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Objective: This research paper aims to evaluate the relative efficacy of pharmacognosy research on the top 40 tertiary hospitals and its pharmacy in India according to Newsweek's 2021 ranking. Purpose: India's pharmaceutical market is now estimated to be worth $50 billion. India is a significant pharmaceutical exporter, reaching more than 200 nations with its exports. This study will help to identify whether the pharma industry is working efficiently in selected Indian hospitals or not. Thus, to create a bench marking for the hospital pharmacies in India. Design/methodology/approach: Data for the study came from public and private hospitals' official websites, financial records, and government notifications. Crosssectional data analyses were done using a Slack-Based model and data envelopment analysis (DEA). STATA was the software used to scrutinize the results effectively. Results: Thirteen hospital pharmacies out of a total of 40 are judged to be relatively technically efficient overall, according to the research. The findings of the slack analysis demonstrate that government hospitals and pharmacies are better able to handle slack and effectively manage input and output. Conclusion: The researchers also set upped a benchmarking hospital both for private and government healthcare settings for pharmacognosy research. Thus, it can be utilized for the betterment of the pharmacies. [ABSTRACT FROM AUTHOR]

Revenue risk is the most critical factor that hampers the long-term viability of highway infrastructure service projects. This paper develops an integrated multi-method decision framework to evaluate and compare multiple revenue risk mitigation strategies for build-operate-transfer (BOT) highway infrastructure concessions. Using the project data of a real highway BOT case project in India, this paper models and evaluates various risk mitigation strategies on different performance metrics using Monte Carlo simulation analysis. The technical efficiencies of the risk mitigation strategies are estimated using data envelopment analysis (DEA) and efficient strategies are identified. The findings suggest that strategies comprising appropriate combinations of minimum revenue guarantee, excess revenue share, and revenue shortfall loan mitigate the revenue risk most efficiently. The decision framework and the findings facilitate efficient public infrastructure contract design by policymakers and set multiple research directions. [ABSTRACT FROM AUTHOR]

Background: Efficiency of production system is calculated by the input spent to amount of output produced. The Agronomic efficiency has been found to be below 48% than the world efficiency standard of 40 kg ha-1. The average fertilizer usage of India is 133 kg ha-1, 232% higher than the global standard fertilizer of 40 kg ha-1. The certified seeds and agriculture machinery usage has been increased by 15% and 13% in India but the efficiency of farm stays behind. Methods: With this back ground the present study applied cost approach variable return to scale Data Envelopment Analysis (DEA) model to evaluate the Indian farms resource using 200 rural farms of Tamil Nadu state. The data envelopment analysis models have the advantage of simultaneous measure of technical, allocative and cost efficiency of the individual farms. Result: The cost efficiency among the respondents ranged from 0 to 1 with a mean efficiency of 0.66. The study found that Decision-Making Units (DMU) have cost inefficiency, suggesting that most of the DMU operated very far away from the efficiency frontier. Allocative inefficiency (0.55) is worse than the technical inefficiency (0.80), implying that the lower economic efficiency is the result of high allocative inefficiency. [ABSTRACT FROM AUTHOR]

Purpose: The Indian leather industry contributes to economic growth at a significant environmental cost. Due to the rising global demand for sustainable leather products, promoting efficient input utilisation has become vital. This study measures input efficiency and its determinants for leather industry in order for it to improve its future performance. Design/methodology/approach: In the first stage, bootstrap data envelopment analysis (DEA) approach is used for measuring efficiency and analysing firms' differences based on their geographical location, organisational structures, urban-rural location and sub-industrial groups. A second stage regression examines efficiency determinants using size, age, skill and capital-labour intensity as the explanatory variables. Findings: Efficiency result shows a significant potential of minimising inputs by 47% provided the firms adopt best practices. West Bengal firms, urban located firms, individual and proprietorship owned firms and leather consumer goods firms are found to be relatively efficient to their counterparts. Size, skilled managerial staff and labour-intensive firms positively affect efficiency. Practical implications: Construction of well-connected roads for accessing urban retail markets and provision of reliable electricity would improve efficiency of rural firms. Small-scale enterprises have a larger share in Indian leather industry; therefore, policy should focus on enhancing the firms' scale and investing in training facilities to skill employed labour for ensuring optimal use of inputs. Originality/value: Previous studies on the leather industry have used the conventional DEA efficiency measurement approach. This study uses DEA bootstrapping model for robust efficiency estimates and provides consistent inferences about the determinants. [ABSTRACT FROM AUTHOR]

