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The paper's aim is to assess performance of firms in the UK retail sector.

Economic efficiencies of 41 retail companies working in the UK between 2000 and 2005 are examined in this study using three related methodologies: data envelopment analysis (DEA), Malmquist productivity index (MPI), a bootstrapped Tobit regression model. DEA is used to calculate technical and scale efficiencies of companies. Two outputs (turnover, profit before taxation) and three inputs (total assets, shareholders funds, and number of employees) are employed for the efficiency measurement. MPI is used to analyze the patterns of efficiency change over the six year period 2000‐2005. DEA efficiencies are then used to test important hypotheses on the impact of environmental variables, namely head office location, type of ownership, years of incorporation, legal form and retail characteristic, on the functioning of the UK retail sector using bootstrapped Tobit regression.

DEA analysis has shown that only ten retail companies are considered as efficient under CRS assumption, and 16 firms under VRS assumption in 2005. MPI results have indicated that about 50 percent of retail companies have registered progress in terms of MPI during 2000 and 2005. Twenty out of 41 retail companies have adopted advanced and efficient retailing technologies during this period. Three environmental variables, namely the type of ownership, legal form and retail characteristic, have been found to play significant roles influencing retail efficiency using bootstrapped Tobit regression.

Data availability has limited the level of analysis in some parts of this study, especially in the bootstrapped Tobit regression.

This study seems to be the first in applying productivity analysis using DEA for the UK retail sector.

The purpose of this paper is to develop several predictive models for estimating the structural construction cost and establish range estimation for the structural construction cost using design information available in early stages of residential building projects.

Information about residential building projects is collected based on project documents from construction companies with regard to the design parameters and the actual structural construction costs at completion. Storey enclosure method (SEM) is fundamental for determining the building design parameters, forming the potential variables and developing the cost estimation models using regression analysis. Nonparametric bootstrap method is used to establish range estimation for the structural construction cost.

A model which is developed from an integration of advanced SEM, principle component analysis and regression analysis is robust in terms of predictability. In terms of range estimation, cumulative probability-based range estimates and confidence intervals are established. While cumulative probability-based range estimates provide information about the level of uncertainty included in the estimate, confidence intervals provide information about the variability of the estimate. Such information could be very crucial for management decisions in early stages of residential building projects.

This study could provide practitioners with a better understanding of the uncertainty and variability included in the cost estimate. Hence, they could make effective improvements on cost-related management approaches to enhance project cost performance.

The paper aims to explore the impact of economic freedom on the efficiency of the Malaysian banking sector.

The analysis is confined into two stages. In the first stage, the bias-corrected data envelopment analysis method is used to compute the efficiency of individual banks. Then bootstrap regressions are used to examine the impact of economic freedom on bank efficiency, while controlling for the potential impacts of contextual variables.

It was found that greater freedom to start new businesses tend to impede the efficiency of banks operating in the Malaysian banking sector. The results indicate that restrictions on the activities of which banks could undertake exert negative impact on their efficiency levels. The empirical findings seem to support for official regulation and supervision of banks by setting the limits on activities which banks could undertake. In addition evidence supporting for government interventions in the foreign exchange and money markets was found.

The purpose of the present paper is to extend the earlier works on the performance of the banking sector in a developing economy and establish empirical evidence on the impact of economic freedom. Although empirical evidence which examines the performance of banking sectors is abundant in the literature, to the best of our knowledge, virtually nothing has been published to address the impact of economic freedom.

The purpose of this paper is to predict implementation cost contingencies for residential construction projects in flood-prone areas, where floods with storms frequently cause serious damage and problems for people.

Expert interviews are conducted to identify the study variables. Based on bills of quantities and project documents, historical data on residential construction projects in flood-prone areas are collected. Pearson correlation analysis is first used to check the correlations among the study variables. To overcome multicollinearity, principal component analysis is used. Then, stepwise multiple regression analysis is used to develop the cost prediction model. Finally, non-parametric bootstrap method is used to develop range estimation of the implementation cost.

A list of project-related variables, which could significantly affect implementation costs of residential construction projects in flood-prone areas, is identified. A model, which is developed based on an integration of principle component analysis and regression analysis, is robust. Regarding range estimation, 10, 50 and 90 percent cost estimates, which could provide information about the uncertainty levels in the estimates, are established. Furthermore, implementation cost contingencies which could show information about the variability in the estimates are determined for example case projects. Such information could be critical to cost-related management of residential construction projects in flood-prone areas.

This study attempts to predict implementation cost contingencies for residential construction projects in flood-prone areas using non-parametric bootstrap method. Such contingencies could be useful for project cost budgeting and/or effective cost management.

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.

The purpose of this paper is to assess the efficiency of agricultural production in South Africa from 1970 to 2014, using an integrated two-stage fuzzy approach.

