• Asia-Pacific Journal of Business
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• v.6 no.2
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• pp.1-17
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• 2015

This study compares the most widely used parametric and non-parametric techniques to measure cost and profit efficiency of banks, namely the Stochastic Frontier Analysis (SFA) and Data Envelopment Analysis (DEA). We formulate the specification form of both stochastic cost and profit frontier models and constant return to scale Cost DEA and Profit DEA models and provide an empirical assessment of the cost and profit frontiers based on a panel dataset of National Commercial Banks (NCBs) and Private Banks (PBs) in Bangladesh over the 2001-2010 period. The cost inefficiency and profit efficiency are slightly higher for PBs than NCBs in case of both SFA and DEA. The coefficients of advance and off-balance sheet items are significant that positively influence the banks in stochastic cost frontier model while the advance, other earning assets, price of borrowed fund are significant and negative effects on the banks in stochastic profit frontier model. The average cost inefficiency and average profit efficiency are recorded with 16.3% and 91% respectively. The highest and lowest cost inefficiency are observed for Janata Bank and United Commercial Bank Limited whilst the highest and lowest profit efficiency are recorded for Eastern Bank Limited and Janata Bank respectively. The average technical and allocative efficiency are 68.8% and 35.9%, respectively in case of CRS cost-DEA model whereas they are 70.3% and 31.8% in case of CRS profit-DEA model. The average cost inefficiency is recorded 6.3% by SFA whereas it is 24.5% by DEA. The average profit efficiency is found 91% by SFA while it is 22.1% by DEA, and SFA method shows better bank efficiency than DEA.

• Journal of the Korean Operations Research and Management Science Society
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• v.35 no.3
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• pp.59-68
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• 2010

Improving productive efficiency is important for strengthening a competitiveness of coastal fisheries. This paper examines the productive efficiency of a sample of coastal gillnet fishing business units by estimating a stochastic frontier analysis (SFA) and a data envelopment analysis (DEA) approaches and compares those estimates obtained from two approaches. The estimated mean productive efficiency by SFA is 77.6% and the mean productive efficiencies obtained for the VRS and CRS DEA are 75.9% and 45.7%, respectively. The joint use of SFA and DEA for estimating efficiency is also discussed.

• The Journal of Fisheries Business Administration
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• v.41 no.3
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• pp.45-58
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• 2010

This study estimated the technical efficiency of coastal composite fishery in Korea by using the data envelopment analysis (DEA) and the stochastic frontier analysis (SFA) methods, and the results on the respective method were compared. In the DEA method, the constant returns to scale (CRS) and the variable returns to scale (VRS) output-oriented DEA models were separated and technical efficiencies were estimated, respectively. The average estimated value of technical efficiency by the SFA method (0.633) was found to be lower than that by the VRS-DEA method (0.738), while it was higher than that by the CRS-DEA method (0.479). It was found that strong correlation exists between the SFA method and the VRS-DEA method. The method which can utilize both methods in mutually complementing way for the estimation of technical efficiency was also considered.

• The Journal of Fisheries Business Administration
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• v.43 no.2
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• pp.67-77
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• 2012

Based on the survey on aquaculture management status in Nohwa-eup, Bogil-myeon, Wando-eup in Wando region, this study aimed to estimate productive efficiencies of abalone aquaculture production using a stochastic frontier approach (SFA) and to find out their determinants. In the analysis, a Cobb-Douglas production function with an inefficiency term that follows an halfnormal distribution was assumed for the estimation of productive efficiencies. Then, based on the outcomes of productive efficiencies, determinants of productive efficiency were investigated using a tobit regression model. Results showed that the average inefficiency was estimated to be 10% and the production size would be a statistically significant variable for the production. In addition, it was shown that the cage installing method would be an important factor affecting to the level of productive efficiency.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.12
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• pp.8719-8727
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• 2015

The purpose of this study is to examine the production efficiency of Rose farm and to explain the factors of the inefficiency. To analysis the production efficiency, SFA(Stochastic Frontier Analysis) and DEA(Data Envelopment Analysis) methods are measured, and then, Tobit regression model is used to analysis the influential factors on the production efficiency. As a result, first, the production efficiency by SFA is 88.4%, and by DEA, results are 78.5% and 85.2% in the CRS and VRS model, respectively. In particular, the production efficiency of the measurement results of the two methods are complementary, it is described in the same order of efficiency of each management body. Second, the results of tobit model shows that 6 input-factors are significant, and seed/nursery and material costs, which have the largest regression coefficient value and positive effect on production efficiency, are the most influential factors. Therefore, the results of this study indicates Rose farm can enhance their management efficiency by increasing amount of the seed/nursery and material costs.

• Asia-Pacific Journal of Business
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• v.10 no.2
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• pp.1-12
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• 2019

Collaborative research and development (R&D) has been encouraged based on the belief that knowledge spill-over is mutually beneficial for partners. Although the benefits are supported by science and technology policy research, the risk of R&D collaboration has not been extensively discussed. Two independent studies suggest that there are risks associated with the overuse of collaborative research frameworks. Two sets of R&D collaboration data were analyzed: between the national bio-technology research program and 1) Data Envelop Analysis (DEA), and 2) between Stochastic Frontier Analysis (SFA). In the case of SFA, output measures were integrated into a single output, with weights extracted from research programme managers' responses to the questionnaire. While the DEA result demonstrated the inefficiency of collaborative research, SFA did not. Unlike previous research highlighting risks associated with disclosing proprietary R&D and potential conflict of interest, our study indicates that the transaction's social cost affects collaborative research efficiency. Therefore, governments promoting R&D collaborations should be carefully managed, and policy makers must reconsider the strict conditions governing compulsory collaborative R&D programs.

