Past PhD theses

Industry reports show that many firms still struggle with creating value from their data because data practices remain confined to data experts. Research has yet to explain how companies develop data practices among a widening audience — a capability denoted as democratization — especially how employees can effectively integrate data within their domain expertise. Simultaneously, the role of data governance — providing the framework for managing data as an asset — must shift from a control function focused on data protection to becoming an enabler of innovation. This thesis elucidates how data democratization and data governance co-evolve to scale data practices, through two interrelated streams of research. Through three essays, the first research stream grounds data democratization in Information Systems (IS) research, identifying it as a capability rooted in practice. We emphasize the necessity of integrating both generic and situated data practices to achieve data democratization. We illustrate how data practices are cultivated through situated learning and practice exchange. Through two essays, the second research stream explains how to govern data to achieve both control and innovation. We introduce systems thinking to position data governance at the intersection of data strategy and data operations within a Viable System Model. We describe the reconfiguration of data governance into archetypes that reflect the evolving strategic role of data. Altogether, our findings advance data management research by providing a clearer understanding of how to scale data practices through the interplay between data democratization and data governance, synergistically driving value creation from data.

In the age of digital transformation, enterprises are increasingly recognizing the value of external data, which originates beyond their four walls. Despite the growing number of datasets and their potential value, external data is sourced in an ad-hoc manner without clear guidelines. This leads to inconsistent sourcing decisions, characterized by a lack of clarity on the object of sourcing and the underlying data sourcing practices. Furthermore, the field of data sourcing lacks extensive research, necessitating urgent action within the Information Systems (IS) research community to bridge this gap. Considering the abovementioned research opportunities, this thesis – through three interrelated research streams – provides foundations for, analyzes, and improves data sourcing practices in the enterprise context. The contributions of the first research stream are an external data sourcing taxonomy (Essay 1), which informs sourcing decisions in an enterprise context, and a reference process to source and manage external data, which is accompanied by explicit prescriptions in the form of design principles (Essay 2). The second research stream proposes a use case-driven assessment of open corporate registers (Essay 3) and, building on the subsequent findings, a method to screen, assess, and prepare open data for use in support of companies’ open data activities (Essay 4). Finally, the third research stream reveals and elaborates on three data sourcing practices developed by companies in response to institutional pressures in the sustainability context (Essay 5). Thus, the outcomes of this thesis enable the transition from ad-hoc acquisition to well-informed, professional data sourcing approaches in the enterprise context.

Wearable devices, such as wearable activity trackers (WATs), are increasing in popularity. Although they can help improve a person's quality of life, they also raise serious privacy issues. Although security aspects of WATs have been widely studied (e.g., Bluetooth security, inference of password or biometrics), as well as privacy-related aspects such as users' attitudes and concerns, we lack knowledge about the privacy of WAT users. Indeed, the security aspects that were studied in prior work are not enough to build a realistic adversary model, as these studies focus mostly on communication protocols and not on large-scale data collection. Furthermore, previous work related to data inference by using WATs focuses on only functionalities rather than on privacy (e.g., better monitoring of activity or health to improve user experience). Moreover, these studies focus only on the inference of behavioral patterns (e.g., activities, consumption) or conditions (e.g., diseases), but none of them investigate the inference of users’ personal attributes (e.g., personality, religion, political views). In this thesis, composed of three research papers and a literature review, we contribute to the WAT security \& privacy research field by analyzing how the data of WAT users can be accessed at a large scale by many potential adversaries, by evaluating how such data can be used to infer users' personal attributes and, finally, by proposing privacy enhancing technologies (PETs) to protect their privacy. Concretely, after analyzing the current literature about WAT security \& privacy, we conduct a user-survey study to better understand the WAT user’s behaviors towards data sharing, especially with respect to third-party applications (TPAs) that can easily be used by adversaries to collect data. We then use a rigorous machine-learning approach to evaluate to what extent users’ psychological profiles (Big 5) can be inferred from WAT data, and we discuss the related consequences on the users’ privacy and society as a whole. Finally, to propose effective and likely-to-be-adopted protection mechanisms, we conduct a user-centered design study by using a participatory design methodology before analyzing and evaluating the proposed designs in order.

The growing use of complex Machine Learning (ML) models, especially in critical domains such as healthcare and finance, raises concerns about security, ethics, and comprehensibility. The field of Interpretable Machine Learning (IML) emerged to provide insights into the workings of these models. In this thesis, we cover interpretability methods for both classical ML and deep neural networks (DNNs). In terms of classical ML methods, the significant contribution of our work is MoDE, a novel technique that generates interpretable, low-dimensional visualizations for large-scale, high-dimensional datasets. MoDE stands out by preserving not only inter-data point distances but also correlations and ordinal scores, offering enhanced data understanding. We also evaluate various explanation methods for DNNs, revealing that the selection of the optimal method depends on the specific task. Moreover, we analyze the robustness of these methods against adversarial attacks, highlighting vulnerabilities and proposing novel attack strategies with sparse perturbations. These findings carry implications for enhancing the robustness of explanation methods.

Furthermore, as an application of IML, we apply IML to recommender systems, utilizing graph neural networks to achieve superior recommendation accuracy and interpretability. By leveraging user interaction subgraphs, we are able to provide in-depth interpretations for recommendations. This dual emphasis on accuracy and interpretability holds promise for enhancing recommender systems in practical business scenarios.

Mobile apps are used to support behavior change goals (e.g., stopping a lousy habit, increasing the physical activity frequency, or learning a new skill). Because these apps are pervasive, they are great tools to reach and help people in their self-improvement path towards better habits and well-being. However, users of these apps fail to reach their objectives because they lack the motivation to attain their goals. App creators might help to support motivation by integrating human motivation theories into their designs. Still, not many apps are theory-grounded. To address this issue, we organized our research around a comprehensive human motivation theory: The Self-Determination Theory (SDT). We explored three research streams: (1) developing an artifact that maps market app features to support the SDT Basic Psychological Needs (BPNs); (2) creating an SDT inspired physical activity app that provided empirical evidence that its design supported the BPNs; (3) providing empirical evidence of an SDT inspired appdesign that contributes to increasing the physical activity and motivation of individuals. The results of our studies show that the SDT can inform the design of behavior change app features. Moreover, these SDT inspired features can be used to create a physical activity app that improves individuals’ intrinsic motivation and physical activity level. This thesis results have practical implications for app designers, policymakers, and health practitioners whose interest lies in creating theory-informed and effective behavior change apps.

In view of the ever-changing organisational environment, it is essential to better understand the role that judgment plays in decision-making so that the organisation can function efficiently. This dissertation's overall research focus is on the role of human judgment in strategic, tactical, and operational decision-making inside organisations, as well as in diverse functional domains, specifically demand forecasting and strategic partner selection. Sustainable partner selection for collaborative networked organisations (CNO) is a strategic decision for long-term collaboration with a lasting impact on partners. The focus of this research includes the impact of human judgment for sustainable partner selection for CNO using multi-criteria decision-making methods. The role of human judgment in the demand planning and forecasting function is the focus at the tactical and operational decision-making levels in this research. This research demonstrates the use of human judgment at the operational decision-making level by modelling demand and in the selection of variables, at the tactical decision-making level in model selection in the case of several conflicting error measures, and in the selection of sustainable partners for collaboration at the strategic decision-making level of an organisation. It is found that the structured approaches proposed in the research articles on using human judgment in demand forecasting and partner selection support the decision-making processes at each respective level.

Empirical research on retail investors has attempted to explain online investor subpar performance by inferior decision-making and behavioural biases. The findings on human reasoning have mostly been based on quantitative transaction and account data provided by a single financial institution. The purpose of this study was to explore opportunities to improve investing processes co-created by investors and online investing platform service providers. Qualitative interviews and analysis of live platforms were used to conduct the research.

