The following qualitative research explores the adoption of Zero Trust in organisations from an organisational and user acceptance perspective. From an organisational perspective, the research highlights essential aspects such as testing the Zero Trust architecture in a pre-adoption phase, involving top management in the planning phase, communicating in a non-technical language, and making end-users feel a personal connection to IS security. The research highlights the importance of balancing the ease of use with security, evaluating the end-user's technical maturity, and carrying out evaluations from a user acceptance perspective. To gather valuable empirical data, the researcher has conducted semi-structured interviews with highly competent respondents within the field of Zero Trust. Most of the literature available today within Zero Trust focuses on technical aspects, and this research is a unique and vital contribution to the limited available research.

The transportation sector is responsible for the majority of airborne particles and global energy consumption in urban areas. Its role in generating air pollution in urban areas is even more critical, as many visitors, commuters and citizens travel there daily for various reasons. Emissions released by transport fleets have an exhaust (tailpipe) and a non-exhaust (brake wears ) origin. Both exhaust and non-exhaust airborne particles can have destructive effects on the human cardiovascular and respiratory systems and even lead to premature deaths. This dissertation aims to estimate the amount of exhaust and brake emissions in a real case study by proposing an innovative methodology. For this purpose, different levels of study have been introduced, including the subsystem level, the system level, the environmental level and the suprasystem level. To address these levels, two approaches were proposed along with a data collection process. First, a comprehensive field survey was conducted in the area of Buonconsiglio Castle and data was collected on traffic and non-traffic during peak hours. Then, in the first approach, a state-of-the-art simulation-based method was presented to estimate the amount of exhaust emissions generated and the rate of fuel consumption in the case study using the VISSIM traffic microsimulation software and Enviver emission modeler at the suprasystem level. In order to calculate the results under different mobility conditions, a total of 18 scenarios were defined based on changes in vehicle speeds and the share of heavy vehicles (HV%) in the modal split. Subsequently, the scenarios were accurately modelled in the simulation software VISSIM and repeated 30 times with a simulation runtime of three hours. The results of the first approach confirmed the simultaneous effects of considering vehicle speed and HV % on fuel consumption and the amount of exhaust emissions generated. Furthermore, the sensitivity of exhaust emissions and fuel consumption to variations in vehicle speed was found to be much higher than HV %. In other words, the production of NOx and VOC emissions can be increased by up to 20 % by increasing the maximum speed of vehicles by 10 km/h. Conversely, increasing the HV percentage at the same speed does not seem to produce a significant change. Furthermore, increasing the speed from 30 km/h to 50 km/h increased CO emissions and fuel consumption by up to 33%. Similarly, a reduction in speed of 20 or 10 km/h with a 100% increase in HV resulted in a 40% and 27% decrease in exhaust emissions per seat, respectively. In the second approach, a novel methodology was proposed to estimate the number of brake particles in the case study. To achieve this goal, a downstream approach was proposed starting from the suprasystem level (microscopic traffic simulation models in VISSIM) and using a developed mathematical vehicle dynamics model at the system level to calculate the braking torques and angular velocities of the front and rear wheels, and proposes an artificial neural network (ANN) as a brake emission model, which has been appropriately trained and validated using emission data collected through more than 1000 experimental tribological tests on a reduced-scale dynamometer at the subsystem level (braking system). Consideration of this multi-level approach, from tribological to traffic-related aspects, is necessary for a realistic estimation of brake emissions. The proposed method was implemented on a targeted vehicle, a dominant SUV family car in the case study, considering real driving conditions. The relevant dynamic quantities of the targeted vehicle (braking torques and angular velocities of the wheels) were calculated based on the vehicle trajectory data such as speed and deceleration obtained from the traffic microsimulation models and converted into braking emissions via the artificial neural network. The total number of brake emissions emitted by the targeted vehicles was predicted for 10 iterations route by route and for the entire traffic network. The results showed that a large number of brake particles (in the order of billions of particles) are released by the targeted vehicles, which significantly affect the air quality in the case study. The results of this dissertation provide important information for policy makers to gain better insight into the rate of exhaust and brake emissions and fuel consumption in metropolitan areas and to understand their acute negative impacts on the health of citizens and commuters.

No description available.

