Application of the european customer satisfaction index to postal services. Structural equation models versus partial least squars

Customer satisfaction and retention are key issues for organizations in today’s competitive market place. As such, much research and revenue has been invested in developing accurate ways of assessing consumer satisfaction at both the macro (national) and micro (organizational) level, facilitating comparisons in performance both within and between industries. Since the instigation of the national customer satisfaction indices (CSI), partial least squares (PLS) has been used to estimate the CSI models in preference to structural equation models (SEM) because they do not rely on strict assumptions about the data. However, this choice was based upon some misconceptions about the use of SEM’s and does not take into consideration more recent advances in SEM, including estimation methods that are robust to non-normality and missing data. In this paper, both SEM and PLS approaches were compared by evaluating perceptions of the Isle of Man Post Office Products and Customer service using a CSI format. The new robust SEM procedures were found to be advantageous over PLS. Product quality was found to be the only driver of customer satisfaction, while image and satisfaction were the only predictors of loyalty, thus arguing for the specificity of postal services

Informed recommender: basing recommendations on consumer product reviews

Recommender systems attempt to predict items in which a user might be interested, given some information about the user's and items' profiles. Most existing recommender systems use content-based or collaborative filtering methods or hybrid methods that combine both techniques (see the sidebar for more details). We created Informed Recommender to address the problem of using consumer opinion about products, expressed online in free-form text, to generate product recommendations. Informed recommender uses prioritized consumer product reviews to make recommendations. Using text-mining techniques, it maps each piece of each review comment automatically into an ontology

Recommender System Based on Consumer Product Reviews

Consumer reviews, opinions and shared experiences in the use of a product is a powerful source of information about consumer preferences that can be used in recommender systems. Despite the importance and value of such information, there is no comprehensive mechanism that formalizes the opinions selection and retrieval process and the utilization of retrieved opinions due to the difficulty of extracting information from text data. In this paper, a new recommender system that is built on consumer product reviews is proposed. A prioritizing mechanism is developed for the system. The proposed approach is illustrated using the case study of a recommender system for digital cameras

Antecedentes de la lealtad en los entornos on line y off line: un análisis empírico aplicado al sector de la aviación civil

El objetivo de esta investigación es analizar la lealtad de los usuarios de líneas aéreas tanto en el entorno online como en el entorno offline. La revisión bibliográfica ha identificado tres antecedentes: la satisfacción, la confianza y el valor percibido. Se ha llevado a cabo un estudio empírico realizándose un total de 1710 entrevistas personales en el aeropuerto del Prat (Barcelona) a usuarios de dos compañías aéreas tradicionales, Iberia y British Airways, y a una compañía low cost, Easyjet. Se trata de las tres compañías que operan con vuelos directos Barcelona-Londres. En el análisis de los datos se han utilizado modelos de ecuaciones estructurales y en particular la técnica utilizada fue el análisis factorial confirmatorio. Los resultados revelan que tanto la satisfacción, como la confianza y el valor percibido explican las relaciones de lealtad entre los pasajeros y las compañías aéreas.