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Data envelopment analysis (DEA) is based on the production possibility set that involves the process of converting resources or inputs to outputs. Accordingly, most DEA models include endogenous variables and need an additional step to find the influence of exogenous variables on the process. The purpose of this paper is to examine the relationship between the efficiency scores of DEA and the exogenous variables using truncated regression analysis with double bootstrapping along with two additional methods.

First, the authors employ DEA for benchmarking the comparative efficiency of the health care institutes. Next, the authors run and compare truncated, ordinary least square (OLS) and Tobit regression analysis using the double bootstrapping algorithm for finding the influence of exogenous variables on the efficiency of the health care institutes.

The authors confirmed the amount of bias for the Tobit and OLS regression models, which was caused by serially correlated errors. Accordingly, the authors chose results from the truncated regression model with double bootstrapping for examining the influence of exogenous or environment variables on the efficiency scores.

The study includes cross-sectional data on health care institutes in the state of Washington, USA. Collecting data in various states or regions over time is left for future studies.

In this study, three exogenous variables such as Medicaid revenues, locations of health care institutes and ownership types are significant for explaining the relationship between the efficiency scores and a group of the exogenous variables. Managers and policy makers need to pay attention to these variables along with endogenous variables for promoting the sustainability of the health care institutes.

The study demonstrates the usefulness of the truncated regression analysis with double bootstrapping for confirming the relationship between the efficiency scores of DEA and a group of exogenous variables, which is rare in the DEA literature.

Measuring the success of microfinance institutions (MFIs) using a single efficiency value and then exploring its determining factors might be misleading. Hence, this study decomposed the efficiency measure into three divisions, namely operational, financial sustainability and social outreach. Subsequently, the authors identified factors affecting these efficiencies in the second stage regression analysis.

This study employed the network data envelopment analysis approach to evaluate each division of efficiency of 90 MFIs from 2013 to 2018 and used second-stage regression techniques (Tobit and Truncated) to examine the effect of institutional factors.

The authors’ efficiency analysis revealed that financial sustainability and social outreach were responsible for the low overall efficiency. The second stage analysis revealed the negative influence of institutional factors such as efficiency wage (particularly among small MFIs) on financial sustainability, social outreach and overall efficiencies. Staff turnover reduced operational, financial and overall efficiencies, particularly for large MFIs. The presence of female board members and staff improved the efficiency of MFIs, thus highlighting the pivotal role of women in the success of MFIs. Besides, the effects of regional location of MFIs, regulation and legal status on efficiencies were further discussed.

The study has uniquely evaluated three different types of efficiency in MFIs and employed conventional techniques for the second-stage regression to identify the determinants of efficiency. The findings will enable managers to make appropriate decisions to enhance their organisational efficiency.

The purpose of this study is to disentangle the drivers of adoption of procurement strategies in situations where small agri-food firms deal with constrained organizational choices. More specifically, the authors investigate the role of transaction costs, capabilities and networks in the definition of feasible “make-or-buy” choices in emerging wine regions.

This article analyzes a unique dataset of small wineries from five US states: Illinois, Michigan, Missouri, New York and Vermont. The reported results derive from both a hurdle model (i.e. a probit model and a truncated regression model) and a tobit model.

The results suggest the importance of trust as a replacement for formal governance structures whenever small firms deal with highly constrained sets of organizational choices. On the other hand, the level of dependence on a limited mix of winegrape varieties and the perception that these varieties are fundamental in building legitimacy help to explain higher rates of vertical integration.

This study is important because it sheds light on organizational constraints that affect millions of farmers across the globe. The study of “make-or-buy” decisions in agri-food supply chains has mostly relied on the implicit assumption that all organizational choices are available to every firm. Nevertheless, limited capabilities and the participation in low-density networks may constrain the ability of a firm to adopt a governance mechanism. Stated organizational preferences and actual organizational choices may thus differ.

This paper aims to focus on assessing the effect which factors that cannot be controlled by the public administrator on the efficiency of municipalities on rendering sewerage services.

