Enticed by the reform of Indian banking sector in the early 1990s and further slowdown in the economy as a result of global financial crisis in late 2000s, the current study analyzes the performance of Indian banks using ...

In this paper, we propose a method to measure competitiveness performance at the subnational level, with an application to Peruvian regions. For this, we propose a benefit-of-the-doubt composite index that summarizes the ...

Public transport systems are often subject to a close regulatory oversight because of their economic and social impacts. In the case of France, this has led to an institutional design that has involved the participation ...

Given the importance of the Chilean dried-fruit market and the characteristics of the industrial process of dehydration, it becomes imperative for companies to measure the efficiency of their production processes in order ...

Ever since the inception of brand values, these have become a benchmark for many data-driven strategies, eventually providing a basis for vertical/horizontal integrations, as well. In recent decades, brands have become ...

The potential of big data has exceeded the expectations of most organizations. However, despite its vast importance and application, some important aspects of big data remain the subject of debate. One of the most sensitive ...

This short communication complements the DEA model proposed by Lovell and Pastor (Eur. J. Oper. Res. 118 (1999), 46-51), by incorporating both positive and negative criteria in the model. As such, we propose a DEA model, ...

Fuzzy logic provides a useful tool for dealing with decisions in which the phenomena are imprecise and vague. The application of non-parametric statistical analysis in the SERVQUAL model could be more meaningful in the ...

Faced with increased competition, business schools seem to have realized that having the strongest brands, hence, a distinct image, is vital to strengthen their presence in the education marketplace. It is in this context ...

Probabilistic or stochastic programming is a framework for modeling optimization problems that involve uncertainty. Stochastic programming models arise as reformulations or extensions of reliability optimization problems ...