The attribution model of achievement motivation has been applied to academic achievement as a way of understanding underachievement and as a basis for developing intervention programs. There has been little applied research in this area, however, that supports the use of the model in school settings. The purpose of the present study was to test the applicability of the model to an actual school setting. Subjects were 149 tenth grade students in a large urban school district. In accordance with the model, specific attributions for success or failure were assessed, as well as subjects' perceptions of the locus, stability, and controllability of attributions. Attribution patterns found in previous analog research were not found in a school setting. Immediate effort attributions were the most prevalent, regardless of performance level or outcome. Causal beliefs were found to relate to performance in ways predicted by the model but also in some ways not predicted. Relationships were generally stronger for high performers. Comparing subjects' perceptions of the dimensional properties of attributions across outcomes showed a strong outcome bias. Attributions were perceived as more internal and stable following successes, consistent with previous research. In addition, a performance level bias was found. Low performers rated attributions as less internal, stable, and controllable following successes and more so following failures than did high performers. This bias, termed the underachievement bias, was discussed in terms of its detrimental effects on school performance. The differences between high and low performers regarding perceptions of dimensionalities were consistent with the predictions of the attribution model. It was concluded that the attribution model is applicable to school settings. Suggestions were made that more applied research be conducted, that intervention programs based on this model should target subjects' perceptions of attributions rather than just the specific attributions themselves, and that because of the differences among subjects in perceptions of dimensional properties of attributions, researchers should obtain a direct measure of subjects' perceptions rather than assuming them.

Market attribution is the problem of interpreting the influence of advertisements onthe user’s decision process. Market attribution is a hard problem, and it happens to be asignificant reason for Google’s revenue. There are broadly two types of attribution models- data-driven and heuristics.This thesis focuses on the data driven attribution modeland explores the viability of using Bayesian Network as market attribution models andbenchmarks the performance against a logistic regression. The data used in this thesiswas prepossessed using undersampling technique. Furthermore, multiple techniques andalgorithms to learn and train Bayesian Network are explored and evaluated.For the given dataset, it was found that Bayesian Network can be used for market at-tribution modeling and that its performance is better than the baseline logistic model. Keywords: Market Attribution Model, Bayesian Network, Logistic Regression.

L’expérience subjective accompagnant un mouvement se construit a posteriori en intégrant différentes sources d’informations qui s’inter-influencent à différents moments tant avant qu’après le mouvement. Cette expérience subjective est interprétée par un modèle d’attribution bayésien afin de créer une expérience d’agentivité et de contrôle sur les mouvements de son propre corps. Afin de déterminer l’apport de l’interaction entre les paramètres considérés par le modèle d’attribution et d’investiguer la présence de disparités inter-individuelles dans la formation de l’expérience subjective du mouvement, une série de 90 pulsations simples de stimulation magnétique transcrânienne (SMT) sur le cortex moteur primaire (M1) suivi de multiples questions sur l’expérience subjective reliée au mouvement provoqué a été effectuée chez 20 participants normaux. Les données objectives du mouvement ont été recueillies par électromyographie (EMG) et capture du mouvement. Un modèle de régression a entre autres été effectué pour chaque participant afin de voir quelle proportion du jugement subjectif pouvait être expliqué par des indices objectifs et cette proportion variait grandement entre les participants. Les résultats de la présente étude indiquent la présence d’une capacité individuelle à se former des jugements subjectifs reflétant adéquatement la réalité comme en témoigne la cohérence entre les différentes mesures d’acuité et plusieurs variables mesurant l’expérience subjective. / Subjective experience is built after the fact by integrating different sources of information that interact with each other at different moments (before, during and after the movement). In order to create subjective experience of agency and control, the characteristics of the movement are interpreted by a Bayesian model. To determine the impact of the interaction between the parameters that enter in that Bayesian attribution model and to investigate possible disparities in the formation of subjective experience of movement between individuals, a series of 90 pulses of single-pulse transcranial magnetic stimulation (TMS) followed by four questions on the subjective experience relative to the movement was conducted on 20 normal subjects. Objective data about the movement was gathered using electromyography (EMG) and motion capture. Individual regression models have been conducted to determine the proportion of the subjective judgements that varied accordingly to the objective parameters of the movement; this proportion varied greatly between participants. The present study proposes that there is an individual capacity to form subjective judgements that adequately represent the reality, as suggested by coherence between different accuracy measures and different variables measuring the subjective experience.