The analyses and evaluations show that investors have not fully adopted the online platform-provided decision support tools because of issues related to usability, user experience, incompatibility with current investing style and lack of transparency – leading to intuition-based decision-making. Investors requested less complexity and a reduction of information overflow by more meaningful data presentation and interpretation. A considerable proportion of investors had not implemented tenets of portfolio theory and asset allocation, preferring position-focused investing. Investors claimed having learned to mitigate the effect of behavioural biases through experience. A modified design science approach was applied to evaluate the potential for improved decision support adoption. The evaluations show that by alternative design, investors can be persuaded to implement portfolio-level management. Investors are a heterogeneous group of users and modularisation of investing services is suggested in order to match the diversity in investor needs and priorities.

This thesis consists of three scientific publications that use machine learning methods to understand industrial processes that were designed to improve environmental quality. The work contributes to efforts to make numerical models for simulation and process control that can be applied to improve equipment designs and process performance.
Chapter 1 reports the analysis of data obtained from continuous torrefaction trials conducted in an integrated pilot plant. The purpose of the work was to gain insights into the process behavior that might be useful in the conception of a process controller that aims to obtain a product having a high and constant calorific value from heterogeneous raw biomass having a low and fluctuating calorific value.
Chapter 2 reports the development of artificial neural network predictive models of data acquired during 12 months of operation of an anaerobic digester treating food wastes. The purpose of the work was to make a predictive model to map process performance, evaluated in terms of CH4 production, to measurable operational parameters. The models were then assessed for use as decision tools to control the digester feeding rate.
Chapter 3 reports the development of a problem-solving approach and a simulation tool for use in designing upflow anaerobic filters (UAF) for wastewater treatment. A multi-layer perceptron (MLP) artificial neural network model was used to run simulations of different design values of the UAF packing diameter, packing material type and the bioreactor height to diameter ratio. The ranked results of simulation were used to select the optimal design values.

Big old companies surprisingly often fail to seize the opportunities that come with new digital technologies. These well-established “incumbent” organisations do not generally lack financial resources for digital innovation. Nor do they lack ideas for new digital products, processes, and services. Rather, their challenge lies in successfully managing the development of digital ideas, especially the digital ideas that come from employees who have only limited IT skills – so-called “non-IT employees”.

This thesis describes our findings from studying incumbent organisations that support non-IT employees in creating digital innovation. We identify three types of calls for ideas—formal, informal, and mixed—that enable non-IT employees to contribute their knowledge, skills, and creativity to the initiation of digital innovation. We find that the success of these calls for ideas relies on the careful orchestration of organisational competences, managerial processes, and individual practices. We describe how a big old company can leverage these elements to better navigate the initiation of digital innovation.

Incumbent organisations face a challenge to simultaneously explore external technologies and knowledge to develop new revenue sources and exploit their existing assets and capabilities to secure existing revenue streams. Exploration and developing digital innovations call for new skills and flexible processes that allow for experimentation, iteration, and even failure, which can be stand in stark contrast to the accumulated skills and processes that have been refined for exploitation over the years. As a remedy, some organisations are choosing to move digital innovation activities into separate organisational units and toward the peripheries of the organisation. This can however hamper access to the existing knowledge base and capabilities in the rest of the organisation. To mitigate this, previous research has highlighted integration: a collaboration process to combine newly explored knowledge with existing knowledge and assets to develop digital innovation outputs. As yet, few research projects have focused specifically on understanding how distinct integration mechanisms can be used to join exploration and exploitation efforts to maximise the value of digital innovation outputs. Anchored in ambidexterity and digital innovation literature and based on qualitative data, we argue that incumbent organisations need to put in place formal integration mechanisms rather than relying on spontaneous knowledge exchange between explorative and exploitative activities. In terms of organisational units dedicated to digital innovation, we put forth the notion that solely setting up such a unit is unlikely to be sufficient as a performance boosting action but that the integration of such units must be carefully considered and managed at multiple levels, from senior management to individual employees’ behaviours.

In the last few decades, the rise of theoretical computer science led to a growing interest in definability problems on quasi-Polish spaces, a class of topological spaces that was recently introduced by de Brecht. The Scott domain, which is the set of all subsets of the integers equipped with the Scott topology, is universal among these spaces, which makes it our main object of study.

In the first part of the thesis, we show that the partial order induced by reductions via continuous functions on the Borel subsets of the Scott domain is ill-founded and contains infinite antichains. Moreover, we show that these properties already occur at the lowest possible level of topological complexity.

To remedy this situation, we then study the partial order induced by total and relatively continuous relations. We fully characterize this partial order on the Borel subsets of the Scott domain by showing that it is isomorphic to the restriction of the Wadge order on the non-self-dual Borel subsets of the Baire space.

In the second part of the thesis, we are interested in the Decomposability Conjecture, an important problem whose object is the decomposability of Borel functions. Using the question tree machinery developed by Duparc, we present new techniques that allow us to tackle this conjecture from a novel perspective.

In the framework of a joint supervision of doctoral studies with the University of Paris (France).

In recent years, active cyber defence has become an integral part of a State’s toolbox to defend against malicious cyber activities. Complementary to their defensive postures in cyberspace, States have opted for a more active stance in order to identify, prevent and respond to cyber operations. This paradigm shift has also been observed in Switzerland since 2012. This dissertation explores the concept of active cyber defence by comparing and defining the terms, its characteristics and typologies. The scope of this dissertation focuses on the Swiss approach and includes the international and domestic legal framework pertaining to active cyber defence. Furthermore, it depicts the Swiss political governance by scrutinizing the roles and responsibilities of stakeholders at the federal level, the inter-agency collaboration and the decision-making processes. In 2019, a case study was conducted to observe how this political framework was applied during the integrated security exercise which simulated a nationwide cyber crisis. Finally, this dissertation provides a set of recommendations that could improve the Swiss framework surrounding the use of active defensive measures in cyberspace.

In the data-driven enterprise, large quantities of data are automatically processed to detect patterns, generate insights, and fuel business processes and business models alike. However, enterprises striving to become data-driven face multiple challenges. First, while a broadening audience of enterprise stakeholders need to work with data, they largely remain lodged in organizational and technical silos. Second, organizations must comply with strengthened data protection regulations to exploit personal data, which is key to successful data-driven business strategies. This dissertation explores the seemingly contradictory objectives of data protection compliance and increased data usage in enterprises. On the one hand, we investigate. enterprise data catalogs (EDC) as emerging platforms that support data democratization in the enterprise context. Based on insights from over 10 real-world EDC pilot projects in multi-national organizations, we identify emerging data-related roles and analyze how EDCs support their typical data needs and usages. On the other hand, we examine the interplay between data protection regulations and data processing practices, by linking compliance requirements to data management capabilities, embedded into a revisited data life cycle for personal data. In collating these two perspectives, we find that creating transparency on enterprise-wide data assets is a cornerstone of both data protection compliance and data democratization. Our contributions inform the redesign of data management practices and call attention to the need for diligent data documentation as essential building block of the conformable and democratized data-driven enterprise.

