• Journal of Korea Technology Innovation Society
• /
• v.3 no.3
• /
• pp.36-52
• /
• 2000

This study takes an issue of efficiency analysis in the presence of network effect utilizing the DEA (Data Envelopment Analysis) framework. Network effect has important policy implication for the regulation of local monopolies which undertake their business through physical network, such as electricity, natural gas, local telephony, etc. If the difference in spatial condition between companies is not controlled properly, the performance comparison and associated incentive regulation bear significant bias. In this study, we propose a methodology to measure the true managerial or technical efficiency apart from efficiency difference accruing from the difference in spatial condition. A series of modified DEA efficiency models are combined to investigate the extent of exogenous and endogenous efficiency component in the Korean natural gas distribution companies. Empirical results show that the network effect plays significant role in determining superficial performance difference.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.37 no.4
• /
• pp.125-138
• /
• 2012

Health care that is considered to be one of the major factors for the quality of life is nowadays receiving a great deal of attention, and thus there is a growing need in Korea to identify the efficiency of national medical service and enhance the competitiveness. Although there exist studies on the medical service efficiency about general hospitals and local hospitals, they mostly deal with the efficiency problems from a domestic and regional perspective. In response, this paper analyzes the competitive efficiency of national medical service with respect to 16 OECD countries, by exploiting Data Envelopment Analysis (DEA) and Malmquist Productivity Index (MPI). Building on the DEA and MPI analysis results, this paper identifies the competitive position of Korean national medical service and suggests implications for the medical service improvement.

• Asia pacific journal of information systems
• /
• v.10 no.1
• /
• pp.19-35
• /
• 2000

Data Envelopment Analysis(DEA), a non-parametric productivity analysis tool, has become an accepted approach for assessing efficiency in a wide range of fields. Despite of its extensive applications, some features of DEA remain bothersome. DEA offers no guidelines to where relatively inefficient DMU(Decision Making Unit) improve since a reference set of an inefficient DMU consists of several efficient DMUs and it doesn't provide a stepwise path for improving the efficiency of each inefficient DMU considering the difference of efficiency. We aim to show that DEA can be used to evaluate the efficiency of System Integration Projects and suggest the methodology which overcomes the limitation of DEA through hybrid analysis utilizing DEA along with machine learning.

• Journal of the Korean Society of Food Culture
• /
• v.31 no.4
• /
• pp.339-347
• /
• 2016

With the interest on operational efficiency due to the rapid growth of food material distribution industry and food service industry, the study adopts DEA (Data Envelopment Analysis) model and examines to measure the technological, pure technical and scale efficiency those companies engaging in the food material distribution and food service business. As a result of analysis, the companies operating integrated business have relatively higher efficiency than those operating only food material distribution or food service companies while the result indicates that three efficiencies don't have significant difference depending on whether affiliated companies or not. In the results from the measuring by DEA.

• Proceedings of the Korea Society for Industrial Systems Conference
• /
• 2006.05a
• /
• pp.77-88
• /
• 2006

Data Envelopment Analysis (DEA), a non-parametric productivity analysis tool, has become an accepted approach for assessing efficiency in a wide range of fields. Despite of its extensive applications, some features of DEA remain bothersome. DEA offers no guidelines to where relatively inefficient DMU(Decision Making Unit) improve since a reference set of an inefficient DMU consists of several efficient DMUs and it doesn't provide a stepwise path for improving the efficiency of each inefficient DMU considering the difference of efficiency. We aim to show that DEA can be used to evaluate the efficiency of System Integration Projects and suggest the methodology which overcomes the limitation of DEA through hybrid analysis utilizing DEA along with machine learning.

• East Asian Economic Review
• /
• v.19 no.2
• /
• pp.167-188
• /
• 2015

The objective of this study is to obtain a better understanding of the current rice production condition in Myanmar through efficiency analysis, especially, to examine the impact of farm mechanization on Myanmar rice production efficiency. For representation of efficiency and the determinants, this paper uses both the data envelopment analysis (DEA) and the stochastic frontier approach (SFA) with variable returns to scale on Myanmar rice production. The efficiency of the rice production was estimated and subsequently the determinants factors were investigated based on the estimated efficiency level of these sample farmers. The empirical evidence finds that farm mechanical tools significantly improve the Myanmar rice production efficiency.

• Journal of Information Technology Services
• /
• v.6 no.1
• /
• pp.65-81
• /
• 2007

Recently, the affiliates of the Ministry of Information and Communication have been conducting a performance evaluation study on IT policy projects, based on the performance evaluation model of the U.S. Office of Management and Budgets, with regard to investment in computerization. The performance evaluation model for the policy project could provide the objective information required for the planning, development, and operation of the policy project. However, the performance of the most policy project is likely to be evaluated using the subjective evaluation criteria of evaluators. Therefore, this case study evaluates the relative efficiency using DEA (Data Envelopment Analysis) in order to improve the performance evaluation method of the digital pioneer' project implemented by the Korea IT Industry Promotion Agency. The improvement value of inefficient Decision Making Units (DMUs) was measured by a static efficiency analysis. The measured value will suggest a objective viewpoint of the performance between DMUs to evaluators. In doing so, it will show an appropriate direction for the policy project to be evaluated successfully.

• The Journal of Society for e-Business Studies
• /
• v.26 no.2
• /
• pp.45-59
• /
• 2021

As software(SW) has been considered as a key driver of the fourth industrial revolution, significant R&D investment has been made by Korean government. Despite the attention and support by the government, systematic analysis on the SW R&D efficiency has not been fully addressed. In this study, the efficiency of SW national research and development projects was analyzed using Data Envelopment Analysis(DEA) techniques. Efficiency was measured from both static and dynamic perspectives based on 1,463 projects conducted by the National IT Industry Promotion Agency(NIPA) from 2008 to 2018. The static efficiency analysis identified the causes of inefficiency as scale and technology problems. As a result of dynamic efficiency analysis, we present a sector-specific response model using an efficiency-stability matrix. This study is meaningful in that efficiency analysis was conducted on the entire SW national R&D project, and static/dynamic efficiency analysis results are expected to be used as a guideline for planning SW national R&D project.

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

• Health Policy and Management
• /
• v.30 no.2
• /
• pp.199-210
• /
• 2020

Background: Based on the importance of ceasing smoking programs to control the regional disparity of smoking behavior in Korea, this study aims to reveal the variation of smoke rate and determinants of it for 229 provinces. An evaluation of the relative efficiency of the cease smoking program under the consideration of regional characteristics was followed. Methods: The main sources of data are the Korean Statistical Information Service and a national survey on the expenditure of public health centers. Multivariate regression is performed to figure the determinants of regional variation of smoking rate. Based on the result of the regression model, clustering analysis was conducted to group 229 regions by their characteristics. Three clusters were generated. Using data envelopment analysis (DEA), relative efficiency scores are calculated. Results from the pooled model which put 229 provinces in one model to score relative efficiency were compared with the cluster-separated model of each cluster. Results: First, the maximum variation of the smoking rate was 16.9%p. Second, sex ration, the proportion of the elder, and high risk drinking alcohol behavior have a significant role in the regional variation of smoking. Third, the population and proportion of the elder are the main variables for clustering. Fourth, dissimilarity on the results of relative efficiency was found between the pooled model and cluster-separated model, especially for cluster 2. Conclusion: This study figured regional variation of smoking rate and its determinants on the regional level. Unconformity of the DEA results between different models implies the issues on regional features when the regional evaluation performed especially on the programs of public health centers.