Measuring the Impact of Advertising on Army Recruiting: Data Envelopment Analysis and Advertising Effectiveness

1990 ◽  
Author(s):  
A. Charnes
Keyword(s):  
Data Envelopment Analysis ◽  
Advertising Effectiveness ◽  
Data Envelopment ◽  
The Impact ◽  
Army Recruiting

Benchmarking the efficiencies of Indonesia’s municipal water utilities using Stackelberg data envelopment analysis

2015 ◽  
Vol 22 (4) ◽  
pp. 588-609 ◽  
Author(s):  
Andreas Wibowo ◽  
Hans Wilhelm Alfen
Keyword(s):  
Data Envelopment Analysis ◽  
The Body ◽  
Water Utilities ◽  
Data Envelopment ◽  
Two Stage ◽  
Restrictive Assumption ◽  
Content Type ◽  
Second Stage ◽  
Municipal Water ◽  
The Impact

Purpose – The purpose of this paper is to present a yardstick efficiency comparison of 269 Indonesian municipal water utilities (MWUs) and measures the impact of exogenous environmental variables on efficiency scores. Design/methodology/approach – Two-stage Stackelberg leader-follower data envelopment analysis (DEA) and artificial neural networks (ANN) were employed. Findings – Given that serviceability was treated as the leader and profitability as the follower, the first and second stage DEA scores were 55 and 32 percent (0 percent = totally inefficient, 100 percent = perfectly efficient), respectively. This indicates sizeable opportunities for improvement, with 39 percent of the total sample facing serious problems in both first- and second-stage efficiencies. When profitability instead leads serviceability, this results in more decreased efficiency. The size of the population served was the most important exogenous environmental variable affecting DEA efficiency scores in both the first and second stages. Research limitations/implications – The present study was limited by the overly restrictive assumption that all MWUs operate at a constant-return-to-scale. Practical implications – These research findings will enable better management of the MWUs in question, allowing their current level of performance to be objectively compared with that of their peers, both in terms of scale and area of operation. These findings will also help the government prioritize assistance measures for MWUs that are suffering from acute performance gaps, and to devise a strategic national plan to revitalize Indonesia’s water sector. Originality/value – This paper enriches the body of knowledge by filling in knowledge gaps relating to benchmarking in Indonesia’s water industry, as well as in the application of ensemble two-stage DEA and ANN, which are still rare in the literature.

A common-weights approach for efficiency evaluation in fuzzy data envelopment analysis with undesirable outputs: Application in banking industry

2020 ◽  
Vol 39 (5) ◽  
pp. 7705-7722
Author(s):  
Mohammad Kachouei ◽  
Ali Ebrahimnejad ◽  
Hadi Bagherzadeh-Valami
Keyword(s):  
Data Envelopment Analysis ◽  
Performance Management ◽  
Banking Industry ◽  
Evaluation Process ◽  
Fuzzy Arithmetic ◽  
Data Envelopment ◽  
Fuzzy Data ◽  
Undesirable Outputs ◽  
Dea Model ◽  
The Impact

Data Envelopment Analysis (DEA) is a non-parametric approach based on linear programming for evaluating the performance of decision making units (DMUs) with multiple inputs and multiple outputs. The lack of the ability to generate the actual weights, not considering the impact of undesirable outputs in the evaluation process and the measuring of efficiencies of DMUs based upon precise observations are three main drawbacks of the conventional DEA models. This paper proposes a novel approach for finding the common set of weights (CSW) to compute efficiencies in DEA model with undesirable outputs when the data are represented by fuzzy numbers. The proposed approach is based on fuzzy arithmetic which formulates the fuzzy additive DEA model as a linear programing problem and gives fuzzy efficiencies of all DMUs based on resulting CSW. We demonstrate the applicability of the proposed model with a simple numerical example. Finally, in the context of performance management, an application of banking industry in Iran is presented for analyzing the influence of fuzzy data and depicting the impact of undesirable outputs over the efficiency results.

Fuzzy C-means based data envelopment analysis for mitigating the impact of units’ heterogeneity

Kybernetes ◽  
2016 ◽  
Vol 45 (3) ◽  
pp. 536-551 ◽  
Author(s):  
Seyed Hossein Razavi Hajiagha ◽  
Shide Sadat Hashemi ◽  
Hannan Amoozad Mahdiraji
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Negative Impact ◽  
Positive Impact ◽  
Efficiency Score ◽  
Data Envelopment ◽  
Content Type ◽  
Fuzzy C Means ◽  
The Impact ◽  
Decision Making Units

