Purpose: The purpose of this study is to investigate and understand the consumer perspective and intentions toward the prospect of a future without third-party cookies.Design/Methodology/Approach: This thesis utilized a combination of exploratory and confirmatory research design. A mixed-method approach was used for the data collection. The qualitative stage included focus groups. This was followed by a quantitative stage, conducted by means of an online survey.Findings: The finding from the focus groups indicated that Internet users have a negative opinion of the prospect of a world without third-party cookies, with positive expectations like improved user experience. Furthermore, results from both the survey and focus groups reveal a glaring lack of information when it comes to the subject of the gradual elimination of third-party cookies. The survey findings show the existence of a connection between user intention and distrust as well as user intention and privacy concerns.Theoretical Implications: Novel contributions have been made in several fields of business administration, specifically, the domains of digital marketing, consumer behavior, and consumer psychology. Contributions have also been provided by blending the theories from the above domains. Furthermore, this study established the addition of the Technology Acceptance Model, and Communication Privacy Management Theory to research the cookieless future, offering new useful insights on the underresearched topic of the cookieless future.Practical/Societal Implications: In the absence of cookies, organizations must improve their communication with online users. Furthermore, organizations and institutions are both responsible for educating everyday online users on topics such as online policies, data collection procedures, and data protection regulations. Moreover, organizations will have to focus on improving the online user experience in the absence of third-party cookies.Limitations/Future Research: Having a larger sample size would have allowed for better generalizability. Furthermore, participants were mostly Millennials (i.e., born between 1981 and 1996) and Gen Z (i.e., born between 1997 and 2012) in the case of both the qualitative and quantitative parts of the study. An even wider distribution in age within the sample could have resulted in a different statistical analysis. Additionally, the survey and the focus groups were conducted exclusively in English, limiting the study to English speakers. Future research could investigate the users’ perceptions and behavior after Google has fully removed third-party cookies. Future research can also take an in-depth look into how the removal of cookies will affect personalized advertisements. Moreover, further studies could also investigate bigger populations. Different populations could be investigated as well: different generations, or different countries. Lastly, a longitudinal study of this thesis after the completed removal of third-party cookies could be a fruitful research direction.Originality: This is the first study that examines consumer perceptions and intentions in the context of the cookieless future, to the authors' knowledge. It is also the first to academically examine the cookieless future using a mixed-method approach, to the authors' knowledge.

The number of mobile applications (i.e., apps) is rapidly growing, as the mobile computing becomes an integral part of the modern user experience. Malicious apps have infiltrated open marketplaces for mobile platforms. These malicious apps can exfiltrate user's private data, abuse of system resources, or disrupting regular services. Despite the recent advances on mobile security, the problem of detecting vulnerable and malicious mobile apps with high detection accuracy remains an open problem. In this thesis, we address the problem of Android security by presenting a new quantitative program analysis framework for security vetting of Android apps. We first introduce a highly accurate proactive detection solution for detecting individual malicious apps. Our approach enforces benign property as opposed of chasing malware signatures, and uses one complex feature rather than multi-feature as in the existing malware detection methods. In particular, we statically extract a data-flow feature on how user inputs trigger sensitive critical operations, a property referred to as the user-trigger dependence. This feature is extracted through nontrivial Android-specific static program analysis, which can be used in various quantitative analytical methods. Our evaluation on thousands of malicious apps and free popular apps gives a detection accuracy (2% false negative rate and false positive rate) that is better than, or at least competitive against, the state-of-the-art. Furthermore, our method discovers new malicious apps available in the Google Play store that have not been previously detected by anti-virus scanning tools. Second, we present a new app collusion detection approach and algorithms to analyze pairs or groups of communicating apps. App collusion is a new technique utilized by the attackers to evade standard detection. It is a new threat where two or more apps, appearing benign, communicate to perform malicious task. Most of the existing solutions assume the attack model of a stand-alone malicious app, and hence cannot detect app collusion. We first demonstrate experimental evidence on the technical challenges associated with detecting app collusion. Then, we address these challenges by introducing a scalable and an in-depth cross-app static flow analysis approach to identify the risk level associated with communicating apps. Our approach statically analyzes the sensitivity and the context of each inter-app communication with low analysis complexity, and defines fine-grained security policies for the inter-app communication risk detection. Our evaluation results on thousands of free popular apps indicate that our technique is effective. It generates four times fewer false positives compared to the state-of-the-art collusion-detection solution, enhancing the detection capability. The advantages of our inter-app communication analysis approach are the analysis scalability with low complexity, and the substantially improved detection accuracy compared to the state-of-the-art solution. These types of proactive defenses solutions allow defenders to stay proactive when defending against constantly evolving malware threats. / Ph. D.

