scholarly journals A cause and effect two-stage BSC-DEA method for measuring the relative efficiency of organizations

2011 ◽  
Vol 1 (1) ◽  
pp. 41-48 ◽  
Author(s):  
Seyed Esmaeel Najafi ◽  
Seyed Aliakbar Ahmadi ◽  
Mohammad Fallah ◽  
Nasser Shahsavaripour
Keyword(s):  
Relative Efficiency ◽  
Two Stage ◽  
Cause And Effect ◽  
Dea Method

Measurement of innovation resource allocation efficiency in civil–military integration enterprises

Kybernetes ◽  
2019 ◽  
Vol 49 (3) ◽  
pp. 835-851 ◽  
Author(s):  
Zilong Wang ◽  
Zhiwen Zhang ◽  
Ng Choon Yeong Jhony
Keyword(s):  
Resource Allocation ◽  
Tobit Model ◽  
Two Stage ◽  
Content Type ◽  
Innovation Activities ◽  
Resource Allocation Efficiency ◽  
Dea Method ◽  
Overall Efficiency ◽  
Positive Effect ◽  
Stage Efficiency

Purpose As a transition economy, China is interested in allocating its limited innovation resources economically, reasonably and efficiently to produce as many outputs as possible with its limited financial and human resources. Nonetheless, what is the efficiency of the allocation of innovative resources for civil–military integration enterprises, and what factors hinder its efficiency improvement? The purpose of this paper is to explore these problems. Design/methodology/approach The improved two-stage network data envelopment analysis (DEA) method is used to measure the overall efficiency and stage efficiency of the innovation resource allocation of 58 Chinese civil–military integration listed companies from 2010 to 2016. Tobit model is used to analyze the influencing factors of resource allocation efficiency. Findings The results indicate that the overall efficiency and stage efficiency of innovation resource allocation fluctuate in varying degrees during the period. The optimization of overall efficiency is restricted by lower efficiency of innovation achievement transformation. Enterprise scale was found to have a significant negative impact on both overall and two-stage efficiencies. Proportion of research and development (R&D) personnel had a positive effect on the overall and two-stage efficiency. Government support had a significant positive effect on the stage of innovation resource development and overall efficiency. Originality/value Previous research studies have used either the DEA or stochastic frontier analysis method to measure the efficiency of innovation activities as a whole and ignored the stage of initial investment to final output in innovation activities. That is, the process in which initial input of R&D resources becomes innovation output, and then becomes economic benefits. Therefore, this paper studies the efficiency of innovation resource allocation of civil–military integration listed companies. The improved two-stage chain network DEA method and Tobit model were used.

scholarly journals Alternative Secondary Goals in Multiplicative Two-Stage Data Envelopment Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Chao Lu ◽  
Haifang Cheng
Keyword(s):  
Data Envelopment Analysis ◽  
Relative Efficiency ◽  
Data Envelopment ◽  
Nonparametric Method ◽  
Two Stage ◽  
Dea Model ◽  
Second Stage ◽  
China Construction Bank ◽  
Overall Efficiency ◽  
Decision Making Units

Data envelopment analysis (DEA) is a nonparametric method for evaluating the relative efficiency of a set of decision-making units (DMUs) with multiple inputs and outputs. As an extension of the DEA, a multiplicative two-stage DEA model has been widely used to measure the efficiencies of two-stage systems, where the first stage uses inputs to produce the outputs, and the second stage then uses the first-stage outputs as inputs to generate its own outputs. The main deficiency of the multiplicative two-stage DEA model is that the decomposition of the overall efficiency may not be unique because of the presence of alternate optima. To remove the problem of the flexible decomposition, in this paper, we maximize the sum of the two-stage efficiencies and simultaneously maximize the two-stage efficiencies as secondary goals in the multiplicative two-stage DEA model to select the decomposition of the overall efficiency from the flexible decompositions, respectively. The proposed models are applied to evaluate the performance of 10 branches of China Construction Bank, and the results are compared with the results of the existing models.

scholarly journals Efficiency of commercial banks operating in Federation of Bosnia and Herzegovina using DEA method

2019 ◽  
Vol 1 (2) ◽  
pp. 106-111
Author(s):  
Admel Husejinović
Keyword(s):  
Data Envelopment Analysis ◽  
Bosnia And Herzegovina ◽  
Commercial Banks ◽  
Relative Efficiency ◽  
Efficiency Measurement ◽  
Data Envelopment ◽  
Significant Difference ◽  
Dea Method ◽  
Small Banks

Main objective of this research is to measure an efficiency of commercial banks operating in Federation of Bosnia and Herzegovina in period 2016-2017. An analysis is conducted over 12 banks that had positive overall profit lost at the end of 2016 and 2017 years published by Banking Agency of Federation of Bosnia and Herzegovina. Data Envelopment Analysis (DEA) method with two input and three output parameters is used for efficiency measurement. Each bank’s efficiency is presented for 2016 and 2017 years. For observed period, large banks showed more efficient than small banks. Based on the results shown in this research and features used in this model there is significant difference in relative efficiency of top two banks and rest of 10 banks.

scholarly journals A Two-Stage DEA to Analyze the Effect of Entrance Deregulation on Iranian Insurers: A Robust Approach

2012 ◽  
Vol 2012 ◽  
pp. 1-24 ◽  
Author(s):  
Seyed Gholamreza Jalali Naini ◽  
Hamid Reza Nouralizadeh
Keyword(s):  
Statistical Analysis ◽  
Relative Efficiency ◽  
Generalized Estimating Equations ◽  
Insurance Market ◽  
Data Envelopment ◽  
Two Stage ◽  
Robust Approach ◽  
Dea Model ◽  
Second Stage ◽  
First Results

We use two-stage data envelopment analysis (DEA) model to analyze the effects ofentrance deregulationon the efficiency in the Iranian insurance market. In the first stage, we propose arobust optimizationapproach in order to overcome the sensitivity of DEA results to any uncertainty in the output parameters. Hence, the efficiency of each ongoing insurer is estimated using our proposed robust DEA model. The insurers are then ranked based on their relative efficiency scores for an eight-year period from 2003 to 2010. In the second stage, a comprehensive statistical analysis usinggeneralized estimating equations(GEE) is conducted to analyze some other factors which could possibly affect the efficiency scores. The first results from DEA model indicate a decline in efficiency over the entrance deregulation period while further statistical analysis confirms that the solvency ignorance which is a widespread paradigm among state owned companies is one of the main drivers of efficiency in the Iranian insurance market.

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