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Since there is no agreement on the consistency of their estimates, the purpose of this paper is to investigate whether parametric stochastic frontier analysis (SFA) and nonparametric data envelopment analysis (DEA) generate consistent bank efficiency assessments.

The authors utilize four alternative efficiency computation models: two DEA technical efficiency models based on constant and variable returns to scale, and two SFA cost efficiency models employing Translog and Fourier functional specifications. An unbalanced panel of 59 Indian banks over 1990‐2007 is employed as a model, developing country, banking market.

The Translog and Fourier specifications in SFA and the constant and variable returns to scale assumptions in DEA are found to rank and identify “best‐practice” and “worst‐practice” approximately in the same order. The association between DEA efficiency estimates and non‐frontier standard performance measures, however, is mixed and inconclusive. Unlike DEA scores, SFA efficiency assessments were found to be consistent with cost and profit ratios and hence are “believable”.

For regulators and bankers alike, the authors' findings highlight the importance of investigating the consistency of efficiency scores across various research methods. They should ensure that frontier‐based efficiency assessments are not simply “artificial constructs” of models' assumptions/specifications.

This paper extends the existing literature by checking jointly the statistical consistency of both DEA technical efficiency scores and SFA cost efficiency scores. The prior studies focus either on technical efficiency or cost efficiency, but not both. Moreover, as far as the authors are aware, this is the first cross‐methodological validation study to focus on bank efficiency in the context of a developing country banking market.

Previous research has found that industry concentration and firm efficiency affect stock returns. However, it is not clear if concentration is a byproduct of efficiency and hence its effect on stock returns is driven by efficiency. This paper aims to examine the relationships between industry concentration, firm efficiency and average stock returns. Mainly, it aims to answer if the effects of industry concentration and firm efficiency on stock returns are independent and significant.

The stochastic frontier approach is used to estimate firm efficiency. The Herfindahl index is used to measure industry concentration. Regression and vector autoregressive analyses are performed to examine cross-sectional and lagged relationships between concentration, efficiency, profitability and stock returns. The characteristics-based benchmark approach is also used to investigate performance of test portfolios.

Industry concentration and firm efficiency have independent and significant effects on average stock returns through profit margins and market shares, which are related to firms’ profitability. Industry concentration has a greater positive impact on market shares than on profit margins, whereas firm efficiency has a greater positive impact on profit margins than on market shares. In sum, highly efficient firms in highly concentrated markets have lower distress risks and hence provide lower average stock returns.

The paper shows the linkages between industry concentration, firm efficiency, profitability and stock returns that have not been documented together in prior studies. Businesses can better understand the impact of concentration and efficiency on market shares and profit margins. Researchers may consider incorporating concentration and efficiency, both of which are meaningful microeconomic variables, into an asset pricing model. Investors can enhance their returns by having a zero-cost portfolio with long and short positions in stocks of firms with different levels of concentration and efficiency.

The purpose of this paper is to propose a performance evaluation framework using an integrated approach of stochastic frontier analysis (SFA) and technique of order preference with similarity to ideal solution (TOPSIS) called efficiency ranking method using SFA and TOPSIS (ERM-ST) specifically in the banking sector where service excellence is of prime importance for business growth.

The proposed approach ERM-ST measures the performance of a DMU in the SFA framework by considering multiple outputs and multiple inputs. It is a non-parametric tool which does not need any prior model assumptions which enhances its applicability in real-life business scenarios. Moreover, the efficiency score obtained using the proposed model ERM-ST lies between 0 and 1, unlike in case of super efficiency data envelopment analysis (DEA) which may go well above 1.

The proposed framework is evaluated for its applicability using two various data sets and is further used to evaluate the performance of a group of 26 public sector banks in India. The results obtained by the proposed method ERM-ST are compared with those obtained by super efficiency DEA using Friedman’s test.

The proposed approach ERM-ST is developed to evaluate the performance of a service unit with multiple outputs and multiple inputs in the SFA framework.

The purpose of this paper is to present a literature review of the fresh produce supply chain management (FSCM). FSCM includes the processes from the production to consumption of fresh produce (fruits, flowers and vegetables).

Literature review is done by systematically collecting the existing literature over a period of 20 years (1989‐2009) and classifying it on the basis of structural attributes such as problem context, methodology and the product under consideration. The literature is also categorized according to the geographic region and year of publication.

There is an increase in interest towards FSCM still there is an absence of a journal with the prime attention towards FSCM. The key finding of this review is that the main interest is towards consumer satisfaction and revenue maximization with post‐harvest waste reduction being a secondary objective. It is revealed from the review that most of the literature is fragmented and is in silos. Lack of demand forecasting, demand and supply mismatch, lesser integrated approach etc are the major causes of concerns.

The authors have taken only the fresh produce (fruits, flowers and vegetables), authors may also look at other perishable items such as milk, meat, etc.

Result shows a product‐problem‐methodology mapping which may serve as a framework for the managers addressing issues in FSCM.

Most of the prior literature reviews are focused on a specific issue such as production planning or inventory management and ignore the broader perspective. There exists a need of having a detailed literature review covering all the operational issues in FSCM. This review fills this gap in the FSCM literature.