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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 concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.

The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.

The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.

The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.

This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.

Decree Law No. 663 introduced a decentralized organizational structure and administration pertaining to Turkish public hospitals in November 2011. This study aims to explore the effects of the public hospital unions (PHUs), which were a result of Decree Law No. 663, on the efficiency and productivity of public hospitals.

Data envelopment analysis (DEA) and DEA-based Malmquist total factor productivity (TFP) index were used from 2011 to 2016. Raw materials and supply expenses, salaries and fringe benefits, other service costs, general administrative expenses, total number of beds, number of specialists, number of residents, number of general practitioners, number of nurses and midwives and other medical officials were used as input variables. Working capital turnover, number of inpatients, number of outpatients and number of surgical operations for Groups A, B and C were used as output variables.

According to the DEA scores, the percentage of efficient hospitals showed a declining trend from 2011 to 2016. The TFP results also showed a decreasing trend from 2011 to 2016.

Providing administrative and financial autonomy to public hospital managers may cause efficiency and productivity losses, which is contrary to expectations.

This study is the first to reveal the impact of decentralization of public healthcare providers on their performance levels in Turkey.

This paper aims to investigate the performance of OECD countries' long-term productivity during the period of 1975–2018.

This study employed different approaches to evaluate how efficiency scores vary with changes in inputs and outputs: Data Envelopment Analysis (CRS, VRS and FDH), TOPSIS and TOPSIS of these scores.

The findings suggest that, during the period of this study, countries with higher freedom of religion and with Presidential democracy regimes are positively associated with higher productivity.

To the best of the authors’ knowledge, this is the first study that uses efficiency models to assess the productivity levels of OECD countries based on several contextual variables that can potentially affect it.

The aim of this study is to estimate the technical efficiency of the massive and economically important crop of rice in Ecuador, and then conduct a comparison between groups of farmers with and without insurance.

The authors use an input-oriented data envelopment analysis approach (DEA) to estimate technical efficiency scores. The DEA is combined with the double bootstrap approach in Simar and Wilson (2007) to study factors that may affect technical efficiency. This method overcomes the traditional two-stage DEA approach frequently used in the efficiency literature. The authors thus research the role of insurance on rice efficiency production using this technique and sizeable field-level survey data from 376 rice farmers distributed in five provinces during the 2019 winter cycle in Ecuador.

Most uninsured rice farmers operate with increasing returns to scale, which means that farms improve their resource use efficiency by increasing their size. However, since scale efficiencies are relatively high, it appears that inefficiencies are explained by inadequate input use. Also, the authors find evidence that insured farmers have a negative relationship with technical efficiency in rice production. In other results, when exploring the influence of additional variables on efficiency, the authors find that parameters related to transplanting, high education, farm size and some locations are positive and statistically significant.

The results of this work are relevant for policymakers interested in evaluating technology performance, risk management instruments and farm efficiency in an industry in a developing country such as rice production in Ecuador.

This paper is the first attempt to estimate farm-level technical efficiency employing the double bootstrap approach to assess the efficiency and its determinants of Ecuadorian rice producers.

This paper aims to measure and unveil the relationship between customer satisfaction and efficiency levels in the most relevant Brazilian airports.

The authors utilize a two-stage network DEA (data envelopment analysis) and AHP (analytic hierarchy process) model as the cornerstones of the study. The first stage of the network productive structure focuses on examining the infrastructure efficiency of the selected airports, while the second stage assesses their business efficiency.

Although the results indicate that infrastructure and business efficiency levels are heterogeneous and widely dispersed across airports, controlling the regression results with different contextual variables suggests that the impact of efficiency levels on customer satisfaction is mediated by a set of socio-economic and demographic (endogenous) and regulatory (exogenous) variables. Furthermore, encouraging investment in airports is necessary to achieve higher infrastructural efficiency and scale efficiency, thereby improving customer satisfaction.

There is a scarcity of studies examining the relationships among customer satisfaction, privatization and airport efficiency, particularly in developing countries like Brazil.

A brief review of earlier studies on the productivity scenario of Indian industry shows that most of the studies analysed are confined to either parametric approach or growth accounting approach of measuring productivity. At the same time, the few studies based on the non-parametric [namely, Malmquist productivity index (MPI)] overlook the returns to scale conditions as well as the bias involved in the estimation of distance functions. Given this backdrop, this study aims to provide a robust measure of productivity, which considers the returns to scale assumptions and correct for the bias involved in the estimation of productivity.

This study empirically tests for the returns to scale that exists in the chemical and chemical products industry in India. The test result suggests that Ray and Desli (1997) approach of MPI is the appropriate one for the present context. Initially, the conventional Ray and Desli (1997) estimation and decomposition of MPI for the period 2001 to 2017 is being used. Subsequently, to correct for the bias in the estimation of efficiency scores used for the estimation of MPI, the bootstrapping algorithm of Simar and Wilson (2007) has been extended into the context of MPI estimation.

