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The purpose of this study is to comprehend the efficiency levels of the Indian textile industry and also its sub-sectors in the light of changing global and national business environment. It is imperative to study the efficiency levels of textile industry for an emerging economy like India, where the industry contributes up to 13 per cent in export earnings, 10 per cent in total industrial production and 2 per cent in gross domestic product (GDP). The study holds an important place in the wake of phasing out of the quota regime existing under the Multi Fibre Agreement (MFA) and the rising competition being faced from countries such as Bangladesh, Vietnam and Cambodia.

The present study attempts to have an in-depth analysis of the efficiency levels in the Indian textile industry using meta-frontier data envelopment analysis, which is a non-parametric linear programming based frontier technique.

The findings highlight that the Indian textile industry is inefficient and has a huge scope of improvement in terms of efficiency. It also confirms the existence of different production functions among the sub-sectors of the industry. Among the different sub-sectors, the proximity of production frontier of readymade garments is the closest to meta-frontier followed by cotton and blended yarn, man-made fibre, cloth and others.

The findings bear strong implications for the policymakers in their attempt to regain the lost competitive position of the Indian textile industry and to enhance its contribution in the economy. As per the findings, policymakers should target the relatively inefficient sub-sectors of textile industry (cloth, man-made fibre, cotton and blended yarn) to infuse more efficiency in these sectors to enhance the market share of the Indian textile industry in the global textiles market.

The current study is a unique addition to the sparse literature on managing efficiencies in the textile industry, particularly of emerging economy like India. Looking at the methodological and geographical coverage of the previous work, it was found that no study has explored and analysed the efficiencies of the sub-sectors in the Indian textile industry using meta-frontier analysis. Therefore, this study will be the first of its kind which seeks to fill such gaps and intends to enrich the available literature.

The purpose of this study is to compare the production efficiencies and frontiers differences of oil-producing countries (OPCs) in four inter-governmental organizations (IGOs) in the international petroleum industry with the aim of providing such countries understanding of group characteristics that help maximize their supply interests.

The empirical analysis is based on 14 years of panel data covering the period from 2000 to 2013. In all 46 unique countries who are members of four IGOs relevant to the international petroleum industry are examined on individual and group bases. The authors use both metafrontier analysis and global frontier difference in examining the group average and group frontiers, respectively.

Groups with high inter and intra-group collaborations which ensure exchange of information, organizational learning and innovation tend to do better than groups with even higher hydro-carbon endowment. Additionally, hydro-carbon resource endowment may not be the solution to group inefficiency without higher endowment in human capital, economic stability, technology and infrastructure.

Choice of inter-governmental organizational membership should be based on the level of inter- and intra-group collaborations, human capital endowment among others and not mere historic links or even resource endowment.

This is among the few studies to compare and rank IGOs. Specifically, it is among the first studies to analyze the petroleum production efficiencies of IGOs involved in the international petroleum industry. This study assesses the performance differences among OPCs with the aim of identifying for OPCs the characteristics of inter-governmental groups that are beneficial to efficiency in upstream petroleum activities.

The purpose of this paper is to consider a new application of stochastic frontier analysis, in which the method is applied to demand data for a food product category, in an attempt to benchmark category consumption and segment food consumers.

In a unified, two‐stage approach, a stochastic frontier model is first estimated and subsequently deviations from the demand frontier are regressed on customer characteristics. The method is illustrated in scanner panel data.

A frontier demand function estimated in scanner data of a frequently‐bought food category has significant and consistent parameters. Specific descriptor variables can explain excessive category demand and profile customers with considerable sales potential.

More work is needed to generalise the usefulness of the proposed model in different food categories. Future research may employ alternative functional specifications and explanatory variables.

The empirical identification of salient characteristics improves consumer understanding and can assist in the design of data‐driven marketing action. Applied researchers can use marketing and demographic variables that are found in standard consumer panels to estimate frontier models.

