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This paper aims to examine the performance efficiency of 56 real estate assets within the rental sector in the UAE to evaluate the relative operation efficiency in relation to revenue generation.

The data envelopment analysis (DEA) approach was used to measure the relative operational efficiency of the studied assets in relation to the revenue performance. This method could produce a more informed and balanced approach to performance measurement.

The outcomes show that scores of efficiencies ranging from 7% to 99% in some of the models. The results showed that on average buildings are 75% relatively less efficient in maintenance, in term of revenue generation, than the benchmark set. Likewise, on average, the inefficient buildings are 60% relatively less efficient in insurance. Result also shows that 95% of the building assets in the sample are by and large operating at decreasing returns to scale. This implies that managers need to considerably reduce the operational resources (input) to improve the levels of revenue.

This study recommends that the FM operational variables that were found to inefficiently contribute to the revenue should be re-examined to test the validity of the findings. This is necessary before generalising or interpolating the results that are presented in this study.

The information obtained about operational performance can help FM managers to understand which improvements in the productivity of inefficient FM resources are required, providing insight into how to reduce operating costs and increase revenue.

This paper adds value in using new FM operational parameters to evaluate the efficiency of the performance of built assets.

In the process of industrial growth, when existing industries go for technology upgradation and new modernised industries emerge, both capital intensity and energy demand of overall industry tend to rise steadily. This poses a serious challenge for sustainable development objectives. Towards this end, enhancing energy efficiency of individual industries is the only remedy. Against this backdrop, the study aims to probe the trends in capital intensities and energy efficiencies of individual industries in India.

This study uses panel data regression analysis on data of two-digit industries from 1980/1981–2016/2017. The statistical analysis includes relevant macroeconomic variables derived from the literature to ascertain the drivers of energy efficiency in industries.

The results brought out that capital deepening due to technology upgradation and modernisation and capital productivity growth are the decisive determinants of energy efficiency growth. Furthermore, the ever-increasing fuel price motivated industries to conserve energy on a steady basis, supplemented by energy conservation-specific policy interventions.

This study recommends policy initiatives to ascertain and address technology gaps industry-wise, so that its subsequent efficient capital utilisation, and energy conservation measures of industries would result in energy efficiency growth in industry. The policy must focus on energy-efficient capital intensification in fabricated metals, leather, textile and wood industries that are found less-energy-efficient despite being less-capital-intensive.

This study empirically explores the capital efficiency of industries by investigating the interaction between capital intensity and energy efficiency at a two-digit industry level. It explores the determinants of energy efficiency and proposes industry-specific policies for energy-efficiency-enhancement of the overall industry.

This paper explores how the manufacturer’s strategic choice (acquisition or investment) impacts product quality in a supply chain comprising two complementary suppliers and a common manufacturer.

The manufacturer faces six strategic choices to improve product quality: acquiring or investing in the high-capable supplier, the low-capable supplier, or both. As the Stackelberg leader, the manufacturer determines which strategy is adopted, while suppliers are separately responsible for components’ quality and wholesale prices. The authors use game theory and calculate the model with Mathematica.

The authors develop analytical models to analyze how acquisition costs, investment proportions, component importance and quality improvement coefficients influence decision-makers. The results show that the highest quality may not benefit the manufacturer. Investing in or acquiring a low-capable supplier is better than a high-capable supplier under certain conditions. If the gaps between two suppliers’ quality improvement coefficients and the importance of two components are dramatic, the manufacturer should choose an investment strategy.

This study contributes to the complementary supply chain management by comparing two kinds of strategies-acquisition and investment, with a high-capable supplier and a low-capable supplier.

Farmers producer organisations (FPOs) play the most crucial role in the agriculture supply chain system, aiming to redress the balance between farming and marketing activities of agricultural produce. The purpose of this study is to assess the performance of FPOs using data envelopment analysis (usually referred to as DEA) on 34 FPO units selected from the state of Rajasthan.

