• International Journal of Advanced Culture Technology
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• v.11 no.1
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• pp.171-180
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• 2023

This study is to develop a performance analysis model for SMEs.Based on similar performance indicators through previous studies, performance indicators for SMEs were rewritten.Through the Korean Journal Citation Index (KCI), 75 related data were classified and a comprehensive SME performance analysis model was developed.Performance analysis was divided into two axes and classified into tables.The horizontal axis is the spatial performance range, which is divided into three areas: performance management by department/function, integrated performance management for the entire organization, and governance performance management requiring policy feedback. The vertical axis is subdivided into short-term, mid-term, and long-term by time and growth stage, and is divided into three parts: technical performance according to technological input, economic performance as organizational performance, and social performance for policy utilization. Then, performance indicators were mapped to each column. As a result of the survey, 28% of technical performance was analyzed as a result of frequency analysis, and performance indicators were organized into five categories: IT, R&D, certification, patent, and innovation. Economic performance was divided into 29%, BSC, HRD, logistics, production quality management, financial support, asset management, etc. 6 categories, social performance 43%, ESG, marketing, export, policy support, consulting, cooperation, etc. 7 categories.Limitations of the study include the narrowness of the survey that derived only performance indicators despite being a meta-analysis, and the performance model was mapped and classified according to growth stage and support period.however Insufficiency of validity due to lack of evidence, performance indicators were developed, but there were limitations in utilization for practical use.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.79-99
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• 2015

The prediction of bankruptcy has been extensively studied in the accounting and finance field. It can have an important impact on lending decisions and the profitability of financial institutions in terms of risk management. Many researchers have focused on constructing a more robust bankruptcy prediction model. Early studies primarily used statistical techniques such as multiple discriminant analysis (MDA) and logit analysis for bankruptcy prediction. However, many studies have demonstrated that artificial intelligence (AI) approaches, such as artificial neural networks (ANN), decision trees, case-based reasoning (CBR), and support vector machine (SVM), have been outperforming statistical techniques since 1990s for business classification problems because statistical methods have some rigid assumptions in their application. In previous studies on corporate bankruptcy, many researchers have focused on developing a bankruptcy prediction model using financial ratios. However, there are few studies that suggest the specific types of bankruptcy. Previous bankruptcy prediction models have generally been interested in predicting whether or not firms will become bankrupt. Most of the studies on bankruptcy types have focused on reviewing the previous literature or performing a case study. Thus, this study develops a model using data mining techniques for predicting the specific types of bankruptcy as well as the occurrence of bankruptcy in Korean small- and medium-sized construction firms in terms of profitability, stability, and activity index. Thus, firms will be able to prevent it from occurring in advance. We propose a hybrid approach using two artificial neural networks (ANNs) for the prediction of bankruptcy types. The first is a back-propagation neural network (BPN) model using supervised learning for bankruptcy prediction and the second is a self-organizing map (SOM) model using unsupervised learning to classify bankruptcy data into several types. Based on the constructed model, we predict the bankruptcy of companies by applying the BPN model to a validation set that was not utilized in the development of the model. This allows for identifying the specific types of bankruptcy by using bankruptcy data predicted by the BPN model. We calculated the average of selected input variables through statistical test for each cluster to interpret characteristics of the derived clusters in the SOM model. Each cluster represents bankruptcy type classified through data of bankruptcy firms, and input variables indicate financial ratios in interpreting the meaning of each cluster. The experimental result shows that each of five bankruptcy types has different characteristics according to financial ratios. Type 1 (severe bankruptcy) has inferior financial statements except for EBITDA (earnings before interest, taxes, depreciation, and amortization) to sales based on the clustering results. Type 2 (lack of stability) has a low quick ratio, low stockholder's equity to total assets, and high total borrowings to total assets. Type 3 (lack of activity) has a slightly low total asset turnover and fixed asset turnover. Type 4 (lack of profitability) has low retained earnings to total assets and EBITDA to sales which represent the indices of profitability. Type 5 (recoverable bankruptcy) includes firms that have a relatively good financial condition as compared to other bankruptcy types even though they are bankrupt. Based on the findings, researchers and practitioners engaged in the credit evaluation field can obtain more useful information about the types of corporate bankruptcy. In this paper, we utilized the financial ratios of firms to classify bankruptcy types. It is important to select the input variables that correctly predict bankruptcy and meaningfully classify the type of bankruptcy. In a further study, we will include non-financial factors such as size, industry, and age of the firms. Thus, we can obtain realistic clustering results for bankruptcy types by combining qualitative factors and reflecting the domain knowledge of experts.

