Data Envelopment Analysis (DEA) is a well-known method used to measure the efficiency of decision making units. In this paper, we study the impact of the financial crisis while integrating DEA efficiency measures with Support Vector Machines (SVM). Moreover, to account for the heterogeneity effect in the efficiency measures, the gap statistical method of Tibshirani, et al., (2001) [Tibshirani, R., Walther, G., & Hastie, T. (2001). Estimating the number of clusters in a data set via the gap statistic. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 63(2), 411-423.] is applied in order to achieve the optimal number of cluster. This study uses December quarterly panel data consisting of Farm Credit Agricultural Banks data from 2005 to 2016. We find strong evidence that the efficiency measures were stationary prior to the financial crisis (2005-2006), during the financial crisis (2007-2009) and post financial crisis (2010-2016). The results further show that the integrated DEA-SVM provide a lower performance during 2007-2009. Furthermore, the results show that the Agricultural banking sector was both efficient and stable over the period being analysed.

During the past three decades, data envelopment analysis (DEA) has been successfully used for measuring the relative efficiency of financial or non-financial firms. This paper presents an empirical investigation to measure the relative efficiency of five Canadian banks versus 6 US big banks using DEA method. The study considers the number of employees and total assets as input and net revenue is used as the output of the DEA model. The data are collected from the official statements of the banks for the fiscal year of 2017. The results indicate that 6 US banks maintained an average efficiency of 0.87 but the average efficiency of Canadian banks was 0.72. While two US banks maintained an efficiency of one, the other US banks demonstrate relatively well in terms of the performance. In Canada, while one bank performed relatively well, the performance of the other Canadian banks were not as good as the US banks.

Data Envelopment Analysis (DEA) is a decision making tool based on linear programming for measuring the relative efficiency of a set of comparable units. DEA helps us identify the sources and level of inefficiency for each of the inputs and outputs. This approach has been used to evaluate the efficiency of the safety department in five construction companies. A three-input, safety workforce, safety training, and safety budget, and two-output, Perfect days and Uptime, constant returns-to-scale (CRS) model was developed. The model indicated the necessary improvements required in the inefficient unit’s inputs and outputs to make it efficient, by identifying what factor is responsible for the low efficiency of performance, and also what factor should be improved in order to improve the efficiency of the safety department. The result shows that the safety department of firm A, B and D are efficient, but Firm C and Firm E can improve their efficiency by reducing inputs up to 3.34% and 6.05%, respectively. The inputs identified for reduction were; number of safety staffs and safety budget for Firm C and E respectively.

Measuring the relative efficiency of similar units has been a popular research especially when the units were mostly non-financial. Even, similar financial units may not be necessarily evaluated based on traditional financial figures such as return of equities, return of assets, etc. In this paper, we present an empirical investigation to measure the relative efficiency of 30 branches of an Iranian bank named Bank Mellat. The study considers four inputs including operating expenses, interest paid, capital expenditures and fixed assets. In addition, we use customers’ bank deposit, commissions and loans paid as output parameters. Using three different data envelopment analyses, the study measures the relative efficiencies of all units. The preliminary results indicate that most banks were working under desirable level of efficiency.

Measuring the relative efficiency of financial units plays essential role for making strategic decisions such as business development, downsizing, etc. This paper presents an empirical investigation to rank different branches of a credit institution named Samen in city of Semnan, Iran. The proposed study uses data envelopment analysis (DEA) for measuring the relative efficiency of 17 units. The results indicate that five units were efficient and using Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), the efficient units are ranked based on some inputs/outputs. The results of this study indicate that most branches of this financial unit performed poorly and a restructure in their businesses is necessary. In addition, the study has provided some evidences that considering employee wage, bank deposit and administration expenses as inputs for DEA implementation seems to provide better results than using total assets and equities.

In this study, we provide six complementary models of data envelopment analysis to analyze the financial efficiency of chosen decision-making units for firms listed on Tehran Stock Exchange. For this purpose, we consider tapered modeling, the concept of accounting costs and economic costs and divide the whole process into the two sub-processes in modeling. After the modeling process, we choose the seventeen companies and thereby, provide a new method for analyzing the financial efficiency. The proposed method seems to be more appropriate than the methods proposed in the past and it can deliver better understanding of real-world cases. Finally, we understand that how this method works for analyzing the financial efficiency of organizations.

Measuring the relative efficiency of banking industry has been a popular subject among both practitioners and academicians. Data envelopment analysis (DEA) has been widely applied for different purposes. This paper presents an empirical investigation to measure the relative efficiency of various banks located in province of Semnan, Iran. The proposed study uses DEA method to rank all units and using Anderson and Peterson method (1993) [Andersen, P., & Petersen, N. C. (1993). A procedure for ranking efficient units in data envelopment analysis. Management Science, 39, 1261-1264], we provide some super efficiency for inefficient units. The study also provides reference numbers for inefficient units and gives some target values for all inefficient units.

Construction operations are blamed as one of important causes of green gas effects. During the past few decades, there has been tremendous efforts to reduce the negative effects of construction operations on environment. This paper presents an application of data envelopment analysis to measure the relative efficiencies of the researches accomplished by various countries around the world on green construction or green management. The study expects countries that produce higher amount of CO2 accomplish more quality research articles. To do this, the study performs a survey using three keywords; namely “green construction”, “green building” and “sustainable building” on Scopus database and found 8692 articles over the period 1965-2019 where one of these keywords, at least, was used in their abstracts, keywords or titles. We also use three measures of h-index, I-10 and total publications repre-sentative of quality and quantity of the outputs produced by researchers. The study considers 28 countries responsible for at least 90% of CO2 emission for measuring the relative efficiency program using data envelopment analysis. The results indicate that Hong Kong was the only efficient country followed by Singapore with relative efficiency of 0.67.

Private schools play important role on developing economy especially in rural areas of Iran and when they operate efficiently it can also be considered as a rich source of income. During the past three decades, data envelopment analysis (DEA) has become a popular technique for measuring the relative performance of non-financial units. In this paper, we present an empirical study to measure the relative efficiency of 11 private universities located in region ten of Islamic Azad university. The proposed study of this paper assigns some points for human resources including university professor and regular employees and considers it along with assets as inputs of DEA model. We also consider the number of graduated students and operating profit as output of our proposed DEA model. The implementation of standard BCC method yields 6 efficient units and to have better results we use another DEA technique. The results of this study present some investment opportunities for management of this private university.

One of the primary concerns in banking industry is to measure the relative importance of banking industry using popular multi criteria decision making (MCDM) techniques such as data envelopment analysis (DEA) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). In this paper, we use these two MCDM techniques to measure the relative efficiencies of 16 private and governmental banks in terms of electronic payment. There are three inputs with DEA methods including the number of issued cards, the number of ATM machines and the number of POSs and there are two outputs including the number of successful ATMs and the number of successful POSs transactions. The proposed study of this paper uses the necessary data of one of Iranian provinces and the results of the implementation of DEA and TOPSIS have indicated that 9 out of 16 banks were efficient. Our study also indicates that mean of relative efficiency for private banks was 82% while this number was 75% for governmental banks.