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It is essential to track the development of resource and pollution intensive industries such as textile, leather, pharmaceutical, etc., under burgeoning pressure of environmental compliance. Therefore, the purpose of this paper is to analyze the progress of Indian leather industry in terms of individual factors and total factor productivity.

This study applies and examines the various concepts of productivity such as labor productivity, capital productivity, material productivity and energy productivity. Further, it assesses and compares the performance of Indian leather industry in Tamil Nadu (TN), West Bengal (WB) and Uttar Pradesh (UP) based on productivity analysis, spatial variations determinants in productivity and technology closeness ratio.

The findings suggest that as per the productivity analysis, WB leather clusters have performed remarkably better in terms of partial factor productivity and technical efficiency (TE), followed by TN and UP. This can be attributed to shifting of leather cluster of WB to a state-of art leather complex with many avenues for resource conservation. Further, the findings reveal that the firm size and partial factor productivities have significant positive correlation with TE which supports technological theory of the firm.

The results of this study can be useful for the policy makers associated with the Indian leather industry especially to design interventions to support capacity building at individual firm level as well as cluster level to enhance the efficiency and productivity of overall industry.

The findings also support the resource dependence theory of firm according to which the larger size firms should reflect on resource conservation practices, for instance the concept of prevention is better than cure based upon 3R (reduce, recycle and reuse) principles.

The paper gives an explanation of the productivity in the leather industry in terms of its factor productivity and TE.

The purpose of this paper is to extend the Du Pont method by connecting productivity and profitability through financial statements focusing on the two most common productivity indicators for companies: total factor productivity (TFP) and labour productivity.

The first part of the paper uses a deductive approach to obtain a new productivity rate of return. The second part applies the methodology of financial statements analysis to develop an empirical application of the findings.

The main finding is a functional relationship among the return on operating assets (ROOA), TFP and labour productivity. From it, the paper obtains a productivity rate of return that synthesizes both productivity measures. The ROOA is broken down into the sum of three parts: productivity, price change, and a crossed effect between turnover and price change.

The model developed in this paper enables analysts and managers to deepen in the causes of margin and turnover and, thus, in the causes of ROOA. To the extent that the separation between productivity and price change effects adds clarity to the knowledge of the causes of ROOA, it creates, at the same time a basis for making more precise decisions in order to improve corporate performance.

This paper differs from other studies by presenting the return of operating assets as a variable that depends on productivity ratios. Financial statement analysis has only occasionally incorporated productivity measures among the variables regarded as the drivers of a companys economic performance.

The use of productivity measurement in relation to the marketing function is relatively new. The central role of marketing can be expressed as the management of consumer demand and the translation of this demand into sales and profits. Marketing productivity analysis is an important tool in evaluating the performance of the marketing department in carrying out this role. It measures the efficiency with which marketing inputs are used to generate target levels of marketing outputs.

This paper explores the impact of international acquisitions on the efficiency and productivity of the cement industry in an emerging economy.

The data envelopment analysis (DEA) and Malmquist index (MI) are used to calculate the partial efficiency and productivity of individual inputs (materials, labour and fixed assets), as well as the total factor efficiency and productivity during the period 2000–2018. DEA and MI are combined with bootstrapping to perform succinct statistical inferences for determining the accuracy of results. In this paper we apply the input-oriented CCR DEA Window model. With respect to the level of analysis, data was collected from individual companies and then aggregated data at the industry level.

The research results show that international acquisitions positively affect efficiency of the cement industry in the long term. Efficiency of capital is lower in the short period after acquisitions. Additionally, international acquisitions positively affect partial productivity, as well as total factor productivity of the cement industry.

The results of the study may be significant for managers and policy makers to design appropriate strategies for the improvement of the cement industry performance over time.

Research in emerging economies related to subject matter is limited, and this is one of the earliest research studies which explore change in efficiency and productivity at the level of Serbian cement industry.

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.

This study aims to examine the impact of country governance and other potential bank-specific characteristics and macroeconomic condition determinants on bank productivity in the period of 2006–2016.

The productivity level of total 167 banks selected from Malaysia, Indonesia, Brunei and Singapore are evaluated using the data envelopment analysis-based Malmquist productivity index method. A panel regression analysis framework based on ordinary least squares, a fixed effect and a random effect models then are used to identify its main determinants.

The empirical findings indicate that the total factor productivity changes of Islamic banks is higher than conventional banks. The liquidity and global financial crisis influence both banks’ productivity. Bank size, credit risk, market power, management efficiency and inflation merely influence Islamic banks’ productivity. On the country governance dimensions, voice and accountability are found to positively influence both banks’ productivity. Regulatory quality and rule of law (RL) significantly influences the conventional parts. Political stability and absence of violence, government effectiveness, RL and control of corruption negatively influence the banks’ productivity, but this influence is only significant for the Islamic banks.

