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The current paper's goal is to examine the productivity of the closed banking sector evidenced from Ethiopia. In addition, the inclusion of intangibles on productivity was examined in the current paper.

First, the standard Malmquist Productivity Index (MPI) was employed for 13 commercial banks for both stages. Second, by excluding the state-owned commercial bank, the analysis employed a bootstrapped MPI for the robust and comprehensive conclusion. Furthermore, from 2010 to 2019, the fixed effect Ordinary Least Square (OLS) regression with balanced panel data was used.

The standard MPI in both stages shows that the productivity of Ethiopian commercial banks is declining. The technological shock was the main reason for the loss. The catch-up in both stages scored above unity, mainly due to the pure efficiency change. Besides, when combined with tangible resources, the inclusion of resource-based view (RBV) proxy variables reduces technological shock regress and ultimately improves productivity change. The bootstrapped MPI also reveals that technological shock is the primary source of the productivity decline. However, efficiency change also contributes to the productivity decline based on this estimation.

Future research could examine the more extensive productivity analysis by considering the primary sources of data collections for resource-based variables.

According to the study's results, banking regulatory authorities and bank management, including the shareholders, should continue to invest in cutting-edge technology to improve the productivity of the banking sector.

This is the first comprehensive study of productivity for Ethiopian commercial banks based on the standard MPI, bootstrapped MPI, and OLS by incorporating all resources into the analysis.

This paper aims to examine the total factor productivity (TFP) change and its components: efficiency change and technical change in microfinance institutions (MFIs) in India operating from 2005 to 2018. The study also scrutinizes the variations in productivity levels across the distinct organizational form and size groups of MFIs. In addition to this, the authors identify the contextual factors that determine TFP growth, catching-up and technology innovation in MFIs.

The study employs a smooth homogeneous bootstrap estimation procedure of Simar and Wilson (1999) for obtaining reliable estimates of Malmquist indices –productivity and its components – in a data envelopment analysis (DEA) framework for individual MFIs. In order to identify the determinants of productivity change and its components, the study follows Simar and Wilson's (2007) guidelines and applies a bootstrap truncated regression model. The double bootstrap procedure performs well, both in terms of allowing correct estimation of bias and deriving statistically consistent productivity estimates in the first and root mean square errors in the second stage of the analysis.

The empirical results reveal that the MFIs have shown average productivity growth of 6.70% during the entire study period. The observed productivity gains are primarily contributed by a larger efficiency increase at the rate of 4.80%, while technical progress occurs at 2.3%. Nonbanking financial companies (NBFC)-MFIs outperformed non-NBFC-MFIs. Small MFIs show the highest TFP growth in terms of size groups, followed by the large MFIs and medium MFIs. The bootstrap truncated regression results suggest that the credit portfolio, size and age of MFIs matter in achieving higher productivity levels.

The practical implication drawn from the study is that the Indian MFI industry might adopt the latest technology and innovations in the products, risk assessment and credit delivery to improve their productivity levels. The industry must focus on enhancing the managerial skill of its employees to achieve a high productivity level.

This study is perhaps the initial attempt to explain the productivity behavior of MFIs in India by deploying a statistically robust double bootstrap procedure in the DEA-based Malmquist Productivity Index (MPI) framework. The authors estimate the bias-adjusted productivity index and its decompositions, which represent more reliable and statistically consistent estimates. For contextual factors responsible for driving productivity change, the study deploys a bootstrap truncated regression approach.

The purpose of this paper is to present a productivity measure for hospital services in Ontario.

The study applied the Malmquist Productivity Index (MPI) to assess the efficiency of hospital services in Ontario, Canada, over the period 2003‐2006. The MPI was decomposed into efficiency change and technological change. Efficiency change was further decomposed into pure efficiency change and scale efficiency change. A bootstrapping technique was also used to obtain confidence intervals for the output oriented MPI and its decompositions.

By estimating confidence intervals it was found that a large number of hospitals did not achieve significant progress in terms of productivity. By taking geometric means of estimates for all years it was observed that while overall productivity and efficiency of hospitals in Ontario declined during the study period, technological progress increased at a rate of 5.95 percent on average.

The present study helps to understand the productivity and technological change and change in technical efficiency in this vital sector of the economy, which is important for policy making identifying improvement opportunities in resource allocation. It was observed that Ontario hospitals did not improve the efficiency with which they employed their inputs (i.e. staff and supplies) over the study period; they did achieve gains through application of technologies.

The paper provides a thorough study on productivity growth of health care services in Ontario using a non‐parametric framework with bootstrapping. It also provides a robust measurement and analysis of the contributions of technology, size of operation and use of inputs to the performance of hospitals in Ontario.

