• Journal of Information Technology Services
• /
• v.16 no.4
• /
• pp.15-31
• /
• 2017

This study analyzes 45 Korea IT service companies from 2012 to 2016 using DEA analysis. Large enterprises, medium enterprises and small and medium enterprises (SMEs). CCR model and BCC model were used for efficiency analysis. Among the various analytical objects, the decision objects which yield the maximum output with minimum input are compared with other analysis objects. The relative inefficiency was measured through this, and Technical Efficiency (TE), Pure Technology Efficiency (PTE), Scale Efficiency (SE), scale profit, reference frequency were analyzed. Also, we analyzed the Technology Gap Ratio (TGR), which is the distance between production function and Meta-Frontier for each firm, using Meta-Frontier analysis. Finally, the Tobit model is used to analyze the sources of efficiency and inefficiency. The inputs are assets, capital, and employees, and the output factor is sales. The analysis shows that large firms are achieving technological achievements more efficiently than small and medium enterprises. As a result, medium-sized enterprises and SMEs can improve efficiency overall through efficient operation of workforce and appropriate combination of inputs such as assets and capital. Also, as a result of the influence factor analysis, it was found that the ratio of the managed asset ratio and the management cost ratio were significant factors influencing the efficiency of the IT service companies. This study suggests the efficiency analysis using DEA for many Korea IT service companies. Inefficient parts of each company are classified according to size and technology. Also, we identify the most efficient companies and analyze the causes of those companies whose profits are lower than their size.

• Korean Management Science Review
• /
• v.31 no.4
• /
• pp.135-150
• /
• 2014

This paper analyzes the operational efficiency of 91 individual retail stores in Seoul by a two-step procedure. In the first step, a data envelopment analysis (DEA) model is used to identify the efficiency scores. Three inputs (store size, number of items, and number of employees) and two outputs (sales and number of customers) are used for the efficiency measurement. In the second step, a Tobit regression model is used to identify the drivers of efficiency. DEA efficiency scores are used to test hypotheses on the impact of five independent variables, namely store age, number of items per store size, number of items per employee, trade area index, and number of competitors. Results of the Tobit analysis show that number of items per store size, number of items per employee, and number of competitors play a significant role in influencing the operational efficiency of retail stores. Managerial implications of the study are discussed.

• Journal of Korea Technology Innovation Society
• /
• v.21 no.4
• /
• pp.1268-1295
• /
• 2018

This study examines the effects of policy mix on R&D efficiency in by using data from 2014 and 2016 Korean Innovation Survey. The DEA-Tobit analysis is used to estimate the impact of policy mix on relative R&D efficiency. As a result of the DEA analysis, the R&D efficiency of the Korean manufacturing industry firms is low, because the R&D investment has not been used effectively. According to the Tobit model, policy mix have a positive effect on R&D efficiency. In particular, the combination of market-oriented, market supply-oriented, and supply demand-oriented policy mix showed a positive relationship with R&D efficiency. R&D portfolio is necessary to improve R&D efficiencies and government has to facilitate a policy mix in view of the nature of firms and Consistency of policy tools.

• The Journal of the Korea Contents Association
• /
• v.19 no.2
• /
• pp.349-367
• /
• 2019

This paper empirically analyzed the factors of operational efficiency after the quasi-public bus operating system in Busan Metropolitan City. DEA analysis result, for CRS the annual efficiency in 2010 of bus routes that can be transferred to subway lines 1 was the highest. The annual efficiency of bus routes that can be transferred to subway lines 2 shows the largest annual average of 0.923 in 2016 under the CRS average gradually improved over the year. Annual efficiency of subway lines No. 3 and transferable bus routes gradually improved from 2009 to 2015, but declined again in 2016. Among 536 routes for four years on 134 routes per year, 205 routes were found to be inefficient. In order to increase efficiency of the 205 routes, it is suggested that the number of routes should be reduced. In addition, the analysis results on DEA using the Tobit calibration, the most significant factors affecting the operational efficiency index were the time taken, followed by the number of passengers and the number of passengers transferred.

• Journal of Industrial Convergence
• /
• v.15 no.2
• /
• pp.7-16
• /
• 2017

The 6th industrialization of agriculture, which promotes the virtuous cycle of creating new value with through the convergence integration of agriculture and contributing to the vitalization of local economy etc. Accordingly, the purpose of this research is to provide implication for the efficiency, vitalization of domestic 6th industrialization per regions Analysis is use of the "2015~2016 Food Agriculture 6th Industrialization Basic Reality Survey Research" report data. As an analytical method DEA(Data Envelopment Analysis), Tobit model were used for results. The following are the summary of hypothesis test. First, 6th industrialization of agriculture identify improve income growth and increase efficiency, Second, Efficiency influence factor analysis shows have a minus (-) relation that yearly rural population increase rate, and secondary industry participation rate. Third, 6th industrialization recognition of the business is plus(+) relation influenced. Through this research, There may be a limit of research data and qualitative factor etc, explanatory power, but it is meaningful in the sense that objective implication was drawn for the enhancement of the 6th industrialization per regions.