Extracting useful knowledge from data sets is a key concept in modern information systems. Consequently, the need of efficient techniques to extract the desired knowledge has been growing over time. Machine learning is a research field dedicated to the development of techniques capable of enabling a machine to \"learn\" from data. Many techniques have been proposed so far, but there are still issues to be unveiled specially in interdisciplinary research. In this thesis, we explore the advantages of network data representation to develop machine learning techniques based on dynamical processes on networks. The network representation unifies the structure, dynamics and functions of the system it represents, and thus is capable of capturing the spatial, topological and functional relations of the data sets under analysis. We develop network-based techniques for the three machine learning paradigms: supervised, semi-supervised and unsupervised. The random walk dynamical process is used to characterize the access of unlabeled data to data classes, configuring a new heuristic we call ease of access in the supervised paradigm. We also propose a classification technique which combines the high-level view of the data, via network topological characterization, and the low-level relations, via similarity measures, in a general framework. Still in the supervised setting, the modularity and Katz centrality network measures are applied to classify multiple observation sets, and an evolving network construction method is applied to the dimensionality reduction problem. The semi-supervised paradigm is covered by extending the ease of access heuristic to the cases in which just a few labeled data samples and many unlabeled samples are available. A semi-supervised technique based on interacting forces is also proposed, for which we provide parameter heuristics and stability analysis via a Lyapunov function. Finally, an unsupervised network-based technique uses the concepts of pinning control and consensus time from dynamical processes to derive a similarity measure used to cluster data. The data is represented by a connected and sparse network in which nodes are dynamical elements. Simulations on benchmark data sets and comparisons to well-known machine learning techniques are provided for all proposed techniques. Advantages of network data representation and dynamical processes for machine learning are highlighted in all cases / A extração de conhecimento útil a partir de conjuntos de dados é um conceito chave em sistemas de informação modernos. Por conseguinte, a necessidade de técnicas eficientes para extrair o conhecimento desejado vem crescendo ao longo do tempo. Aprendizado de máquina é uma área de pesquisa dedicada ao desenvolvimento de técnicas capazes de permitir que uma máquina \"aprenda\" a partir de conjuntos de dados. Muitas técnicas já foram propostas, mas ainda há questões a serem reveladas especialmente em pesquisas interdisciplinares. Nesta tese, exploramos as vantagens da representação de dados em rede para desenvolver técnicas de aprendizado de máquina baseadas em processos dinâmicos em redes. A representação em rede unifica a estrutura, a dinâmica e as funções do sistema representado e, portanto, é capaz de capturar as relações espaciais, topológicas e funcionais dos conjuntos de dados sob análise. Desenvolvemos técnicas baseadas em rede para os três paradigmas de aprendizado de máquina: supervisionado, semissupervisionado e não supervisionado. O processo dinâmico de passeio aleatório é utilizado para caracterizar o acesso de dados não rotulados às classes de dados configurando uma nova heurística no paradigma supervisionado, a qual chamamos de facilidade de acesso. Também propomos uma técnica de classificação de dados que combina a visão de alto nível dos dados, por meio da caracterização topológica de rede, com relações de baixo nível, por meio de medidas de similaridade, em uma estrutura geral. Ainda no aprendizado supervisionado, as medidas de rede modularidade e centralidade Katz são aplicadas para classificar conjuntos de múltiplas observações, e um método de construção evolutiva de rede é aplicado ao problema de redução de dimensionalidade. O paradigma semissupervisionado é abordado por meio da extensão da heurística de facilidade de acesso para os casos em que apenas algumas amostras de dados rotuladas e muitas amostras não rotuladas estão disponíveis. É também proposta uma técnica semissupervisionada baseada em forças de interação, para a qual fornecemos heurísticas para selecionar parâmetros e uma análise de estabilidade mediante uma função de Lyapunov. Finalmente, uma técnica não supervisionada baseada em rede utiliza os conceitos de controle pontual e tempo de consenso de processos dinâmicos para derivar uma medida de similaridade usada para agrupar dados. Os dados são representados por uma rede conectada e esparsa na qual os vértices são elementos dinâmicos. Simulações com dados de referência e comparações com técnicas de aprendizado de máquina conhecidas são fornecidos para todas as técnicas propostas. As vantagens da representação de dados em rede e de processos dinâmicos para o aprendizado de máquina são evidenciadas em todos os casos