• Journal of Korea Trade
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• v.26 no.7
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• pp.127-144
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• 2022

Purpose - This study aimed to analyze the dynamic effects of progress in globalization on firm performance by employing individual companies' financial statement datasets. Design/methodology - The analysis leveraged the variables of operating revenue (OPRE) and pre-tax profit and loss (PLBT) as measurement variables for firm performance over 2011-2019. As a proxy variable for globalization, the trade index, a subordinate indicator of the KOF Globalization Index, was used. Through panel regression analysis, the relationship among those variables was ascertained, and the local projection (LP) method was subsequently utilized to identify dynamic effects. A subsample analysis was further performed by classifying companies based on their sizes and industries to determine the differential effects of globalization on each group. Findings - The panel regression analysis derived positive effects of an increasing degree of globalization on OPRE of Korea maritime and fishery firms. However, the impulse response functions, obtained from the LP, showed that in the short run, globalization affects PLBT negatively but in the long run, it gradually converted into a positive effect. In addition, according to the subsample analysis based on company size, the effects of globalization on OPRE became greater as each company became larger. Moreover, the industry-based analysis showed heterogeneous effects, depending on the industries in which the maritime and fishery companies operated. Originality/value - The analysis of the dynamic effects of globalization on firm performance, which revealed that the effects vary depending on the time points, is the important contribution of this study. The results also suggest that the effects of globalization vary depending on the company size and industry.

• Journal of the Korean Association of Geographic Information Studies
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• v.10 no.4
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• pp.75-86
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• 2007

The subjects were fifteen dong at downtown of Busan. LQ index and cluster analysis were used to research space functions of downtown in both years, that is to say, 2000 and 2005. At the analysis of LQ index in 2000, the secondary industries were specialized at traditional markets as well as large-scaled commercial districts, while the tertiary industries were done at financial business districts. LQ index in 2005 did not make change mostly comparing with the one in 2000: But, main businesses at downtown that belonged to the tertiary industries rapidly dwindled at old downtown to make appearance at Seomyeon of new downtown. Main businesses at old downtown in the past moved to new downtown to dwindle main functions at old downtown. The cluster analysis was done by using LQ index to classify into three clusters. The first cluster was old downtown that functions of downtown dwindle, and the second cluster was residence area, and the third cluster was the area where space function at downtown was very much active. The findings were as follow: Firstly, various kinds of urban regeneration plans should be made to control dwindling of old downtown and to establish comprehensive and systematic plans on new downtown. Secondly, downtown space functions could be placed depending upon specialization of each industry so that industries being suitable to the area should be introduced to construct development base.

• Journal of Korea Port Economic Association
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• v.32 no.3
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• pp.1-20
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• 2016

The objective of this study was to select world top 20 major container terminals that belong to Korea and China as comparison units to analyze their efficiency and productivity trend. Super Efficiency Data Envelopment Analysis(SE-DEA) and Malmquist Productivity Index(MPI) models were applied to this study. Compared with previous studies, we narrowed down the scope of the comparison units to terminals and conducted more detailed analysis to present more meaningful implications to the actual operation. The different result from existing studies, SE-DEA analysis implied that the major terminals of Busan New Port showed similar efficiency and productivity with China's terminals. In MPI analysis, the average index of Korean and China's terminals is 1.051 and 1.049. The analysis indicates that the productivity was improved 5.1% and 4.9% annually. From 2012 to 2015, the average terminal TCI index of South Korea and China were 1.032 and 1.0318-the main factor which influences MPI. This means improving productivity mainly rely on investment in new equipment and technology upgrades rather than improve operational efficiency over the past few years. Future management decisions should consider more aggressive marketing to increase the volume and improve operational efficiency to enhance productivity. Further research should apply the overall efficiency of the methodology considering the financial diversification, terminal capacity, service levels, and other factors.

