on results, we finally suggested a direction to go forward in order to achieve a sustainable growth in the field. After Asia financial crisis, the growth rate of the forestry sector was getting stable with the fast recovery of Korean economy. While the main drivers of growth in the field was labor and capital accumulation in 1980s and 1990s, the main driver of growth has been the increment of capital accumulation since 2000. As the result of our analysis for forecasting the potential growth in the field, the contribution of labor, capital, TFP in total growth is expected as 0.09%, 1.58%, and -0.01%, respectively. The potential growth rate of the forestry sector during 2012-2020 is predicted to be 1.65% and the total production will become 36.25 trillion won.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.27 no.1
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• pp.55-70
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• 2024

The loss of biodiversity poses a significant threat not only to business sustainability and investment risk but also to societal well-being. Nature serves as a crucial driver for long-term business viability and economic prosperity. The Task Force on Nature-related Financial Disclosures (TNFD), established in September 2023, mandates that companies assess and disclose their impacts on nature. Despite this, many businesses lack a full understanding of their reliance on and impact upon natural capital and ecosystem services, leading to insufficient disclosures. This study evaluates the applicability of TNFD's assessment methodologies and indicators within a domestic context, highlighting the condition of nature and ecosystem services, and exploring potential synergies with national biodiversity policies. Our analysis suggests that TNFD necessitates a unique approach to the spatial and temporal data and methodologies traditionally employed in environmental impact assessments. This includes assessing the reciprocal influences of corporate activities on natural capital and ecosystem services via the LEAP framework. Moreover, in industries where the choice of specific indicators depends on unique sectoral traits, developing a standardized strategy for data and assessment indicators-adapted to local conditions-is crucial due to the variability in the availability of assessment tools and data. The proactive engagement of the private sector in ecosystem restoration projects is particularly promising for contributing towards national biodiversity objectives. Although TNFD is in its nascent phase, its global adoption by numerous companies signifies its potential impact. Successful implementation of TNFD is anticipated to deepen businesses' and financial institutions' understanding of natural capital and ecosystem services, thereby reinforcing their commitment to sustainable development.

• KDI Journal of Economic Policy
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• v.40 no.1
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• pp.45-66
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• 2018

This paper measures the extent of comovements in stock returns between Korea and three major countries (China, Japan and the U.S.) using industry-level data for Korea from 2003 to 2016 in the spirit of the international capital asset pricing model. It also examines what drives the comovements between Korea and the three countries. We find that the comovements of Korean stock returns with those of the U.S. and Japan became smaller after the global financial crisis. In contrast, the comovement in stock returns between Korea and China became larger after the crisis. After an additional analysis, we conclude that trade linkage is the main driver of the comovements between Korea and the three countries.