Aproximación teórica de la lealtad de marca: enfoques y valoraciones

[ES] El concepto de lealtad no es nuevo en el mercadeo, por el contrario ha sido uno de los temas más investigados, sin embargo no se ha llegado a unificar criterios sobre su definición por ser un fenómeno complejo. Inicialmente, el estudio de la lealtad se abordó desde dos corrientes diferentes: como una actitud, donde se dan cabida sentimientos y afectos positivos a favor de una marca; como un comportamiento efectivo, materializado en compras repetidas de la misma marca. Luego, se consideró una corriente que plantea que la medición de la lealtad no concierne exclusivamente a la valoración del comportamiento de recompra o al compromiso, sino a ambos. El objetivo de este artículo es el describir los aspectos más relevantes del concepto de lealtad de marca, a partir de la revisión y análisis teórico, específicamente su definición, enfoques, métodos de medición y tipos, para presentar algunas consideraciones finales.

Proficiency, Attitude and Conventions in Minority Languages

In this paper we study a simple mathematical model of a bilingual community in which all agents are f luent in the majority language but only a fraction of the population has some degree of pro ficiency in the minority language. We investigate how different distributions of pro ficiency, combined with the speaker´attitudes towards or against the minority language, may infl uence its use in pair conversations.

Measurement of polycyclic aromatic hydrocarbons in ambient air by fluorescence spectroscopic techniques

250 p. + anexos

Virtual Sensors for On-line Wheel Wear and Part Roughness Measurement in the Grinding Process

Grinding is an advanced machining process for the manufacturing of valuable complex and accurate parts for high added value sectors such as aerospace, wind generation, etc. Due to the extremely severe conditions inside grinding machines, critical process variables such as part surface finish or grinding wheel wear cannot be easily and cheaply measured on-line. In this paper a virtual sensor for on-line monitoring of those variables is presented. The sensor is based on the modelling ability of Artificial Neural Networks (ANNs) for stochastic and non-linear processes such as grinding; the selected architecture is the Layer-Recurrent neural network. The sensor makes use of the relation between the variables to be measured and power consumption in the wheel spindle, which can be easily measured. A sensor calibration methodology is presented, and the levels of error that can be expected are discussed. Validation of the new sensor is carried out by comparing the sensor's results with actual measurements carried out in an industrial grinding machine. Results show excellent estimation performance for both wheel wear and surface roughness. In the case of wheel wear, the absolute error is within the range of microns (average value 32 mu m). In the case of surface finish, the absolute error is well below R-a 1 mu m (average value 0.32 mu m). The present approach can be easily generalized to other grinding operations.