Nowadays, to fulfill growing power requirements at reasonable prices, like other European countries, the Indian electricity market is now more oriented towards renewable energy resources. Today, the wind energy industry has grown from a marginal activity to a multi-billion-dollar business in India's power production sector because of its comparatively safer and positive environmental features. Though, there are several wind energy power plants generating electricity in India's different geographical locations, assessing their performance is a crucial task and an important target for stakeholders. In the present study, an attempt is made to quantitatively assess the relative operational efficiencies of 14 wind power plants in India during 2016–2017 to 2019–2020 employing a two-stage data envelopment analysis Tobit model. Further, the sensitivity analysis is implemented in the present study to assess the robustness and efficacy of the data envelopment analysis models with different combinations of inputs and outputs. Data envelopment analysis results indicate that 14% of India's wind power plants were operated at the most productive scale during the observed period 2016–2017 to 2019–2020. The Tobit regression results indicate that the wind turbines' age adversely affects production efficiency. In contrast, the site elevation has a significant positive impact on the operational efficiency of wind power plants. Findings from the present study may help stakeholders and policy regulators in the wind industry to identify the key factors influencing the performance of ongoing wind power plants in India and optimize operational strategies and policies. [ABSTRACT FROM AUTHOR]

This paper aims to provide an analysis of the agricultural productivity of food grains across the states of India. Despite agriculture's diminishing role as a share of overall gross domestic product (GDP) in India, it plays a crucial role by providing a large proportion of jobs to the workforce. Acknowledging the central role of agriculture in the economy as well as the diversity between the states in terms of availability of resources, this study analyses the impact of various inputs like land area, fertilizers, electricity and labour use on the production efficiency of food grains at the state level from 2006 to 2017 using Data Envelopment Analysis (DEA) and Malmquist Productivity Index (MPI). The results highlight the variation in the performance of states with five out of selected 20 states experiencing efficiency under the CCR model of DEA and nine out of 20 states being efficient under the BCC model of DEA. MPI results indicate that 11 out of 20 states have progressed in terms of MPI during the period under consideration. The paper highlights the possibility of increasing agricultural production with optimizing the inputs by focusing on efficient resource deployment and enhanced production management. The paper investigates the relationship between various agricultural inputs and food grain production efficiency using DEA and provides a way to improve on the allocation of these resources across various Indian states. [ABSTRACT FROM AUTHOR]

Financial inclusion programs are used as a major economic development tool in emerging economies. Human development is the major determinant of financial inclusion, creating opportunities for people to better access financial services. The study's major aim is to examine the technical efficiency of financial inclusion of Indian states by Data Envelopment Analysis using human development as input. To achieve this objective, a 3‐dimensional Financial Inclusion Index (FII) is constructed for 28 major Indian states from 2010 to 2017. Empirical findings suggest most Indian states are under low and medium financial inclusion, and states with better human development have better FII status. Crucially, technical efficiency results reveal states with better human development perform better in terms of FII. The inconsistency in FII and human development ranks is majorly found in North‐eastern and high‐income Indian states. Therefore, policymakers in India should focus on promoting human development in low Human Development Index states. [ABSTRACT FROM AUTHOR]