One of the most commonly used techniques to examine business performance is the application of DEA. The application of DEA requires the selection of inputs and outputs. This study takes three inputs and three outputs based on the insights drawn from the field survey. While the input variables consist of total assets, paid-up capital and the number of economic activities, the three output variables are turnover, net profit and number of members benefitted. Broadly, these variables encapsulate the operational performance of the business units.

This study’s findings reveal that the estimated relative efficiency score of the input-oriented CCR (Charnes, Cooper, and Rhodes) model ranges from 0.06 to 1. Interestingly, only one FPO has reported a relative efficiency (RE) score of one, whereas the remaining FPOs fall below the efficiency frontier. However, 15 FPOs report an RE score of one in the output-oriented CCR approach. Considering the estimates obtained in the input- and output-oriented BCC (Banker, Charnes and Cooper) models, this study found that about 20% of the FPOs report an efficiency score greater than 0.80. Moreover, three FPOs are on the frontier line. An examination of the scale efficiency score in the input-oriented model, 45% of the FPOs have an efficiency score greater than 0.80, whereas almost all FPOs achieve a scale efficiency score greater than 0.80 in the output-oriented model. Overall, the results imply that the FPOs should place greater emphasis on the efficient utilisation of the inputs to enhance the overall business performance and productivity.

The findings of this study provide vital insights into the specific inputs and outputs that determine the performance efficiency of FPOs and identify the potential areas for improving the existing inefficient FPOs.

This study contributes to the repository of the existing empirical studies in three distinct ways. First, the authors hardly found any previous studies that quantitatively assess the business performance of FPOs using the DEA technique. Second, the effort to identify the slacks associated with each input and output variable in input- and output-oriented models gives insights on improvable areas for inefficient FPOs. Third, the authors attempt to demystify the empirical obfuscations by highlighting the major challenges FPOs face in the state of Rajasthan.

Due to its high leverage nature, a bank suffers vitally from the credit risk it inherently bears. As a result, managing credit is the ultimate responsibility of a bank. In this chapter, we examine how efficiently banks manage their credit risk via a powerful tool used widely in the decision/management science area called data envelopment analysis (DEA). Among various existing versions, our DEA is a two-stage, dynamic model that captures how each bank performs relative to its peer banks in terms of value creation and credit risk control. Using data from the largest 22 banks in the United States over the period of 1996 till 2013, we have identified leading banks such as First Bank systems and Bank of New York Mellon before and after mergers and acquisitions, respectively. With the goal of preventing financial crises such as the one that occurred in 2008, a conceptual model of credit risk reduction and management (CRR&M) is proposed in the final section of this study. Discussions on strategy formulations at both the individual bank level and the national level are provided. With the help of our two-stage DEA-based decision support systems and CRR&M-driven strategies, policy/decision-makers in a banking sector can identify improvement opportunities regarding value creation and risk mitigation. The effective tool and procedures presented in this work will help banks worldwide manage the unknown and become more resilient to potential credit crises in the 21st century.

Because of the importance of the wheat industry in the economy, a real-featured performance measurement approach is essential for the wheat production process. The purpose of this paper is to develop a data envelopment analysis (DEA) model that is fully compatible with the wheat production process so that managers and farmers can use it to evaluate the efficiency of wheat farms for strategic decisions.

A dynamic multi-stage network DEA model is developed to evaluate the efficiency of wheat production farms in short-term (two-year) and long-term (eight-year) periods.

The results of this study show that because of the lack of long-term planning and excessive reliance on rain, most of the investigated regions have no stability in efficiency, and the efficiency of the regions changes in a zigzag manner over time. Among studied regions, only the Hashtrood region has high and stable efficiency, and other regions can follow the example of this region's cultivation method.

To the best of the authors’ knowledge, this study is the first one that uses the dynamic multi-stage network DEA considering every other year cultivation method and direct–indirect inputs in the agricultural section.