本研究建構一社會網路互動下的新凱因斯動態隨機一般均衡模型，探討效用基礎下波茲曼分配背後的網路結構，以及，社會網路對新凱因斯動態隨機一般均衡模型參數的影響。根據本論文模擬結果，效用基礎下波茲曼分配背後所隱含的社會網路結構呈現局部區域性連結拓璞，此結論與熱力學對波茲曼分配中粒子互動方式的假設相同，然而，區域性連結之網路結構（如環狀網）並非目前實證研究所觀察到的網路型態（如冪分布網路或高群集係數之小世界網路），故吾人是否得以直接利用效用基礎下波茲曼分配來描述社會上人與人之間的互動現象必需更忱慎考量之。另外，社會網路互動也將使新凱因斯動態隨機一般均衡模型之參數估計產生偏誤，依本研究估計結果觀之，只要加入社會互動，總合需求曲線中實質利率之參數估計將為正號，即實質利率對產出缺口的影響為負向影響，也就是文獻上的投資儲蓄迷思(IS puzzle)，若進一步觀察社會網路結構對該實證迷思的影響則可發現當社會網路群聚程度越高時，該估計偏誤將越嚴重。 / We construct a social network-based New Keynesian DSGE (Dynamic Stochastic General Equilibrium) Model to investigate the underlying social network structure derived from the performance-based Boltzmann-Gibbs model, and thus interpret the process that social network structures affect the estimation bias in the New Keynesian DSGE framework. According to our simulation results, the underlying social network structure derived from the performance-based Boltzmann-Gibbs model should be local. This finding is consistent with the study of thermodynamics, which the Boltzmann-Gibbs distribution is based upon, i.e. the local interaction. However, it contradicts not only the purpose of combining the performance-based Boltzmann-Gibbs machine and New Keynesian DSGE model, but also empirical studies of social network structures in the real world. Accordingly, maybe we have to consider further whether the performance-based Boltzmann-Gibbs machine is a suitable tool for calibrating social interaction under the stylized New Keynesian DSGE framework. Furthermore, if we embedded interaction behavior in the stylized New Keynesian model, the so-called “IS Puzzle” can be consequently observed. We also realized that “IS Puzzle” is connected with network structures. The more clustering the network structure is, the more significant “IS Puzzle” would be.

Carefree handling refers to the ability of a pilot to operate an aircraft without the need to continuously monitor aircraft operating limits. At the heart of all carefree handling or maneuvering systems, also referred to as envelope protection systems, are algorithms and methods for predicting future limit violations. Recently, envelope protection methods that have gained more acceptance, translate limit proximity information to its equivalent in the control channel. Envelope protection algorithms either use very small prediction horizon or are static methods with no capability to adapt to changes in system configurations. Adaptive approaches maximizing prediction horizon such as dynamic trim, are only applicable to steady-state-response critical limit parameters. In this thesis, a new adaptive envelope protection method is developed that is applicable to steady-state and transient response critical limit parameters. The approach is based upon devising the most aggressive optimal control profile to the limit boundary and using it to compute control limits. Pilot-in-the-loop evaluations of the proposed approach are conducted at the Georgia Tech Carefree Maneuver lab for transient longitudinal hub moment limit protection. Carefree maneuvering is the dual of carefree handling in the realm of autonomous Uninhabited Aerial Vehicles (UAVs). Designing a flight control system to fully and effectively utilize the operational flight envelope is very difficult. With the increasing role and demands for extreme maneuverability there is a need for developing envelope protection methods for autonomous UAVs. In this thesis, a full-authority automatic envelope protection method is proposed for limit protection in UAVs. The approach uses adaptive estimate of limit parameter dynamics and finite-time horizon predictions to detect impending limit boundary violations. Limit violations are prevented by treating the limit boundary as an obstacle and by correcting nominal control/command inputs to track a limit parameter safe-response profile near the limit boundary. The method is evaluated using software-in-the-loop and flight evaluations on the Georgia Tech unmanned rotorcraft platform- GTMax. The thesis also develops and evaluates an extension for calculating control margins based on restricting limit parameter response aggressiveness near the limit boundary.

