Xari Rovira

A measure of perceived performance to assess resource allocation

Performance measurement is a key issue when a company is designing new strategies to improve resource allocation. This paper offers a new methodology inspired by classic importance–performance analysis (IPA) that provides a global index of importance versus performance for firms. This index compares two rankings of the same set of features regarding importance and performance, taking into account underperforming features. The marginal contribution of each feature to the proposed global index defines a set of iso-curves that represents an improvement in the IPA diagram. The defined index, together with the new version of the diagram, will enable the assessment of a firm’s overall performance and, therefore, enhance decision making in the allocation of resources. The proposed methodology has been applied to a Taiwanese multi-format retailer and managerial perceptions of performance and importance are compared to assess the firm’s overall performance.

Kernel Functions over Orders of Magnitude Spaces by Means of Usual Kernels. Application to Measure Financial Credit Risk

This paper lies within the domain of learning algorithms based on kernel functions, as in the case of Support Vector Machines. These algorithms provide good results in classification problems where the input data are not linearly separable. A kernel is constructed over the discrete structure of absolute orders of magnitude spaces. This kernel will be applied to measure firms’ financial credit quality. A simple example that allows the kernel to be interpreted in terms of proximity of the patterns is presented.

Generalisation Improvement of Radial Basis Function Networks Based on Qualitative Input Conditioning for Financial Credit Risk Prediction

The rating is a qualified assessment about the credit risk of bonds issued by a government or a company. There are specialised rating agencies, which classify firms according to their level of risk. These agencies use both quantitative and qualitative information to assign ratings to issues. The final rating is the judgement of the agencys analysts and reflects the probability of issuer default. Since the final rating has a strong dependency on the experts knowledge, it seems reasonable the application of learning based techniques to acquire that knowledge. The learning techniques applied are neural networks and the architecture used corresponds to radial basis function neural networks. A convenient adaptation of the variables involved in the problem is strongly recommended when using learning techniques. The paper aims at conditioning the input information in order to enhance the neural network generalisation by adding qualitative expert information on orders of magnitude. An example of this method applied to some industrial firms is given.

Evaluating student-internship fit using fuzzy linguistic terms and a fuzzy OWA operator

Personnel selection is a well-known problem that is made difficult by incomplete and imprecise information about candidate and position compatibility. This paper shows how positions, which satisfy candidates interests, can be identified with fuzzy linguistic terms and a fuzzy OWA operator. A set of relevant positions aligned with a students interests is selected using this approach. The implementation of the proposed method is illustrated using a numerical example in a business application.