Big Data and Analytics (BDA), which describes the ever-growing volumes of digital data and the application of advanced analytics technologies, promises significant value creation opportunities across industries. Even though companies increase their investments, their BDA initiatives fall short of expectations and they struggle to guarantee a return on investments. This lack raises fundamental questions about how companies actually change and adapt their data-related capabilities, especially regarding the quality and use of data, to create value from BDA. Considering these gaps, this thesis – through five interrelated essays – investigates how companies adapt their enterprise data management (EDM) capabilities to create additional business value from BDA. The thesis outcomes show that BDA has profound implications on EDM practices. Companies can create value from their data assets only by managing data quality and data use (e.g., analytics) in correspondence. Establishing the corresponding management structurs and awareness for data require time and commitment over a longer period. Usually, there are many different data initiatives in companies that first need to be consolidated. The thesis outcomes can support practitioners in consolidating these initiatives and formulating a comprehensive EDM approach. First, by gaining a unified view of the analytics initiatives and managing the corresponding capabilities as an integrated enterprise analytics platform (see Essay I). Second, by adapting or establishing data governance designs with a focus on data availability and data monetization (see Essay II and Essay III). Third, by showing practitioners options to run data quality more efficiently with the support of artificial intelligence, specifically machine learning (see Essay IV). Fourth, by supporting the practitioners in setting up an enterprise data catalog to facilitate data use and data democratization for a broad audience within organizations (see Essay V). Thus, the thesis outcomes can support companies in setting up corresponding programs that provide them with a long-term directive to become more data driven.

The operational continuity of critical infrastructures (CIs) is vital for the functioning of modern societies. Yet, these CIs are monitored/managed by an interdependent ecosystem of information systems, exposing CIs to the systemic risk of cascading failures. Consequently, CIs require an information-systems defense capability – i.e., the ability to prevent, detect and respond to information systems’ failure. In order to ensure such a capability, the field of computer & information security develops a myriad of technologies. However, security incidents are caused by inappropriate organizational design and/or human-behavior aspects, at least as often as by inefficient IT design. Following this logic, information systems are apprehended as socio-technical systems constituted by a nexus of technologies (material resources) and human agents (human, and knowledge resources) who employ such technologies. Building on prior research on organizational capabilities and security economics, I explore the organizational design and human behavior aspects that are necessary for CIs to build an information-systems defense capability. Investigating the case of three specific critical infrastructures and their context, I deconstruct this capability into material, human, and knowledge resources, and I explore how they should be acquired to build such a capability. My contributions are threefold: 1) a model that helps to preempt the effect of disruptive technologies on the optimal level of investment in cyber-security, providing a framework in order to select and invest in the most effective technologies; 2) a recruitment framework in order to attract scarce IT-specialists; 3) a model in order to foster tacit-knowledge absorption related to cyber-security. Policy recommendations and a research agenda for future work are presented.

We live in an age where we witness how machines learned to talk, beat us at video games, dream, paint and advance making scientific discoveries. However, having little or no underlying principle that explains the working of these learning systems, leads to a lack of confidence in their efficiency. An inevitable consequence of the lack of trust and certainty, is limited productionalization of machine learning. This notably holds for sensitive domain applications, such as precision medicine, engineering or self-driving vehicles to name a few.

This thesis is an attempt to make a contribution towards more understandable and trustworthy machine learning models, humans’ pivotal asset for achieving (general) artificial intelligence. A recurring theme throughout the text will be the information-theoretic perspective of learning systems. In particular, we are interested in compression techniques, which have their interpretation as a “measure of intelligence”. Such interpretation is motivated by the fact that “being able to compress well, is closely related to - acting intelligently”. The intuition is that shedding redundancy from the data leads to meaningful summarizations and pattern discovery i.e generalization, one way of recognising intelligent behaviour. This thesis is a result of combining four papers which all relate to information compression. We startbypresenting anefficient approach for extracting relevant information from large datasets which enables human to consume (parse) big data. In particular, the focus in chapitre 2 is on distributed clustering recast as information compression. We then continue in chapitre 3, towards informationtheoretic causal discovery, where an optimal code length description is leveraged in distinguishing causes from effects in observational data. Although not yet entirely established, an information-theoretic interpretation is been posed as theoretical grounding and explanation of deep learning models. This brings us to chapitre 4, where we propose a generative hybrid model that exhibits explorative and flexible properties at the same time. Our generative vii model benefits from the nonparametric modeling of copulas on top of a compressed version of the training data (i.e. embedded features of an autoencoder), that makes up for an efficient, easy add-on technique, which altogether paves its way to myriad of applications. In the last chapter of the thesis, we present simple, scalable, single-model uncertainty estimates constructed by benefiting from the most informative feature representations (outputs of the last layer) of a deep model.

In conclusion, by tackling causal, generative and uncertainty-aware machine learning methods, we hope to increase the integrity and aid the adaptation of such models in any domain of application.

Climate change is pushing us to find new ways to manage energy production, distribution, and consumption. At the consumption end, smart energy meters, energy monitoring devices and applications, and renewable energy technologies such as solar photovoltaic and battery storages empower energy consumers to evolve into prosumers: the producers and consumers of energy. This research studies the prosumer role in the sustainable energy system.

The research presents two main perspectives on prosumerism; it explores both the micro-foundations and macro-level influences on the energy prosumers. The research results are displayed in the form of six articles published in international peer-reviewed journals and conferences. The first two articles make propositions about the prosumer role as part of the changing socio-technical energy and innovation system. The next two articles focus on understanding the micro-level impact on the energy prosumers and examine the producer–consumer, in particular, as a co-creator of energy-related innovations. The remaining two articles address the impact of macro-level policies on prosumers.

Theoretical contributions of the research are related to the novel research framework that combines the concepts from the socio-technical multi-level perspective, innovation studies, and policy research. Practical contributions of the study are related to the understanding of the micro-foundations of prosumer interests toward innovation co-creation activities. Practitioners benefit from evidence concerning the differences between consumers and prosumers, which may help them in better designing products and services. Policy-makers may benefit from the findings related to the policy analysis that studies policy influence on the prosumers and compares different prosumer activities with policy mixes and calls for a more holistic and systemic approach for the development of the prosumer related policies.

In the framework of a joint supervision of doctoral studies with Tampere University.

A considerable share of mathematical research has its roots in physics and has progressed to present form through abstraction and generalization. Topological dynamics for instance traces back to the study of physical systems changing over time. Its objects of enquiry are flows, which generalize a variety of real-world systems in biology, economics and engineering. Topological dynamics is concerned with the asymptotic or long term behavior of flows, answering questions like: will the system eventually return to its initial state?
A game-changing theorem by Kechris, Pestov, and Todorcevic established a connection between topological dynamics and Ramsey theory. Contrary to topological dynamics, the genesis of Ramsey theory lies in abstract mathematics. Results in this field state that one can find well-organized subsets within large disorganized sets, or find order in chaos, as they are commonly popularized. A prototypical example is that in a group of 6 people there are always at least three people which either all know each other or are all strangers.
Ramsey theory has numerous applications, from number theory – Green-Tao's famous proof that prime numbers contain arbitrarily long arithmetic progressions, for example – to theoretical computer science – decidability of constraint satisfaction problems, which have consequences in A.I. research, for instance.
In this thesis, we establish new results in projective Fraïssé theory, a field with deep implications to the study of topological dynamics of groups of homeomorphisms of compact spaces.

In the framework of a joint supervision of doctoral studies with the Università degli Studi di Torino.

The series of interactions between service providers and customers are called customer journeys. These customer journeys, today, are highly personalized, due to the new devices and technologies that are available. At the same time, new methods are required to help businesses better understand customer behavior. Business process management (or BPM) is a discipline that aims to optimize a company's processes. Process Mining is a branch that complements BPM by providing a data-driven analysis approach. In this dissertation, we investigate the ways in which process mining and BPM can help to increase businesses' comprehension of customer journeys. One of the key findings is that both the process mining framework and the XES standard for storing event logs in process mining settings are relevant for customer journeys. We show that some process mining activities can be applied as-is while other techniques need to take into account the specifics of customer journeys. In particular, we contribute by proposing new algorithms for discovering, enhancing, and exploring customer journeys. We also propose new techniques for predicting next customer interactions. Overall, we contribute by leveraging process mining know-how to improve customer journey analytics; two disciplines that were, to the best of our knowledge, never before considered together.