Extracting useful knowledge from data sets is a key concept in modern information systems. Consequently, the need of efficient techniques to extract the desired knowledge has been growing over time. Machine learning is a research field dedicated to the development of techniques capable of enabling a machine to \"learn\" from data. Many techniques have been proposed so far, but there are still issues to be unveiled specially in interdisciplinary research. In this thesis, we explore the advantages of network data representation to develop machine learning techniques based on dynamical processes on networks. The network representation unifies the structure, dynamics and functions of the system it represents, and thus is capable of capturing the spatial, topological and functional relations of the data sets under analysis. We develop network-based techniques for the three machine learning paradigms: supervised, semi-supervised and unsupervised. The random walk dynamical process is used to characterize the access of unlabeled data to data classes, configuring a new heuristic we call ease of access in the supervised paradigm. We also propose a classification technique which combines the high-level view of the data, via network topological characterization, and the low-level relations, via similarity measures, in a general framework. Still in the supervised setting, the modularity and Katz centrality network measures are applied to classify multiple observation sets, and an evolving network construction method is applied to the dimensionality reduction problem. The semi-supervised paradigm is covered by extending the ease of access heuristic to the cases in which just a few labeled data samples and many unlabeled samples are available. A semi-supervised technique based on interacting forces is also proposed, for which we provide parameter heuristics and stability analysis via a Lyapunov function. Finally, an unsupervised network-based technique uses the concepts of pinning control and consensus time from dynamical processes to derive a similarity measure used to cluster data. The data is represented by a connected and sparse network in which nodes are dynamical elements. Simulations on benchmark data sets and comparisons to well-known machine learning techniques are provided for all proposed techniques. Advantages of network data representation and dynamical processes for machine learning are highlighted in all cases / A extração de conhecimento útil a partir de conjuntos de dados é um conceito chave em sistemas de informação modernos. Por conseguinte, a necessidade de técnicas eficientes para extrair o conhecimento desejado vem crescendo ao longo do tempo. Aprendizado de máquina é uma área de pesquisa dedicada ao desenvolvimento de técnicas capazes de permitir que uma máquina \"aprenda\" a partir de conjuntos de dados. Muitas técnicas já foram propostas, mas ainda há questões a serem reveladas especialmente em pesquisas interdisciplinares. Nesta tese, exploramos as vantagens da representação de dados em rede para desenvolver técnicas de aprendizado de máquina baseadas em processos dinâmicos em redes. A representação em rede unifica a estrutura, a dinâmica e as funções do sistema representado e, portanto, é capaz de capturar as relações espaciais, topológicas e funcionais dos conjuntos de dados sob análise. Desenvolvemos técnicas baseadas em rede para os três paradigmas de aprendizado de máquina: supervisionado, semissupervisionado e não supervisionado. O processo dinâmico de passeio aleatório é utilizado para caracterizar o acesso de dados não rotulados às classes de dados configurando uma nova heurística no paradigma supervisionado, a qual chamamos de facilidade de acesso. Também propomos uma técnica de classificação de dados que combina a visão de alto nível dos dados, por meio da caracterização topológica de rede, com relações de baixo nível, por meio de medidas de similaridade, em uma estrutura geral. Ainda no aprendizado supervisionado, as medidas de rede modularidade e centralidade Katz são aplicadas para classificar conjuntos de múltiplas observações, e um método de construção evolutiva de rede é aplicado ao problema de redução de dimensionalidade. O paradigma semissupervisionado é abordado por meio da extensão da heurística de facilidade de acesso para os casos em que apenas algumas amostras de dados rotuladas e muitas amostras não rotuladas estão disponíveis. É também proposta uma técnica semissupervisionada baseada em forças de interação, para a qual fornecemos heurísticas para selecionar parâmetros e uma análise de estabilidade mediante uma função de Lyapunov. Finalmente, uma técnica não supervisionada baseada em rede utiliza os conceitos de controle pontual e tempo de consenso de processos dinâmicos para derivar uma medida de similaridade usada para agrupar dados. Os dados são representados por uma rede conectada e esparsa na qual os vértices são elementos dinâmicos. Simulações com dados de referência e comparações com técnicas de aprendizado de máquina conhecidas são fornecidos para todas as técnicas propostas. As vantagens da representação de dados em rede e de processos dinâmicos para o aprendizado de máquina são evidenciadas em todos os casos