The paper introduces stochastic frontier analysis as a means to determine consumer differences in food demand. This is an important area for retailers, producers and researchers.

Examines 88 academic member libraries of the Association of Research Libraries (ARL) to determine their relative cost efficiency, using stochastic frontier regression and data envelopment analysis (DEA) methods. Both methods give average ARL cost efficiencies of around 80 percent. This places academic ARL libraries in the same range of efficiency as other institutions, including for‐profit and non‐profit institutions. Many libraries are above 80 percent efficiency. For those below, some speculation is given for the lower efficiency. The lack of an output measure for the use of electronic sources may contribute to lower efficiency for a few libraries. Large staff size and a large number of serial subscriptions do predict lower efficiency, but this is not a necessary consequence. The DEA model allows us to determine increasing, constant, or declining returns to scale for research libraries. From this, it appears research libraries with expenditures between $10,000,000 and $20,000,000 are operating at the most efficient scale. Since the methods used are outside the repertoire of most LIS research, a conceptual explanation is provided.

This paper aims to evaluate the performance of basic metals industry in India and analyze its determinants, using data envelopment analysis (DEA) method. It also intends to compare the results through conventional two-stage and bootstrap-based inferences.

Considering technical efficiency as a measure of performance, this paper specifically investigates whether the participation of a firm in the global market affects its performance. The conventional two-stage procedure is used to test the export intensity and firm performance nexus. The bootstrap-based algorithms (by Simar and Wilson, 2007) are used to correct the bias and serial correlation issues involved in the conventional approach.

The result shows a negative relation between export intensity and firm performance while following the conventional procedure. Even after accounting for serial correlation, the relation remains more or less similar to that of conventional analysis. However, a strong negative relation between export intensity and firm performance is not observed in a more reliable inference obtained after correcting for possible bias as well as serial correlation.

This paper is based on cross-sectional analysis, and a more reliable result can be obtained by considering a larger sample and longer period.

This paper shows how the conventional two-stage procedure may result in misleading inferences due to bias in the estimation of efficiency scores and the serial correlation during the second stage inferential analysis. This paper also empirically exemplifies how the double bootstrap DEA procedure can overcome these limitations of the conventional two-stage approach.

The purpose of this paper is to use the concept of meta-frontiers data envelopment analysis (DEA) to compare the technical efficiencies of banks in selected Southeast Asia countries in the periods of 1998-2012.

The authors evaluate bank efficiency in Indonesia, Malaysia, Thailand and the Philippines by means of DEA, and the authors employ a meta-frontiers approach to calculate efficiency scores in a cross-country setting.

The analysis shows that even there are some similarities in the process of financial reforms undertaken in the selected countries, the observed efficiency levels of banks vary substantially across the market.

It is crucial to take into consideration of different technologies in explaining the efficiency differences.

The purpose of this paper is to estimate the relative efficiencies of banks of the Indian domestic banking sector by employing various models of data envelopment analysis (DEA) using the panel data of the recent decade (2008–2017). The paper provides a comparative analysis of these models based on the efficiency outputs. It compares the performance of banks based on their ownership and sizes and studies the decade-long trend of productivity using Malmquist indices.

This paper estimates overall technical, pure technical and scale efficiencies of 21 public sector banks and 17 private banks. It compares the descriptive statistics of efficiency estimates found out through 18 different DEA models and compares them using two non-parametric statistical tests. It studies the difference in efficiencies based on ownership and size by applying the same statistical tests. It employs the Malmquist index method to study the technological and technical progress in the banks’ productivity over the decade of FY 2008–FY 2017.

During FY 2016–2017, only 9 out of 38 banks were overall technically efficient with the whole sample having a mean overall technical inefficiency of 5 percent with scale inefficiency contributing more than pure technical inefficiency. The comparative study ascertains that private sector and public sector banks (PSBs) possess efficiencies that are similar based on super-efficiency slack-based model – variable returns to scale and non-oriented, a model that the authors argue to be the most suitable for the real-life business banking scenarios whereas the private sector banks possess better efficiency than the PSBs. The Malmquist indices prove that private sector banks have a higher increase in productivity based on both technological progress and efficiency improvements whereas PSBs had a loss of efficiency and comparatively less improvement in technology.