Country governance has received surprisingly little attention in the banking industry over the past few decades. Majority of the studies that examine the effect of governance on bank performance have focused more on the micro governance dimension. Thus, to the best of the researcher’s knowledge, no study has been done to address the effect of country governance on the productivity of the Islamic and conventional banks.

The purpose of this paper is to describe the process of productivity management and potential expert systems applications at each stage of productivity analysis. Based on literature reviews it discusses the strengths and limitations of these technologies. Describes several tasks in the measurement, interpretation and evaluation phases and examines the appropriateness of an expert systems application. Finds that expert systems applications could be useful in interpretation and evaluation. Focuses on productivity analysis at the organizational‐level only. Opines that business managers with limited or no knowledge of productivity models may want to have expert systems applications developed to diagnose problems and take corrective actions in a timely manner. The paper could be useful to business practitioners as well as researchers. Contributions include a detailed description of productivity analysis and how and where expert systems applications could make a difference. Productivity management is critical for long‐term business survival.

Excavator productivity calculations embrace myriad variables, which in turn, can be modelled in several ways. A key productivity variable is operator competence (O _c ) because this can impact on so many of the other variables. Earlier research has studied excavator productivity, but little has attempted to simultaneously model productivity variables in relation to O _c . The purpose of this paper is to address the void in extant literature.

A numeric, theoretical analysis is undertaken using the Caterpillar® hydraulic excavator productivity model to estimate excavator production, given: first, variance in modifying factors based on derived maximum and minimum values; and second, variance resulting from linear calculations based on excavator operator competence.

Excavator productivity resulting from incremental variance of modifying factors in isolation is shown to be linear except, in the case of bucket payload. Simultaneous application of modifying variables results in a greater, curvilinear productivity trend; while it is demonstrated that quantification of key modifying factors can to a significant extent be related to operator competence.

Findings add to productivity literature generally and to that of plant and equipment more specifically. Results will help productivity estimation of excavation in a practical sense while informing subsequent design of an empirical academic research of this problem.

Originality relates principally to determining modifying factor ranges and their analysis of simultaneous effect on each other, especially, as influenced on assumptions of operator competence.

The purpose of this paper is to discover the most important productivity determinants of Yemeni microfinance institutions. In addition, this study tests the most appropriate tool to measure productivity in such unique industry.

The authors applied data envelopment analysis (DEA) with the variable return to scale after testing the technology return to scale assumption. Then, they used DEA with bootstrapping technique to overcome the borne biasness in the conventional DEA analysis. Finally, the authors presented the Hicks–Moorsteen (total factor productivity [TFP]) as the most suitable tool for the technology presented in this study.

In this paper, the authors found a prolonged deterioration in the productivity scores of microfinance institutions in Yemen. This study highlights the importance of operating in rural areas to improve micro finance institutions’ (MFIs’) productivity. In contrast, they found no significant differences in productivity, neither between microfinance banks and non-governmental organizations nor between Islamic and non-Islamic MFIs.

This study extends previous research in the area of productivity and its determinants. It also adds to the body of productivity knowledge and methodology within the context of the microfinance industry in Yemen.

The study discovered new productivity determinants and re-assessed the importance of some already known ones. These determinants have been studied for the first time in Yemen’s microfinance industry and have contributed to answer the question of what is the most suitable productivity method that should be used. This study proved that the Hicks–Moorsteen TFP and the variable return to scale assumption are the only suitable methods to study productivity in the microfinance industry.

Total factor productivity (TFP) has become a prominent concept in agriculture economics and policy over the last three decades. The main aim of this paper is to obtain a detailed picture of the field via bibliometric analysis to identify research streams and future research agenda.

The data sample consists of 472 papers in several bibliometric exercises. Citation and collaboration structure analyses are employed to identify most important authors and journals and track the interconnections between main authors and institutions. Next, content analysis based on bibliographic coupling is conducted to identify main research streams in TFP.

Three research streams in agricultural TFP research were distinguished: TFP growth in developing countries in the context of policy reforms (1), TFP in the context of new challenges in agriculture (2) and finally, non-parametric TFP decomposition based on secondary data (3).

This research indicates agenda of future TFP research, in particular broadening the concept of TFP to the problems of policy, environment and technology in emerging countries. It provides description of the current state of the art in the agricultural TFP literature and can serve as a “guide” to the field.