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

Moderating effects of management control systems and innovation on performance. Simple methods for correcting the effects of measurement error for interaction effects in small samples

In the accounting literature, interaction or moderating effects are usually assessed by means of OLS regression and summated rating scales are constructed to reduce measurement error bias. Structural equation models and two-stage least squares regression could be used to completely eliminate this bias, but large samples are needed. Partial Least Squares are appropriate for small samples but do not correct measurement error bias. In this article, disattenuated regression is discussed as a small sample alternative and is illustrated on data of Bisbe and Otley (in press) that examine the interaction effect of innovation and style of use of budgets on performance. Sizeable differences emerge between OLS and disattenuated regression

Social network measures for "nosduocentered" networks, their predictive power on performance

Our purpose in this article is to define a network structure which is based on two egos instead of the egocentered (one ego) or the complete network (n egos). We describe the characteristics and properties for this kind of network which we call “nosduocentered network”, comparing it with complete and egocentered networks. The key point for this kind of network is that relations exist between the two main egos and all alters, but relations among others are not observed. After that, we use new social network measures adapted to the nosduocentered network, some of which are based on measures for complete networks such as degree, betweenness, closeness centrality or density, while some others are tailormade for nosduocentered networks. We specify three regression models to predict research performance of PhD students based on these social network measures for different networks such as advice, collaboration, emotional support and trust. Data used are from Slovenian PhD students and their s

Simultaneous estimation of indirect and interaction effects using structural equation models

Interaction effects are usually modeled by means of moderated regression analysis. Structural equation models with non-linear constraints make it possible to estimate interaction effects while correcting for measurement error. From the various specifications, Jöreskog and Yang's (1996, 1998), likely the most parsimonious, has been chosen and further simplified. Up to now, only direct effects have been specified, thus wasting much of the capability of the structural equation approach. This paper presents and discusses an extension of Jöreskog and Yang's specification that can handle direct, indirect and interaction effects simultaneously. The model is illustrated by a study of the effects of an interactive style of use of budgets on both company innovation and performance

Correcting Measurement Error Bias in Interaction Models with Small Samples

Several methods have been suggested to estimate non-linear models with interaction terms in the presence of measurement error. Structural equation models eliminate measurement error bias, but require large samples. Ordinary least squares regression on summated scales, regression on factor scores and partial least squares are appropriate for small samples but do not correct measurement error bias. Two stage least squares regression does correct measurement error bias but the results strongly depend on the instrumental variable choice. This article discusses the old disattenuated regression method as an alternative for correcting measurement error in small samples. The method is extended to the case of interaction terms and is illustrated on a model that examines the interaction effect of innovation and style of use of budgets on business performance. Alternative reliability estimates that can be used to disattenuate the estimates are discussed. A comparison is made with the alternative methods. Methods that do not correct for measurement error bias perform very similarly and considerably worse than disattenuated regression

Networks of PhD Students and Academic Performance: A Comparison across Countries

In this article we compare regression models obtained to predict PhD students’ academic performance in the universities of Girona (Spain) and Slovenia. Explanatory variables are characteristics of PhD student’s research group understood as an egocentered social network, background and attitudinal characteristics of the PhD students and some characteristics of the supervisors. Academic performance was measured by the weighted number of publications. Two web questionnaires were designed, one for PhD students and one for their supervisors and other research group members. Most of the variables were easily comparable across universities due to the careful translation procedure and pre-tests. When direct comparison was not possible we created comparable indicators. We used a regression model in which the country was introduced as a dummy coded variable including all possible interaction effects. The optimal transformations of the main and interaction variables are discussed. Some differences between Slovenian and Girona universities emerge. Some variables like supervisor’s performance and motivation for autonomy prior to starting the PhD have the same positive effect on the PhD student’s performance in both countries. On the other hand, variables like too close supervision by the supervisor and having children have a negative influence in both countries. However, we find differences between countries when we observe the motivation for research prior to starting the PhD which increases performance in Slovenia but not in Girona. As regards network variables, frequency of supervisor advice increases performance in Slovenia and decreases it in Girona. The negative effect in Girona could be explained by the fact that additional contacts of the PhD student with his/her supervisor might indicate a higher workload in addition to or instead of a better advice about the dissertation. The number of external student’s advice relationships and social support mean contact intensity are not significant in Girona, but they have a negative effect in Slovenia. We might explain the negative effect of external advice relationships in Slovenia by saying that a lot of external advice may actually result from a lack of the more relevant internal advice