With cloud and mobile computing, a new category of software products emerges as mass-market information systems (IS) that addresses distributed and heterogeneous end-users. Understanding user requirements and the factors that drive user adoption are crucial for successful design of such systems. Existing IS theories and models contribute to a theoretical understanding of the adoption and use of IS in mass-markets, however they are criticized for not being able to drive actionable insights on IS design as they consider the IT artifact as a black-box. We argue that IS needs to embrace market research techniques to understand and empirically assess user preferences and perceptions in order to integrate the "voice of the customer" in a mass-market scenario. We aim at supporting the design of mass-market IS by establishing a reliable understanding of consumer’s preferences for multiple factors combing functional, non-functional and economic aspects. We apply our findings to the privacy-aware design of mass-market IS and evaluate their implications on user adoption.

Economic offshoring has been a major trend for the past 30 years. Spreading supply-chains always thinner across the globe to reach lower labor costs has shown its limits in terms of intellectual property risks, responsiveness to demand volatility and disruptions, ethical and environmental impact... Moreover, recent research has shown that local production in high-cost countries can be competitive when taking mismatch costs into account, and with an adequate product portfolio strategy.
But still, the offshoring momentum continues. Why?
In the first part of this thesis, I explore offshoring through the lens of behavioral decision-making, and take a first step toward the greater goal of identifying heuristics at play in offshoring decisions. I developed a software-based trial that puts 200 participants from various background in front of an offshoring decision focused on a mismatch costs problem: offshoring production to benefit from lower costs but facing demand uncertainty, or producing locally at a higher-cost once demand is known.
In the second part of the thesis, I present a simulation-game that I developed to help transmit these research insights to students, managers and policymakers. Indeed, my goal with this research project is to make a new body of knowledge on reshoring accessible to a wide public through an active learning approach – gamification, trial and errors, experiential learning – so that the scientific breakthroughs do not stay locked in the laboratory, and instead spread to empower practitioners by improving their “heuristic toolbox”.

Software problems do not only induce high financial loss, but also sometimes induce human loss. Those problems are due to the presence of software bugs, failures, errors, and defects in software systems. These software anomalies, and in particular the software defects, have a huge impact not only on business activities but also on the cost of developing and maintaining these software systems. In order to identify their sources, particularly the ones causing severe impacts on the systems’ operations, we conducted two case studies. We analyzed software defects of two systems over a period of a year and a half. We classified these software defects, according to their trigger factors and according to their severity impact. Conducting these studies led us to propose “the origins of severe software defects method” order to identify trigger factors that cause severe software defects on a given evolving system. We also found that the group of technology trigger factors causes more severe defects than the other groups of trigger factors for this type of systems.

This thesis studies the value of responsiveness for a manufacturer. In practical terms, responsiveness allows a manufacturer to wait until actual customer demand can be observed. The benefits of this come from minimized waste, when less unwanted goods are produced, and increased sales as more of the actual demand can be met. If a manufacturer uses responsiveness to provide services, and for example, customization of products to its customers, responsiveness can be a strategic advantage in competition. This work contributes to the understanding of when the higher cost of responsiveness can be justified and manufacturer should invest in local capacity instead of low-cost offshore manufacturing.

The first essay, investigates an approach called the Volatility Portfolio and Option-based Costing. This approach suggests building a balanced portfolio of products with high and low time-sensitivity to maximize the benefits from responsiveness. This advice is contrary to the common intuitive solution. With four applications in cases from different industries, we show how the approach delivers value and how to move from theory to practice.

It has been shown that innovation follows manufacturing. Using company cases, I build a hypothesis of how responsiveness leads to higher innovation, because of local problem-solving and customer-driven innovations. The contribution from the essay, is that companies should think about learning and innovation as they make their production decisions.

Currently used models can systematically underestimate the value of lead time reduction. This can be significant for products with short sales period and a clearance price that varies in the share of unsold inventory. Third essay, demonstrates reasons for this underestimation and provides tools to fix it.

This thesis presents a behavioral economics contribution to the security of information systems. It focuses on security information sharing (SIS) between operators of critical infrastructures, such as systemic banks, power grids, or telecommunications. SIS is an activity by which these operators exchange cybersecurity-relevant information, for instance on vulnerabilities, malwares, data breaches, etc. Such information sharing is a low-cost and efficient way by which the defenders of such infrastructures can enhance cybersecurity. However, despite this advantage, economic (dis)incentives, such as the free-rider problem, often reduce the extent to which SIS is actually used in practice. This thesis responds to this problem with three published articles.

The first article sets out a theoretical framework that proposes an association between human behavior and SIS outcomes. The second article further develops and empirically tests this proposed association, using data from a self-developed psychometric survey among all participants of the Swiss Reporting and Analysis Centre for Information Assurance (MELANI). SIS is measured by a dual approach (intensity and frequency), and hypotheses on five salient factors that are likely associated with SIS outcomes (attitude, reciprocity, executional cost, reputation, trust) are tested. In the third article, policy recommendations are presented in order to reduce executional costs, which is found to be significantly and negatively associated with SIS. In conclusion, this thesis proposes multiple scientific and practical contributions. It extends the scientific literature on the economics of cybersecurity with three contributions on the human factor in SIS. In addition, regulators will find many recommendations, particularly in the area of governance, to support SIS at the legislative level. This thesis also offers many avenues for practitioners to improve the efficiency of SIS, particularly within Information Sharing and Analysis Centers (ISACs) in charge of producing Cyber Threat Intelligence in order to anticipate and prevent cyberrisks.

It is commonly acknowledged that business model innovation carries enormous opportunities for incumbent organizations, especially when driven by digital transformation. However, less is known and discussed about the challenges for off-line born organizations – i.e. established before the diffusion of the Internet - which attempt to tackle this journey of change. In this context, thanks to my research setting, based on the collaboration between the BISA team at UNIL and SAP AG, I contribute to the business model domain with two research streams.

First, I address the process of business model management, analyzing phases that go beyond business model design. I observe this process in practice, complementing the predominantly conceptual literature. I contribute to the research by identifying two approaches to business model management: a deterministic and waterfall approach, characterized by a high level of certainty and confidence by the management team; and a discovery-driven approach, in which numerous design and evaluation iterations are performed before business model implementation.

Second, I study the design of business models for connected products. Phenomena like internet of things and smart cities require a complex network of actors in which organizations, individuals, and objects exchange value. Existing business model representations are not fully capable of describing such networks, having rather generic elements and components. Therefore, I take a first step towards new means of representation, proposing a taxonomy of design elements to represent business models for cyber-physical systems, the combination of physical and computational processes at the foundation of connected products. The main contribution of this research is a specific set of actors’ roles, the type of value they exchange and perceive, as well as their dominance in the network.

In this doctoral dissertation, I relate three studies performed to address the challenge of a visual inquiry tool for identity communication in the context of startups and small and medium enterprises. The challenge being: how to develop a visual inquiry tool (a tool on which a team of stakeholders with different backgrounds could try and solve their challenge in a designerly way) especially tailored to help them tackle the issue of communicating a coherent brand identity to all their different stakeholders. These three chapters (or studies) have been developed within a design science paradigm of research, which allows to develop knowledge through both theoretical and in the form of artefacts to tackle a practical problem. The main contributions of this dissertation are: 1) a brand identity ontology based on an extensive literature review, which addresses the semantic issues found in the brand identity literature and gives us the opportunity to explore and redefine the concept in terms of a conceptual model and 2) an identity communication map, this is derived from the ontology but is this time directly aimed at practitioners. It addresses the challenge of creating a coherent and structured identity communication especially in the context of startup and SMEs. And lastly, 3) by analyzing existing visual inquiry tools, we derived a design theory for managing any business challenge in a designerly way. This last contribution aims at supporting future designers and researchers when developing such artefacts. The view proposed in this thesis is  highly interdisciplinary, but focuses mainly on design and proposes to adopt a new approach when solving management problems.

