• Journal of Home Health Care Nursing
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• v.27 no.1
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• pp.16-28
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• 2020

Purpose: The aim of this study was to evaluate the efficiency of long-term care visiting nursing centers in communities using data envelopment analysis (DEA). Methods: Data were collected using a self-reported questionnaire. The average number of staff per 6 months and total space of center were used as input variables. The average number of clients per 6 months and the average profits per 6 months were used as output variables. EMS Window version 3.1 was used to measure the efficiency scores. Descriptive statistics and tobit regression were applied to analyze the general characteristics of the variables and the factors affecting efficiency scores. Results: The average efficiency of 30 long-term care visiting nursing centers in communities was approximately 66.9% on technical efficiency analysis, and 79.1% on scale efficiency analysis. Eight nursing centers on technical efficiency analysis and 12 centers on scale efficiency analysis had 100.0% efficiency. Conclusion: Our findings reveal that long-term care visiting nursing centers in communities have low operational efficiency. Therefore, it is essential to institute policies and regulations to improve the efficiency of visiting nursing centers and to strengthen the business competencies of center officers.

• Journal of Society of Preventive Korean Medicine
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• v.17 no.3
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• pp.103-114
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• 2013

Objectives : This study aimed to analyze the management efficiency of Korean Medicine hospitals for recent 10 years(2001~2010) using the Data Envelop Analysis(DEA) model. Methods : We collected the management data of 23 Korean Medicine hospitals for DEA model from the Korean Oriental Medicine Hospitals' Association (KOMHA). Input variables of DEA model are numbers of beds, numbers of doctors, numbers of nurses and numbers of other staffs of each Korean Medicine hospitals. Output variables are numbers of inpatients and numbers of outpatients of each Korean Medicine hospitals. Based on the DEA model, we calculated the efficiency score of each Korean Medicine hospital and compared it by hospital's ownership, location, and size. Results : Average DEA efficiency scores of Korean Medicine hospitals by year ranged from 0.86 to 0.92. Private owned hospitals showed higher efficiency scores than the university affiliated hospitals with statistical significance (p=0.001). And Korean Medicine hospitals located in capital region of Korea(Seoul City, Incheon City, Gyeonggi-do) and the rest Korean Medicine hospitals did not show statistical difference (p=0.516). Lastly, Korean Medicine hospitals with different size did not show statistical difference in management efficiency (p=0.499). Conclusion : We have found that Korean Medicine hospitals management efficiency have not changed throughout 10 years, and that different ownership forms of Korean Medicine hospital show statistical difference in management efficiency while location, and size do not.

• Korea Journal of Hospital Management
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• v.24 no.4
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• pp.57-69
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• 2019

Purpose: This study aimed to analyze the job performance efficiency of nurses in the Neonatal Intensive Care Unit (NICU) by using the Data Envelopment Analysis (DEA). Additionally, the study aimed to provide a detailed method to improve the currently inefficient way in which nurses perform their jobs by differentiating the reference group of more efficient nurses, and to compare the characteristics of the more efficient group of nurses to those of the less efficient group of nurses. Methodology: This study evaluated the relative job performance efficiency of nurses by applying DEA to 43 nurses in the NICU. The input variables for the efficiency analysis were working career (years), time spent in direct nursing care (hours), overtime (hours), and job-related training (hours); the output variables were the job performance scores of professional practice, research, leadership, and education. Data were analyzed using SPSS IBM 23.0 and Open Source DEA (OSDEA). Findings: The relative job performance efficiency of the 43 nurses was 0.933, and 20 nurses were evaluated as more efficient. In addition, the study confirmed the possibility of improving the overall job performance efficiency by improving leadership, while controlling the current input variables. Lastly, the more efficient nurses had significantly higher job performance scores for research (t=2.028, p=0.049), leadership (t=2.036, p=0.048), and education (t=2995, p=0.005) than those who were less efficient. Practical Implications: It is suggested that job performance be evaluated using DEA to improve the overall job performance efficiency of NICU nurses. The analysis results from DEA for nurses becomes evidence in support of establishing individualized goals for each nurse, thus resulting in a foundation for systematic human resource management of nurses, and ultimately contributing to increase in the job performance efficiency of nurses.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.1
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• pp.84-99
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• 2014