[ES] ¿Cómo es la atribución en un entorno de omnicanal? Se puede determinar una distinción importante en contraste con la atribución en un entorno multicanal. Además de proporcionar el proceso de análisis de marketing, una especificación del proceso estándar intersectorial para la minería de datos (CRISP¿DM), se utiliza un enfoque de método mixto secuencial para analizar la cuestión principal de la investigación. En el primer paso de esta investigación se analizan las características y los requisitos de atribución eficiente en un entorno omnicanal. A partir de entrevistas semiestructuradas con expertos y de un proceso de investigación bibliográfica holística estructurada, se identifica claramente la falta de un enfoque de atribución omnicanal. Los enfoques de atribución existentes se identifican mediante la realización de un proceso estructurado de revisión de la literatura. Estos enfoques identificados se evalúan aplicando los resultados de las entrevistas semiestructuradas con expertos, es decir, los requisitos y características de una atribución omnicanal eficiente. Ninguno de los enfoques de atribución identificados cumple con la mayoría de los requisitos de omnicanal analizados. Al tener la brecha de investigación ¿ la falta de un enfoque de atribución de omnicanales ¿ claramente identificada, se desarrolla un enfoque de atribución de omnicanales en la segunda parte de esta investigación presentada. Utilizando la metodología MAP, la principal laguna de investigación se llena proporcionando el Holistic Customer Journey (HCJ): una base de datos lista para el omni¿canal y un enfoque de atribución de omni¿canal correspondiente. Entre otras cosas, el enfoque de atribución desarrollado consiste en una clasificación de aprendizaje automático. Esta investigación presentada es la primera en utilizar información de casi 240.000.000 de conjuntos de datos de interacción, que contienen información entre dispositivos y entre plataformas. Todas las fuentes de datos subyacentes son proporcionadas por una de las plataformas inmobiliarias más grandes de Alemania. / [CAT] Com és l'atribució en un entorn de omnicanal? Es pot determinar una distinció important en contrast amb l'atribució en un entorn multicanal. A més de proporcionar el procés d'anàlisi de màrqueting, una especificació del procés estàndard intersectorial per a la mineria de dades (CRISP¿DM), s'utilitza un enfocament de mètode mixt seqüencial per analitzar la qüestió principal de la investigació. En el primer pas d'aquesta investigació s'analitzen les característiques i els requisits d'atribució eficient en un entorn omnicanal. A partir d'entrevistes semiestructurades amb experts i d'un procés de recerca bibliogràfica holística estructurada, s'identifica clarament la falta d'un enfocament d'atribució omnicanal. Els enfocaments d'atribució existents s'identifiquen mitjançant la realització d'un procés estructurat de revisió de la literatura. Aquests enfocaments identificats s'avaluen aplicant els resultats de les entrevistes semiestructurades amb experts, és a dir, els requisits i característiques d'una atribució omnicanal eficient. Cap dels enfocaments d'atribució identificats compleix amb la majoria dels requisits de omnicanal analitzats. En tenir la bretxa de recerca ¿ la manca d'un enfocament d'atribució de omnicanales ¿ clarament identificada, es desenvolupa un enfocament d'atribució de omnicanales a la segona part d'aquesta investigació presentada. Utilitzant la metodologia MAP, la principal llacuna de recerca s'omple proporcionant el Holistic Customer Journey (HCJ): una base de dades a punt per al omni¿canal i un enfocament d'atribució de omni¿canal corresponent. Entre altres coses, l'enfocament d'atribució desenvolupat consisteix en una classificació d'aprenentatge automàtic. Aquesta investigació presentada és la primera a utilitzar informació de gairebé 240.000.000 de conjunts de dades d'interacció, que contenen informació entre dispositius i entre plataformes. Totes les fonts de dades subjacents són proporcionades per una de les plataformes immobiliàries més grans d'Alemanya. / [EN] What does attribution in an omni¿channel environment look like? A major distinction can be determined in contrast to attribution in a multi¿channel environment. Besides providing the Marketing Analytics Process, a specification of the Cross¿industry standard process for data mining (CRISP¿DM), a sequential mixed method approach is utilized to analyze the main research question. Within the first step of this presented research characteristics, and requirements of efficient attribution in an omni¿channel environment are analyzed. Based on semi¿structured expert interviews and a holistic structured literature research process, the lack of an omni¿channel attribution approach is clearly identified. Existing attribution approaches are identified by conducting the structured literature review process. Those identified approaches are evaluated by applying the results of the semi¿structured expert interviews - the requirements and characteristics of efficient omni¿channel attribution. None of the identified attribution approaches fulfill a majority of the analyzed omni¿channel requirements. By having the research gap - the lack of an omni¿channel attribution approach - clearly identifed, an omni¿channel attribution approach is developed in the second part of this presented research. Utilizing the MAP methodology, the main research gap is filled by providing the Holistic Customer Journey (HCJ): an omni¿channel ready data foundation and a corresponding omni¿channel attribution approach. Among other things the developed attribution approach consists of a machine learning classification. This presented research is the first to utilize information from almost 240.000.000 interaction data sets, containing crossdevice and cross¿platform information. All underlying data sources are provided by one of Germany's largest real¿estate platforms. / Nass, O. (2019). Optimizing Online Marketing Efficiency By Analyzing the Mutual Influence of Online Marketing Channels with Respect to Different Devices [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/122296 / TESIS