A brief review of earlier studies on the productivity scenario of Indian industry shows that most of the studies analysed are confined to either parametric approach or growth accounting approach of measuring productivity. At the same time, the few studies based on the non-parametric [namely, Malmquist productivity index (MPI)] overlook the returns to scale conditions as well as the bias involved in the estimation of distance functions. Given this backdrop, this study aims to provide a robust measure of productivity, which considers the returns to scale assumptions and correct for the bias involved in the estimation of productivity.

This study empirically tests for the returns to scale that exists in the chemical and chemical products industry in India. The test result suggests that Ray and Desli (1997) approach of MPI is the appropriate one for the present context. Initially, the conventional Ray and Desli (1997) estimation and decomposition of MPI for the period 2001 to 2017 is being used. Subsequently, to correct for the bias in the estimation of efficiency scores used for the estimation of MPI, the bootstrapping algorithm of Simar and Wilson (2007) has been extended into the context of MPI estimation.

The results from the conventional Malmquist productivity estimates testifies to an improvement of total factor productivity (TFP) in seven out of 16 years under consideration. On the contrary, TFP growth is recorded only in the four years throughout the period after the bias correction. A greater discrepancy between the two measures has been found in the case of scale change factor component of MPI.

The technical change (TC) component positively influences TFP, whereas scale change factor (SCF) deteriorates the TFP condition of this industry. It will be appropriate for these firms to identify and operate under an optimal scale of operation, along with reaping the benefits of technological change. From a methodological perspective, researchers should consider the potential bias that arise in estimation of TFP and use a larger sample whenever possible.

This paper brings in a new perspective to the existing literature on industrial productivity. As against earlier studies, this study empirically tests the returns to scale of the sector under consideration and uses the most appropriate approach to measure productivity. The effect of sampling bias on TFP and its components is analysed.

The purpose of this paper is to provide a robust version of the Malmquist Productivity Index (MPI) in order to evaluate hotels' productivity levels during the Great Recession.

Based on the order-m frontiers, we apply a robust version of an MPI. We decompose the productivity into three robust components. We use a sample of hotels operating in the Balearic Islands and Canary Islands, and we decompose and evaluate their productivity levels during the period 2004–2013. Moreover, we evaluate hotels' productivity performance during the pre-crisis period, the US subprime crisis period, the global financial crisis (GFC), the sovereign debt crisis period and the after-crisis period.

Our findings show that productivity did not deteriorate due to the adverse effects of economic crisis. This is mainly driven by increased technical change and the ability to operate at optimal scales. The long-term investment in innovation policies which are related to services and processes, appear to be the dominating feature behind hotels' productivity levels, which helped the hotel industry to recover quickly from the Great Recession.

The vast majority of empirical studies evaluating the productivity change in the hotel industry apply MPIs which are based on data envelopment analysis (DEA). However, the productivity measurement which is based on the nonparametric framework is sensitive to sample characteristics. In order to avoid such shortcomings, we apply a robust version of the MPI.

The nature and extent of productivity change of ten commercial banks in Malaysia is analysed over the period 2000‐2001. Utilising non‐parametric methodology, Data Envelopment Analysis (DEA) and Malmquist total factor productivity index (MPI), individual bank efficiency and productivity changes which took place within this period is estimated. The MPI calculated within the framework of DEA further decompose productivity growth into technical efficiency change and technological change. The results from this analysis indicate that total factor productivity increased in all eight banks except for EON, which remain the same while PBB, recorded a decrease in productivity. AFB recorded the highest growth in total factor productivity. The growth in productivity is attributed to technological change rather than technical efficiency change.

This study aims to investigate the productivity level of family takaful in Malaysia and Indonesia from 2014 to 2019. Productivity can support corporate sustainability, which is one of the company’s goals.

The measurement of the productivity level in this study involved applying the Malmquist Productivity Index (MPI) method. The input variables used consisted of equity, total expenses and total investment. The output variables consisted of total profit and investment income. In addition, this research used the orientation of the output and intermediation.

Throughout the study period, the Indonesian family takaful had an average total factor productivity change (TFPCH) of about 0.945. In other words, it did not reach optimal productivity. It is more due to the low value of technological change (TECHCH). On the contrary, family takaful companies in Malaysia had called productivity, showing a TFPCH of about 1.041. Again, this is mainly due to an increase in TECHCH and efficiency change, but it is still low in pure technical efficiency change.

This study focuses on factors that exist in internal takaful companies. Neither micro- nor macroeconomic variables that can affect productivity levels have been measured. In addition, this study only analyzed two countries out of the 11 countries in the Southeast Asian region.

