• Proceedings of the Korea Port Economic Association Conference
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• 2006.08a
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• pp.253-276
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• 2006

This paper has shown the trend of competition positioning of 26 Korean ports in 1994, 1999, and 2003 by using BCG matrix which consists of relative market shares, growth rate of cargo handling, and also growth rate and CCR and BCC efficiency scores with scale efficiency scores in the vertical and horizontal axes. The empirical main results are as follows. First, Incheon Port, Pyungtag Port, Gwangyang Port, Busan Port, Pohang Port and Woolsan Port have shown their competitive positioning in terms of market share and growth rate. Second, Pyungtag Port, Wando Port, Tongyoung Port, Gohyun Port, Samcheog Port, and Okgae Port have their competitive positioning in terms of growth rate and scale efficiency scores. The main policy implication of this paper is to emphasize that BCG matrix method using in this paper can give seaport manager the basic information for planning the future port management for enhancing the competitive positioning among Korean seaports.

• The Journal of Fisheries Business Administration
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• v.41 no.3
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• pp.129-151
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• 2010

This study is to analyze the efficiency of Korean fishing ports using DEA. First, the study calculated the efficiency scores based on a CCR-BCC framework and hence technical, pure technical, and scale efficiency scores are seperated for the 38 fishing ports under study. The Average of technical, pure technical, and scale efficiency are turned out to be 0.6834, 0.8582, and 0.7774 respectively. The 15 fishing ports are fully efficient under the constant returns to scale while 21 fishing ports under the variable returns to scale. Second, the super efficiency scores are also calculated under the radial model without the consideration of slacks. The highest score is turned out to be 4.4984 for the P16 fishing port with the average score of 0.9652 for the entire fishing ports. Nevertheless, P16 fishing port has showed up only once as a reference set. On the other hand, P34 fishing port has showed up 11 times as a reference set, which scored the second highest score of 2.9815. Finally the super efficiency scores are calculated under the non-radial model with the explicit consideration of slacks. Now the P34 fishing port scored the highest score of 2.3424 with even 15 times referred to a bench-mark. Therefore the importance of P34 fishing port is emphasized once again on the field of bench-marking for the efficiency of fishing ports. When the targets for the input factors to improve the efficiency of each DMU are calculated the area of fishing port needs the most adjustment to be reduced for 40.36% on the average, while the cosignment sales area does the least adjustment for 13.70%.

• Journal of Distribution Science
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• v.12 no.8
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• pp.5-15
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• 2014

Purpose - The method of TV home shopping is a kind of retail method that provides the viewer with information about products and, further, sells the products to consumers through the media of television. The domestic home-shopping industry has been expanding since 1995, and there are six companies in this arena as of 2012. In this study, we evaluate the management efficiency of TV home-shopping companies and provide suggestions for improving efficiency, using the DEA (data envelopment analysis) model. Hence, we expect to contribute to the progress of the companies' efficiency and the development of the TV home-shopping industry, where deepening competition is inevitable because it is experiencing the maturing market stage in its life cycle. Research design, data, and methodology - Efficiency is the ratio of the quantity of input to the quantity of output of a product or service. It is necessary to estimate aggregate inputs and aggregate outputs, which are calculated by applying a weighting to a number of input and output factors, to measure the efficiency. The DEA model is divided into the CCR model and the BCC model. The CCR model is a basic model that assumed constant returns to scale (CRS), and the BCC model extends the CCR model to accommodate technologies exhibiting variable returns to scale (VRS), and concerns only the technical efficiency without considering the efficiency of returns to scale. In this study, we consider six companies each year from 2008 to 2012 as a DMU (Decision Making Unit) and analyze the differences in efficiency for each company in each year. Furthermore, we evaluate the operating characteristics of TV home-shopping companies, using three models, in accordance with the overall performance, profitability, and marketability of the business. Results - The result of the analysis, using DEA models, shows that Hyundai Home Shopping (2009, 2010, 2011), GS Home Shopping (2011), NS Home Shopping (2011) and CJ O Shopping (2012) possess MPSS (most productive scale size), with a score 1.0 in CCR, BCC, and scale efficiency. Particularly, Hyundai Home Shopping is shown to be the most efficient in terms of overall business performance, marketability, and profitability. The overall efficiency of the home shopping industry has displayed an increasing trend since 2008, even though it decreased marginally in 2012; further, we can observe that home shopping companies operate with increasing efficiency with the passage of time. Conclusions - Home shopping companies have focused on market expansion rather than profits, as they displayed better efficiency in marketability than increase in profitability during the period 2008-2012. In addition, the main reason for the increased efficiency in the home shopping industry is the market expansion through the revenue increase of each home shopping company. This study can be used as a reference when home shopping companies attempt to devise future strategies, as it suggests efficiency benchmarks and development levels for each home shopping company.

