Cement & material sector is instrumental in infrastructural development of any economy. The same holds about Saudi cement sector which has contributed substantially to the economic and construc-tion boom in Kingdom of Saudi Arabia (KSA). The cement sector of KSA holds the highest place among other GCC countries. Though, during the last couple of years, the sector seems to be grap-pled with capacity and weighted down due to certain reasons. Nevertheless, almost all of the cement companies in KSA are underplaying their actual capacities. Still, plenty of untapped growth oppor-tunities for cement sector are available in KSA and other GCC countries. Henceforth, considering the growth potential and taking cue from the current scenario of KSA cement sector, the current study endeavors to measure the efficiency of listed cement companies in KSA. The study endeavors to be engrossed in identifying a set of companies which plays on efficiency frontier. Therefore, the technical efficiency performance of fourteen listed cement companies in KSA was measured using Data Envelopment Analysis (DEA) methodology. Two basic models of DEA methodology (i.e. CRS & VRS) were used to estimate the pure and technical efficiency of identified DMUs over a pe-riod of four years from 2016 to 2019. The study reveals that over a period of four years and on an average efficiency scale, only 23% of the firms were purely technically efficient on a CRS scale, while 46% of the firms were technically efficient. Only 23% of the firms were scale and technically efficient. Though, companies in the sector have a vast potential to outperform on efficiency front. Yet, the overall efficiency level among Saudi cement companies are remained depressing. Moreo-ver, the study has noticed that the companies which are inefficient did not have a considerable dis-tance from the efficiency frontier. The study also provided significant insights on the input factors causing inefficiency and suggestion to achieve the total technical efficiency. Furthermore, the effi-ciency analysis also provided benchmarking firms, which are efficient under several criteria for oth-ers to imitate their best practices for becoming a significant player on efficiency frontier.

This paper examines the production efficiency and its determinants of 3301 rice farms in Vietnam. Our empirical results from data envelopment analysis (DEA) technique show that rice farms perform on average level of efficiency. There are differences in production of each farm related to some demographic factors and educational level. Results from Tobit model indicate that education played an important role in helping farmer improve their efficiency. In addition, policies on increasing the cultivation area may also promote the farm efficiency.

This study evaluates the technical efficiency, productivity change of financial companies listed in the Malaysian stock exchange (Bursa Malaysia) and examines the effects of productivity change on efficiency over the period 2007–2016. Moreover, this study also concentrates on the ranking of financial companies according to their efficiency scores. Data Envelopment Analysis (DEA) is uti-lized on a Malmquist Productivity Index in order to calculate the financial companies’ efficiency scores. The results of this study show that some firms were fully efficient. The results implied that these companies were in optimal control of their inputs or resources to generate the maximum outputs. Also, the results indicate a tremendous productivity gain was mostly because of a positive shift in frontier technology and positive shift in technical efficiency. This study is significant because it helps to identify the efficient companies from the financial sector in Malaysia based on multiple inputs and outputs by using the DEA model. Common misspecification problems observed that instability of efficiency scores over productivity.

The six GCC states share similar economic, geographic and socio-cultural characteristics and also face with similar challenges in terms of energy perspective. This study plans to focus on the eco-nomic and energy efficiency of the six GCC states. In the process, the study ranks the GCC states in terms of their efficiency scores. These efficiencies are computed through Data Envelopment Analysis. The economic efficiency is calculated for all six GCC states. Capital and labor are the inputs and GDP is the output. In this survey, Saudi Arabia maintains the highest efficiency score of 0.94, closely followed by Qatar (0.92), Kuwait (0.89), Bahrain (0.83), Oman (0.81) and UAE (0.67). There is a huge gap between the economic efficiency scores of Saudi Arabia and UAE. The environmental efficiency scores are calculated using CO2 emissions as output and electric power consumption and energy as input. Again, the highest efficiency score is for Saudi Arabia (0.91) followed by Oman (0.87), Kuwait and Bahrain have a tie for the 3rd position with a score of 0.74. Finally, the laggards are UAE (0.65) and Qatar (0.62). Again, there is a huge gap between the best and the worst performers. The case of two countries is worth mentioning. Qatar is ranked second in terms of economic efficiency while it was ranked sixth in terms of economic efficiency. Oman was ranked fifth in terms of economic efficiency while it was ranked second in terms of environmental efficiency. Finally, an average of economic and environmental efficiency are taken to compute the composite index. Saudi Arabia has the first place followed by Oman, Kuwait, Bahrain, Qatar and UAE.