The analyses performed to date focus mainly on technical efficiency due to the low importance of public outputs value. For it, a four‐stage process was followed in order to observe the overall effect produced by operation size and environmental conditions on efficiency index estimated by data envelopment analysis.

The results obtained show that operation size and environmental conditions have a significant impact on efficiency indices and on reducing the variability between indices; however, the type of management proprietorship, public vs private, does not impact the degree of efficiency.

As regards decisions stemming from intermunicipal comparison, it can be seen that a drastic decrease occurs in input reduction and output increase percentages geared towards making the municipality under observation efficient in relation to those initially detected, without taking into consideration factors which cannot be controlled by the administrator.

This paper is pioneering in knowing the intensity of the effect of certain factors on the benchmarking process.

First, this study assesses firms’ efficiency of transforming intellectual capital (IC) components into firm performance. Second, this study examines (1) cubic S-curve relationship between board independence and IC efficiency and (2) how firm size moderates the cubic S-curve relationship.

This study employs a stochastic nonparametric envelopment of data (StoNED) framework to estimate IC efficiency, which is derived from the estimation process of transforming structural, relational and human capitals into accounting- and market-based performance indicators. This study conducts regression analyses on 1,104 firm-year observations of Taiwanese semiconductor firms over the period of 2011–2018.

StoNED results suggest that sample firms' IC efficiency can be relatively improved by approximately 80%. Regression results indicate that a cubic S-curve relationship between board independence and IC efficiency exists, and firm size moderates the nonlinear effects.

Overall, this study highlights the importance of examining the nonlinear effect of board independence on IC efficiency from the perspective of agency theory, and the moderating effect from firm size, which may suggest availability of resources from the resource-based view of the firm.

This study contributes to the literature through the innovative application of an efficiency-based tool for evaluating IC efficiency. The cubic S-curve relationship between board independence and IC efficiency also points to the policy concerning the appropriate number of independent directors on board.

The purpose of this paper is to examine the factors influencing the choice and the amount of cocoa beans sold to public and private licensed buying companies in the Western North of Ghana.

The study was conducted in the Western North of Ghana. Cragg’s Double Hurdle model was used to examine the factors influencing the choice of licensed buying company (LBC) whereas Kendall’s coefficient of concordance was employed in analysis of the marketing challenges.

The results showed that non-price incentives determine the choice and the amount cocoa beans sold to an LBC. Specifically, education, years of experience in cocoa farming and timely payment of sold cocoa beans positively influence the choice of public LBC. However, off farm job participation, provision of credit facilities and extension services affect the choice of private LBC as marketing outlet. Perceived low price of cocoa beans, inadequate credit support, and adjustment of scales used in weighing of cocoa beans were identified as the most important challenges confronting farmers.

The research provides important information on non-price incentives influencing cocoa marketing outlet decision as well as the marketing challenges faced by farmers which can contribute to improving internal marketing efficiency of the cocoa industry in Ghana. Besides, this study also extends the frontiers in terms of methodological approach by adopting Cragg’s Double Hurdle Model in addressing the research question.

The research provides important information on non-price incentives influencing cocoa marketing outlet decision as well as the marketing challenges faced by farmers which can contribute to improving internal marketing efficiency of the cocoa industry in Ghana. Besides, this study also extends the frontiers in terms of methodological approach by adopting Cragg’s Double Hurdle Model in addressing the research question.

Research on strategic choices available to the firm are often modeled as a limited number of possible decision outcomes and leads to a discrete limited dependent variable. A limited dependent variable can also arise when values of a continuous dependent variable are partially or wholly unobserved. This chapter discusses the methodological issues associated with such phenomena and the appropriate statistical methods developed to allow for consistent and efficient estimation of models that involve a limited dependent variable. The chapter also provides a road map for selecting the appropriate statistical technique and it offers guidelines for consistent interpretation and reporting of the statistical results.