Les travaux décrits dans cette thèse apportent une contribution au problème de la navigation autonome de robots mobiles dans un contexte de vision indoor. Il s’agit de chercher à concilier les avantages des différents paradigmes d’architecture de contrôle et des stratégies de navigation. Ainsi, nous proposons l’architecture RHIZOME (Robotic Hybrid Indoor-Zone Operational ModulE) : une architecture unique de contrôle robotique mettant en synergie ces différentes approches en s’appuyant sur un système neuronal. Les interactions du robot avec son environnement ainsi que les multiples connexions neuronales permettent à l’ensemble du système de s’adapter aux conditions de navigation. L’architecture RHIZOME proposée combine les avantages des approches comportementales (e.g. rapidité de réaction face à des problèmes imprévus dans un contexte d’environnement dynamique), et ceux des approches délibératives qui tirent profit d’une connaissance a priori de l’environnement. Cependant, cette connaissance est uniquement exploitée pour corroborer les informations perçues visuellement avec celles embarquées. Elle est représentée par une séquence de symboles artificiels de navigation guidant le robot vers sa destination finale. Cette séquence est présentée au robot soit sous la forme d’une liste de paramètres, soit sous la forme d’un plan. Dans ce dernier cas, le robot doit extraire lui-même la séquence de symboles à suivre grâce à une chaine de traitements d’images. Ainsi, afin de prendre la bonne décision lors de sa navigation, le robot traite l’ensemble de l’information perçue, la compare en temps réel avec l’information a priori apportée ou extraite, et réagit en conséquence. Lorsque certains symboles de navigation ne sont plus présents dans l’environnement de navigation, l’architecture RHIZOME construit de nouveaux lieux de référence à partir des panoramas extraits de ces lieux. Ainsi, le robot, lors de phases exploratoires, peut s’appuyer sur ces nouvelles informations pour atteindre sa destination finale, et surmonter des situations imprévues. Nous avons mis en place notre architecture sur le robot humanoïde NAO. Les résultats expérimentaux obtenus lors d’une navigation indoor, dans des scenarios à la fois déterministes et stochastiques, montrent la faisabilité et la robustesse de cette approche unifiée. / The work described in this dissertation is a contribution to the problem of autonomous indoor vision-based mobile robot navigation, which is still a vast ongoing research topic. It addresses it by trying to conciliate all differences found among the state-of-the-art control architecture paradigms and navigation strategies. Hence, the author proposes the RHIZOME architecture (Robotic Hybrid Indoor-Zone Operational ModulE) : a unique robotic control architecture capable of creating a synergy of different approaches by merging them into a neural system. The interactions of the robot with its environment and the multiple neural connections allow the whole system to adapt to navigation conditions. The RHIZOME architecture preserves all the advantages of behavior-based architectures such as rapid responses to unforeseen problems in dynamic environments while combining it with the a priori knowledge of the world used indeliberative architectures. However, this knowledge is used to only corroborate the dynamic visual perception information and embedded knowledge, instead of directly controlling the actions of the robot as most hybrid architectures do. The information is represented by a sequence of artificial navigation signs leading to the final destination that are expected to be found in the navigation path. Such sequence is provided to the robot either by means of a program command or by enabling it to extract itself the sequence from a floor plan. This latter implies the execution of a floor plan analysis process. Consequently, in order to take the right decision during navigation, the robot processes both set of information, compares them in real time and reacts accordingly. When navigation signs are not present in the navigation environment as expected, the RHIZOME architecture builds new reference places from landmark constellations, which are extracted from these places and learns them. Thus, during navigation, the robot can use this new information to achieve its final destination by overcoming unforeseen situations.The overall architecture has been implemented on the NAO humanoid robot. Real-time experimental results during indoor navigation under both, deterministic and stochastic scenarios show the feasibility and robustness of the proposed unified approach.

Mapping of the human genome has been instrumental in understanding diseasescaused by changes in single genes. However, disease mechanisms involvingmultiple genes have proven to be much more elusive. Their complexityemerges from interactions of intracellular molecules and makes them immuneto the traditional reductionist approach. Only by modelling this complexinteraction pattern using networks is it possible to understand the emergentproperties that give rise to diseases.The overarching term used to describe both physical and indirect interactionsinvolved in the same functions is functional association. FunCoup is oneof the most comprehensive networks of functional association. It uses a naïveBayesian approach to integrate high-throughput experimental evidence of intracellularinteractions in humans and multiple model organisms. In the firstupdate, both the coverage and the quality of the interactions, were increasedand a feature for comparing interactions across species was added. The latestupdate involved a complete overhaul of all data sources, including a refinementof the training data and addition of new class and sources of interactionsas well as six new species.Disease-specific changes in genes can be identified using high-throughputgenome-wide studies of patients and healthy individuals. To understand theunderlying mechanisms that produce these changes, they can be mapped tocollections of genes with known functions, such as pathways. BinoX wasdeveloped to map altered genes to pathways using the topology of FunCoup.This approach combined with a new random model for comparison enables BinoXto outperform traditional gene-overlap-based methods and other networkbasedtechniques.Results from high-throughput experiments are challenged by noise and biases,resulting in many false positives. Statistical attempts to correct for