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The purpose of this paper is to provide lines of action based on the results aimed at improving the future levels of productivity of the travel agencies in Alicante.

Estimates of output‐oriented Malmquist Productivity Indices and the values of their components will be calculated. Furthermore, in order to avoid problems that may arise from these results (which are based on finite samples that are sensitive to sampling variations) bootstrapping techniques (smoothed bootstrap) are used. Lastly, the Mann‐Whitney‐U test is used in order to accept or reject the influence that different factors have on productivity levels.

This study is a continuation of previous studies analysing the variation productivity of 22 travel agencies located in Alicante (Spain) over the period 2008‐2010. The results obtained from applying the afore‐mentioned methods do not provide substantially different conclusions to those obtained in previous studies. The authors can observe that the technological component (T) is the main factor, which these companies use to sustain their productivity levels and, thanks to this, the negative effects of a poor application of economies of scale are neutralised. In light of this, and for the whole of the period 2008‐2010, the inappropriate management of resources and the inability to obtain higher output levels played an insignificant role. Perhaps, a higher amount of information online about both economics and efficiency could be a good strategy to help these agencies to improve their results.

The impossibility of increasing the amount of data for carrying out the study, given the scarcity of statistical sources available and the reticence of the travel agencies to provide us with more information.

Although other studies have been previously carried out using Malmquist indices and bootstrapping techniques to analyse the productivity of travel agencies in other countries or cities, this is the first to be undertaken in Spain using the smoothed bootstrap method based on output‐oriented models, so as to examine this field of study from a new perspective.

When there are few treated clusters in a pure treatment or difference-in-differences setting, t tests based on a cluster-robust variance estimator can severely over-reject. Although procedures based on the wild cluster bootstrap often work well when the number of treated clusters is not too small, they can either over-reject or under-reject seriously when it is. In a previous paper, we showed that procedures based on randomization inference (RI) can work well in such cases. However, RI can be impractical when the number of possible randomizations is small. We propose a bootstrap-based alternative to RI, which mitigates the discrete nature of RI p values in the few-clusters case. We also compare it to two other procedures. None of them works perfectly when the number of clusters is very small, but they can work surprisingly well.

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.

The current paper's goal is to examine the productivity of the closed banking sector evidenced from Ethiopia. In addition, the inclusion of intangibles on productivity was examined in the current paper.

First, the standard Malmquist Productivity Index (MPI) was employed for 13 commercial banks for both stages. Second, by excluding the state-owned commercial bank, the analysis employed a bootstrapped MPI for the robust and comprehensive conclusion. Furthermore, from 2010 to 2019, the fixed effect Ordinary Least Square (OLS) regression with balanced panel data was used.

The standard MPI in both stages shows that the productivity of Ethiopian commercial banks is declining. The technological shock was the main reason for the loss. The catch-up in both stages scored above unity, mainly due to the pure efficiency change. Besides, when combined with tangible resources, the inclusion of resource-based view (RBV) proxy variables reduces technological shock regress and ultimately improves productivity change. The bootstrapped MPI also reveals that technological shock is the primary source of the productivity decline. However, efficiency change also contributes to the productivity decline based on this estimation.

Future research could examine the more extensive productivity analysis by considering the primary sources of data collections for resource-based variables.

According to the study's results, banking regulatory authorities and bank management, including the shareholders, should continue to invest in cutting-edge technology to improve the productivity of the banking sector.

This is the first comprehensive study of productivity for Ethiopian commercial banks based on the standard MPI, bootstrapped MPI, and OLS by incorporating all resources into the analysis.

This paper aims at contributing to the research on the role played by corruption in the health procurement by use non-parametric techniques to examine whether the efficient execution of Italian public contracts for healthcare infrastructures is affected by socio-economic variables (including the level of “environmental” corruption) in the area where the work is localised and by the institutional features of the contracting authority.

A data envelopment analysis (DEA) is applied to a sample of 405 contracts during the period 2000-2005. Smoothed bootstrap techniques to calculate confidence intervals for the estimated efficiency parameters along with different non-parametric tests and kernel density estimates are used.

Results show that “environmental” corruption negatively influences the performance of healthcare infrastructures. Furthermore, healthcare contracting authorities appear to be less efficient than other public bodies acting as procurers.

The paper highlights the role of environmental corruption in the provision of healthcare infrastructures.

The aim of this paper is to provide a new and simple approach to the empirical assessment of the internal efficiency of national regulatory authorities (NRAs) based on outcomes on regulated markets. Moreover, in order to illustrate this approach, the authors apply the proposed methodology to telecommunications.

First, this paper defines the production process of a NRA to identify correctly the inputs and the outputs of regulatory activity. Second, with reference to the telecommunications sector this paper provides estimates for the inputs and output identified. Third, by treating each NRA as a decision making unit (DMU) this paper carries out a data envelopment analysis (DEA) used in conjunction with a smoothed bootstrap technique.