IoT is a technology where different devices are equipped with internet connection which makes it possible to control them and exchange data over internet. IoT can be thought of as an umbrella term covering a broad and ever-growing range of services and technologies. One of the segments within IoT is the smart home ecosystem. The tremendous development the last decade within smartphones, wearable devices and broadband has created new ways to connect individual devices in the home (Qasim and Abu-Shanab, 2016; Jeong et al, 2016; Wilson et al, 2017; Hubert et al, 2017). This creates a synergy effect; by connecting multiple devices to a system new value is created. Energy, home controls, security, communication and entertainment services are all included in the smart home (Miller, 2015; Wilson et al, 2017). Even though the concept of smart homes has a large potential it seems like it has not reached its full potential and the diffusion of the innovation among the consumers is still at an early stage (Balta-Ozkan et.al, 2013; Yang et.al 2017). So far, many studies have been performed on the technical aspects of IoT and smart home ecosystems but less attention has been paid on the consumer point of view and what determinants that play a role in the intention to adopt the technology (Yang, Lee, and Zo. 2017). In addition, previous studies have mainly focused of one single device and has not considered the entire ecosystem (Yang, Lee, and Zo. 2017). Therefore, the purpose with this thesis is to study what are the key determinants for the intention to adopt smart homes from an ecosystem point of view. To fulfill the purpose known theoretical models regarding intention to adopt technology have been used to develop a research model. The basis to establish the research model has been the theory of innovation adoption, TRA, TPB, TAM, VAM and UTAUT. Based on the literature four determinants were selected to be included in the model; these were cost, perceived ease of use, perceived usefulness and individualization. The first three are all included in the mentioned theoretical models and have previously been proven to be important for intention to adopt. The last one, individualization is derived from the field of product differentiation. In the literature it is mentioned that the possibility to refine, adjust and modify may be crucial for the user (Dodgson et.al. 2008). With this background it was interested to include individualization as a determinant in the research model and study how it impacts intention to adopt. In addition to the determinants one moderator was included; the composition of the household. In order to collect the empirical data a survey was conducted using the snowball sampling approach via Facebook and LinkedIn. The survey consisted of two sections where the first section aimed to collect background information about the respondent and the second section consisted of questions regarding the determinants. In the second section the respondents were asked to respond according to a 5-point Likert scale. The used questions in the survey was predefined in the literature. Study results show that consumers’ use intention is shaped by individualization, perceived usefulness and perceived ease of use. Cost was found not to be statistically significant. Neither was the composition of the household.

網路科技不斷進步，服務創新與商業模式陸續推出。線上影音分享網站為目前當紅的領域，但對於網站該如何設計以及使用者為何使用影音分享網站都未有明確準則。因此本研究藉由科技接受行為相關理論的回顧，配合影音分享網站之特性，以Davis(1989)的科技接受模式為基礎，結合相關重要變數，提出概念性架構。目的為找出可能影響網站使用者的相關變數、並瞭解Web2.0影音網站使用者之使用意圖。 實驗方法採用線上問卷方式，在回收的501份問卷中，得到492份有效問卷，以結構方程模式進行研究模式分析。分析結果顯示，研究模式之適配度均達到應有標準。 研究結論章節中會說明本研究之管理意涵，並將研究結果提供給未來欲設立Web2.0影音分享網站的設計者，在網站建立初期，將有限資源投注在重要的變數上，使網站可達到最大效益。 / As the advance of Internet technology continues new business models are emerging in the market. Online video sharing website is the hottest application nowadays, but there is little study on designing the website and why the users using the website. In this research, we propose a conceptual model based on the technology acceptance model developed by Davis (1989) and this model integrating the important variables due to the extant research of relevant theory of technology acceptance and characteristics of video sharing website. The data collection was used the online survey, and we got the 492 eligible data and the analysis was used the Structural Equation Model (SEM). According to the result, the model fit was qualified. This research will give some management implication for designers who want to set up a video sharing website, this research provides the information on how to invest the limited resource on the critical variables in order to maximize the service value in the conclusion section in this paper.