thesechallenges have led to a reduction in coverage. Both limitations can be remediedusing prioritisation tools such as MaxLink, which ranks genes using guiltby association in the context of a functional association network. MaxLink’salgorithm was generalised to work with any disease phenotype and its statisticalfoundation was strengthened. MaxLink’s predictions were validatedexperimentally using FRET.The availability of prioritisation tools without an appropriate way to comparethem makes it difficult to select the correct tool for a problem domain.A benchmark to assess performance of prioritisation tools in terms of theirability to generalise to new data was developed. FunCoup was used for prioritisationwhile testing was done using cross-validation of terms derived fromGene Ontology. This resulted in a robust and unbiased benchmark for evaluationof current and future prioritisation tools. Surprisingly, previously superiortools based on global network structure were shown to be inferior to a localnetwork-based tool when performance was analysed on the most relevant partof the output, i.e. the top ranked genes.This thesis demonstrates how a network that models the intricate biologyof the cell can contribute with valuable insights for researchers that study diseaseswith complex genetic origins. The developed tools will help the researchcommunity to understand the underlying causes of such diseases and discovernew treatment targets. The robust way to benchmark such tools will help researchersto select the proper tool for their problem domain. / <p>At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 5: Manuscript. Paper 6: Manuscript.</p>

Le but de cette thèse est de comprendre comment les entreprises peuvent faire augmenter une expérience donnant naissance à l’engagement des consommateurs grâce aux nouveaux médias (comme les vidéos du Web, les dispositifs de téléphonie mobile et la télévision "traditionnelle") afin de stimuler le comportement actif des clients et de redéfinir des stratégies commerciales de marketing. Nous avons structuré notre analyse sur trois études d’approche.Dans la première étude, nous avons décrit comment l'engagement personnel avec le contenu et l'engagement social interactif (résultant du sens perçu de la communauté, du sentiment d'appréciation intrinsèque et de la participation à l'expérience) influencent différemment le comportement actif et passif sur des sites de télévision sociale. Nous avons testé des hypothèses en estimant un modèle d'équation structurale avec les données d'une enquête sur un groupe de 814 utilisateurs de télévision sociale aux Etats-Unis et en Europe. Dans la deuxième étude, nous examinons l'influence de l'intrusion dans la vie privée sur la relation entre l'engagement expérientiel (c'est à dire l'engagement personnel et l'engagement interactif et social) et l'utilisation active et passif et nous avons testé ces hypothèses (379 utilisateurs) en tenant compte de services de géolocalisation sur téléphonie mobile. Dans la troisième étude, nous avons élargi notre cadre conceptuel et étudié les effets de l'engagement social interactif sur l'identité sociale et l'appréciation des marques. Le modèle a été validé expérimentalement en menant une enquête sur des pages de fans de Facebook de 20 grandes marques internationales situées en Europe et aux Etats-Unis (panel de 387 personnes). Les résultats émergeant des trois études prouvent que l'engagement expérientiel a des effets positifs sur le comportement du consommateur (actif et passif) en ligne et qu'il contraste avec l'effet négatif de l'atteinte à la vie privée. Les résultats obtenus confirment les effets positifs de l'engagement social et interactif sur les rapports affectifs des consommateurs pour une marque et le plein effet de l'identité sociale. De manière plus spécifique, les annonceurs publicitaires, qui forcent les expériences pouvant influencer l'engagement social et interactif, peuvent aussi influer sur l'identité sociale et le rapport avec une marque. / The thesis aims to understand how companies can leverage on consumer experiential engagement in new-media based social media environments (using video on the web, handheld devices and web 2.0) in order to stimulate active behavior and redefine commercial marketing strategies. We structure our analysis on a three studies approach. The first study describes how Personal Engagement with the content and Social-Interactive Engagement (resulting from the perceived sense of community, intrinsic enjoyment and participation experience) differently influence both active and passive behavior. We test hypotheses with survey data from a sample of 814 US and EU social TV users. In study 2 we examine the influence of privacy intrusiveness on the relation between Experiential Engagement (Personal and Social-interactive Engagement) and active and passive use and we test it (n=379) with reference to mobile location-based social networking applications in EU and US. In study 3 we develop a conceptual model in which social-interactive engagement influences social identity directly and brand love indirectly through the mediating effect of social identity. The model was empirically validated (n=387) on the Facebook fan pages of 20 leading international brands in EU and the US. Findings emerging from the three studies show that Experiential Engagement has positive effects on the consumer behavior online (active and passive) and it may contrast the negative effect of privacy intrusiveness. The results obtained show also a positive effects of social-interactive engagement on consumer-brand affective relationships (brand love) and the full mediating effect of social identity. More specifically advertisers, leveraging on experiences that influence social-interactive engagement can influence the social identity and the relationship with the brand