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The study aims to focus on data envelopment analysis for assessing the microfinance institutions (MFIs) efficiency over the footings of its undesirable output, i.e. non-performing loans (NPLs). The attention is not only to evaluate the efficiency but also to identify the variable wise inefficiencies incorporating the quality of the portfolio.

The paper assessed MFI efficiency using three different methods of treatment of undesirable output to portray the significant difference. It also has used an advanced methodological model, i.e. weighted Russell directional distance model (WRDDM), under the non-radial assumption that allowed us to find the variable-wise inefficiency contribution. The study also investigated the efficiency differences concerning ownership, including all sizes of MFIs.

The study findings evidence the fall in efficiency score as NPL integrated, and it is found to be statistically significant. In the context of inefficiency assessment, among all input and output variables, total employees and operating expenses, portfolio quality inefficiencies are the leading causes of MFI inefficiencies. Undesirable output inefficiency accounts for almost one-third part of the total inefficiencies and remaining due to input inefficiencies. It is significant to draw attention that there is no improvement in undesirable output inefficiency. By contrast, input inefficiencies retained gains for two years and gradually showed a decreasing trend throughout 2015–2017.

The authors have used balanced panel data of 72 Indian MFIs for five years' period from 2013–2017 whose complete data were available in the Microfinance Information Exchange.

The paper has focused on identifying the inefficiencies that are needed to be focused on to attain efficiency. It could provide vital information to the managers, policymakers in identifying the causes of inefficiencies, which is crucial to improve for long-term sustainability. It will be a roadmap for benchmarking, strategy building and policy-making processes.

The findings of the study help in finding the benchmarking information for the inefficient decision-making units to identify the target units that need particular attention to focus. These practices could give a positive outcome, not only for institutions but also for the MFI clients.

The study provides an insight in to variable-wise inefficiency measurement using advanced model WRDDM in Indian context MFIs.

The purpose of this paper is to examine the relationship between rural banks’ efficiency and their lending provision for micro and small businesses (MSBs) in West Java Indonesia. Rural banks are special banks that are generally located in the district and sub-district areas and they are very involved in providing loans to MSBs.

The study includes 212 rural banks in various districts in West Java province over the 2012–2016 period. Data envelopment analysis is employed to obtain banks’ technical efficiency and panel data analysis is used to reveal the impact of rural banks’ efficiency on their loan provision to MSBs.

The findings reveal that technical efficiency of the rural banks has a significant positive impact on their loan provision to MSBs in West Java Indonesia. These results have underscored the importance of rural banks in maintaining and increasing their bank efficiency levels to enhance their capacity in providing loans to MSBs.

The results of this study have brought some implications for practitioners (rural bank management) to maintain and improve their efficiency in order to expand their capacity to lend to MSBs. The roles of Otoritas Jasa Keuangan or the Indonesia Financial Services Authority in monitoring the efficiency of rural banks and overseeing the provision of their loans to MSBs are also very necessary in ensuring good performance of rural banks in terms of both aspects, respectively.

This study highlights the importance of rural banks in providing loans to MSB segments. The contribution of rural banks in stimulating the development of MSBs is believed to be able to produce positive social implications in terms of empowering the economic and social life of MSBs in their local communities.

The study fills the literature gap by revealing a significant relationship between bank efficiency and loan provision for MSBs in the context of rural banks.

This study examines dynamics of global and regional financial market efficiency; and how specific features of the market and other conditions influence variability in such efficiency.

The study employs fixed effects statistical approach in its examination of how specific features of financial markets influence variability in its efficiency.

This study finds that individual IMF defined economic regions tend to exhibits significantly different financial market efficiency characteristics given specific market features and conditions. In regional level comparative analysis (e.g. Europe, Africa, Asia–Pacific etc.) this study finds that incidence of financial market uncertainty is the dominant condition with significant effect on financial market efficiency across all the IMF regions. In the global level analysis, empirical estimates presented suggest that financial market uncertainty, financial institutional depth and financial institutional efficiency tend to have significant positive influence on global financial market efficiency all things being equal. In the same analysis however, this study finds that financial market and financial institutional access growth has significant negative impact on financial market efficiency.

The uniqueness of this study compared to related ones found in the literature stems from its focus on financial market efficiency at the global, and IMF defined regional block level instead of on a specific economy as often found in the literature. Additionally, in contrast to other related studies, this study further examines the role of global financial market uncertainty in its financial market efficiency analysis. Financial market uncertainty variable may be unique to this study because the variable is derived through an econometric process from a base variable.