In this study, determinant input-output variables are identified for calculating Data Envelopment Analysis (DEA) efficiency scores relating to evaluating the efficiency of government-sponsored research and development (R&D) projects. In particular, this study proposes a systematic framework of design and analysis of experiments, called "all possible DEAs", for pinpointing DEA determinant input-output variables. In addition to correlation analyses, two modified measures of time series analysis are developed in order to check the similarities between a DEA complete data structure (CDS) versus the rest of incomplete data structures (IDSs). In this empirical analysis, a few DEA determinant input-output variables are found to be associated with a typical public R&D performance evaluation logic model, especially oriented to a mid- and long-term performance perspective. Among four variables, only two determinants are identified : "R&D manpower" ($x_2$) and "Sales revenue" ($y_1$). However, it should be pointed out that the input variable "R&D funds" ($x_1$) is insignificant for calculating DEA efficiency score even if it is a critical input for measuring efficiency of a government-sonsored R&D project from a practical point of view a priori. In this context, if practitioners' top priority is to see the efficiency between "R&D funds" ($x_1$) and "Sales revenue" ($y_1$), the DEA efficiency score cannot properly meet their expectations. Therefore, meticulous attention is required when using the DEA application for public R&D performance evaluation, considering that discrepancies can occur between practitioners' expectations and DEA efficiency scores.

• Journal of the Korean Society for Railway
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• v.12 no.6
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• pp.1030-1036
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• 2009

In 1990's, in Europe and some advanced nations, the structural reform of the railroad industry for improving the productive efficiency of the railroad industry and competitive power had been progressed. This paper empirically explores the relationship between railway restructuring and productive efficiency in the railway industry. We use Data Envelopment Analysis (DEA) to construct efficiency scores, and explain these scores, using Tobit regression analysis by using variables reflecting institutional factors and organizational type. Our results suggest that vertical separation, infrastructure and services are separated, and horizontal separation, passenger service and freight service are separated, improve productive efficiency. We also find that market competition has positive effect on the efficiency, but independent management from the government has negative effect, which is in line with economic intuition as well as with expectations on the railway restructuring. As a consequence, increased independence without sufficient competition and adequate regulation may deteriorate incentives for productive efficiency.

• Journal of Korean Society for Quality Management
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• v.32 no.2
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• pp.77-92
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• 2004

It is difficult but very necessary to measure the productivity of container terminals as logistics service provider. It is meaningful to find the appropriate inputs and outputs of the logistics service delivery systems and to measure the relationship between these inputs and outputs. This study proposes a model of evaluating the efficiency of container terminals. The evaluation consists of three phases. First, DEA(Data Envelopment Analysis) phase, determines the efficiency score and weights of DMUs(Decision Making Unit). This phase performs through four steps : selection of DMU, selection of DEA model, determination of input and output factors, calculation of efficiency score and weights for each DMU. Secondly, CEM (Cross Evaluation Model) phase, is to calculate the cross-efficiency scores of DMUs. This phase performs through three steps: selection of CEM, determination of cross-efficiency score for each DMU and development of cross-efficiency matrix. Finally, average cross-efficiency analysis phase is to compute the average cross-efficiency score. The proposed model discriminates among DMUs and ranks DMUs, whether they are efficient or inefficient.