Purpose: This work reports on a study to measure tractor owners' (mostly farmers') repurchase behavior (RPB). While earlier studies have focused on the technical aspects of the tractors, none as yet have considered farmer intention and behavior for predicting purchase decision. A conceptual model was built considering all possible antecedents of farmers' RPB based on in-depth interviews and discussions with marketing managers. Interviews with customers were used to understand these antecedents or interactions with salespersons, either directly or indirectly during conversations or visits to stores. The authors have attempted to develop a scale on farmers' consumption experience from the perspective of farmers. Design/methodology/approach: The model was validated using Smart-PLS, and the best tractor brand was identified using data envelopment analysis (DEA). At the village level, snowball sampling was adopted to identify potential tractor owners who had repurchased the same brand or a higher model as respondents. Findings: Findings reveal that all the paths were found to be significant. Farmers' consumption experience (FE) seems to be the biggest predictor of RPB, followed by image, satisfaction and trust. The newly introduced construct FE has a significant effect on farmers' RPB. DEA results further indicate that most tractor companies function with 100% efficiency. Research limitations/implications: The study was carried out in India; it can be extended to other countries. Also, the sample was collected from one state in India and is cross-sectional in nature, so it cannot be generalized. Originality/value: First, the authors developed a conceptual model considering all possible antecedents of RPB. No studies had yet developed a scale on FE. Second, the authors created a benchmark for the various preferred tractor brands from the farmers' perspective using DEA. [ABSTRACT FROM AUTHOR]

Purpose: Data envelopment analysis (DEA) has wide applications in the agricultural sector to evaluate the efficiency with crisp input and output data. However, in agricultural production, impreciseness and uncertainty in data are common. As a result, the data obtained from farmers vary. This impreciseness in crisp data can be represented in fuzzy sets. This paper aims to employ a combination of fuzzy data envelopment analysis (FDEA) approach to yield crisp DEA efficiency values by converting the fuzzy DEA model into a linear programming problem and machine learning algorithms for better evaluation and prediction of the variables affecting the farm efficiency. Design/methodology/approach: DEA applications are focused on the use of a common two-step approach to find crucial factors that affect efficiency. It is important to identify impactful variables for minimizing production adversities. In this study, first, FDEA was applied for efficiency estimation and ranking of the paddy growers. Second, the support vector machine (SVM) and random forest (RF) were used for identifying the key leading factors in efficiency prediction. Findings: The proposed research was conducted with 450 paddy growers. In comparison to the general DEA approach, the FDEA model evaluates fuzzy DEA efficiency giving the user the flexibility to measure the performance at different possibility levels. Originality/value: The use of machine learning applications introduces advanced strategies and important factors influencing agricultural production, which may help future research in farms' performance. [ABSTRACT FROM AUTHOR]

This study combines a data envelopment analysis, a dynamic computable general equilibrium model with estimated secondary material flows for circular economy, based on economist Joseph Schumpeter's macroeconomic theory, to develop a novel soft-link model to determine the efficiency of forty-three dark-fermentative technology of biohydrogen, and technology improvement impacts on biohydrogen output and supply price for six major emerging Asian countries. The integrated model is found to be feasible. This study finds that efficiency of continuous technology significantly exceeds that of batch technology. Biomass substrate concentration is the most important input in the generation of biohydrogen statistically; pH influences the efficiency of the batch technology, and the efficiency of continuous production technologies significantly exceeds that of batch technologies, but still have a gap to improve to full production efficiency for most of continuous technologies. India and China generate highest output growth of biohydrogen in baseline scenario. Japan and India can most benefit from improvements in batch and continuous biohydrogen production technology. The models and results of this study provides guidelines and references for decision-makers in industry and government who are responsible for reforming future energy policy. • A soft-link DEA-CGE model was developed, and its effectiveness confirmed. • Efficiency and improvement potential of dark fermentation technologies are provided. • Efficiency of continuous technology significantly exceeds that of batch technology. • Biomass substrate concentration is the most important input in generation of bioH2. • Japan and India are the countries that benefit most from improved bioH2 technology. [ABSTRACT FROM AUTHOR]

