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The measurement capabilities of the data envelopment analysis (DEA) models are used to train the artificial neural network (ANN) models for the best performance modeling of the sawmills in Ontario. The bootstrap DEA models measure robust technical efficiency scores and have benchmarking abilities, whereas the ANN models use abstract learning from a limited set of information and provide the predictive power.

The complementary modeling approaches of the DEA and the ANN provide an adaptive decision support tool for each sawmill.

The trained ANN models demonstrate promising results in predicting the relative efficiency scores and the optimal combination of the inputs and the outputs for three categories (large, medium and small) of sawmills in Ontario. The average absolute error in predicting the relative efficiency scores varies from 0.01 to 0.04, and the predicted optimal combination of the inputs (roundwood and employees) and the output (lumber) demonstrate that a large percentage of the sawmills shows less than 10% error in the prediction results.

The purpose of this study is to develop an integrated DEA-ANN model that can help in the continuous improvement and performance evaluations of the forest industry working under uncertain business environment.

This work aims to analyse the relationship between public health efficiency and well-being considering a panel of 102 Italian provinces from 2000 to 2016 and evaluates if there are omitted variable biases and endogeneity biases and also evaluates if there are heterogeneous effects among provinces with different income levels.

We use a multi-input and output bootstrap data envelopment analysis to assess public health efficiency. Then, we measure well-being indices using the min-max linear scaling transformation technique. A two-stage least squares model is used to identify the causal effect of improving public health efficiency on well-being to account for time-invariant heterogeneity, omitted variable bias and endogeneity bias.

After controlling for important economic factors, the results show a significant effect of an accountable and efficient public health system on well-being. Those effects are concentrated in the North, the most economically, geographically and environmentally advantageous areas.

The use of the sample mean, probably the oldest and most used method for aggregating the indicators, could be affected by variable compensation, with consequent misleading results in the process of constructing the well-being index. Another limitation is the use of lagged values of the main predictor as an instrument in the instrumental variables setting because it could lead to information loss. Finally, the availability of data over a long period of time.

The findings could help policymakers adopt measures to strengthen the public health system, encourage private providers and inspire countries worldwide.

These results draw the attention of local authorities, who play an important role in designing and implementing policies to stimulate local public health efficiency, which puts individuals in the conditions of achieving overall well-being in their communities.

For the first time in Italy, a panel of well-being indices was constructed by developing new methodologies based on microeconomic theory. Furthermore, for the first time, the assessment of the relationship between public health efficiency and well-being is carried out using a panel of 102 Italian provinces.

This paper aims to estimate the technical efficiency (TE) and its determinants in the handloom micro-enterprises of Assam (India) using the double-bootstrap data envelopment analysis (DEA) technique.

The study uses a random sample of 340 handloom micro-entrepreneurs from the three districts of Assam in India. The double-bootstrap DEA was used to calculate the TE and its determinants.

The findings reveal that handloom enterprises are only 60% technically efficient, suggesting room for improvement. The bootstrap truncated regression results demonstrate that the handloom firms’ TE is influenced by both entrepreneur-specific and firm-specific factors.

The implication lies in the fact that the management of a firm may figure out how much it can reduce its input utilization to produce the existing amount of output so that it can move along the TE ladder. Moreover, it can crosscheck the factors to weed out inefficiency.

This paper has made two significant contributions to the extant literature. Firstly, it fills the gap by way of accounting the TE of handloom micro-enterprises, which has so far been neglected. Secondly, it used the bootstrap approach, which otherwise is very rare in the discourse on the Indian manufacturing industry, let alone in the micro, small and medium scale enterprises sector.

The main purpose of this paper is to determine the bias-corrected efficiencies and rankings of the selected hotels and restaurants (H&Rs) in India.

The data for the Indian H&R sector are collected from the Prowess database. The bootstrap data envelopment analysis (DEA) based on a constant return to scale (CRS), variable return to scale-input oriented (VRS-IP) and variable return to scale-output oriented (VRS-OP) are applied on H&Rs to obtain the bias-corrected efficiencies.

It is found that relative efficiencies using basic DEA methods of all the 45 H&Rs of India are overestimated. These efficiencies are corrected using bias correction through bootstrap DEA methods. The bounds for the efficiencies of each H&R are computed using all the adopted methods. All H&Rs are ranked using bias-corrected efficiencies, and the linear trend between ranks suggests that the H&Rs are ranked almost similarly by all the adopted methods.

To improve efficiency, Indian H&R companies must rethink their personnel needs by enhancing their workforce management capabilities. The government needs to extend more support to this sector by introducing a liberal legislation framework and supporting infrastructure policies.

There is a paucity of studies on H&Rs in India. The current study focused on measuring bias-corrected efficiencies of the selected H&Rs of India. This study is one of the few initiatives to explore bias-corrected efficiencies extensively using the bootstrap DEA method.

This paper investigates the impact of credit risk shocks on the evolution of banking efficiency in China.

This paper introduces credit risk as a bad output into a bootstrap data envelopment analysis (bootstrap-DEA) model.

During a credit risk shock, the efficiency levels of both state-owned commercial banks and joint-stock commercial banks are significantly higher than those of urban/rural commercial banks, and the efficiency differences between these banks further increase during a period of economic slowdown. This paper also finds that the efficiencies of joint-stock commercial banks are the most sensitive to credit risk shocks; these banks are the first to be affected and the first to completely adjust. However, urban/rural commercial banks adjust very slowly.

Most scholars still use the traditional DEA method to estimate China's banking efficiency. The bootstrap-DEA method is clearly able to obtain a more exact estimated efficiency score. In fact, in comparison with the bootstrap-DEA model, we found that the traditional DEA method overestimates China's banking efficiency, and this is an especially serious problem for those banks that have a high efficiency score.