• Health Policy and Management
• /
• v.27 no.4
• /
• pp.296-303
• /
• 2017

Background: This study purposed to analyze regional factors related to gastric cancer screening rate provided by national cancer screening program in Korea. Methods: The unit of analysis was administrative districts of si gun gu level. Dependent variable was regional gastric cancer screening rate provided by national cancer screening program, and regional variables were selected to represent the regional characteristics such as demographic, health behavior and status, socioeconomic, and health resource. Tobit regression was applied for the analysis. Results: Analysis results showed that gastric cancer screening rate was varied depending on regions from 47.8% to 69.1%. Tobit regression showed that gastric cancer screening rate had negative relationships with smoking rate, financial independence rate, and National Health Insurance premium per capita. And regional gastric cancer screening rate had positive relationships with sex ratio and number of gastric cancer screening center. Conclusion: Regional characteristics should be considered in establishing regional policies for increasing the gastric cancer screening rate.

• Korea Journal of Hospital Management
• /
• v.2 no.1
• /
• pp.183-202
• /
• 1997

Health care organizations have been faced with financial difficulties under turbulent health care environment. This situation led hospitals to concentrate their efforts to improve their managerial efficiency in various ways. This study aims to evaluate technical efficiency of 31 tertiary hospitals in Korea and find determinants which are closely related with hospital efficiency. Data envelopment analysis(DEA) and Tobit Model were adopted for study. For the analysis, human resource factors such as number of physicians, nurses, and administrative staffs are used as input variables and the number of inpatients and outpatients) are used as output variables. Among 31 hospitals, in CCR model, 8 hositals showed efficiency score 1 which means they have been operated in very efficient ways and BCC model showed 13 of 31 hospitals as efficient organizations. Next, we analyzed determinants which are closely related with hospital efficiency. By using Tobit model, the study showed hospital size, Quality of care, value added per capita, and revenue per patient were closely related with hospital efficiency, However, it appeared that financial status of hospitals(i.e : making profit or not) was not related with hospital efficiency.

• Journal of Korea Port Economic Association
• /
• v.24 no.4
• /
• pp.349-361
• /
• 2008

There has been abundant empirical research undertaken on the technical efficiency of Korean ports. Most studies have focused on the use of parametric and non-parametric techniques to analyse overall technical efficiency. This paper utilizes data for the period 2000-07 to offer a heterogeneous perspective on the overall efficiency of Korean ports. The framework assumes that ports use one input to produce one output; the output and input include port export(import) and regional export(import). This paper also employs panel analysis and heteroscedastic Tobit model to show the effect of the explanatory variables on the port efficiencies. The panel analysis shows that the regional export/total export has negative effect on the export efficiency while the regional import/total import has not any relations with the import efficiency. The heteroskedastic Tobit model shows that both regional export ratio and regional import ratio have negative effects on the efficiency while the gross regional domestic product has not any significant relations with the import efficiency.

• The Journal of Fisheries Business Administration
• /
• v.40 no.3
• /
• pp.147-167
• /
• 2009

The objective of this study is to analyze the determinants of at-home laver consumption expenditures using the data from a survey of households implemented in 2009. It happened that non-response ratios of monthly expenditures on dry laver and flavored laver among sampled households are 18.8% and 25.6%. Accordingly, this study tries to analyze the determinants affecting at-home laver consumption expenditures by using type II tobit model, one of sample selection models, to deal with sample selection bias caused from non-response data. Analysis results show the age variable positively affects expenditures on dry laver but negatively contributes to expenditures on flavored laver. In addition, the household size, the household's income, the degree of preference for laver have positive relationships with both expenditures. Household size elasticity and income elasticity of the expenditure on dry laver are estimated as 0.220 and 0.251. In the case of flavored laver, these elasticities are estimated as 0.484 and 0.261. Such analysis results can provide information on division of the at-home laver consumption market into groups with high willingness to expense and implementation of detailed marketing strategies to increase at-home laver consumption. The methodology of this study can be applied to consumer preference analysis on other marine products and other analyses on sample with non-response data in the fishery research.

• The Journal of Asian Finance, Economics and Business
• /
• v.8 no.1
• /
• pp.741-750
• /
• 2021

The research aims to analyze the level of efficiency by grouping banks during the period 2017 - 2018 into category 1 and category 2 banks and then dividing them as Regional Development Banks (BPD) and Non-BPD Conventional Commercial Banks (BUK) within each category. The research objects are banks within the categories BPD and BUK comprised 18 BPDs and 35 BUKs. The research methodology uses 3 stages, first, using Data Envelopment Analysis (DEA) we measure the level of bank efficiency; second, using the Tobit regression model we evaluate the effect of financial performance on DEA efficiency, and third, using the Mann-Whitney test we determine whether there is a difference in the efficiency of category 1 and 2 banks. The results showed that there was a decrease in the efficiency of category 1 and 2 banks but on average, the efficiency of category 1 banks is higher than category 2 banks. The estimation results of the Tobit regression model show that only the ROA variable affects the efficiency level of category 1 banks, while category 2 banks are influenced by NPL and ROA variables. In the Mann-Whitney test, it was proven that there were differences in efficiency between BUK and BPD in category 1 and 2 banks.