• Korean Journal of Agricultural Science
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• v.39 no.2
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• pp.279-289
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• 2012

This study was conducted to survey and diagnose operation status of the agricultural machinery rental service, analyse and compare operational efficiency among 82 city and county ATDEC (agricultural technology development and extension center) using the DEA (Data Envelopment Analysis) method, and recommend future direction, for improvement of the business. Input variables were invested budget and labor, and output variable was rental return. Percentages of return to investment on the rental service were calculated as 68.3% and 63.9% when analyzed with CCR (Charnes, Cooper and Rhodes) and BCC (Banker, Charnes and Cooper) models, respectively, indicating inefficiency of the service operation. Increase of rental charge would increase efficiency by 63.9~68.3% depending on models, and decrease of financial and labor investment would improve the efficiency by about 11.3%. Technical efficiency would be more important than scale efficiency, therefore adjustment of over-invested budget and labor needed to be made together with increase of rental charge to improve the operation. Among the ATDECs providing the rental service, 6 (7.3%), 43 (52.4%), and 33 (40.2%) were in state of CRS (constant return to scale), IRS (increasing return to scale), and DRS (decreasing return to scale), respectively. These indicated public aspects of the rental system, over-investment, lack of output component for input component, meaning that scale income would be increased by qualitative expand of rental charge. Efficiency analysis of the rental system by region showed that efficient ATDECs to be benchmarked by others were in the order of DMU-70, DMU-54, DMU-29, DMU-5, DMU-22, DMU-2, and DMU-61. More comprehensive and extensive survey and analyses would be necessary in the future.

• Agribusiness and Information Management
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• v.8 no.2
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• pp.1-8
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• 2016

The present study has aimed at analyzing the technical and scale efficiencies of credit utilization by the farmer-borrowers in Chittoor district of Andhra Pradesh, India. DEA approach was followed to analyze the credit utilization efficiency and to analyze the factors influencing the credit utilization efficiency, log-linear regression analysis was attempted. DEA analysis revealed that, the number of farmers operating at CRS are more in number in marginal farms (40%) followed by other (35%) and small (17.5%) farms. Regarding the number of farmers operating at VRS, small farmers dominate the scenario with 72.5 per cent followed by other (67.5%) and marginal (42.5%) farmers. With reference to scale efficiency, marginal farmers are in majority (52.5%) followed by other (47.5%) and small (25%) farmers. At the pooled level, 26.7 per cent of the farmers are being operated at CRS, 63 per cent at VRS and 32.5 per cent of the farmers are either performed at the optimum scale or were close to the optimum scale (farms having scale efficiency values equal to or more than 0.90). Nearly 58, 15 and 28 percents of the farmers in the marginal farms category were found operating in the region of increasing, decreasing and constant returns respectively. Compared to marginal farmers category, there are less number of farmers operating at CRS both in small farmers category (15%) and other farmers category (22.5%). At the pooled level, only 5 per cent of the farmers are operating at DRS, majority of the farmers (73%) are operating at IRS and only 22 per cent of the farmers are operating at CRS indicating efficient utilization of credit. The log-linear regression model fitted to analyze the major determinants of credit utilization (technical) efficiency of farmer-borrowers revealed that, the three variables viz., cost of cultivation and family expenditure (both negatively influencing at 1% significant level) and family income (positively influencing at 1% significant level) are the major determinants of credit utilization efficiency across all the selected farmers categories and at pooled level. The analysis further indicate that, escalation in the cost of cultivation of crop enterprises in the region, rise in family expenditure and prior indebtedness of the farmers are showing adverse influence on the credit utilization efficiency of the farmer-borrowers.