Purpose: The present study investigates how the performance of Greek bank branching varies when the external environment causes dramatic changes that are reflected in recession and capital control effects.

Design/Methodology: A unique dataset of accounting Profit and Loss statements of retail branches of a systemic Greek commercial bank, closely supervised by the European Central Bank (ECB), is utilized. A profit bootstrap Data Envelopment Analysis (DEA) model is selected to measure the bank branch efficiency. The derived efficiency estimates are analyzed through a second-stage panel data regression analysis against a set of efficiency drivers related to branch profitability, diversification of income, branch size, and branch activity.

Findings: The results indicate that recession negatively affects branch efficiency in the short and long run. The occurrence of recession significantly intensifies the efficiency premium of branch profitability, reduces the efficiency premium of diversification of income (i.e., a negative efficiency effect is recorded during the early recession period), while mitigating the generally negative efficiency effect of branch size. The analysis of efficiency effects from the deep recession period that encompasses capital controls reveals the importance of diversification of income for the improvement of profit efficiency at bank branch level.

Originality/Value: This is the first branch banking study that explores branch efficiency alteration and the dynamic of branch efficiency drivers when the economy suddenly enters recession and afterwards when conditions are becoming extremely difficult and consequently capital controls are imposed on the economy.

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.

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.

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.

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.

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.

The study attempts to examine the bias-adjusted financial and operational efficiency estimates of microfinance institutions (MFIs) operating in the Eastern Europe and Central Asia (ECA) region during the financial year 2017–2018. In addition, the study also identifies the responsible factors determining the financial and operational performances of MFIs operating in the ECA region.

The study employs two-stage bootstrap data envelopment analysis (DEA). In the first stage, the authors incorporate the bootstrap procedure in the DEA framework as suggested by Simar and Wilson (2000) to estimate the bias-corrected efficiency scores of 67 sample MFIs. In order to identify the drivers of efficiency level, the study deploys the bootstrap truncated regression model following the Simar and Wilson (2007) guidelines in the second stage of analysis.

The authors note from the empirical results that MFIs operating in the ECA region are relatively more financially efficient (0.588) than socially efficient (0.496). However, none of the MFIs were found to be operating at best-practice frontier while considering the bias-adjusted efficiency estimates. Further, the results of second stage of analysis confirm that corporate governance, that is, board size has positive and statistically significant impact on MFIs’ performances. In addition, the bad credit quality deteriorates both financial revenue and operational efficiency. Moreover, the MFIs’ size, profit status and debt-to-equity ratio were also found to be statistically significant to determine the operational and financial efficiency of MFIs in the ECA region.

The study provides the robust efficiency estimates and factors responsible to determine the financial and operational efficiency of MFIs operating in the ECA region. Further, the empirical results of the study provide the inputs and further direction to the policymakers, regulators, practitioners and managers in framing the policy and optimal operating strategies for ECA MFIs industry.

The study extends the DEA analysis by incorporating the bootstrap procedure in DEA model to estimate the bias-adjusted efficiency scores which are more reliable and robust. In addition, bootstrap truncated regression has been applied to identify the drivers of efficiency. Moreover, in the literature there is no single study which has deployed the double bootstrap DEA framework to examine the financial and operational efficiency estimates and its drivers.

In a world where sustainability is a major aim at all socioeconomic levels, social entrepreneurship plays an important role in achieving the goals that have been set. The purpose of this study is to broaden the knowledge of social start-ups, social incubators and founding teams, highlighting the value of each one. The aim is to use quantitative analysis to determine the possible link between social incubators and social start-up success and identify the founding team profile of social start-ups from each sector according to a sector-based ranking.

Bootstrap data envelopment analysis (DEA) was used to calculate the efficiency of social incubators and social start-ups and thus quantify the impact, in terms of increased efficiency, of social incubators on social start-ups. Then, using cross-efficiency methodology, a synthetic index was used to analyse the founding team profile of social start-ups. The study is based on primary data from a survey of Spanish social incubators and social start-ups.

The study provides strong quantitative evidence of the positive effect of social incubators on the development of social start-ups. The size of this effect exceeds the know-how of start-ups. In terms of efficiency gains, this research quantifies the impact of social incubators on this entrepreneurial ecosystem. This impact exceeds 35%. The study also shows that the strongest social start-ups are in the food and information and communication technology (ICT) sectors. The founding teams in these cases have a strong business background, have a high educational level, receive subsidies and express a desire to retain control of the company.

There is an extensive literature dedicated to the analysis of the behaviour and characteristics of traditional incubators, accelerators and start-ups. However, despite the recent rise of social entrepreneurship, studies of social incubators and social start-ups remain scarce. This study provides two novel findings. (1) It shows the importance of creating a social start-up in a context where it receives support throughout all its development stages, providing quantitative insight into the contribution of social incubators and social start-ups. (2) It reveals the profile of founding teams in the highest-ranked business sectors.

The purpose of this paper is to estimate advertising efficiency in the Spanish beer industry and to analyse the effects of several environmental variables and brand portfolio scope on advertising efficiency scores.

A two-stage double bootstrap procedure is used. In the first stage, advertising efficiency is estimated using a bootstrapped data envelopment analysis on a multiple input-output model of advertising. In the second stage, a bootstrapped truncated regression model is estimated to identify the determinants of advertising efficiency. Both stages are estimated simultaneously. The empirical application is carried out on a sample of Spanish brewers between 2007 and 2014.

Results show low advertising efficiency scores and highlight the effects that environment and brand portfolio scope have on these estimates.

For the first time, this paper analyses the effect of environmental variables and the brand portfolio scope on advertising efficiency in the beer industry.