In this thesis, I investigate two specific subjects in data science, namely demand forecasting and causality inference, dividing this thesis in two main parts. The first part aims at improving demand forecasting accuracy that impacts supply chain performance. It consists of three articles aiming at studying how to enhance demand forecasting accuracy using pertinent data (e.g. operational transaction data, weather data, socio-economic data, etc.). Each article explores a new statistical approach on the supply chain optimization through demand forecasting accuracy. We found that considered pertinent data have a significant impact on demand forecasting accuracy with reductions in percentage errors up to 48%. These results can be used to justify and motivate the integration of pertinent data in the decision making process in order to better anticipate demand volumes and reduce costs due to excess inventory or stock shortages.

The goal of the second part is to infer the causal relationship in the case of non-linearity and heteroscedasticity, meaning when the variance is not constant. We provide a bivariate multiplicative noise model (Causal Heteroscedastic Model, CHM) that we extend to the multiplicative case. This two-steps method infers the intrinsic causal mechanism ; it consists of applying a causal additive model on the BAMLSS (bayesian additive model for location, scale and shape) fitted values of the estimated parameters. The simulation study provides an accuracy of 0.97 on average, and the application of CHM on financial indices shows an un-lagged causal effect of the shares on the index they compose.

Digitalization and changing customer demands prompt many organizations to scrutinize their existing and develop new business models (BMs). Against this backdrop, the BM concept has flourished as a key theoretical and practical device for modeling, analyzing, describing, and designing how firms create, deliver, and capture value. Despite the rapidly growing body of literature on BMs, prior research provides very little insight into the challenges that arise when we shift focus from design to BM management as a holistic process, and from one to multiple BMs per firm. Based on qualitative and exploratory research designs, this cumulative dissertation examines three interrelated aspects in the realm of BM management in the multi-BM firm: BM management processes, BM tools, and BM portfolios.

First, it determines activities, phases and characteristics of BM management processes, especially in comparison to the theoretical and idealistic processes proposed in earlier literature. It uncovers two distinct kinds of BM management processes: discovery-driven and deterministic. Second, it analyzes social practices and knowledge boundaries between communities of practice in BM innovation, providing implications for BM tool design. Third, it conceptualizes BM portfolio management and provides a classification of BM combinations based on their sources of value creation.

Taken together, this dissertation analyzes rich empirical data to cast light on the actual mechanisms and social practices that unfold in organizations when managing multiple BMs. Results point at inherent variety and dynamics in the ways how BMs are managed along their lifecycle, vis-à-vis complex social processes. It hereby contributes a complementary perspective to prevalent scientific discussions on what BMs are, by focusing on BMs as something that people do.

Realistic models of human mobility are critical for modern day applications, specifically for recommendation systems, resource planning and process optimization domains. Given the rapid proliferation of mobile devices equipped with Internet connectivity and GPS functionality today, aggregating large sums of individual geolocation data is feasible. The thesis focusses on methodologies to facilitate data-driven mobility modeling by drawing parallels between the inherent nature of mobility trajectories, statistical physics and information theory. On the applied side, the thesis contributions lie in leveraging the formulated mobility models to construct prediction workflows by adopting a privacy-by-design perspective. This enables end users to derive utility from location-based services while preserving their location privacy. Finally, the thesis presents several approaches to generate large-scale synthetic mobility datasets by applying machine learning approaches to facilitate experimental reproducibility.

Cross-boundary teams (those consisting of members across functions and organizations) have been considered in the past two decades as the most effective strategy for organizations to undertake complex and innovative projects. While these teams are well-equipped to undertake such projects as they can tap on their diverse set of knowledge and skills, these differences also increase the costs and difficulties of collaborating. In this dissertation, I relate the

design science research project I undertook to address three challenges that such teams encounter frequently, i.e. the challenge of coordinating, cooperating, and solving wicked problems.
The particularity of this dissertation is that I integrate works in information systems, psycholinguistics and sociology to develop both descriptive and prescriptive knowledge on cross-boundary challenges. The prescriptive knowledge consists (1) of two artifacts (i.e., the Coopilot App and the Team Alignment Map) for the challenges of coordination and cooperation, and (2) a design theory that helps designers develop visual inquiry tools. The descriptive knowledge consists of (1) a process model for team coordination through conversation and (2) a conceptual model that informs how team members can overcome the three challenges by entering a process of joint inquiry.

Overall, I argue that for future research to contribute with prescriptive guidance for the variety of challenges cross-boundary teams can encounter, cross-boundary teamwork should be conceived of as a process of joint inquiry. Through this dissertation I also provide an illustration of how design science researchers can contribute to the lack of prescriptive and actionable knowledge for cross-boundary teamwork.

Abstract

In the framework of a joint supervision of doctoral studies with the Università degli Studi di Torino

abstract EN.pdf  (84 Ko)

Every day we make tens of guesses. How likely is Macron to win the second round of the French presidential elections of 2017? Is Brazil or Germany going to win the football world cup of 2018? Is Roger Federer or Grigor Dimitrov going to win the finals in the Rotterdam Open? This thesis investigates and models how people make such guesses. The first chapter addresses the first of those questions: How likely is something to happen? It demonstrates how we can extend an already existing theory of how we judge how likely something is so that it makes more refined predictions about our behavior. The rest of the thesis focuses on the other two questions: Which of two items is better on some dimension? The second chapter outlines how we can predict how long people will take to make such guesses and what brain regions will be active while they are making those guesses. The last chapter demonstrates that there are systematic differences between how people guess when faced with real-world questions and with typical, artificial, experimental questions. One major difference is that in the real world some objects are more familiar than other objects and people are likely to guess that the more familiar object is better. The results of this thesis help us to better understand how we make decisions and can be used by, for example, software application designers or marketing specialists to distribute information in a way that reduces the effort that we exert when faced with a decision.

Today, to find meeting points or information on public transportation, we frequently use our mobile devices and, more specifically, the location-based services installed on them. These applications are extremely convenient to use and help us on a daily basis. However, we sacrifice, sometimes without realizing it, our privacy by sharing our locations with location-based services, hence, giving private information about ourselves to companies that own these services. Indeed, information can be easily extracted from our location history, in particular, our frequently visited places, our hobbies, even our identity. This is more critical when these services, in order to create new content, build mobility-prediction models about our lives. These companies collect a large amount of personal data that can be used for commercial or malicious purposes. Consequently, it is crucial to create new algorithms and architectures that preserve our location privacy when we use location-based services. This thesis focuses on the issues exposed above. This is particularly relevant today when we know that the new European General Data Protection Regulation (EU GDPR), which aims at preserving the privacy of individuals, came into effect on May, 25^th 2018, and this implies that European and some Swiss companies must be compliant with it.

In recent years, cyber operations and malicious cyber activities have become common means of achieving strategic national interests. Their increasingly disruptive effects, which have destabilized international peace and security and fueled geopolitical instability, have catapulted cyber risks to the national and international security agenda. This dissertation explores Swiss foreign policy as an instrument conducive to international cyber stability. However, while cyberspace has developed into a distinct realm of interstate relations, I argue that states’ behavior in that domain is embedded in the existing international order, which is conducive to international peace and stability. My overall conclusion is that, for the time being, no new rules, instruments or policy responses are needed to delineate acceptable interstate behavior. This standpoint reflects the Swiss foreign policy between 2012 and 2017 to the extent that the chosen policy instruments and diplomatic processes in the cyber realm do not differ significantly from those in other Swiss policy areas. I illustrate it via three qualitative case studies, highlighting not only the multilateral venues of Switzerland’s engagement in the cyber realm (i.e. the UN and the OSCE) but also various norms and measures to build both confidence and cyber security capacity.