Although handloom is a significant source of livelihood for millions of people in India, it performs poorly compared to other sectors of the economy, which may be the root of technical inefficiency. Until now, to measure technical efficiency, no studies have been carried out; therefore, the purpose of this study is to estimate the technical efficiency in the handloom micro-enterprises in India.

This study includes 427 handloom micro-entrepreneurs from the Indian state of Assam. Using bootstrap truncated regression, the data envelopment analysis (DEA) was used to calculate the technical efficiency and identify the factors responsible for inefficiency.

The findings of this study reveal that handloom enterprises are 75% pure technically efficient, suggesting room for input reduction. The bootstrap truncated regression results show that education, prior experience, modern technology, ICT, bank loan, training, gender and location significantly influence the technical efficiency of handloom enterprises.

Despite recent advances in the DEA method, this study used a traditional form of DEA. This study used only one output and a limited set of inputs. Better results could have been obtained by expanding the number of inputs and output. Finally, the data for this study has been obtained from a very narrow geographic area. The production practices of the handloom enterprises in other parts of the region and other states might vary considerably.

Technical efficiency measurement has management implications for businesses because it allows entrepreneurs to determine how much less input is required to produce the same output. A meticulous analysis can pinpoint the causes of inefficiency.

This paper aims to make two significant contributions to the extant literature. First, to the best of the authors’ knowledge, no published document has analyzed the technical efficiency of handloom micro-enterprises anywhere in the world. The authors fill this void by systematically analyzing the technical efficiency of the handloom industry in Assam.

The purpose of this paper is to provide a total factor productivity index for the African banking industry. It also investigates the impact of some internal and external determinants affecting bank productivity.

The biennial Malmquist productivity index and various regression models (ordinary least squares, Tobit and truncated bootstrapped regression) are employed in analyzing data from 120 banks in 24 African countries from 2007 to 2012.

The results indicate a general decline in productivity of banks in Africa, largely due to inadequate technological progress. State banks are found to be more productive than foreign and private banks. The regression analyses showed that non-executive directors, leverage, management quality, credit risk, competition and exchange rate have significant impact on bank productivity, but ownership and CEO-duality do not.

The results have implications for management of banks, governments and regulators. It shows the need for policy and investments that improve state-of-the art technology. The findings also seem to suggest poor management practices in input usage, especially in operational management, as well as costs emanating from non-interest sources. Bank managers need to address these deficiencies to improve productivity in African banking markets.

A major contribution of this paper is the productivity index provided for the African banking industry. This study is also the first to apply the biennial Malmquist to analyze productivity in the African banking industry.

The purpose of this study is to benchmark the performance of state-owned coal-fired power plants (CFPPs) and test whether plant-specific knowledge in terms of quality of coal, size, age and make of plant contribute to an improvement in plant efficiency.

The methodology that is utilized in the study follows a nonparametric approach of data envelopment analysis (DEA) with sensitivity analysis and Tobit regression model. The input-oriented DEA models are applied to evaluate the overall, pure technical and scale efficiencies of the CFPPs. Further, slack analysis is conducted to identify modes to improve the efficiency of the inefficient plants. Sensitivity analysis based on peer count and the removal of variables is carried out to identify the benchmark power plant. Through Tobit and bootstrap-truncated regression model, the paper investigates whether a plant's specific knowledge influences its efficiency.

The DEA analysis demonstrates that nine plants are technically purely efficient.The slack analysis reveals that reducing the consumption of oil is the most effective way to improve the efficiency of inefficient plants. Mattur plant is the benchmark for most of the inefficient plants. Regression result suggests that quality of coal and size of plant significantly affect the inefficiency of the sample plants. Bharat Heavy Electrical Limited MAKE plant achieved higher efficiency in comparison to mixed MAKE.

This study is one of the few published studies that benchmark the performance of state-owned CFPPs. This research carried out taking some new uncontrollable parameters of power plant utilities of India. Research work also identifies the possible causes of inefficiency and provides measures to improve the efficiency of the inefficient power plant.