Recovering Euclidean deformable models from stereo-motion

In this paper we present a novel structure from motion (SfM) approach able to infer 3D deformable models from uncalibrated stereo images. Using a stereo setup dramatically improves the 3D model estimation when the observed 3D shape is mostly deforming without undergoing strong rigid motion. Our approach first calibrates the stereo system automatically and then computes a single metric rigid structure for each frame. Afterwards, these 3D shapes are aligned to a reference view using a RANSAC method in order to compute the mean shape of the object and to select the subset of points on the object which have remained rigid throughout the sequence without deforming. The selected rigid points are then used to compute frame-wise shape registration and to extract the motion parameters robustly from frame to frame. Finally, all this information is used in a global optimization stage with bundle adjustment which allows to refine the frame-wise initial solution and also to recover the non-rigid 3D model. We show results on synthetic and real data that prove the performance of the proposed method even when there is no rigid motion in the original sequence

Predictors of knowledge creation performance. A quantitative qualitative comparative study of European doctorandi

Aquesta tesi forma part d'un projecte destinat a predir el rendiment acadèmic dels estudiants de doctorat portat a terme per l'INSOC (International Network on Social Capital and Performance). El grup de recerca INSOC està format per les universitats de Girona (Espanya), Ljubljana (Eslovènia), Giessen (Alemanya) i Ghent (Bèlgica). El primer objectiu d'aquesta tesi és desenvolupar anàlisis quantitatius comparatius sobre el rendiment acadèmic dels estudiants de doctorat entre Espanya, Eslovènia i Alemanya a partir dels resultats individuals del rendiment acadèmic obtinguts de cada una de les universitats. La naturalesa internacional del grup de recerca implica la recerca comparativa. Vam utilitzar variables personal, actitudinals i de xarxa per predir el rendiment. El segon objectiu d'aquesta tesi és entendre de manera qualitativa perquè les variables de xarxa no ajuden quantitativament a predir el rendiment a la universitat de Girona (Espanya). En el capítol 1, definim conceptes relacionats amb el rendiment i donam un llistat de cada una de les variables independents (variables de xarxa, personals i actitudinals), resumint la lliteratura. Finalment, explicam com s'organitzen els estudis de doctorat a cada un dels diferents països. A partir d'aquestes definicions teòriques, en els pròxims capítols, primer presentarem els qüestionaris utilitzats a Espanya, Eslovènia i Alemanya per mesurar aquests diferents tipus de variables. Després, compararem les variables que són relevants per predir el rendiment dels estudiants de doctorat a cada país. Després d'això, fixarem diferents models de regressió per predir el rendiment entre països. En tots aquests models les variables de xarxa fallen a predir el rendiment a la Universitat de Girona. Finalment, utilitzem estudis qualitatius per entendre aquests resultats inesperats. En el capítol 2, expliquem com hem dissenyat i conduït els qüestionaris en els diferents països amb l'objectiu d'explicar el rendiment dels estudiants de doctorat obtinguts a Espanya, Eslovènia i Alemanya. En el capítol 3, cream indicadors comparables però apareixen problemes de comparabilitat en preguntes particulars a Espanya, Eslovènia i Alemanya. En aquest capítol expliquem com utilitzem les variables dels tres països per crear indicadors comparables. Aquest pas és molt important perquè el principal objectiu del grup de recerca INSOC és comparar el rendiment dels estudiants de doctorat entre els diferents països. En el capítol 4 comparem models de regressió obtinguts de predir el rendiment dels estudiants de doctorat a les universitats de Girona (Espanya) i Eslovènia. Les variables són característiques dels grups de recerca dels estudiants de doctorat enteses com una xarxa social egocèntrica, característiques personals i actitudinals dels estudiants de doctorat i algunes carecterístiques dels directors. Vam trobar que les variables de xarxa egocèntriques no predien el rendiment a la Universitat de Girona. En el capítol 5, comparem dades eslovenes, espanyoles i alemnayes, seguint la metodologia del capítol 4. Concluïm que el cas alemany és molt diferent. El poder predictiu de les variables de xarxa no millora. En el capítol 6 el grup de recerca dels estudiants de doctorat és entès com una xarxa duocèntrica (Coromina et al., 2008), amb l'objectiu d'obtendre informació de la relació mútua entre els estudiants i els seus directors i els contactes d'ambdós amb els altres de la xarxa. La inclusió de la xarxa duocèntrica no millora el poder predictiu del model de regressió utilitzant les variales egocèntriques de xarxa. El capítol 7 pretèn entendre perquè les variables de xarxa no predeixen el rendiment a la Universitat de Girona. Utilitzem el mètode mixte, esperant que l'estudi qualitatiu pugui cobrir les raons de perquè la qualitat de la xarxa falla en la qualitat del treball dels estudiants. Per recollir dades per l'estudi qualitatiu utilitzem entrevistes en profunditat.