During the past century, humans have increased atmospheric CO₂ concentration by at least a third, which is considered as the most important long-lived “forcing” of climate change. Scientists all over the world are responsible to do their best on offering practical solutions to reduce the effects of CO₂ on environment. This paper uses data envelopment analysis to measure the effects of researches accomplished by scientists from 30 countries which are representative of producing over 80% of CO₂ in the world. The study uses 10034 articles published in Scopus database from 1959 to March, 2019. The study uses the amount of CO₂ produced by each country as the input and total publications, h-index and I-10 as the output of the DEA model. The results indicate that despite the fact that China was responsible for producing nearly 30% of the CO₂, the scientists of this country contributed the least on carbon dioxide emissions. In addition, United Kingdom was responsible for about one percent of CO₂ emissions but the researchers of UK performed the best in terms of offering good quality studies.

Banking industry has significant contribution in development of economies of developing countries. Most banks execute their operations through different branches. Therefore it is important to measure the relative efficiencies of these branches. Data envelopment analysis (DEA) is one of the most useful tools in measuring banks’ performance. The present paper aims to extract ranking pattern of banks based on performance evaluation using DEA analysis. In the present research, 120 bank branches of Bank Shahr in city of Tehran were selected and their efficiencies were evaluated using DEA technique. The results are discussed and compared with similar studies.

The primary purpose of this study is to measure the technical efficiency of 30 doctorate-granting universities, the universities or the higher education institutes with PhD training programs, in Vietnam, applying the sensitivity analysis of data envelopment analysis (DEA). The study uses eight sets of input-output specifications using the replacement as well as aggregation/disaggregation of variables. The measurement results allow us to examine the sensitivity of the efficiency of these universities with the sets of variables. The findings also show the impact of variables on their efficiency and its “sustainability”.

Performance measurement is one of the primary tasks in every organization and it helps organization setup appropriate targets for future. There are several ways to assess the performance of the organization, and data envelopment analysis (DEA) is one of the most important methods. DEA is an appropriate technique to measure the efficiency of decision-making units, but does not provide complete information about the unit & apos; s productivity. Malmquist productivity index is one of the methods for measuring productivity and the main advantage of this index is the possibility of the productivity changes over time. In this study, DEA is used to measure service performance and economic foundation so that the performance of four companies within a 3-month period were evaluated. In this work, the cost of the system and investment were used as inputs, revenue and profit were considered as the output and constant returns to scale (CCR) was used to analyze the system, which yields 0.9 for all units when all input/output data were considered in crisp form. The efficacy of different units using interval data also showed different productivity compared with the crisp model. The productivity measures of the companies in three-month period were studied using the Malmquist based productivity index and the results were analyzed.

During the past few years, there have been tremendous efforts on development of Iranian banking industry in an attempt to fight against monopoly of banking system. Bank Pasargad is one of the biggest Iranian banks established for offering retail, commercial as well as investment banking services. The firm was established in 2005 as part of the government & apos; s privatization of the banking system. This paper presents an empirical investigation to determine the most efficient locations for development of this bank. The study uses three main criteria, customer services (C1), Equipment (C2) and Profitability (C3) to compare different alternatives for establishing new branches in city of Tehran, Iran. Using fuzzy data envelopment analysis the study has determined that center of the city could receive the most important attraction followed by north region, west region as well as south west.

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.