In India, the operational performance of the refinery is influenced by many factors. It is important to identify those key drivers which can assist the refineries to uphold and succeed in day-to-day production activities. Therefore, the purpose of this study is to evaluate the operational efficiency of seven Indian oil refineries during the period 2010 to 2018.

In this work, a two-stage empirical analysis is proposed. In the first stage, the data envelopment analysis (DEA) – variable return to scale model is used to evaluate the operational efficiency of the Indian oil refineries. The ordinary least square (OLS), random effect generalized least square (GLS) and Tobit model are used in the second stage to identify the key determinants of efficiency and to explain the variation in refinery efficiency.

The first-stage DEA results showed that the Numaligarh Refinery Limited and Chennai Petroleum Corporation Limited are found to be more efficient than the rest of the sampled refineries and attained their efficiency scores of 0.993 and 0.981, respectively, during the study period. The second-stage regression analysis suggested three explanatory variables: refinery structure, utilization rate and distillate yield, which are found to be significant in explaining variations in refinery efficiency.

This study provides valuable information that would help policymakers to formulate policies toward improving the efficiency of underperforming Indian refineries, which reduces the excessive use of resources and gives a competitive advantage.

This study proposes the first-ever application of the profit frontier DEA model for assessing the operational efficiency of oil refineries and explains the variation in refinery’s efficiency using OLS, GLS as well as the Tobit model.

This paper examines the financial ratios that may have a significant effect on the efficiency in Malaysian listed companies. Nine financial ratios measure seven variables which are firm visibility, tangibility, working capital, leverage, liquidity, productivity and profitability.

Data are collected from 108 public listed companies in Malaysia. The data extracted from companies' annual reports for three years 2012–2014. STATA software analysis is used to examine these relationships.

The results show each of tangibility and liquidity have negative relationships with efficiency ratio. In against of that, profitability, working capital and productively positively link to efficiency. Leverage which is measured by two ratios – Debt ratio and Debt equity ratio – shows mix results. Debt ratio shows a positive but not significant relationship with efficiency ratio and Debt equity ratio shows a negative significant relationship with efficiency ratio.

The results benefit companies, investors, economists and governments regulators in Malaysia-to understand the efficiency determinants, so help to make the right decision to enhance the efficiency level in companies which leads to enhance the amount of investments which in turn, enhance the country's economy in general.

This study differs than previous studies number of aspects: first the study covers a three years' period between 2012 and 2014, this period presents the movement of Malaysian current into depreciation with more than 45 percent of its value. Second, in the Malaysia context, this study examines new variables such as firm visibility, tangibility, and productivity. Third, the results of this study will help managers, shareholders, investors, regulators and other parties to make right decisions that will enhance the level of firm efficiency which enhances the investments and the economy of Malaysia.

Operational performance is critical for the banking sector for both managers and other stakeholders as it strongly affects the overall performance of the banking system. Traditional performance measures such as ratio analysis encountered certain shortcomings. At this juncture, data envelopment analysis (DEA) approaches are increasingly applied in bank efficiency studies. However, basic DEA models ignored the interactions between consecutive terms and focused primarily on measuring performance independently for each study period. All this is required to develop an operational performance model that can enable the long-term decision model.

An attempt has been made to develop a dynamic DEA within a non-radial category to measure interconnection activities considering non-performing loans as an undesirable link. This study uses the Indian banking dataset from 2015 to 2019. The study's research design directs three directions: ‘comparison of the dynamic DEA with the traditional static DEA model, areas of inefficiencies that are investigated for each factor using the factor efficiency index and the robustness results highlighting the performance difference between bank categories.'

Comparing with static DEA results, the study confirms that the dynamic model best measures long-term operational performance due to the linkage between consecutive terms. The efficiency analysis concludes that the input factor that requires the most improvement is ‘fixed assets' and ‘deposits'. The output factor that needs the most progress is ‘non-interest income'. The robustness of the developed model is proven by ownership categories present within the Indian banking system. At a significance level of 10%, the result of both the separate and dynamic model for privately owned banks is significantly better than that of publicly owned banks.

This paper proposes an operational efficiency model for Indian banks in line with undesirable output. The mean factor efficiency analysis related to non-radial DEA modelling enhances managerial flexibilities in determining improvement initiatives.

The purpose of this paper is to discuss a comprehensive literature survey of studies focusing on the efficiency and productivity of the banking sector using parametric and non‐parametric frontier techniques.