• The Journal of Society for e-Business Studies
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• v.19 no.3
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• pp.91-105
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• 2014

This study purposed to benchmark the number of patients who visited an oriental medicine hospital from its surrounding regions using data envelopment analysis (DEA) model, and to analyze the relationships between regional characteristics and efficiency scores from DEA. Study data was collected from one oriental medicine hospital operated in a metropolitan city in Korea. Patient locations were identified at the smallest administrative district, Dong, and number of patients was calculated at the Dong level based on the address of patients in hospital information system. Socio-demographic variables of each Dong were identified from the Statistics of Korea web-sites. DEA was used to benchmark the number of patients between Dongs and to compute the efficiency scores. Tobit regression analysis model was applied to analyze the relationship between efficiency scores and regional variables. 6 Dongs were identified as efficient after DEA. In Tobit analysis, number of medical aid recipients and number of total population in each Dong was significant in explaining the differences of efficiency scores. The study model introduced the application of DEA model in benchmarking the patients between regions. It can be applied to identify the number of patients in each region which a hospital needs to improve their performances.

• Korea Journal of Hospital Management
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• v.4 no.2
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• pp.219-241
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• 1999

This study used the Data Envelopment Analysis, a mathematical linear programming method, to evaluate cost efficiency of hospitals in Korea. DEA method was applied to 244 hospitals: 31 bankrupt hospitals and 213 survived hospitals. Among the 213 sound hospitals, 11 hospitals showed efficiency score 100, but more than 40 hospitals recorded efficiency scores lower than 60. This result implies that more hospitals can be bankrupt in the restructuring process of the industry within 1-2 years. Among the 31 bankrupt hospitals, the highest technical efficiency score was 0.821 and 11 hospitals showed technical efficiency lower than 0.6. This implies that selective financial support based on cost efficiency by the government will be valuable to prevent bankruptcy of these hospitals. The logistic analysis showed statistically significant relationship between bankruptcy and efficiency of hospitals in Korea.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.198-200
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• 2022

With the expansion of the global supply chain, the efficiency of maritime logistics is considered a crucial factor for countries' trade and competitiveness. Nevertheless, prior research has not thoroughly evaluated the efficiency of maritime logistics, including countries' ports and shipping capacities. Accordingly, this study examines integrated maritime logistics efficiency at the national level using DEA-CCR, BCC, and super-efficiency DEA. Furthermore, this study identifies a difference between the selected countries' maritime logistics efficiency and LPI (Logistics Performance Index) through Spearman's correlation test as an ad-hoc analysis. From this, Asian countries showed higher efficiency and European countries showed higher LPI scores. These results might be derived from this difference in port-city development patterns. Additionally, the main cause of inefficiency in Europe and Japan might be attributed to high fleet capacity of control. Consequently, this study can provide valuable implications for coastal countries to set more efficient directions for maritime logistics investment and policy.

• The Journal of Information Systems
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• v.29 no.3
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• pp.145-157
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• 2020

Purpose This study proposed an approach for predicting the efficiency rating of the cultural tourism festivals using DEA and machine learning techniques. The cultural tourism festivals are selected for the best festivals through peer reviews by tourism experts. However, only 10% of the festivals which are held in a year could be evaluated in the view of effectiveness without considering the efficiency of festivals. Design/methodology/approach Efficiency scores were derived from the results of DEA for the prediction of efficiency ratings. This study utilized BCC models to reflect the size effect of festivals and classified the festivals into four ratings according the efficiency scores. Multi-classification method were considered to build the prediction of four ratings for the festivals in this study. We utilized neural networks and SVMs with OAO(one-against-one), OAR(one-against-rest), C&S(crammer & singer) with Korea festival data from 2013 to 2018. Findings The number of total visitors in low efficient rating of DEA is more larger than the number of total visitors in high efficient ratings although the total expenditure of visitors is the highest in the most efficient rating when we analyzed the results of DEA for the characteristics of four ratings. SVM with OAO model showed the most superior performance in accuracy as SVM with OAR model was not trained well because of the imbalanced distribution between efficient rating and the other ratings. Our approach could predict the efficiency of festivals which were not included in the review process of culture tourism festivals without rebuilding DEA models each time. This enables us to manage the festivals efficiently with the proposed machine learning models.