scholarly journals Nested dynamic network data envelopment analysis models with infinitely many decision making units for portfolio evaluation

2020 ◽  
Author(s):  
Tsung-Sheng Chang ◽  
Kaoru Tone ◽  
Chen-Hui Wu
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Dynamic Network ◽  
Network Data ◽  
Data Envelopment ◽  
Portfolio Evaluation ◽  
Network Data Envelopment Analysis ◽  
Analysis Models ◽  
Decision Making Units

Measuring Performance of Dynamic and Network Structures by SBM Model

2015 ◽  
pp. 527-558
Author(s):  
N. Aghayi ◽  
Z. Ghelej Beigi ◽  
K. Gholami ◽  
F. Hosseinzadeh Lotfi
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Dynamic Network ◽  
Black Box ◽  
Time Dependent ◽  
Data Envelopment ◽  
Network Efficiency ◽  
Sbm Model ◽  
Radial Model ◽  
Decision Making Units

The conventional Data Envelopment Analysis (DEA) model considers Decision Making Units (DMUs) as a black box, meaning that these models do not consider the connection and the inner structures of DMUs. Moreover, these models consider that the activities of DMUs in each time are independent of other times, but in the real world, the inner structures of DMUs are complicated, and the activities of DMUs are dependent on other times. Therefore, in this chapter, the authors consider DMUs with network structure and the activity of each DMU in each time dependent to activity of other times, so they call this structure a dynamic network. To this end, in this chapter, models are suggested to evaluate the dynamic network efficiency based on the SBM model, which is a non-radial model of three types with respect to orientation: input-oriented, output-oriented, and non-oriented.

scholarly journals The Sustainability Performance of Chinese Banks: A New Network Data Envelopment Analysis Approach and Panel Regression

2019 ◽  
Vol 11 (6) ◽  
pp. 1622 ◽  
Author(s):  
Yantuan Yu ◽  
Jianhuan Huang ◽  
Yanmin Shao
Keyword(s):  
Data Envelopment Analysis ◽  
Scale Effects ◽  
Estimation Method ◽  
Dynamic Network ◽  
Generalized Least Squares ◽  
Foreign Banks ◽  
Network Data ◽  
Significant Heterogeneity ◽  
Data Envelopment ◽  
Network Data Envelopment Analysis

This paper develops a new network data envelopment analysis (DEA) model that simultaneously integrates the non-convex metafrontier and undesirable outputs and which is super efficient at performing dynamic network slacks-based measures. The model is employed to discuss the efficiency of 36 commercial banks in China during the years 2010–2014. The efficiency of these banks shows significant heterogeneity and the efficiency of most foreign banks has much room for improvement. Regarding both the non-convex metafrontier and the group frontier, state-owned banks perform the best, followed by joint-stock banks, with foreign banks performing the worst; the same is true for the technology gap ratios. The empirical results produced by the feasible generalized least squares estimation method indicate that liquidity and scale effects exert positive impacts on bank efficiency. An alternative estimation method confirmed that the conclusions were robust.

scholarly journals Dynamic network data envelopment analysis for university hospitals evaluation

2016 ◽  
Vol 50 (0) ◽  
Author(s):  
Maria Stella de Castro Lobo ◽  
Henrique de Castro Rodrigues ◽  
Edgard Caires Gazzola André ◽  
Jônatas Almeida de Azeredo ◽  
Marcos Pereira Estellita Lins
Keyword(s):  
Data Envelopment Analysis ◽  
Best Practice ◽  
Assessment Tool ◽  
Health Planning ◽  
Dynamic Network ◽  
Medical Residents ◽  
Network Data ◽  
Data Envelopment ◽  
Research Projects ◽  
Network Data Envelopment Analysis

ABSTRACT OBJECTIVE To develop an assessment tool to evaluate the efficiency of federal university general hospitals. METHODS Data envelopment analysis, a linear programming technique, creates a best practice frontier by comparing observed production given the amount of resources used. The model is output-oriented and considers variable returns to scale. Network data envelopment analysis considers link variables belonging to more than one dimension (in the model, medical residents, adjusted admissions, and research projects). Dynamic network data envelopment analysis uses carry-over variables (in the model, financing budget) to analyze frontier shift in subsequent years. Data were gathered from the information system of the Brazilian Ministry of Education (MEC), 2010-2013. RESULTS The mean scores for health care, teaching and research over the period were 58.0%, 86.0%, and 61.0%, respectively. In 2012, the best performance year, for all units to reach the frontier it would be necessary to have a mean increase of 65.0% in outpatient visits; 34.0% in admissions; 12.0% in undergraduate students; 13.0% in multi-professional residents; 48.0% in graduate students; 7.0% in research projects; besides a decrease of 9.0% in medical residents. In the same year, an increase of 0.9% in financing budget would be necessary to improve the care output frontier. In the dynamic evaluation, there was progress in teaching efficiency, oscillation in medical care and no variation in research. CONCLUSIONS The proposed model generates public health planning and programming parameters by estimating efficiency scores and making projections to reach the best practice frontier.

Conventional versus Islamic bank efficiency: A dynamic network data-envelopment-analysis approach

2020 ◽  
pp. 1-13
Author(s):  
Md Abul Kalam Azad ◽  
Muzalwana Binti Abdul Talib ◽  
Kwek Kian Teng ◽  
Paolo Saona
Keyword(s):  
Data Envelopment Analysis ◽  
Dynamic Network ◽  
Policy Implications ◽  
Network Data ◽  
Data Envelopment ◽  
Islamic Banks ◽  
Network Dea ◽  
Dea Model ◽  
Network Data Envelopment Analysis ◽  
Conventional Banks

This study compares the efficiency of conventional and Islamic banks in Malaysia by engaging in a dynamic three-step (production, intermediation, and profitability) network data envelopment analysis (DEA). The inputs and outputs for the DEA model are selected based on the CAMELS rating. The major contributions of this study are threefold. First, this study investigates the efficiency of Malaysian banks using a novel dynamic network DEA model. Second, the Malaysian banking industry is found to be efficient in creating earning assets rather than in creating loans or profit. The results reveal that only a few banks in Malaysia have been efficient in converting deposits and equities into profit. Third, Islamic banks, in general, have been performing efficiently in the production and profitability approaches. Conventional banks, in contrast, are found to have been efficient in the intermediation approach. Policy implications are derived from the main conclusions.

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