Family takaful companies can use the productivity index as one of the bases of evaluation in managing their resources to enhance optimal output. Furthermore, the management of family takaful companies in Indonesia needs to focus more on technological innovation and delivery of services to increase productivity. Meanwhile, family takaful companies in Malaysia can maintain their technology usage and efficiency to operate productively. The government in both countries is expected to actively accelerate the growth of family takaful companies by producing regulatory products that strengthen the industry. Specifically, the government in Indonesia needs to make regulations that support technology improvement.

There is still not much research that examines family takaful’s productivity level using the MPI. The MPI is an appropriate tool to evaluate the productivity of family takaful companies. Thus, family takaful companies can improve their quality by assessing the productivity index value.

The purpose of this paper is to attempt to measure the performance of the Indian banking sector in terms of efficiency and productivity levels and their determinants during the post-reform period.

The present study is a novel attempt as it has used pooled data for a duration of 15 years (i.e. 1997/1998-2010/2011) from 59 selected banks for estimating the Hicks-Moorsteen (HM) total factor productivity (TFP) index.

Poor technical efficiency has experienced with scale efficiency change exerting dominant factors; whereas relatively better productivity growth has been experienced by the banks with major contributions from technical change components. The study found relatively underestimated efficiency and productivity levels by traditional data envelopment analysis-based Malmquist index. Additionally, the study brings into account the results for external and environmental determining factors contributing to the TFP growth.

Using HMTFP indices has helped to eliminate certain drawbacks of data envelopment and provided the more elaborative decomposition of productivity growth along with their components so as to have lucid and multidimensional insights about the performance of the Indian banking industry after the initiation of financial reforms.

The purpose of this paper is to provide new empirical evidence on the impact of credit risk on China banks' total factor productivity.

The paper employs the Malmquist Productivity Index (MPI) which allows for the examination of five different indices: total factor productivity change (TFPCH); technological change (TECHCH); efficiency change (EFFCH); pure technical efficiency change (PEFFCH); and scale efficiency change (SECH) indices.

The empirical findings indicate that the State Owned Commercial Banks (SOCB), Joint Stock Commercial Banks (JSCB), and City Commercial Banks (CCB) have exhibited lower TFPCH levels with the inclusion of risk factor. It was found that the JSCB and CCB have exhibited lower TFPCH due to TECHCH, while the SOCB have exhibited lower TFPCH due to EFFCH. The empirical findings suggest that the inclusion of credit risk factor has resulted in a higher JSCB EFFCH levels. On the other hand, the SOCB and CCB have exhibited a lower EFFCH levels due to SECH and PEFFCH, respectively.

The results clearly highlight the importance of credit risk and lending quality in determining the total factor productivity change of banks operating in the China banking sector. The author demonstrates that the inclusion of credit risk factor has resulted in a lower TFPCH level of all banks operating in the China banking sector. Thus, excluding the credit risk factor from the analysis on the China banking sector may potentially bias the result upwards.

In an environment of heavy government influence over the lending process, a large proportion of loans extended by Chinese banks over the years have gone bad. Policymakers should prevent the flow of new non‐performing loans by separating bad clients from banks that are being restructured and recapitalized in the reform of the banking sector.

By employing the Malmquist Productivity Index (MPI), the present paper contributes to the existing literature by examining, for the first time, the impact of credit risk on China banks' total factor productivity. To the best of the author's knowledge, this type of analysis is completely missing from the literature in regard to the China banking sector.

The purpose of this study is to propose a comprehensive benchmarking approach to help subsidiaries of a company to implement continuous improvement (CI).

The proposed approach classifies subsidiaries of a company into the three stages of CI. After that, the proposed approach employs data envelopment analysis (DEA)-super slacks-based measure (SBM) model and Malmquist Productivity Index (MPI) to evaluate the operational efficiency of the subsidiaries and selects benchmarking targets and sets benchmarking goals based on the operational efficiency for benchmarking of input and output criteria. Then, the proposed approach suggests a four-step benchmarking process for benchmarking of detailed efficiency. Finally, the proposed approach makes the subsidiaries achieve CI by implementing the input and output benchmarking and the efficiency benchmarking.

The results show that the proposed approach can help subsidiaries of a company to implement a staged benchmarking which considers input and output criteria and Malmquist productivity and efficiency comprehensively.

Generally, benchmarking is implemented in many aspects. However, due to the restriction of a company’s resource level, a practical and staged benchmarking is preferred. This study proposes a comprehensive approach to benchmark systematically and gradually, and provides a more reasonable benchmarking process for implementing CI.