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Juries of PhD: Stéphane Ribot

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Title:Adoption of Big Data And Cloud Computing Technologies for Large Scale Mobile Traffic Analysis
Affiliation:Université de Lyon 3
Defense date:2016-09-23
Directors:Danielle Boulanger
Professeur émérite, Université Jean Moulin Lyon 3

Jean-Fabrice Lebraty
Professeur des Universités, Université Jean Moulin Lyon 3

Evaluators:Frédérique Biennier
Professeur, INSA de Lyon

Régis Meissonier
Professeur des Universités, Université de Montpellier

Suffragants:Marc Favier
Professeur des Universités, Université de Grenoble



Abstract: A new economic paradigm is emerging as a result of enterprises generating and managing increasing amounts of data and looking for technologies like cloud computing and Big Data to improve data-driven decision making and ultimately performance. Mobile service providers are an example of firms that are looking to monetize the collected mobile data. To make meaningful progress, sometimes scientists need to consider previous research and how it can be adapted to address today's challenges. This thesis studies two new technologies - cloud computing and Big Data - through the lens of past research by such social science giants as Goodhue and Davis as well as more recent influencers such as Thompson, Venkatesh, Dishaw and Strong as well as DeLone and McLean. We use the Task-Technology Fit (TTF) theory to examine the drivers and consequences of successful task completion by data scientists in the context of mobile data traffic analysis. The purpose of this thesis is to address the research question: To what extent does cloud computing and Big Data technology contribute to the tasks undertaken by data scientists? In answering this question, we address cloud computing and Big Data together for the first time, particularly in the context of mobile service providers. In order to uncover what is needed to create a new user adoption model, we study scientific research on mobile traffic analysis as it relates to Big Data and cloud computing. In addition to considering existing architectures and their limitations, we examine the current research on cloud-based architectures and current conclusions. To gain a thorough understanding of Big Data and how it should be addressed, we delve into the various aspects of Big Data, define the data value chain, identify major Big Data tools and determine the main challenges of Big Data analysis and the criticisms of the previous research. Our thesis explores cloud computing determinants of adoption and Big Data determinants of adoption at the user level. Identifying the determinants of cloud computing and Big Data adoption is about as straightforward as defining the technologies and identifying their key characteristics. We also define the role of the data scientist. Previous research shows that data scientists have inherently broad roles and can fall into multiple subgroups with varying levels of expertise. Data scientists possess the aggregated skillset and understanding that enables them to extract, analyze and visualize data in order to bring real value to the company. In this thesis, we employ a quantitative research methodology and operationalized using a cross-sectional survey so temporal consistency could be maintained for all the variables. The TTF model was supported by results analyzed using partial least square (PLS) structural equation modeling (SEM), which reflects positive relationships between individual, technology and task factors on TTF for mobile data analysis. Our research makes two contributions: the development of a new TTF construct - task Big Data/cloud computing technology fit model - and the testing of that construct in a model overcoming the rigidness of the original TTF model by effectively addressing technology through five subconstructs related to technology platform (Big Data) and technology infrastructure (cloud computing intention to use). Our new framework takes into account factors and characteristics for Big Data and cloud computing, task, technology, individual, TTF and utilization. Based on a hypotetico-deductive approach, and supported by Adoption Theories, Our hypotheses were tested using the input from a survey involving 169 researchers, who were participants to Mobile traffic Analysis Conferences such as MIT NetMob and the Orange Data for Development (D4D) Challenge. In the hypotheses, we address the current research gaps by seeking to evaluate the TTF in relation to the mobile traffic analytics process, Big Data applications, Cloud infrastructure, the individual characteristics related to the data scientist, utilization and perceived impact/results. Our research and study uncovered several notable main and secondary findings. The main finding can be explained as: "The end justifies the means." In the scope of mobile traffic analysis, using Big Data and cloud technology in concert with one another is required. If we want to progress, we have to not only use, but also master both of those technologies. This is a necessary requirement to evolve data-driven decision making for mobile service providers. Just as we have no choice but to use the Internet, Big Data and cloud computing technology will become mandatory. Secondarily, and contrary to the current hype, we found that only data value and data velocity contribute to the task5technology fit. Further, the idea of the super scientist is unrealistic. Organizations should become much more specific about the role of the data scientist and the resulting expectations. Finally, individual experience and competency reflect the technology adoption. Cloud technology exhibits higher significance than Big Data, which exemplifies that technology maturity increases the fit. In addition, adoption should be facilitated with a tight integration between Big Data and cloud. We found that the use of cloud infrastructure very significantly and positively affects the implementation effectiveness of Big Data applications and use will be positively related to TTF. These findings provide direction to mobile service providers for the implementation of cloud5based Big Data tools in order to enable data-driven decision-making and monetize the output from mobile data traffic analysis. We believe the proposed model can be generalized enough to extend the recommendations to companies who intend to adopt Big Data and cloud computing as part of their new IT capabilities.
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