Critically reviewing 106 studies published across the world from 1994 to 2011, a conceptual framework is developed for the studies assessing the efficiency and productivity of the banking industry using non‐parametric DEA frontier approach.

Both the frontier approaches, parametric and non‐parametric, are gaining an edge over the traditional financial performance measures. In the non‐parametric approach, data envelopment analysis (DEA) is widely applied to measure a bank's efficiency and productivity. Studies conducted in developed countries such as the USA, the UK and Europe are now emerging with the new concepts of banking efficiency.

These findings are based only on the critical review of 106 studies. This study suggests the direction for future research and identifies the gap in existing literature with the development of a conceptual model.

This study is original in nature and included literature published in recent issues of 2011.

The main aim of this paper is to develop a supply chain efficiency framework to improve overall business performance in the competitive era. This paper offers a critical literature review on supply chain efficiency that aims to reveal the basic research that has been carried out, the problem areas and requirements for the efficiency in the new era of the supply chain.

The methodology followed during this research involves beginning with a wide base of articles lying at the supply chain intersection, performance measurement topics, and then screening the list to concentrate on supply chain efficiency.

Findings show that supply chain efficiency in the modern era remains an open research field. This research contributes to the supply chain literature by clarifying the supply chain efficiency definition, defining key measurements and variables for supply chain efficiency and developing a supply chain efficiency framework to improve overall performance.

This study will be very useful to the scholars working in this field. The proposed framework would help researchers and academicians to understand every dimension and variable of supply chain efficiency, allowing practitioners to measure efficiency levels and identify improvement measures. This framework would also act as a comprehensive guide for future studies and business practices.

As there are several state-of-the-art review papers on various supply chain areas, there is a lack of literature available on supply chain efficiency studies that can provide a comprehensive framework for researchers on related literature. Thus, the present study seeks to bridge this gap in the supply chain literature. Also, this study will provide a strong basis for researchers and academicians to apply the supply chain efficiency measurement system to the dynamic supply chain.

The main objective of this paper is to present a holistic approach for measuring overall bank efficiency and its decomposition in intermediation and profitability efficiencies.

Two-stage network data envelopment analysis (NDEA) model has been used for obtaining intermediation and profitability efficiencies along with overall bank efficiency. Additionally, bootstrap truncated regression has also been adopted to explore the influential predictors of two stages.

A comparative analysis between Indian private-sector and public-sector banks showed that the former is efficient than the latter in profitability efficiency stage. Another interesting finding is that none of the banks is efficient in overall study tenure. Finally, outcomes of bootstrap truncated regression show that differences in intermediation efficiency are explained by firm size, return on asset, market share and ownership while profitability stage is determined by diverse, gross domestic product and ownership.

This study will guide the Indian banking sector to act on which they are lagging, for the betterment of their overall performances. Finally, parameters like loan waives and disposal income of non-performing assets (NPAs) are not considered because of the unavailability of information in the output measures of NDEA model.

This paper not only provides a detailed performance assessment of Indian banks but also examines banks’ internal efficiency by deposits as an intermediary measure.

The purpose of this paper is to examine the efficiency of the banking sector in Qatar. The paper utilizes 15 banks comprising Islamic, conventional and foreign banks for the duration of 2007 to 2011.

Data envelopment analysis (DEA) technique is applied to compute technical efficiency, pure technical efficiency and scale efficiency. Also, Malmquist productivity index (MPI) is used to identify the sources of productive efficiencies of the banks.

The results suggest that Qatari banks are operating below optimum performance and thus there is still room for improvement. While conventional banks are the most efficient in Qatar in terms of technical and pure technical efficiencies, Islamic banks are most efficient in terms of scale efficiency. Besides, pure technical inefficiency dominated scale inefficiency in the Qatari banking sector. Moreover, as compared to the Islamic banks, conventional and foreign banks recorded a reduction in average technical efficiency during the duration of the 2008/2009 global financial crisis. In terms of productivity progress, all the Qatari banks were experiencing a decline in productivity mainly attributed to less technological innovation in the banking sector of Qatar.

Most of the banks in Qatar do not have published data before 2007 and after 2011.

There is less technological innovation in the banking sector of Qatar. Hence, bank managers in Qatar should focus on educating customers about modern banking technologies and other innovative banking services in Qatar.

This study is a pioneering effort in the application of DEA and MPI to study about the banking sector in Qatar.