This study has a limitation of choosing a single model of inputs and outputs. Improved insights can be drawn by employing more models based on different inputs and outputs. Further, relevance of each input and output can be examined using a regression-based feedback mechanism (Ouenniche and Carrales, 2018). The influence of environmental factors on the efficiencies can be studied using second-stage regression models and the relationship between efficiency scores and financial ratios can be examined.

This study is based on the panel data of the recent decade (2008–2017) and provides insights into the efficiency scenario of the Indian banking industry and how it changed over the past decade, to the leadership of banks, the banking regulators and the policy makers. The comparative analysis of DEA models based on a sample of Indian banks is first of its kind in the Indian context and helps the researchers to select an appropriate model and delve into further research on the same.

The purpose of this paper is to evaluate the extent of technical efficiency in 27 public sector banks operating in India and to provide strict ranking to these banks.

Two popular data envelopment analysis (DEA) models, namely, CCR model and Andersen and Petersen's super‐efficiency model, were utilized. The cross‐section data for the financial year 2004/2005 were used for obtaining technical efficiency scores.

The results show that only seven of the 27 banks are found to be efficient and thus, defined the efficient frontier; and technical efficiency scores range from 0.632 to 1, with an average of 0.885. Thus, Indian public sector banks, on an average, waste the inputs to the tune of 11.5 percent. Andhra Bank has been observed to be the most efficient bank followed closely by Corporation Bank. Further, the banks affiliated with SBI group turned out to be more efficient than the nationalized banks. The regression results incisively indicate that the exposure to off‐balance sheet activities, staff productivity, market share and size are the major determinants of the technical efficiency.

The practical implication of the research findings is that apart from the proportional reduction of all inputs equivalent to the amount of technical inefficiency, most of the inefficient public sector banks need to reduce the use of the physical capital and augment non‐interest income to project themselves on the efficient frontier.

This paper is the first to provide a strict ranking of Indian public sector banks on the basis of super‐efficiency scores.

This chapter examines the effect of the division of labor from a Bayesian viewpoint. While organizational reforms are crucial for cost reduction in the Japanese water supply industry, the effect of labor division in intra-organizational units on total costs has, to the best of our knowledge, not been examined empirically. Fortunately, a one-time survey of 79 Japanese water suppliers conducted in 2010 enables us to examine the effect. To examine this problem, a cost stochastic frontier model with endogenous regressors is considered in a cross-sectional setting, because the cost and the division of labor are regarded as simultaneously determined factors. From the empirical analysis, we obtain the following results: (1) total costs rise when the level of labor division becomes high; (2) ignoring the endogeneity leads to the underestimation of the impact of labor division on total costs; and (3) the estimation bias on inefficiency can be mitigated for relatively efficient organizations by including the labor division variable in the model, while the bias for relatively inefficient organizations needs to be controlled by considering its endogeneity. In summary, our results indicate that integration of internal sections is better than specialization in terms of costs for Japanese water supply organizations.

Historically standard regression has been used to assess performance in marketing, especially of salespeople and retail outlets. A model of performance is estimated using ordinary least squares, the residuals are computed, and the decision-making units, say store managers, ranked in the order of the residuals. The problem is that the regression line approach characterizes average performance. The focus should be on best performance. Frontier analysis, especially stochastic frontier analysis (SEA), is a way to benchmark such best performance. Deterministic frontier analysis is also discussed in passing. The distinction between conventional ordinary least squares analysis and frontier analysis is especially marked when heteroscedasticity is present. Most of the focus of benchmarking has been on identifying the best performing units. The real insight, though, is from explaining the benchmark gap. Stochastic frontier analysis can, and should, model both phenomena simultaneously.