The results from the conventional Malmquist productivity estimates testifies to an improvement of total factor productivity (TFP) in seven out of 16 years under consideration. On the contrary, TFP growth is recorded only in the four years throughout the period after the bias correction. A greater discrepancy between the two measures has been found in the case of scale change factor component of MPI.

The technical change (TC) component positively influences TFP, whereas scale change factor (SCF) deteriorates the TFP condition of this industry. It will be appropriate for these firms to identify and operate under an optimal scale of operation, along with reaping the benefits of technological change. From a methodological perspective, researchers should consider the potential bias that arise in estimation of TFP and use a larger sample whenever possible.

This paper brings in a new perspective to the existing literature on industrial productivity. As against earlier studies, this study empirically tests the returns to scale of the sector under consideration and uses the most appropriate approach to measure productivity. The effect of sampling bias on TFP and its components is analysed.

This study aims to determine how much company participation in a type of cluster affects its economic performance.

This study includes companies operating in seven industries (automotive, engineering, textiles, information technology (IT) services, furniture, packaging and nanotechnology) in the Czech Republic. The companies are divided into three groups: members of institutionalized cluster, operating in the same region (natural clusters) and operating in other regions. Data envelopment window analysis is used to measure their performance for 2009–2019.

Results show that the effect of clustering differs among industries. Companies in three industries (automotive, engineering, nanotechnology) reveal a positive impact of the cluster initiative on performance growth. Two industries (textile, packaging) with companies operating in a natural cluster show better performance than those in an institutionalized cluster. Moreover, the IT services and the furniture industries show no positive effect of clustering on corporate performance.

This research includes 686 companies from seven industries and monitored for 11 years. On the one hand, the sample includes a relatively high number of companies overall; but on the other hand, the sample is relatively small, especially for nonclustered companies. The reason is the lack of available financial statements for small companies.

From the perspective of practical cluster policy, the authors can recommend that monitoring the performance of member companies in clusters must be one of the criteria for evaluating the success of a cluster, such as cluster initiatives.

This study distinguishes between long-standing natural clusters in a given industry and institutionalized ones that have emerged because of a top-down initiative. An original database is created for clustered and nonclustered companies in seven industries, covering the entire Czech Republic.

This study clarifies the operational performance of fashion companies during the coronavirus pandemic. Meanwhile, improvement strategies have been provided in the post-pandemic era.

The static and dynamic perspectives were combined to comprehensively analyze the operational performance of fashion companies before, during and after the COVID-19 outbreak. A comparative analysis among five representative countries was conducted to achieve global conclusions. Additionally, data envelopment analysis (DEA) theory and various DEA models were employed for the analysis.

The fashion industry has not achieved overall effectiveness. American companies have the best operational performance, followed by European and Chinese companies. In contrast, the impact of the pandemic on American companies was severe, whereas Chinese and European companies showed operational resilience. In addition, the pandemic had a devastating influence on the global fashion industry. This resulted in a decline in total factor productivity, and the main reason was technological regress. Furthermore, labor redundancy is a critical issue for the fashion industry in the post-pandemic era, even if it shows a decrease because of the pandemic.

The existing theory on the fashion industry during the pandemic was improved by expanding the time and geographical dimensions and integrating the advantages of various DEA models. Scientific improvement strategies were presented in the post-pandemic era with application value.

This paper aims to examine how the diversity of educational levels within bank boards influences the efficiency and stability of banks operating in the Middle East and North Africa (MENA) region. Unlike previous studies, this analysis also investigates the role of board gender diversity in moderating the relationship between board educational level diversity and bank efficiency and financial stability in MENA.

In this study, a sample of 77 banks in the MENA region spanning the years 2011 to 2018 is used. The relationship between the presence of highly educated directors on the board, bank efficiency and stability is assessed using the ordinary least squares method. Additionally, the authors use the Generalized Method of Moments technique to correct endogeneity problem.

This study establishes a positive association between the presence of directors with advanced educational backgrounds on bank boards and bank efficiency and stability. Furthermore, the inclusion of women on the board strengthens this relationship.

These findings have important implications for policymakers and regulators in the MENA region, suggesting that promoting diversity policies that encourage the participation of highly educated directors on bank boards can contribute to enhanced efficiency and financial stability. Policymakers may also consider implementing quotas or guidelines to improve gender diversity in board appointments, thereby fostering bank performance in the region.

This study stands out for its innovation and distinctiveness, as it delves into the connection between board educational level diversity and bank efficiency in the MENA region. Notably, it surpasses previous research by investigating the moderating role of board gender diversity, thus offering valuable insights into the complex interplay between these two facets of board diversity. This contribution enriches the existing literature by providing novel perspectives on board composition dynamics and its influence on bank efficiency and stability.