• Korean Journal of Social Welfare
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• v.52
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• pp.117-141
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• 2003

This study is to identify the efficiency in Busan social welfare agencies between input factors and output factors. For this purpose, gathered are the 2001 services reports of those study agencies. This study used 4 difference model, model 1; comparing input factor(social worker number & labor cost) about output factor(the total number of program used person), model 2; comparing input factor(the total number of social welfare agencies staff & the total working expenses) about output factor(the total number of program used person), model 3; comparing input factor(the total number of volunteer, social welfare agencies staff & a period of operation) about output factor(the total number of program used person), model 4; comparing input factor(the total number of volunteer, social welfare agencies staff, a period of operation & the total working expenses) about output factor(the total number of program used person). Charnes's study(1978) provided an analytical tool for efficiency services output of non-profit organizations, and DEA(Data Envelopment Analysis) was a analytical framework for evaluating the impact of social service outcome. the finding are as follows : (1) In the results of comparing 4 models as same standard, we can find 35-55%(16-25) efficiency agencies among the 45 social welfare agencies. (2) For all DMU becoming the efficiency 1 to standard of output factor, model 1; 33 agencies are increasing the social worker number($\Delta$0.8 number), 10 agencies are raising the labor cost of social worker($\Delta$1,189,000 Won), model 2; 30 agencies are increasing the total number of social welfare agencies staff($\Delta$1.25 number), 14 agencies are raising the total working expenses($\Delta$1,447,000 Won), model 3; 8 agencies are increasing the total number of social welfare agencies staff($\Delta$2.26 number), 14 agencies are increasing the total number of volunteer($\Delta$52 number), and 10 agencies are increasing a period of operation($\Delta$13 month), model 4; 24 agencies are increasing the total number of social welfare agencies staff($\Delta$1.8 number), 12 agencies are raising the total working expenses($\Delta$5,017,000 Won), 12 agencies are increasing the total number of volunteer($\Delta$43.2 number), and 23 agencies are increasing a period of operation($\Delta$16 month).

• Journal of Korea Port Economic Association
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• v.26 no.4
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• pp.188-206
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• 2010

Data Envelopment Analysis(DEA) is an operations research-based method for measuring the performance efficiency of decision units that are characterized by multiple inputs and outputs. DEA has been applied successfully as a performance evaluation tool in many fields. However, it has not been extensively applied in the shipbuilding industry. This paper applied the input-oriented DEA model, and Malmquist indices to the 7 shipbuilding firms to measure the efficiency and productivity changes during the period of 2004 to 2009. The Malmquist indices will be decomposed into three components such as pure efficiency change, scale efficiency change, and technical change. The empirical results show the following findings. First, the DEA findings indicate that main source of inefficiency is scale rather than pure technical. Second, the Malmquist indices show that an overall decrease in productivity.

• Journal of Korea Port Economic Association
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• v.25 no.3
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• pp.139-164
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• 2009

The purpose of this paper is to investigate the value chain efficiency of Korean port investment by using the newly developed multi-year and multi-stage value chain efficiency model of DEA(Data Envelopment Analysis). Inputs[port investment amount, cargo handling capacity, and berthing capacity], and outputs[cargo handling amount, number of ship calls, revenue, and score of customer service satisfaction] are used during 14 years(1994-2007) for 20 Korean seaports by using two kinds of DEA models. Empirical main results are as follows: First, Model 1 shows that the ranking order of multi-stage value chain efficiency is Stage 2, Stage 3-1, Stage 1, and Stage 3-2. And according to the value chain average efficiency scores, ranking order is stages 2, 1, 3-1, and 3-2. In Model 2, 3(Incheon, Mogpo, and Jeju) out of 9 ports show the ranking order of Stages 2, 3-2, 3-1, and 1. And value chain average efficiency scores rank in order of Stages 2, 3-2, 3-1, and 1. Second, the difference among the value chain efficiency scores of each stage comes from the efficiency deterioration of all ports except Stages 2 and 1 in Model 1. In Model 2, value chain efficiency scores among the Stages 3-1, 3-2 compared to Stage 1 were deteriorated. The main policy implication based on the findings of this study is that the manager of port investment and management of Ministry of Land, Transport and Maritime Affairs in Korea should introduce the multi-year, multi-stage value chain efficiency method for deciding the port investment amount and evaluating the effect of port investment after considering the empirical results of this paper carefully.