Purpose The purpose of this paper is to ascertain the performance of Indian hospitals in recent past and derive meaningful insights for policy makers and practicing managers in this area.Design/methodology/approach This paper analyses the technical efficiency of select Indian private hospitals using three related methodologies: data envelopment analysis (DEA), Malmquist Productivity Index (MPI) and Tobit regression. Two output variables (i.e. total income and profit after tax) and four input variables (i.e. cost of labour, net fixed assets, current assets and other operating expenses) were selected for the purpose of the study.Findings DEA analysis has shown that 14 out of 37 hospitals are found to be efficient under the Cooper and Rhodes model of DEA and 20 out of 37 hospitals are efficient under the Banker, Charles and Cooper model of DEA. The empirical results pertaining to MPI indicate an overall productivity progress in the private Indian hospital industry during the study period, which is largely due to technological advancement in the industry. Tobit regression demonstrates that chain affiliated, specialized and multi-city located hospitals exhibit a higher technical efficiency.Research limitations/implications This study has a limitation with reference to the unavailability of data on the input and output parameters of the model. The data related to the number of beds, number of doctors, number of nurses, etc., were not available for the period under consideration.Originality/value This study seems to be one of the few studies applying productivity and performance analysis using DEA, MPI and Tobit regression for the Indian private hospital industry. [ABSTRACT FROM AUTHOR]

In the context of urban bus transport, the efficiency study of the bus routes, the impact of the contextual factors on the efficiency, and environmental performance measurement have not been paid enough attention. For performance evaluation, scholars mostly apply conventional data envelopment analysis (DEA) model whose limitation regarding handling the undesirable parameter limits its usefulness in the presence of undesirable outputs. Here, we propose a Shannon entropy-based output-oriented consolidated DEA model for the holistic performance measurements of the bus routes. Apart from operational and financial performance measurement, we introduce a novel 'emission metric' for environmental performance assessment of the bus routes. We show the context-specific implementation of several modified DEA models for measuring the efficiencies of the bus routes and integrate the efficiencies from different models into a single score using Shannon entropy approach. We apply the proposed method on 21 bus routes of Kolkata, an urban city in India. Our method facilitates consistent and more improved score in case of profit-making and loss-making routes, respectively. According to our analysis, route length, route overlapping, number of intermediate stoppages, and cost structure play a crucial role in the performance. We provide several routespecific as well as policy-level recommendations. [ABSTRACT FROM AUTHOR]

With a continuously growing population, India faces a real challenge in fulfilling the target of food security for all of her citizen. Parallel to increment of food production, better processing of it as well plays a vital role in ensuring nutrient food to all in a sustainable fashion. Food processing industry may plays a vital role in this direction and, therefore, occupies an important p lace in India's overall economic growth, in general and that of her manufacturing sector, in particular. For its significant share to the industrial output and both direct and indirect employment generating capacity, food processing industry is considered to be a remarkable pooling factor for the prosperity of agricultural sector as well. In this study, we explore the performance of the production units of this industry. We have analyzed data on Annual Financial Statements of food processing companies, collected through Prowess database of Centre for Monitoring Indian Economy (CMIE) during 2000-2015. We use a two stage methodology for our analyses. Using data envelopment analysis (DEA) method in the first stage, we obtain technical efficiency (TE) score of the individual production units of the industry. In this connection, we also examine the extent of diverse technologies used across its various subsectors, through a measure called technology closeness ratio (TCR), which we explain in the paper in details. We then explain such TE scores in terms of certain company -specific variables using simple ordinary least squares (OLS) regression method, to have an idea on the factors that may influence such performance. Results suggest that the units producing dairy products and sugar recorded relatively lower level of TE, whereas those producing vegetable oil and products are more technically efficient as compared to other sub-sectors within the industry. Overall TCR varies from 0.63 (for sugar and dairy products) to 0.93 (for vegetable oil and products), implying thereby that the former two have an opportunity to improve their overall performance to a sizeable extent through technological upgradation, whereas the latter does not have much scope to improve themselves in this regard, and can improve their overall performance only through more efficient utilization of available resources! Moreover, observed evidence of positive impact of R and D intensity and infrastructural factor on performance have a remarkable implication on the role of the government for providing necessary infrastructural facilities and (probably) initiating innovative scheme(s) to make research and development activities more attractive to them!. [ABSTRACT FROM AUTHOR]