Depuis des décennies, la seule obsession de la stratégie organisationnelle des entreprises est de pouvoir fidéliser les consommateurs en les incitants à acheter le produit ou service qu’ils offrent afin de pouvoir améliorer leurs performances financières. Les réseaux sociaux vont transformer en profondeur le fonctionnement organisationnel des entreprises. Les réseaux sociaux dévoilent la structure organisationnelle d'une entreprise car ils sont également les porteurs d'information « confidentielles » livrées sans réserve par des employés peu discrets. Les RS sont une force puissante avec lesquelles les entreprises doivent dorénavant composer, à l’heure où beaucoup d’entre elles tentent de se réinventer et de se projeter dans un monde en pleine mutation. D’après Stéphane Hugon, 2012, Le digital est le révélateur d’une transformation de la société. Les jeunes sont dans le court, l’intense ; une temporalité qui colle aux outils comme les réseaux sociaux. Ces transformations impactent directement la culture du travail et sa structure organisationnelle. / For decades, the only obsession of organizational business strategy is to build consumer loyalty to justify the buying intention of the product or service they offer in order to improve financial performance. Social networks will fundamentally transform the organizational functioning of companies. Social networks unveil the organizational structure of a company because they are the holders of "confidential" information delivered unreservedly by little discreet employees. Social Media is a powerful force / tool, which companies must now deal with, at a time when many of them are trying to reinvent themselves and to plan for a changing world. According to Stéphane Hugon, 2012 the digital world is a hint of a transformation in our society. Young people are more oriented to the just in time, instant information and the intensity if the new era; a temporality that sticks to tools such as social networks. These changes directly influence the work culture and organizational structure.

La sélection de cibles en mouvement est une tâche courante et complexe dans l'interaction homme-machine (IHM) en général et en particulier dans le domaine de la réalité virtuelle (RV). La prédiction de l'action est une solution intégrale pour aborder les problèmes liés à l'interaction. Cependant, les techniques actuelles de prédiction sont basées sur le suivi continu des actions de l'utilisateur sans prendre en compte la possibilité que les actions d'atteinte d'une cible puissent avoir une composante importante préprogrammée—cette théorie est appelé la théorie du contrôle préprogrammé.En se basant sur la théorie du contrôle préprogrammé, cette thèse explore la possibilité de prédire les actions, avant leur exécution, de sélection d'objets en mouvement. Plus spécifiquement, trois niveaux de prédiction d'action sont étudiés : 1) la performance des actions, mesurée par le temps de mouvement (TM) nécessaire pour atteindre une cible, 2) la difficulté prospective (DP), qui représente la difficulté subjective de la tâche estimée avant son exécution, 3) l'intention de l'utilisateur, qui indique la cible visée par l'utilisateur.Dans le cadre de cette thèse, des modèles de prédiction d'intention sont développés à l'aide des arbres de décision ainsi que des fonctions de classement—ces modèles sont évalués dans deux expériences en RV. Des modèles 1-D et 2-D de DP pour des cibles en mouvement basés sur la loi de Fitts sont développés et évalués dans une expérience en ligne. Enfin, des modèles de TM avec les mêmes caractéristiques structurelles des modèles de DP sont évaluées dans une expérience 3-D en RV. / Selection of moving targets is a common, yet complex task in human–computer interaction (HCI), and more specifically in virtual reality (VR). Action prediction has proven to be the most comprehensive enhancement to address moving-target selection challenges. Current predictive techniques, however, heavily rely on continuous tracking of user actions, without considering the possibility that target-reaching actions may have a dominant pre-programmed component—this theory is known as the pre-programmed control theory.Thus, based on the pre-programmed control theory, this research explores the possibility of predicting moving-target selection prior to action execution. Specifically, three levels of action prediction are investigated: 1) action performance measured as the movement time (MT) required to reach a target, 2) prospective difficulty (PD), i.e., subjective assessments made prior to action execution; and 3) intention, i.e., the target that the user plans to reach.In this dissertation, intention prediction models are developed using decision trees and scoring functions—these models are evaluated in two VR studies. PD models for 1-D, and 2-D moving- target selection tasks are developed based on Fitts' Law, and evaluated in an online experiment. Finally, MT models with the same structural form of the aforementioned PD models are evaluated in a 3-D moving-target selection experiment deployed in VR.