Social networks and performance in knowledge creation. An application and a methodological proposal

El objetivo de esta tesis es predecir el rendimiento de los estudiantes de doctorado en la Universidad de Girona según características personales (background), actitudinales y de redes sociales de los estudiantes. La población estudiada son estudiantes de tercer y cuarto curso de doctorado y sus directores de tesis doctoral. Para obtener los datos se ha diseño un cuestionario web especificando sus ventajas y teniendo en cuenta algunos problemas tradicionales de no cobertura o no respuesta. El cuestionario web se hizo debido a la complejidad que comportan de las preguntas de red social. El cuestionario electrónico permite, mediante una serie de instrucciones, reducir el tiempo para responder y hacerlo menos cargado. Este cuestionario web, además es auto administrado, lo cual nos permite, según la literatura, unas respuestas mas honestas que cuestionario con encuestador. Se analiza la calidad de las preguntas de red social en cuestionario web para datos egocéntricos. Para eso se calcula la fiabilidad y la validez de este tipo de preguntas, por primera vez a través del modelo Multirasgo Multimétodo (Multitrait Multimethod). Al ser datos egocéntricos, se pueden considerar jerárquicos, y por primera vez se una un modelo Multirasgo Multimétodo Multinivel (multilevel Multitrait Multimethod). Las la fiabilidad y validez se pueden obtener a nivel individual (within group component) o a nivel de grupo (between group component) y se usan para llevar a cabo un meta-análisis con otras universidades europeas para analizar ciertas características de diseño del cuestionario. Estas características analizan si para preguntas de red social hechas en cuestionarios web son más fiables y validas hechas "by questions" o "by alters", si son presentes todas las etiquetas de frecuencia para los ítems o solo la del inicio y final, o si es mejor que el diseño del cuestionario esté en con color o blanco y negro. También se analiza la calidad de la red social en conjunto, en este caso específico son los grupos de investigación de la universidad. Se tratan los problemas de los datos ausentes en las redes completas. Se propone una nueva alternativa a la solución típica de la red egocéntrica o los respondientes proxies. Esta nueva alternativa la hemos nombrado "Nosduocentered Network" (red Nosduocentrada), se basa en dos actores centrales en una red. Estimando modelos de regresión, esta "Nosduocentered network" tiene mas poder predictivo para el rendimiento de los estudiantes de doctorado que la red egocéntrica. Además se corrigen las correlaciones de las variables actitudinales por atenuación debido al pequeño tamaño muestral. Finalmente, se hacen regresiones de los tres tipos de variables (background, actitudinales y de red social) y luego se combinan para analizar cual para predice mejor el rendimiento (según publicaciones académicas) de los estudiantes de doctorado. Los resultados nos llevan a predecir el rendimiento académico de los estudiantes de doctorado depende de variables personales (background) i actitudinales. Asimismo, se comparan los resultados obtenidos con otros estudios publicados.

A proposal for the diagnosis of uncertain dynamic systems based on interval models

The performance of a model-based diagnosis system could be affected by several uncertainty sources, such as,model errors,uncertainty in measurements, and disturbances. This uncertainty can be handled by mean of interval models.The aim of this thesis is to propose a methodology for fault detection, isolation and identification based on interval models. The methodology includes some algorithms to obtain in an automatic way the symbolic expression of the residual generators enhancing the structural isolability of the faults, in order to design the fault detection tests. These algorithms are based on the structural model of the system. The stages of fault detection, isolation, and identification are stated as constraint satisfaction problems in continuous domains and solved by means of interval based consistency techniques. The qualitative fault isolation is enhanced by a reasoning in which the signs of the symptoms are derived from analytical redundancy relations or bond graph models of the system. An initial and empirical analysis regarding the differences between interval-based and statistical-based techniques is presented in this thesis. The performance and efficiency of the contributions are illustrated through several application examples, covering different levels of complexity.

Simplification, approximation and deformation of large models

The high level of realism and interaction in many computer graphic applications requires techniques for processing complex geometric models. First, we present a method that provides an accurate low-resolution approximation from a multi-chart textured model that guarantees geometric fidelity and correct preservation of the appearance attributes. Then, we introduce a mesh structure called Compact Model that approximates dense triangular meshes while preserving sharp features, allowing adaptive reconstructions and supporting textured models. Next, we design a new space deformation technique called *Cages based on a multi-level system of cages that preserves the smoothness of the mesh between neighbouring cages and is extremely versatile, allowing the use of heterogeneous sets of coordinates and different levels of deformation. Finally, we propose a hybrid method that allows to apply any deformation technique on large models obtaining high quality results with a reduced memory footprint and a high performance.