This paper talks about the measurement of efficiency and productivity of non-life insurance firms in India. This study is focused on twelve private non-life insurance firms and four public sector non-life insurance firms of India in the period 2008-09 to 2012-13. Data Envelopment Analysis (DEA) coupled with Malmquist productivity Index is used in measuring the efficiency as well as productivity of the Indian non-life insurers. The study attempts to use commission plus management expenses and capital as input parameters and net premium and investment income as output parameters. The technical efficiency of these firms is measured and analysed through CRS and VRS methods. [ABSTRACT FROM AUTHOR]

This article evaluates the performance of 29 electricity distribution divisions of the Indian state Uttarakhand in terms of overall efficiency through data envelopment analysis (DEA) and compares the result with an analysis based on cross efficiency—a method for peer-appraisal. The introduction of cross efficiency highlights the falsely indicated efficient divisions through the measurement of a “false positive index.” The main objective of this work is to identify the apparent “overall efficient” divisions as indicated by DEA analysis solely. Cluster analysis provides more appropriate targets to be used as a benchmark by the poorly performing divisions. The findings of this article can be instrumental to managers and policy makers to adopt measures that would be beneficial to the improvement of inefficient divisions. [ABSTRACT FROM PUBLISHER]

Purpose – The purpose of this paper is to analyze the performance of Indian retailers in recent past and derive meaningful insight for practicing managers in this area.Design/methodology/approach – This paper analyses the economic efficiencies of select Indian retailers using three related methodologies: Data Envelopment Analysis (DEA), Malmquist Productivity Index (MPI) and Bootstrapped Tobit Regression.Findings – DEA analysis has shown that five retail firms out of selected 18 are found as efficient under the CCR model of DEA and seven out of 18 retail firms are efficient under the BCC model of DEA. MPI results indicate that 61 percent of the firms have progressed in terms of the MPI during the period under consideration. The Bootstrapped Tobit Regression shows that number of retail outlets and mergers and acquisitions can be considered as the driving forces influencing efficiency of retailers in India. Research limitations/implications – The paper has a limitation with reference to the availability of data for a few retail outlets, especially in the modeling through the Bootstrapped Tobit Regression. Originality/value – This study seems to be the first in applying productivity analysis using DEA, MPI and Bootstrapped Tobit Regression for the Indian retail sector. [ABSTRACT FROM AUTHOR]

Abstract: This paper evaluates relative performance of the Electric Distribution Divisions (EDDs) of an Indian State, Uttarakhand for the period 2005–2008, through application of Data Envelopment Analysis (DEA). To improve the operational efficiency of EDDs, Uttarakhand Power Corporation Limited (UPCL) disintegrated several divisions into smaller ones over this period. However, microlevel examination revealed the ineffectiveness of this process during the period of analysis. Therefore, in the present work an alternative reorganization model for selecting the EDDs for disintegration and for selecting the optimum scale for disintegration is proposed based on the efficiency analysis of 2007. The model is verified by comparing the mean efficiency score of the EDDs derived using proposed model with that of existing ones. It is found that overall efficiency of proposed model is higher than the existing system. Reliability of the overall efficient EDDs is also examined to check the robustness of the CCR result for the same year. This study gives an assessment of UPCL to monitor and diagnose the changes in the efficiency of EDDs, so it will help regulators to formulate more effective policies on deregulation and disintegration and to determine the appropriate efficiency improvement measures when imposing yardstick competition on electric utilities. [Copyright &y& Elsevier]