Random graphs theory has been an important tool to model and solve problems related to real world networks. Although those problems come from very different fields, such as for example social networks, electrical power grids, and Internet network, they share an important common feature such as a very large number of elements. Due to their large and intricate structures those problems have been first studied in terms of their elements (nodes) and connections between those elements (edges).

Very often the problems are so complicated that a complete description of the dynamics happening in the whole network is impossible. Hence there has been given a lot of attention to the local properties of the network such as how many nodes are involved in a process or how to estimate the probability that the elements of the network will interact with each other in order to produce a certain result.

In this thesis we will focus the attention on a particular category of neural network, i.e., a network which mimics the dynamics and the connectivity of neurons in the brain. The nodes of the network are representing neurons, while the edges connecting them are potential synaptic connections. We propose and analyse a random graph model which may predict synaptic formation of a network and formation of connected clusters which communicate with each other. In particular, in the resulted networks the probability of connections depends on the distance.

In the framework of a joint supervision of doctoral studies with the University of Lund.

During the past four decades, due to miniaturization computing devices have become ubiquitous and pervasive. Today, the number of objects connected to the Internet is increasing at a rapid pace and this trend does not seem to be slowing down. These objects, which can be smartphones, vehicles, or any kind of sensors, generate large amounts of data that are almost always associated with a spatio-temporal context. The amount of this data is often so large that their processing requires the creation of a distributed system, which involves the cooperation of several computers. The ability to process these data is important for society. For example: the data collected during car journeys makes it possible to avoid traffic jams, to know about the need to organize a carpool, or to plan the maintenance interventions to be carried out on the road network. The application domains are therefore numerous, as are the problems associated with them. The articles that make up this thesis deal with systems that share two key characteristics: a spatio-temporal context and a decentralized architecture. In addition, the systems described in these articles revolve around three temporal perspectives: the present, the past, and the future. Systems associated with the present perspective enable a very large number of connected objects to communicate in near real-time, according to a spatial context. Our contributions in this area enable this type of decentralized system to be scaled-out on commodity hardware, i.e., to adapt as the volume of data that arrives in the system increases. Systems associated with the past perspective, often referred to as trajectory indexes, are intended for the access to the large volume of spatio-temporal data collected by connected objects. Our contributions in this area makes it possible to handle particularly dense trajectory datasets, a problem that has not been addressed previously. Finally, systems associated with the future perspective rely on past trajectories to predict the trajectories that the connected objects will follow. Our contributions predict the trajectories followed by connected objects with a previously unmet granularity. Although involving different domains, these contributions open the possibility of being able to deal with these problems more generically in the near future.

Information technologies (IT) have had a massive impact on the capacities of organizations to access and treat information, which have eventually increased their productivity. They have become so integrated in routines that without them, organizations are unable to operate. As an example, in August 2016, Delta Airlines was obliged to cancel almost 2,000 flights because its central system broke down. With the growing capacity of IT, business applications (e.g., enterprise systems) have been supporting increasingly complicated and individual tasks. However, these applications are often chosen based on an organization’s objectives with little consideration for individual needs. They push standardized routines that ask employees to change theirs. This results in a large part of employees being unsatisfied with the way business applications support their activities. With the increasing capacities of mobile applications, many employees have shifted from desktop to mobile applications. Because mobile devices are personal, their applications should be designed to adapt to individual work patterns. 
However, despite their popularity and efficiency, very few studies have investigated the designs of mobile applications and their uses in organizational contexts. This dissertation addresses this gap through three interrelated research streams: Research stream 1 investigates the interplay between individual routines and mobile apps as IT artifact. To do so, it looks into the roles that mobile apps play in supporting the ostensive and performative aspects of individual routines and the underlying design of mobile apps’ user interfaces based on two field studies: customer interactive support and routine patient care. Research stream 2 looks into the capacities of mobile checklists, i.e. checklists that are executed on mobile devices, to codify and execute routines. Checklists are a very efficient structure to support individual routines, as described in existing literature and also in the two mobile apps analyzed in the research stream 1. Given the roles of checklists and their frequent uses in mobile applications, I investigate how organizational knowledge is codified and adapted to different contexts as well as how tasks are documented and validated. Research stream 3 seeks to analyze the structures and the components of individual routines in order to describe, assess and improve them. It intends to understand the extent to which activity patterns are structured vs. unstructured and the uses of IT artifacts in these patterns. Thus, it investigates the use of maturity models and process mining to support organizations in analyzing and improving their routines. 
To conclude this dissertation, I discuss the application of my contributions in view of an ongoing project involving the use of smart glasses to support individual routines as well as the links between this dissertation and existing research in human-computer interaction. 

Over the past decades the electricity supply has been reliable in Switzerland. However, it is uncertain how the electricity supply will evolve in the long-term given the potential changes in the generation-mix in Switzerland, resulting from the nuclear phase-out and the increasing share of PV. The objective of this research is to elaborate on the concept of security of supply in the electricity sector (SoES), and to analyse in particular the case of Switzerland.

We develop a system dynamics model to analyse the impact of these changes on SoES in the long-term. Our results show that with the current regulatory framework, the only investments committed to are those assumed for PV and wind energy until 2035. Consequently, generation adequacy deteriorates progressively and the country becomes a net importer. Given the recent large investments in pumped-storage power plants (PSP), we also analyse how the changes in the Swiss electricity market threaten their profitability. We develop an algorithm to simulate PSP operation and integrate it into our model. Although the changes in the generation-mix lead to higher price differences, the drop of available cheap energy lead to low arbitrage opportunities. As current electricity systems are very complex, the elements in our model are not the only ones affecting the SoES.. Based on a literature review, we develop a framework comprising twelve dimensions, which cover all aspects of long-term SoES. We provide at least one metric for each dimension.

Our overall conclusion is that the security of supply is threatened in Switzerland. In particular, the nuclear phase-out, whatever its timing, will have major effects on prices and on the country’s self-sufficiency. Our framework can be used to monitor the electricity market over time in order to provide insights about the expected evolution of all the aspects of SoES and provide guidance for action.

Sensory information processing is a key process in the brain because it involves many sensory inputs. Some of them are relevant and should induce a motor or cognitive response. In addition, many irrelevant stimuli reach sensory pathway and should be ignored. Synaptic plasticity in the central nervous system is a general process that enhances or decreases sensory responses according to the temporal pattern of stimuli. My main aim is to study synaptic plasticity in the somatosensory pathway, mainly in the thalamo-cortical loop. Sensory information from rodent whiskers is sent from the whisker follicle to the contralateral area of the thalamus and from the thalamus to the barrel cortex (BC). In this Doctoral Thesis we performed extracellular in vivo recordings in the BC and thalamus of urethane anesthetized rats and mice in order to unravel the mechanisms of synaptic plasticity and sensory processing. We observed that repetitive stimulation at frequencies at which the animal explores the environment induced Jong-term potentiation (LTP). In addition, low frequency stimulation could induce LTP or long-term depression (LTD) depending on the intracellular Ca²⁺ concentration during the stimulation time period. This long-term plasticity depended on NMDA receptors activation and the activation of muscarinic and nicotinic cholinergic receptors. Through an optogenetic study we showed that the basal forebrain (BF), the main source of acetylcholine (Ach) to the neocortex, sent its projections in an organized way. Consequently, the Ach-depending facilitation of cortical responses occurs in a very specific manner. We also found that the postero-medial thalamic nucleus (POM) regulated BC whisker responses through GABAergic (ɣ-aminobutyric-acid: GABA) neurons located in upper cortical layers.