Purpose – Data envelopment analysis (DEA) is a non-parametric model that is developed for evaluating the relative efficiency of a set of homogeneous decision-making units that each unit transforms multiple inputs into multiple outputs. However, usually the decision-making units are not completely similar. The purpose of this paper is to propose an algorithm for DEA applications when considered DMUs are non-homogeneous. Design/methodology/approach – To reach this aim, an algorithm is designed to mitigate the impact of heterogeneity on efficiency evaluation. Using fuzzy C-means algorithm, a fuzzy clustering is obtained for DMUs based on their inputs and outputs. Then, the fuzzy C-means based DEA approach is used for finding the efficiency of DMUs in different clusters. Finally, the different efficiencies of each DMU are aggregated based on the membership values of DMUs in clusters. Findings – Heterogeneity causes some positive impact on some DMUs while it has negative impact on other ones. The proposed method mitigates this undesirable impact and a different distribution of efficiency score is obtained that neglects this unintended impacts. Research limitations/implications – The proposed method can be applied in DEA applications with a large number of DMUs in different situations, where some of them enjoyed the good environmental conditions, while others suffered from bad conditions. Therefore, a better assessment of real performance can be obtained. Originality/value – The paper proposed a hybrid algorithm combination of fuzzy C-means clustering method with classic DEA models for the first time.

scholarly journals Evaluation of Water Use Efficiency of 31 Provinces and Municipalities in China Using Multi-Level Entropy Weight Method Synthesized Indexes and Data Envelopment Analysis

2019 ◽  
Vol 11 (17) ◽  
pp. 4556 ◽  
Author(s):  
Boyang Sun ◽  
Xiaohua Yang ◽  
Yipeng Zhang ◽  
Xiaojuan Chen
Keyword(s):  
Data Envelopment Analysis ◽  
Water Use Efficiency ◽  
Water Use ◽  
Water Shortage ◽  
Data Envelopment ◽  
Entropy Weight ◽  
Entropy Weight Method ◽  
Weight Method ◽  
Use Efficiency ◽  
The Impact

China’s water shortage problem is becoming increasingly severe. Improving water use efficiency is crucial to alleviating China’s water crisis. This paper evaluates the water use efficiency of 31 provinces and municipalities in China by using the data envelopment analysis (DEA) method. When the usual DEA model has too many indexes selected, it will cause the majority of the decision making units (DMUs) efficiency values be one, which leads to invalid evaluation results. Therefore, by using the entropy weight method, a new synthetic set of indexes is constructed based on the original indexes. The new synthetic set of indexes retains the full information of the original indexes, and the goal of simplifying the number of indexes is achieved. Simultaneously, by empowering the original indexes, the evaluation using synthetic indexes can also avoid the impact of industrial structure and labor division on water use efficiency. The results show that in China’s northeastern grain producing areas, water use efficiency is higher due to the high level of agricultural modernization. The provinces in the middle reaches of the Yangtze River have the lowest water use efficiency due to water pollution and water waste. In general, China’s overall water use efficiency is low, and there is still much room for improvement.

RELAXING THE IMPACT OF EXTREME UNITS IN DATA ENVELOPMENT ANALYSIS

2012 ◽  
Vol 11 (05) ◽  
pp. 893-907 ◽  
Author(s):  
YIANNIS G. SMIRLIS ◽  
DIMITRIS K. DESPOTIS
Keyword(s):  
Data Envelopment Analysis ◽  
Original Data ◽  
Efficiency Score ◽  
Data Envelopment ◽  
Programming Technique ◽  
Data Set ◽  
Usual Practice ◽  
Dea Models ◽  
Production Possibility Set ◽  
The Impact

Data envelopment analysis (DEA) is a nonparametric linear programming technique for measuring the relative efficiency of decision making units (DMUs) on the basis of multiple inputs and outputs. DEA assessments, however, are proved to be sensitive to extreme units that deviate substantially in their input/output patterns. In this paper we introduce an approach for handling extreme observations in DEA, i.e., observations that exhibit irregularly high values in some outputs and/or low values in some inputs. Unlike the usual practice of removing such observations, we retain them in the production possibility set reducing their impact on the other units. Our modeling approach is based on the concept of diminishing returns, assuming that the contribution of an output (input) to the efficiency score diminishes as the output increases beyond a pre-specified level, i.e., the level beyond which a value is characterized as extreme. According to our approach the original data set is transformed to an augmented data set, where standard DEA models can then be applied, remaining thus in the grounds of the standard DEA methodology. We illustrate our approach with a numerical example.

Determinants of Efficiency in the Malaysian Banking Sector: Evidence from Semi-parametric Data Envelopment Analysis Method

2016 ◽  
Vol 4 (2) ◽  
pp. 151-172 ◽  
Author(s):  
Fadzlan Sufian
Keyword(s):  
Data Envelopment Analysis ◽  
Banking Sector ◽  
Productive Efficiency ◽  
Data Envelopment ◽  
Panel Regression ◽  
Analysis Method ◽  
Bank Efficiency ◽  
Panel Regressions ◽  
Parametric Data ◽  
The Impact

This article follows Simar and Wilson’s (2007 , Journal of Econometrics, 136(1), 31–64) two-stage procedure to analyse the efficiency of the Malaysian banking sector. In the first stage, we employ the data envelopment analysis (DEA) method to compute the efficiency of individual banks during the period 1999–2008. We then use panel regressions to examine the impact of ownership on bank efficiency while controlling for the potential impacts of contextual variables. The DEA results indicate an increase in efficiency over the sample period. The results from the panel regression suggest that productive efficiency is positively related to bank size, capitalization and foreign ownership. On the other hand, the publicly listed and government-owned banks have been relatively inefficient in their intermediation function.

Sign in / Sign up

Export Citation Format

Share Document