L'auteur n'a pas fourni de résumé en français / This thesis focuses on semi-autonomous wheelchair navigation. We aim to design asystem respecting the following constraints.Safety: The system must avoid collisions with objects and specially with humans present in the scene.Usability: People with motor disabilities and elders often have problems using joysticks and other standard control devices. The use of more sophisticated and human-like ways of interacting with the robot must be addressed to improve the acceptance and comfort for the user. It is also considered that the user could just be able to move one finger and so the request of human intervention should be as reduced as possible to accomplish the navigation task.Compliance:} The robot must navigate securely among obstacles while reducing the frustration caused to the user by taking into account his intentions at different levels; final destination, preferred path, speed etc.Respect of social conventions: When moving, the robot may considerably disturb people around it, especially when its behavior is perceived as unsocial. It is thus important to produce socially acceptable motion to reduce disturbances. We will also addresses the issue of determining those places where the robot should be placed in order become part of an interacting group.In this work we propose to estimate the user's intention in order to reduce thenumber of necessary commands to drive a robotic wheelchair and deal withambiguous or inaccurate input interfaces. In this way, the wheelchair can be incharge of some part of the navigation task and alleviate the user involvement.The proposed system takes into account the user intention in terms of the finaldestination and desired speed. At each level, the method tries to favor themost ``reasonable'' action according to the inferred user intention.The user intention problem is approached by using a model of the user based onthe hypothesis that it is possible to learn typical destinations (those wherethe user spends most of his time) and use this information to enhance theestimation of the destination targeted by the user when he is driving therobotic wheelchair.A probabilistic framework is used to model the existent relationship betweenthe intention of the user and the observed command. The main originality of theapproach relies on modeling the user intentions as typical destinations and theuse of this estimation to check the reliability of a user's command to decidehow much preeminence it should be assigned by the shared controller whenmanaging the robot's speed.The proposed shared-control navigation system considers the direction of thecommands given by the user, the obstacles detected by the robot and the inferreddestination to correct the robot's velocity when necessary. This system is basedon the dynamic window approach modified to consider the input given by the user,his intention, the obstacles and the wheelchair's dynamic constraints tocompute the appropriate velocity command.All of the results obtained in this thesis have been implemented and validatedwith experiments, using both real and simulated data. Real data has beenobtained on two different scenarios; one was at INRIA's entry hall and the otherat the experimental apartment GERHOME.

Current bio-robotic assistive devices have developed into intelligent and dexterous machines. However, the sophistication of these wearable devices still remains limited by the inherent difficulty in controlling them by sensing user-intention. Even the most commonly used sensing method, which detects the electrical activity of skeletal muscles, offer limited information for multi-function control. An alternative bio-sensing strategy is needed to allow for the assistive device to bear more complex functionalities. In this thesis, a different sensing approach is introduced using Pulsed-Wave Doppler ultrasound in order to non-invasively detect small tendon displacements in the hand. The returning Doppler shifted signals from the moving tendon are obtained with a new processing technique. This processing technique involves a unique way to acquire raw data access from a commercial clinical ultrasound machine and to process the signal with Fourier analysis in order to determine the tendon displacements. The feasibility of the proposed sensing method and processing technique is tested with three experiments involving a moving string, a moving biological beef tendon and a moving human hand tendon. Although the proposed signal processing technique will be useful in many clinical applications involving displacement monitoring of biological tendons, its uses are demonstrated in this thesis for ultrasound-based user intention analysis for the ultimate goal of controlling assistive exoskeletal robotic hands.