This paper aims to define the efficiency achieved by urban transport companies in Spain concerning the resources they use, considering the type of management used for implementation, public-private, and size.

This study consisted of an analysis of the efficiency of 229 public-private urban transport operators during the period 2012–2021 using Data Envelopment Analysis, the Malmquist Index and inference estimators to determine productivity, efficiency change into Pure Technical Efficiency Change (PTECH), and scale efficiency change.

Based on the efficiency analysis, the authors concluded that of the 229 companies studied, more than 35 were inefficient in all analysed periods. Considering the sample used, direct management is considered significantly more efficient. It cannot be concluded that the size of these companies influences their efficiency, as the data show unequal development behaviours in the studied years.

This study provides arguments on whether there is a significant difference between the two types of management in the urban transport sector. It also includes firm size as a study variable, which has not been previously considered in other studies related to urban transport efficiency. Efficiency should be a crucial factor in determining funding allocation in this sector, as it encourages operators to optimize and improve their services.

Estimation of (in)efficiency became a popular practice that witnessed applications in virtually any sector of the economy over the last few decades. Many different models were deployed for such endeavors, with Stochastic Frontier Analysis (SFA) models dominating the econometric literature. Among the most popular variants of SFA are Aigner, Lovell, and Schmidt (1977), which launched the literature, and Kumbhakar, Ghosh, and McGuckin (1991), which pioneered the branch taking account of the (in)efficiency term via the so-called environmental variables or determinants of inefficiency. Focusing on these two prominent approaches in SFA, the goal of this chapter is to try to understand the production inefficiency of public hospitals in Queensland. While doing so, a recognized yet often overlooked phenomenon emerges where possible dramatic differences (and consequently very different policy implications) can be derived from different models, even within one paradigm of SFA models. This emphasizes the importance of exploring many alternative models, and scrutinizing their assumptions, before drawing policy implications, especially when such implications may substantially affect people’s lives, as is the case in the hospital sector.

The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.

The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.

The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.

The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.

This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.

This study investigates the role of market concentration and efficiency in banking system stability during the COVID-19 pandemic. We empirically test the hypothesis that market concentration and efficiency are significant determinants of bank performance and stability during the time of crises, using a sample of 575 banks in 20 countries in the Middle East and North Africa (MENA).

The main sources of bank data are the BankScope and BankFocus (Bureau van Dijk) databases, World Bank development indicators, and official websites of banks in MENA countries. This study combined descriptive and analytical approaches. We utilize a panel dataset and adopt panel data econometric techniques such as fixed/random effects and the Generalized Method of Moments (GMM) estimator.

The results reveal that market concentration negatively affects bank profitability, whereas improved efficiency further enhances bank performance and contributes to the banking sector’s overall stability. Furthermore, our analysis indicates that during the COVID-19 pandemic, bank stability strongly depended on the level of market concentration, but not on bank efficiency. However, more efficient banks are more profitable and stable if the banking institutions are Islamic. Similarly, Islamic banks with the same level of efficiency demonstrated better overall financial performance during the pandemic than their conventional peers did.

The main limitation is related to the period of COVID-19 pandemic that was covered in this paper (2020–2021). Therefore, further investigation of the COVID-19 effects on bank profitability and risk will require an extended period of the pandemic crisis, including 2022.

This study provides information that will enable bank managers and policymakers in MENA countries to assess the growing impact of market concentration and efficiency on the banking sector stability. It also helps them in formulating suitable strategies to mitigate the adverse consequences of the COVID-19 pandemic. Our recommendations are useful guides for policymakers and regulators in countries where Islamic and conventional banking systems co-exist and compete, based on different business models and risk management practices.

The authors contribute to the banking stability literature by investigating the role of market concentration and efficiency as the main determinants of bank performance and stability during the COVID-19 pandemic. This study is the first to analyze banking sector stability in the MENA region, using both individual and risk-adjusted aggregated performance measures.