• Journal of Digital Convergence
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• v.18 no.5
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• pp.145-155
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• 2020

The purpose of this study investigates the relationship between business strategy and cost behavior according to the corporate life cycle. To do so, I first examine the relationship between cost behavior and business strategy. And then, I analyze the effect of the business strategy on cost behavior conditional upon corporate life cycle based on listed Korean firms from 2000 to 2019. Specifically, the business strategy index is calculated by using the six continuous financial factors and the Prospector and Defender was defined on the score. Also, the corporate life cycle is distinguished based on the cash flow pattern. I find the evidence that the Prospector firms are likely to strengthen the asymmetry of cost behavior (cost stickiness), and examined that such a relationship was most strongly represented in the Introduction. This study is meaningful in that this is a more comprehensive analysis by examining business strategy and cost strategy according to the corporate life cycle and expand the application of financial information by using financial indicators to distinguish business strategies.

• Environmental and Resource Economics Review
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• v.19 no.3
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• pp.689-711
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• 2010

This study attempts to find the relationship between achievement of environmental industry and development of financial sector by analyzing cross-sectional data for OECD countries. Development of financial industry has a positive effect on environmental industry. Green financing also affects improvement of environmental quality. Demand for environmental quality is positively influenced by GDP per capita, while financial crisis has a bad effect on environmental performance. Government-led green financing in Korea has a major role in the Korean environmental policy for years. While market-oriented green financing improves the efficiency of regulation, government-led green financing may delay internalization of environmental cost due to its subsidizing effect. Further study should include systematic analysis on the scope of green financing and its qualitative attribute and fundamental causality between green financing and environmental improvement.

• Economic Analysis
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• v.27 no.4
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• pp.43-69
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• 2021

As the trade linkages and the financial relationship between China and Korea grow stronger, China's influence on Korea is also growing larger. Therefore, it is meaningful to examine key features of Chinese monetary policy operations and the current situation, and to analyze the transmission mechanism of China's monetary policy shocks onto the Korea economy. China's monetary policy shocks can have an impact on the Korea economy through the trade, financial and oil-price channels. In the trade channel, an expansionary Chinese monetary policy can increase Korea's exports of intermediate goods to China under the vertical trade structure, via the vertical trade integration effect. Meanwhile, the expenditure switching effect and the income demand effect show no statistical significance. In the financial and oil-price channels, expansionary Chinese monetary policy shocks can decrease the interest rate and increase both stock prices and the consumer price index in Korea through changes in global portfolio capital flows, interest rates, and raw material prices.

• Journal of the Society of Disaster Information
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• v.8 no.4
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• pp.336-346
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• 2012

In recent years, the number of unforeseen weather hazards increased significantly, and subsequent financial loss in public and private sector has increased as well. Research has been increasingly focused on the actual damage assessment in the event of a disaster through effective and comprehensive disaster countermeasures to deal with and improve a better anti-disaster strategy. Through investigation of existing domestic and foreign property loss related to current damage analysis, the comparison and analysis in terms of current estimated methodology of the full extent of the damage results is proposed. In this study, a more efficient damage index and natural disaster damage assessment is suggested and more specifically, the basic standards of declared disaster areas and the strength of private property damage index will be also be discussed.

• Journal of Korea Port Economic Association
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• v.32 no.3
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• pp.47-66
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• 2016

Once it has been built, a container terminal is impossible to move to another location. It is hard to rectify wrong decisions in a container terminal. This highlights the importance of decision making for a container terminal. The port management about a container terminal has developed from a cargo interface location between sea and land transport, to the standardization of information and procedures due to globalization among global shipping and terminal operators. This research focuses on the current states of market and management for global container terminal operators by investigating up-to-date data for them. The current market states for global container terminal operators are analyzed by using by Herfindahl-Hirschman Index. The analyses of current management states for global container terminal operators are divided into profitability analysis, activity analysis, and bankruptcy risk analysis. Finally, global container terminal operators are clustered into three groups by the current management states.