• Health Policy and Management
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• v.25 no.2
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• pp.97-106
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• 2015

Background & Methods: The purpose of this research is to estimate the efficiency of the pharmaceutical firms and the determinants of their efficiency. Stochastic frontier analysis(SFA) and panel study are applied to the data of 60 domestic pharmaceutical firms from 2006 to 2012. Results & Conclusion: First, the result of the stochastic frontier analysis shows that overall efficiency of the pharmaceutical firms is increasing as time goes by. However, if firms are classified by the scale, the larger firms show more efficiency and if classified by the degree of innovativeness, the innovative firms show more efficiency compared to the non-innovative firms. This evidences show that the scale and R&D investment have significant relationships with the efficiency of the pharmaceutical firms. Therefore, it is necessary to increase the national level of investment for the fundamental researches to vitalize R&D of the new drugs. Second, the result of estimation of the determinants of efficiency shows that the firms with larger sales promotion expenses and entertainment expenses have less efficiency compared to the other firms. This can be explained by the structural characteristics of the small generic pharmaceutical firms. Therefore, the government had better make the pharmaceutical firms to reduce sales promotion and entertainment expenses and increase R&D expenses by introducing systems such as global budgeting system on medicine or reference pricing system.

• The Journal of Fisheries Business Administration
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• v.47 no.2
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• pp.1-14
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• 2016

This study applied the Stochastic Frontier Analysis to estimate which independent variable affects to efficiency of aquaculture household. This study used wage and facility scale as input variables, sales volume as an output variable to estimate efficiency. Also, the study used region, species, water quality to estimate technical inefficiency factors of the model. The data used for this study were obtained by the operating costs survey using 1:1 interview method. The study selected translog production model with technical inefficiency term estimated as half-normal distribution. In addition, the study used pearson and spearman correlation coefficient among efficiency estimating models. Also, the study analysed differences among estimated efficiencies through t-test, and showed us 0.1793 in species, 0.4677 between Geojae and Masan.

• Journal of the Korean Operations Research and Management Science Society
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• v.40 no.1
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• pp.117-128
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• 2015

An organization must restructure itself once it makes outsourcing decisions. Outsourcing is a strategy that a company adopts to direct internal efforts into strengthening its core business processes while contracting other companies for strategically less critical tasks thereby improving organizational production efficiency, and the strategy inevitably involves organizational restructuring. While public service organizations have increasingly embraced outsourcing as well, only a handful of studies analyze the change in efficiency from organizational restructuring. Therefore, this study uses Stochastic Frontier Analysis (SFA) to test whether efficiency improved in Korea Expressway Corporation's restructured branch offices. Korea Expressway Corporation evaluated and grouped its branches based on road extensions management, traffic volume, the number of snow days, road deterioration, and the number of junctions to differentially allocate its personnel and budget. The extent of efficiency improvement in each group was compared, and results show that inefficiency did not exist in any of the groups. The efficiency index among the three group categories was similar across the board; 9 offices in the A-group with its expanded personnel and budget had an efficiency index of 0.954, 12 offices in the B-group with unchanged personnel and budget conditions had an index of 0.950, and 12 offices in the C-group with reduced personnel and budget had an index of 0.937. SFA facilitates efficiency analyses because it does not require pre-restructuring data. This study is easy to apply in practice and empirically tests efficiency improvement in organizational restructuring based on data collected from all the branch offices in Korea Expressway Corporation.

• Journal of Technology Innovation
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• v.17 no.2
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• pp.131-158
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• 2009

We examine the effects of environmental or organizational factors on the performance of TLOs(technology transfer offices) in the PRIs(Public research institutes) using SFA(Stochastic Frontier Analysis), a technique for estimating the efficiency of DMUs(decision making units). In SFA, independent variables are assumed to determine the efficient production technique(production frontier) or affect the efficiency of DMUs. Previous researchs show that input variables such as number of personnel, R&D expenditure affect the production frontier while environmental or organizational variables affect the efficiency. We tried to estimate various types of models to find out whether environmental or organizational variables affect output variables differently from the previous research. Main empirical findings are as follows. First, R&D expenditure tends to increase all output variables considered. Second, environmental factors such as type of institutions and location of institutions affect the level of outputs. Third, organizational factors such as reward system for technology transfer also appear to affect the output variables. Fourth, environmental or organizational variables affect the production frontier directly rather than affect the efficiency of DMUs. Lastly, the efficiency of each DMU appear to be 1 or near to 1. Since almost all DMUs are equally efficient, it may not be effective to evaluate technology transfer activities of PRIs by efficiency criteria. We believe that this research should be complemented by additional data. More general types of production function need to be considered, and new techniques with concepts like output distance functions need to be developed to analyse multiple outputs simultaneously.