Thesis in joint-supervision with the Universidad Autónoma de Madrid

Full text of the thesis avaiable on Serval: https://serval.unil.ch/notice/serval:BIB_E66BE6750A09

Predictions that information technology (IT) will become a dominant driver in patient care delivery continue to proliferate. While IT’s potential benefits in healthcare are manifold, past research has shown that the digitalization of medicine remains more of a promise than a reality. Given the current limitations of IT in healthcare, in this dissertation, I argue that we need prescriptive design knowledge on how IT artifacts ought to be to function in patient care delivery.

Investigating two dominant IT artifacts in healthcare, electronic health record (EHR) systems and mobile medical apps, this dissertation unpacks the ‘black box’ of IT artifacts and sheds light on the design and the effective use of IT artifacts in routine patient care. The dissertation comprises three interrelated research streams, each taking a specific angle to study the aforementioned aspects. Research stream 1 provides a conceptual framework on the interdependencies between routines in patient care and EHR systems and devises two strategies to objectively assess and improve the effective use of EHR systems. Rather than studying design and use of EHR systems separate from each other, our framework suggests combine the two. Research stream 2 investigates routines at the individual level and describes the affordances of mobile apps to accommodate differences in EHR system use among individual physicians. Research stream 3 centers around the patient and studies the design of medical apps to provide a way for patients to self-diagnose their acute symptoms and to enhance the monitoring of an illness. This research stream presents design principles that effective medical apps should possess in order to engage the patient in the delivery of care. The theoretical contributions can be classified as mid-range theories and inform design practice by being specific about both users (i.e. patients and physicians) and IT artifacts (i.e. EHR systems and medical apps).

Motivated by both economic and environmental reasons, there has been a boost in the number of studies attempting to improve the energy efficiency of data centers (DCs). However, after analyzing the literature related to the improvement of the efficiency of DCs, we spotted several gaps that are assessed in this thesis.

In the first paper, we study how operations management principles apply to DCs running scientific jobs. In particular, we test Little’s law, the law of variability and the law of utilization by using data from three major scientific data centers. Results show that both, Little’s law and the law of utilization, hold, while the law of variability does not. These findings give insights into how DC operations can be improved by applying operations management principles to DCs.

The second paper reviews the most commonly used performance indicators in DCs. We analyze these indicators and find several drawbacks. To overcome these drawbacks, we develop a new performance indicator that has all the characteristics that a normal performance indicator should have according to the literature. The proposed indicator is evaluated using data from three DCs and by controlled lab-tests.

In the third paper, a two-step method to model and forecast the Load-at-Risk of computing systems is proposed. Data from a Finnish company’s computing system is used to validate the method developed. Results show that the two-step method successfully models and forecasts the Load-at-Risk of computing systems. This provides operators of computing systems with better information regarding how high the workload of the system could be with a significance level.

To sum up, this research provides means to assist the managers and operators of computing systems to improve the efficiency at which their computing resources are operated.

This thesis is devoted to the study of non-Borel ∆12 pointclasses of the Baire space, using reductions by continuous functions. This work is divided in three main parts. In the first one, we generalise results obtained by Duparc and Louveau to provide a complete description of the Wadge hierarchy of the class of increasing di˙erences of coanalytic sets, under some determinacy hypothesis. In a second part, we study some ∆12 pointclasses above the class of increasing di˙erences of coanalytic sets, and give a fragment of the Wadge hierarchy for those classes. Finally, we apply our results and techniques to theoretical computer science and more precisely to the study of regular tree languages, that is sets of labeled binary trees that are recognized by tree automata.

Thesis in co-supervision with the Université Paris-Diderot (Paris 7)

In this thesis, we develop tools to study the influence of predictors on multivariate distributions. We tackle the issue of conditional dependence modeling using generalized additive models, a natural extension of linear and generalized linear models allowing for smooth functions of the covariates. Compared to existing methods, the framework that we develop has two main advantages. First, it is completely flexible, in the sense that the dependence structure can vary with an arbitrary set of covariates in a parametric, nonparametric or semiparametric way. Second, it is both quick and numerically stable, which means that it is suitable for exploratory data analysis and stepwise model building. Starting from the bivariate case, we extend our framework to pair-copula constructions, and open new possibilities for further applied and methodological work. Our regression-like theory of the dependence, being built on conditional copulas and generalized additive models, is at the same time theoretically sound and practically useful.

The ubiquity of mobile devices and particularly smartphones has caused the emergence of a new trend of distributed applications known as Proximity-Based Mobile (PBM) applications. These applications enable a user to interact with others in a defined range and for a certain time duration for different purposes such as social networking, dating, gaming and driving. The goal of this thesis is to introduce a set of programming abstractions and algorithms that can be used for building PBM applications in a category of mobile networks, called mobile ad hoc networks (MANETs). In fact, the characteristics of MANETs make them a promising technology to enable PBM applications. However, the existing abstractions and algorithms in the literature of MANETs are not fully adequate for building PBM applications. Thus, in this thesis we define proximity-based durable broadcast and proximity-based neighbor detection as the main requirements of PBM applications. Then, in each part of the thesis, we introduce abstractions and algorithms which address one of these requirements. 

Cloud computing and its three facets (Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS)) are terms that denote new developments in the software industry. In particular, PaaS solutions, also referred to as cloud platforms, are changing the way software is being produced, distributed, consumed, and priced. Software vendors have started considering cloud platforms as a strategic option but are battling to redefine their offerings to embrace PaaS. In contrast to SaaS and IaaS, PaaS allows for value co-creation with partners to develop complementary components and applications. It thus requires multisided business models that bring together two or more distinct customer segments. Understanding how to design PaaS business models to establish a flourishing ecosystem is crucial for software vendors. This doctoral thesis aims to address this issue in three interrelated research parts. First, based on case study research, the thesis provides a deeper understanding of current PaaS business models and their evolution. Second, it analyses and simulates consumers’ preferences regarding PaaS business models, using a conjoint approach to find out what determines the choice of cloud platforms. Finally, building on the previous research outcomes, the third part introduces a design theory for the emerging class of PaaS business models, which is grounded on an extensive action design research study with a large European software vendor. Understanding PaaS business models from a market as well as a consumer perspective will, together with the design theory, inform and guide decision makers in their business model innovation plans. It also closes gaps in the research related to PaaS business model design and more generally related to platform business models.

This work investigates for the most part cooperation dilemmas in society when the population is structured as a complex network or when agents lay in space and can migrate. Cyclic games are also investigated in the framework of migration. Cooperation is modeled by two-player games where two players can choose between two available strategies which are cooperation and defection. Among other games, we study the prisoner’s dilemma. In that game mutual cooperation is the best choice but the structure of the game leads selfish agents to both defect. This is due to the fact that the temptation to defect is strong and the so called sucker payoff earned by a cooperator against a defector is very low. Using this game and others, we first study the evolution of cooperation on weighted networks and on spatial networks. Then we study the evolution of cooperation when the players can migrate in space in order to improve their payoffs. We find that when the weights are attributed according to some degree-weight correlations on a social network the cooperation can be strongly improved. In a second part we show that particular spatial hierarchical topologies which are embedded in space lead to particularly high levels of cooperation. In a third part, exploring migration, we find that when agents imitate their neighbors randomly while they migrate opportunistically, cooperation spreads in the population.

A large portion of the Internet traffic today is due to media streaming and this trend is still growing, as testified by the success of services like Skype, Spotify and Netflix. Media streaming consists in sending video or audio content in a continuous flow of data over the Internet and in playing this content at its arrival. Since computing resources such as bandwidth, memory and processing are limited, delivering multimedia content in a scalable manner is a key challenge. This PhD thesis addresses the issue of scalable media streaming in large-scale networks.

