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The purpose of this paper is to evaluate the functioning of the main Spanish and Portuguese containers ports to observe if they are operating below their production capabilities.

To achieve the above-mentioned objective, one possible method is to calculate the data envelopment analysis (DEA) efficiency, and the scale efficiency (SE) of targets, and in order to consider the variability across different samples, a bootstrap scheme has been applied.

The results showed that the DEA bootstrap-based approach can not only select a suitable unit which accords with a port’s actual input capabilities, but also provides a more accurate result. The bootstrapped results indicate that all ports do not need to develop future investments to expand port infrastructure.

The proposed DEA bootstrap-based approach provides useful implications in the robust measurement of port efficiency considering different samples. The study proves the usefulness of this approach as a decision-making tool in port efficiency.

This study is one of the first studies to apply bootstrap to measure port efficiency under the background of the Spain and Portugal case. In the first stage, two models of DEA have been used to obtain the pure technical, and the technical and SE, and both the input-oriented options: constant return scale and variable return scale. In the second stage, the bootstrap method has been applied in order to determine efficiency rankings of Iberian Peninsula container ports taking into consideration different samples. Confidence interval estimates of efficiency for each port are reported. This paper provides useful insights into the application of a DEA bootstrap-based approach as a modeling tool to aid decision making in measuring port efficiency.

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.

The purpose of this paper is to assess the performance of Greek fossil fuel‐fired power stations employing a data envelopment analysis (DEA) model combined with bootstrapping.

DEA is used to derive aggregate performance indicators using data on inputs and desirable and undesirable outputs for a sample of fossil fuel‐fired power stations. The statistical significance of the derived aggregate performance indicators is assessed via the bootstrapping approach.

The results suggest that the power stations in the sample are considerably more inefficient than revealed by the initial point estimates of inefficiency. Moreover, the non‐lignite‐fired stations of the sample are on an average more efficient than the lignite‐fired stations.

DEA represents a useful framework for exploring the current state to derive aggregate performance indicators of power stations, and moreover, the statistical properties of these metrics can be assessed via the bootstrapping approach.

The bootstrapping approach in DEA shows its superiority over DEA models that do not address the uncertainty surrounding point estimates. The DEA bootstrapping model used in this study to model environmental performance in the power station electricity production setting provides bias correction and confidence intervals for the point estimates and it is therefore more preferable.

The derivation of aggregate performance indicators of Greek fossil fuel‐fired power stations is an important addition to the existing literature on energy economics. The paper is also innovated in providing the statistical properties of the derived performance metrics.

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.

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.

DEA is a favored method to investigate the efficiency of institutions that provide educational services. We measure the efficiency of German universities especially from the students’ perspective. Since 1998, the Centrum für Hochschulentwicklung (CHE) evaluates German universities annually. The CHE ranking consists of three ranking groups for different indicators, but they do not create a hierarchy of the universities. Thus, a differentiation of the universities ranked in the same group is not possible. Based on the CHE data set, especially the surveys among students, we evaluate teaching performance from the students’ point of view using data envelopment analysis (DEA). DEA enables us to identify departments that – in the students’ perspective – are efficient in the sense that they provide high quality of education. As a method for performance evaluation, we apply a DEA bootstrap approach. By the use of this approach, we incorporate stochastic influences in the data and derive confidence intervals for the efficiency. Based on data generated by the bootstrap procedure, we are able to identify stochastic efficient departments. These universities serve as a benchmark to improve teaching performance.

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 purpose of this paper is to analyse the performance of the Indian non-life (general) insurance sector in terms of efficiency, productivity and returns-to-scale economies. In addition to this, it identifies the determinants of efficiency.

This study employs a two-stage data envelopment analysis (DEA) bootstrap approach to estimate the level and determinants of efficiency. In the first stage, the DEA bootstrap approach is employed to estimate bias-corrected efficiency scores. In the second stage, the truncated bootstrapped regression is used to identify the effect of firm-level characteristics on the efficiency of insurers. Moreover, the bootstrapped Malmquist index is used to examine the productivity growth over the observation period 2005–2016.

The bootstrapped DEA results show that the Indian non-life insurance sector is moderately technical, scale, cost and allocative efficient, and there is a large opportunity for improvement. Moreover, the results reveal that the public insurers are more cost efficient than the private insurers. It is also evident that all the insurers irrespective of size and ownership type are operating under increasing returns to scale. Malmquist index results divulge an improvement in productivity of insurers, which is attributable to the employment of the best available technology. Bootstrapped DEA and bootstrapped Malmquist index results also show that the global financial crisis of 2008 has not severely affected the efficiency and productivity of the Indian non-life insurance sector. The truncated regression results spell that size and reinsurance have a statistically significant negative relationship with efficiency. It also shows a statistically significant positive age–efficiency relationship.

The results hold practical implications for the regulators, policy makers, practitioners and decision makers of the Indian non-life insurance companies.

This study is the first of its kind that comprehensively investigates different types of robust efficiency measures, determinants of efficiency, productivity growth and returns-to-scale economies in the Indian non-life insurance market for an extended time period.

Data Envelopment Analysis (DEA) is a nonparametric mathematical programming technique used to measure the relative efficiency of the production organization's operations. This paper presents the theoretical measures of the railway systems, along with the bootstrap DEA analysis. A DEA model is applied to evaluate the relative efficiency of railway operations of 29 UIC (Union Internationale des Chemins de fer) countries, based on the data obtained from the International UIC publications. The bootstrap DEA analysis provides information (bias estimates) on the sensitivity of the DEA efficiency index to the sampling variations. The model results are analyzed and evaluated in terms of their relative operational performance efficiency. The model results facilitate an organization's decision-making by providing valuable information.

This study aims to propose a hotel life cycle model and applies this model to examine the development of international tourist hotels (ITHs) in Taipei.

In this study, a two-stage approach is proposed to examine the life cycles of 20 ITHs in Taipei. First, we evaluate the overall and departmental efficiencies by using a two-layer bootstrap data envelopment analysis (DEA) model. Second, we divide the phases of the hotel life cycle by incorporating two objective indicators, namely, the average efficiency change rate (AECR) and the annual average efficiency (AE).

The results show not only that the efficiency scores derived from the bootstrap DEA model could help assess the performance of individual ITHs but also that the resulting AECR and AE could help to objectively classify the development of the hotels under study into the following phases: initial, growth, maturity and recession and regeneration phases.

The method proposed in, and the results obtained from, this study can provide the stakeholders of the ITHs in Taipei with an alternative to the existing subjective enterprise life cycle (ELC) model for identifying these ITHs’ stages of development using quantitative and objective criteria.

Existing hotel management research rarely focuses on hotel life cycle analysis, likely due to the adoption of subjective criteria by the conventional ELC model, which limits the practical application of the research. To improve on the conventional ELC model, our proposed quantitative approach involves dividing the hotel life cycle by employing two objective indicators and then empirically presenting the results.