• Environmental and Resource Economics Review
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• v.9 no.2
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• pp.349-371
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• 2000

Data Envelopment Analysis model is a linear programming based technique for measuring the relative performance of organizational units where the presence of multiple inputs and outputs makes comparison difficult. A common measure for relative efficiency is weighted sum of outputs divided by weighted sum of inputs. DEA model allows each unit to adopt a set of weight that shows it in the most favorable light in comparison to the other unit. In this paper, we present the mathematical background and characteristics of DEA model, and give a short case study where we apply the DEA model to evaluate the relative efficiencies of 51 global electricity companies. The technical efficiency and scale efficiency are also to be investigated. Generating capacity and the number of employees are used for input data, and revenue, net profit and electricity sales are used for output data. We find that the companies with 100% relative efficiency are only 9 among 51 electricity companies. And the technical and scale efficiency of KEPCO is 98.7% and 78.89%, respectively. This means that the inefficiency of KEPCO is caused by the scale inefficiency. The analysis shows that the employees should be decreased by 15% at minimum to get the 100% efficiency. The result suggests that KEPCO needs the structural reform to improve the efficiency.

• Journal of Korea Port Economic Association
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• v.32 no.2
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• pp.123-135
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• 2016

This study uses Data Envelopment Analysis(DEA) to investigate the management efficiency of Korean shipping companies based on business administration costs such as corporate entertainment, advertisement, and labor costs. We analyze shipping enterprises listed on the Korean stock market of the period of 2010-2014. Corporate entertainment, advertisement and labor costs are used as input variables and sales and net income are used as output variables. We use technical efficiency, pure technical efficiency, scale efficiency and returns to scale to propose a plan to improve the efficiency of inefficiency decision-making units (DMUs). The results of the efficiency analysis show that six of the DMUs in the technical efficiency of CCR model and eight of the DMUs in the pure technical efficiency of BCC model are in efficient state. In terms of return to scale, six of the DMUs(24% of all DMUs) show increasing returns to scale, while 13 DMUs(52% of all DMUs) showdecreasing returns to scale. Because multiple efficient state for DMUs exist in the technical efficiency analysis, we conduct a super efficiency analysis. The results show that the efficient state of the twomost efficient DMUs are 1.314 and 1.243, respectively. This implies that these DMUs could maintain their current levels of the efficiency if they increase the amount spent on advertisements, corporate entertainment and labor costs by 31.4% and 24.3%. respectively. We conclude this study by providing the efficiency states of each DMU and target for improving the inefficiencies in each case.

• The Korean Journal of Financial Management
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• v.20 no.2
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• pp.263-291
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• 2003

The purpose of this paper is to analyse the change in the productivity of Korean life insurance industry by Generalized Malmquist productivity indices. Generalized Malmquist indices will be decomposed into three components such as pure efficiency change, scale efficiency change, and technical change. The principal findings indicate an overall increase in productivity driven more by technical progress than pure technical efficiency and scale efficiency.

• Journal of Korea Port Economic Association
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• v.24 no.4
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• pp.47-70
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• 2008

The purpose of this study is to estimate total factor productivity(TFP) growth by stochastic frontier function and to grasp contributing factors of its growth rate by decomposing the total factor productivity into efficiency change, technical progress, scale change, and allocation change. Annual growth rate of total factor productivity for 1990-2003 is 0.019 (1.9%), higher than that of overall industry (0.010). The main component of TFP growth is not efficiency change but technical progress. Contributing factors of total factor productivity growth are change of allocation efficiency in port industry, technical progress in sea-transportation industry, and change of scale efficiency in transportation-equipment industry. The change of total factor productivity shows a decreasing trend since late in the 1990s. The annual technical efficiency of port-logistics industry is less than that of overall industry. Capital elasticity for output (0.391) is higher than labor elasticity (0.227), but scale economy of port-logistics industry is 0.618, which is far from optimal scale economy.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.22 no.4
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• pp.301-315
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• 2011

DEA(Data Envelopment Analysis) is useful to measure the relative efficiency of organizational units where the presence of multiple inputs and outputs. This study applied DEA-CCR and DEA-BCC to evaluate the technical, pure technical, and scale efficiency of 29 university libraries. The input variables were number of books, print edition expenses, building space, staff, number of seats. As output variables we estimated: reader visits, number of borrowed items, number of visitors. It was found out that number of libraries with 100% relative efficiency among 29 libraries were 13. Also the results shows that main reason of inefficiency was from scale rather than from pure technical. Many inefficient libraries were operations of increasing return to scale.