Many firms in the service industry face the problem of disparate results in terms of efficiency. This problem is a cause of concern for many big organizations such as banks, hotels, courier companies, and so on. In particular, the last decade has witnessed continuous changes in regulation, technology and competition in the global financial services industry, and Indian banks are no exception. Rising cost-income ratios and declining profitability reflect increased competitive pressure. To assess the stability of the banking system, it is therefore crucial to benchmark the performance of banks operating in India. An efficient banking system contributes in an extensive way to higher economic growth in any country. Thus, studies of banking efficiency are very important for policy makers, industry leaders and many others who are reliant on the banking sector. The present study investigates the technical efficiency of Indian banks, segmented in terms of ownership. For this purpose, the data envelopment analysis (DEA) model was used with five input variables (viz. borrowings, deposits, fixed assets, net worth, and operating expenses) and four output variables (advances & loans, investments, net interest income, and non-interest income), and the efficiency scores were calculated for a sample of forty-nine major banks operating in India. [ABSTRACT FROM AUTHOR]

Abstract: In this paper DEA (Data envelopment analysis), a non-parametric approach to frontier analysis, is applied to evaluate the relative performance of 29 EDDs (Electricity Distribution Divisions) of an Indian hilly state, namely Uttarakhand. Input oriented DEA is applied to evaluate the relative overall efficiency, technical efficiency and scale efficiency of EDDs. The results indicate that numerous divisions have scope for improvement in overall efficiency. Most of the utilities are inefficient due to their scale inefficiency rather than technical inefficiency. Further Slack analysis is carried out to formulate improvement directions for relatively inefficient divisions. Particular areas are identified, which are to be improved for overall efficiency enhancement through sensitivity analysis. Different alternatives for reorganization of the EDDs are explored in order to obtain most favorable and balanced scale. The result is envisaged to be instrumental to policy makers and managers to increase the operational efficiency of the EDDs leading to higher profitability of the state electricity board. [ABSTRACT FROM AUTHOR]

Application of Data Envelopment Analysis (DEA) as a Multicriterion Decision Making (MCDM) methodology is tested for Sri Ram Sagar Project, Andhra Pradesh, India to select the suitable irrigation planning alternative. Three different criterion functions of DEA, namely minimizing deviation variable Dj (Min Dj), minimizing maximum deviation (Minmax), and minimizing the sum of deviations Σ Dj (Minsum) are applied for the same DEA constraint set. These criterion functions are evaluated under the framework of Multi Objective Linear Programming (MOLP). Highest efficiency rated irrigation planning alternative is chosen to be the best for each of the above criterion functions. The results are compared with those obtained by discrete MCDM methods, PROMETHEE and EXPROM. It is found that ranks obtained by DEA are reasonably close to those obtained by the above mentioned MCDM methods, PROMETHEE and EXPROM. [ABSTRACT FROM AUTHOR]

In agricultural sector, farm efficiency evaluation is an important means of farm management. To evaluate the farm efficiency for effective allocation of agricultural resources, data envelopment analysis (DEA) is used. Commonly, a two-stage regression analysis is used to treat the obtained efficiency values on a set of explanatory variables. However, majority of the past studies explained the variables influencing efficiency rather than efficiency prediction. This paper aims to use DEA in combination with Machine learning approach to examine and predict the impact of environmental variables on farms' performance. Random forest (RF) algorithm has been employed, which is one of the most useful machine learning algorithms of recent times. First, DEA was used to evaluate efficiency of all the farms. Then the RF method was employed to examine the variables crucial in predicting farm performance. This multi-stage model was applied to 450 paddy producers in rural Eastern India. The results of the RF algorithm revealed that land ownership, Kisan Credit Card (KCC), and educational status were the most crucial variables which affected the performance of the paddy producers. With the identification of the major factors influencing agricultural production, new policy actions may be developed to assist the small farmers. Furthermore, this joint DEA-Machine-learning approach may help future researchers not only to investigate but also to predict the impact of the important environmental variables on farms' performance. • Agriculture utilizes huge resources in terms of mechanization, power, chemical fertilizers, seeds, irrigation, and labour. • Effective use of resources is crucial. DEA and cross-efficiency matrix were used for evaluating farm performance. • A machine learning based approach has been used to examine the impact of environmental variables on farms' performance. • Land ownership and agricultural credit availability impact the performance of smallholders in paddy cultivation. [ABSTRACT FROM AUTHOR]