PurposeThe aim of this paper is to illustrate the value of data envelopment analysis (DEA) for strategic analysis and performance management in the hotel industry.Design/methodology/approachThe paper uses a world‐wide sample of hotel companies and two cases to illustrate how DEA can be used to develop strategic guidelines to improve organizational performance.FindingsThe study shows that DEA can be used for strategic design and performance management through the analysis of two cases. Additionally, for the sample of 83 hotel companies, there are three main conclusions: a focused strategy performs better than a diversification strategy; for the bulk of the sample, the scale efficiency is higher than the pure technical efficiency, hence hotel managers should concentrate on productivity improvements (that is how to transform inputs into outputs) and not on scale issues (such as increases or decreases in the size of operations); and the majority of the hotel companies in the sample are operating under decreasing returns‐to‐scale, which implies that a decrease in the size of the companies would have a positive effect on the average efficiency level of the industry.Research limitations/implicationsThe paper has two limitations: the performance index created from the efficient frontier of the DEA model is a function of the hotel companies in the sample rather than an absolute measure; and the variables used as inputs and outputs for the DEA model were exclusively taken from the financial statements, which limits the strategic analysis.Practical implicationsThe DEA allows managers to analyze performance in terms of productivity and scale, to identify benchmarks (or peer units), to determine the targets (or optimum values) for inputs and outputs, and to detect slacks in the usage of resources or the production of outputs. Therefore, this methodology provides more insights for performance management than the traditional ratio analysis commonly used in the hotel industry.Originality/valueThe study is one of the few in the hotel industry to use DEA. The paper contributes to that corresponding literature by using: a larger sample size; a world‐wide sample of hotel companies; a longitudinal analysis (three years); and two illustrative cases to show how the information of a DEA model can be used for strategic analysis and performance management.

A Hidden Layer Learning Vector Quantization (HLVQ), neural networklearning algorithm is used for correcting the outputs of Multilayer Perceptrons (MLP) for predicting corporate bankruptcy. We call this method HLVQ-C, and it is shown that it outperforms both discriminant analysis and traditional neural networks while significantly reducing type I error, which is the type of error that has the highest costs for banks. Moreover, our approach gives an estimation of the prediction robustness thus providing a useful measure of credit risk, which is of great interest for banks, insurance companies and creditors in general. We also show that unbalanced samples, containing more financially sound firms than bankrupt firms, place a strong bias on the classifiers thus leading to a deterioration of type I error accuracy. Although many studies have been published on bankruptcy prediction using neural networks or discriminant analysis, they used mainly US or UK samples of very limited size. Our study is based on industrial French firms, uses a data-set of 583 bankrupt firms over the period 1998-2000 and tests the effects of different proportions of non-bankrupt firms in the sample. Attention was also given to feature selection to reduce the dimensionality of the problem.

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Abstract. In this paper we propose a supervised version of the Isomap algorithm by incorporating class label information into a dissimilarity matrix in a financial analysis setting. On the credible assumption that corporates financial status lie on a low dimensional manifold, nonlinear dimensionality reduction based on manifold learning techniques has strong potential for bankruptcy analysis in financial applications. We apply the method to a real data set of distressed and healthy companies for proper geometric tunning of similarity cases. We show that the accuracy of the proposed approach is comparable to the state-of-the-art Support Vector Machines (SVM) and Relevance Vector Machines (RVM) despite the fewer dimensions used resulting from embedding learning.

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