This paper provides evidences that NRAs operating in new accession countries look more efficient in pursuing dynamic efficiency goals than (more experienced) NRAs operating in other European countries, while they are less efficient when the regulatory outcomes are measured in terms of retail efficiency.

The lack of data on NRAs' personnel and budget requires a cautious discussion of the results.

This paper enables one to get a better understanding of the effectiveness of different regulatory measures.

Once a measure of productive (internal) efficiency of a regulatory body has been obtained, it is then possible to embed this information in a more general analysis aimed at disentangling the effectiveness of regulatory measures from the ability (efficiency) of NRAs in putting them into force.

The evaluation of regulators' productive efficiency, as well as the identification of its determinants, has been almost completely neglected in the literature. With this paper we start to fill this gap.

The purpose of this paper is to estimate technical efficiency obtained from both data envelopment analysis (DEA) and stochastic frontier approaches using household survey data for rice farming households in Vietnam.

A bootstrap method is used to provide statistical precision of the DEA estimator. Technical efficiency is modeled as a function of household and production factors.

The results from the deterministic, semi‐parametric and parametric approaches indicate that among other things, technical efficiency is significantly influenced by primary education and regional factors. In addition, scale efficiency analysis shows that many farms in Vietnam are operating with less than optimal scale of operation.

The study is among the first that employ a bootstrap method and compare estimates from both Data Envelopment Analysis (DEA) and stochastic frontier approaches.

The purpose of this paper is to present a productivity measure for hospital services in Ontario.

The study applied the Malmquist Productivity Index (MPI) to assess the efficiency of hospital services in Ontario, Canada, over the period 2003‐2006. The MPI was decomposed into efficiency change and technological change. Efficiency change was further decomposed into pure efficiency change and scale efficiency change. A bootstrapping technique was also used to obtain confidence intervals for the output oriented MPI and its decompositions.

By estimating confidence intervals it was found that a large number of hospitals did not achieve significant progress in terms of productivity. By taking geometric means of estimates for all years it was observed that while overall productivity and efficiency of hospitals in Ontario declined during the study period, technological progress increased at a rate of 5.95 percent on average.

The present study helps to understand the productivity and technological change and change in technical efficiency in this vital sector of the economy, which is important for policy making identifying improvement opportunities in resource allocation. It was observed that Ontario hospitals did not improve the efficiency with which they employed their inputs (i.e. staff and supplies) over the study period; they did achieve gains through application of technologies.

The paper provides a thorough study on productivity growth of health care services in Ontario using a non‐parametric framework with bootstrapping. It also provides a robust measurement and analysis of the contributions of technology, size of operation and use of inputs to the performance of hospitals in Ontario.

The purpose of this paper is to introduce error ellipse into the bootstrap method to improve the reliability of small samples and the credibility of the S-N curve.

Based on the bootstrap method and the reliability of the original samples, two error ellipse models are proposed. The error ellipse model reasonably predicts that the discrete law of expanded virtual samples obeys two-dimensional normal distribution.

By comparing parameters obtained by the bootstrap method, improved bootstrap method (normal distribution) and error ellipse methods, it is found that the error ellipse method achieves the expansion of sampling range and shortens the confidence interval, which improves the accuracy of the estimation of parameters with small samples. Through case analysis, it is proved that the tangent error ellipse method is feasible, and the series of S-N curves is reasonable by the tangent error ellipse method.

The error ellipse methods can lay a technical foundation for life prediction of products and have a progressive significance for the quality evaluation of products.

Control charts are designed to be effective in detecting a shift in the distribution of a process. Typically, these charts assume that the data for these processes follow an approximately normal distribution or some known distribution. However, if a data-generating process has a large proportion of zeros, that is, the data is intermittent, then traditional control charts may not adequately monitor these processes. The purpose of this study is to examine proposed control chart methods designed for monitoring a process with intermittent data to determine if they have a sufficiently small percentage of false out-of-control signals. Forecasting techniques for slow-moving/intermittent product demand have been extensively explored as intermittent data is common to operational management applications (Syntetos & Boylan, 2001, 2005, 2011; Willemain, Smart, & Schwarz, 2004). Extensions and modifications of traditional forecasting models have been proposed to model intermittent or slow-moving demand, including the associated trends, correlated demand, seasonality and other characteristics (Altay, Litteral, & Rudisill, 2012). Croston’s (1972) method and its adaptations have been among the principal procedures used in these applications. This paper proposes adapting Croston’s methodology to design control charts, similar to Exponentially Weighted Moving Average (EWMA) control charts, to be effective in monitoring processes with intermittent data. A simulation study is conducted to assess the performance of these proposed control charts by evaluating their Average Run Lengths (ARLs), or equivalently, their percent of false positive signals.