Fuzzy technique for order preference by similarity to ideal solution is used to assess the relative efficiency of agriculture in South Africa over the course of the years in the first stage. In the second stage, fuzzy regressions based on different rule-based systems are used to predict the impact of socio-economic and demographic variables on agricultural efficiency. They are compared with the bootstrapped truncated regressions with conditional α levels proposed in Wanke et al. (2016a).

The results show that the fuzzy efficiency estimates ranged from 0.40 to 0.68 implying inefficiency in South African agriculture. The results further reveal that research and development, land quality, health expenditure–population growth ratio have a significant, positive impact on efficiency levels, besides the GINI index. In terms of accuracy, fuzzy regressions outperformed the bootstrapped truncated regressions with conditional α levels proposed in Wanke et al. (2015).

Policies to increase social expenditure especially in terms of health and hence productivity should be prioritized. Also policies aimed at conserving the environment and hence the quality of land is needed.

The paper is original and has not been previously published elsewhere.

Firms are working in a complex environment in which the updated information increase the pace of precise decision making and reduce the risk of wrong decisions. Therefore, discovering firms’ performance is a major issue. The purpose of this paper is to evaluate the efficiency of Iranian plastic producing companies by using data envelopment analysis (DEA). It also discovers various drivers that significantly affect the efficiency of enterprises.

The authors studied a sample of 17 manufacturing firms to examine the relative efficiency of companies. They, then, evaluated the effects of efficiency drivers and used two methods for these purposes: DEA and bootstrapped Tobit regression model.

The study has shown that two manufacturing firms out of selected 17 are efficient under the Charnes, Cooper, and Rhodes model. Also, nine out of 17 plastic producing companies are productive under the Banker, Charnes, and Cooper model. The results of Tobit regression shows that only two efficiency drivers out of four have a significant positive influence on the efficiency of plastic producing firms.

Considering one industry and country limits the generalizability of the results provided. Besides, data availability has limited the analysis in some parts, particularly in bootstrapped Tobit regression.

The authors listed this section into benchmarking and strategical management; more importantly, the suggestions for improving the chemical industry and its future evolution are presented.

The paper is classified into two issues: the efficiency of plastic producing firms in Iran and evaluating the reason for inefficiency, apart from internal managerial procedures.

The purpose of this paper is to assess the efficiency determinants of mergers and acquisitions (M&A) in the context of Latin American airlines based on business-related variables commonly found in the literature. The idea is to identify preferable potential airline matches in light of fleet mix, ownership structure and geographical proximity.

In order to achieve the objective, all possible combinations of M&A pairs are considered in the analysis, which is developed in a two-stage approach. First, the M&A Data Envelopment Analysis model efficiency and returns-to-scale estimates are computed. Then, robust regression and multinomial logistic regression are respectively used to discriminate these estimates in terms of such business-related variables.

The results reveal that these different contextual variables significantly impact virtual efficiency and returns-to-scale levels. Private ownership, passenger focus and a better match between aircraft size and demand for flights appear to be key drivers for merged airline efficiency.

The study makes theoretical contributions, though limited to analyzing Latin American airlines only. The use of bootstrapped robust/multinominal logistic regression, compared to the methods adopted by previous literature studies, generates more accurate and robust results related to the efficiency drivers due to its special feature and ability to allow the discrimination of increasing, decreasing, and constant returns to scale in light of a given set of contextual variables.

This study examines the pure effect of the merging activity on efficiency gains. Not only private ownership but also a hybrid public–private ownership has a positive influence on virtual efficiency, suggesting an important governmental role in promoting M&A in the airline industry.

The authors present an original take on the issue of airline mergers by exploring what are the major drivers possibly involved in efficiency gains of potentially merged (virtual) airlines. The authors identify preferable potential airline matches where efficiency gains would be positive in light of business-related variables such as fleet mix, ownership structure and geographical proximity. The analysis also includes an assessment of the impact of contextual variables such as cargo type, ownership structure and geographical proximity in relation to the strategic fit of mergers considering the resulting efficiency and returns-to-scale scores of virtually merged airlines. To the authors’ knowledge, no previous research has addressed these issues in Latin American airlines. Further research directions for this industry are also discussed.

The purpose of this paper is to analyze the performance of Indian retailers in recent past and derive meaningful insight for practicing managers in this area.

This paper analyses the economic efficiencies of select Indian retailers using three related methodologies: Data Envelopment Analysis (DEA), Malmquist Productivity Index (MPI) and Bootstrapped Tobit Regression.

DEA analysis has shown that five retail firms out of selected 18 are found as efficient under the CCR model of DEA and seven out of 18 retail firms are efficient under the BCC model of DEA. MPI results indicate that 61 percent of the firms have progressed in terms of the MPI during the period under consideration. The Bootstrapped Tobit Regression shows that number of retail outlets and mergers and acquisitions can be considered as the driving forces influencing efficiency of retailers in India.

The paper has a limitation with reference to the availability of data for a few retail outlets, especially in the modeling through the Bootstrapped Tobit Regression.

This study seems to be the first in applying productivity analysis using DEA, MPI and Bootstrapped Tobit Regression for the Indian retail sector.

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.