The client-server model is a common approach to streaming, where media consumers (clients) establishes a connection with a media server, somewhere on the Internet. In this model, when the number of consumers increases, more dedicated servers must be added to the system, which tends to be expensive. The peer-to-peer (P2P) approach offers an alternative and naturally scalable solution, where each peer can act as both client and server. Most of the proposed P2P streaming solutions focus on routing to achieve scalability. However, routing alone is limited when resources are insufficient, which is where replication can help.

In this thesis, we propose a family of replication-based streaming protocols. Our first two protocols, named ScaleStream and ReStream, adaptively replicate media content in different peers, based on the demand in the neighborhood of each peer, in order to increase the number of consumers that can be served in parallel. These solutions are adaptive in the sense that they take into account resources constraints like bandwidth capacity of peers, in order to decide when to add or remove replicas. Our two last protocols, named EagleMacaw and TurboStream, are also replication-based but they in addition optimize media routing to improve efficiency and reliability, and to reduce latency.

L’évolution de l’environnement économique, des chaînes de valeur et des modèles d’affaires des organisations augmentent l’importance de la coordination, qui peut être définie comme la gestion des interdépendances entre des tâches réalisées par des acteurs différents et concourants à un objectif commun. De nombreux moyens sont mis en œuvre au sein des organisations pour gérer ces interdépendances. A cet égard, les activités de coordination bénéficient massivement de l’appui des technologies de l’information et de communication (TIC) qui sont désormais disséminées, intégrées et connectées sous de multiples formes tant dans l’environnement privé que professionnel. Dans ce travail, nous avons investigué la question de recherche suivante : comment l’ubiquité et l’interconnectivité des TIC modifient-elles les modes de coordination ?

A travers quatre études en systèmes d’information conduites selon une méthodologie design science, nous avons traité cette question à deux niveaux : celui de l’alignement stratégique entre les affaires et les systèmes d’information, où la coordination porte sur les interdépendances entre les activités ; et celui de la réalisation des activités, où la coordination porte sur les interdépendances des interactions individuelles. Au niveau stratégique, nous observons que l’ubiquité et l’interconnectivité permettent de transposer des mécanismes de coordination d’un domaine à un autre. En facilitant différentes formes de coprésence et de visibilité, elles augmentent aussi la proximité dans les situations de coordination asynchrone ou distante. Au niveau des activités, les TIC présentent un très fort potentiel de participation et de proximité pour les acteurs. De telles technologies leur donnent la possibilité d’établir les responsabilités, d’améliorer leur compréhension commune et de prévoir le déroulement et l’intégration des tâches.

La contribution principale qui émerge de ces quatre études est que les praticiens peuvent utiliser l’ubiquité et l’interconnectivité des TIC pour permettre aux individus de communiquer et d’ajuster leurs actions pour définir, atteindre et redéfinir les objectifs du travail commun.

Cooperation and coordination are desirable behaviors that are fundamental for the harmonious development of society. People need to rely on cooperation with other individuals in many aspects of everyday life, such as teamwork and economic exchange in anonymous markets. However, cooperation may easily fall prey to exploitation by selfish individuals who only care about short-term gain. For cooperation to evolve, specific conditions and mechanisms are required, such as kinship, direct and indirect reciprocity through repeated interactions, or external interventions such as punishment.

In this dissertation we investigate the effect of the network structure of the population on the evolution of cooperation and coordination. We consider several kinds of static and dynamical network topologies, such as Barabási-Albert, social network models and spatial networks. We perform numerical simulations and laboratory experiments using the Prisoner's Dilemma and coordination games in order to contrast human behavior with theoretical results.

Thesis in joint-supervision with the University of Madrid (Carlos III)

Electricity is a strategic service in modern societies. Thus, it is extremely important for governments to be able to guarantee an affordable and reliable supply, which depends to a great extent on an adequate expansion of the generation and transmission capacities. Cross-border integration of electricity markets creates new challenges for the regulators, since the evolution of the market is now influenced by the characteristics and policies of neighbouring countries.

There is still no agreement on why and how regions should integrate their electricity markets. The aim of this thesis is to improve the understanding of integrated electricity markets and how their behaviour depends on the prevailing characteristics of the national markets and the policies implemented in each country.

We developed a simulation model to analyse under what circumstances integration is desirable. This model is used to study three cases of interconnection between two countries. Several policies regarding interconnection expansion and operation, combined with different generation capacity adequacy mechanisms, are evaluated.

The thesis is composed of three papers. In general, we conclude that electricity market integration can bring benefits if the right policies are implemented. However, a large interconnection capacity is only desirable if the countries exhibit significant complementarity and trust each other. The outcomes of policies aimed at guaranteeing security of supply at a national level can be quite counterintuitive due to the interactions between neighbouring countries and their effects on interconnection and generation investments.

Thus, it is important for regulators to understand these interactions and coordinate their decisions in order to take advantage of the interconnection without putting security of supply at risk. But it must be taken into account that even when integration brings benefits to the region, some market participants lose and might try to hinder the integration process.

This thesis deals with combinatorics, order theory and descriptive set theory.
The first contribution is to the theory of well-quasi-orders (wqo) and better-quasi-orders (bqo). The main result is the proof of a conjecture made by Maurice Pouzet in 1978 his thèse d’état which states that any wqo whose ideal completion remainder is bqo is actually bqo. Our proof relies on new results with both a combinatorial and a topological flavour concerning maps from a front into a compact metric space. The second contribution is of a more applied nature and deals with topological spaces. We define a quasi-order on the subsets of every second countable T0 topological space in a way that generalises the Wadge quasi-order on the Baire space, while extending its nice properties to virtually all these topological spaces.
The Wadge quasi-order of reducibility by continuous functions is wqo on Borel subsets of the Baire space, this quasi-order is however far less satisfactory for other important topological spaces such as the real line, as Hertling, Ikegami and Schlicht notably ob-served. Some authors have therefore studied reducibility with respect to some classes of discontinuous functions to remedy this situation. We propose instead to keep continuity but to weaken the notion of function to that of relation. Using the notion of admissible representation studied in Type-2 theory of effectivity, we define the quasi-order of re-ducibility by relatively continuous relations. We show that this quasi-order both refines the classical hierarchies of complexity and is wqo on the Borel subsets of virtually every second countable T0 space – including every (quasi-)Polish space.

Thesis in joint-supervision with the Université Paris-Diderot (Paris 7)

Dans cette thèse, nous étudions les évolutions des systèmes d’information. Nous nous intéressons plus particulièrement à l’étude des facteurs déclencheurs d’évolution, ce qu’ils représentent et comment ils permettent d’en apprendre d’avantage sur le cycle de vie des systèmes d’information.

Pour ce faire, nous avons développé un cadre conceptuel pour l’étude des évolutions qui tient compte non seulement des facteurs déclencheurs d’évolution, mais également de la nature des activités entreprises pour évoluer. Nous avons suivi une approche Design Science pour la conception de ce cadre conceptuel. Selon cette approche, nous avons développé itérativement le cadre conceptuel en l’instanciant puis en l’évaluant afin de raffiner sa conception. Ceci nous a permis de faire plusieurs contributions tant pratiques que théoriques.

La première contribution théorique de cette recherche est l’identification de 4 facteurs principaux déclenchant les évolutions. Ces facteurs sont des éléments issus de domaines généralement étudiés séparément. Le cadre conceptuel les rassemble dans un même outil pour l’étude des évolutions. Une autre contribution théorique est l’étude du cycle de vie des systèmes selon ces facteurs. En effet, l’utilisation répétée du cadre conceptuel pour la qualification des évolutions met en lumière les principales motivations des évolutions lors de chaque étape du cycle de vie. En comparant les évolutions de plusieurs systèmes, il devient possible de mettre en évidence des modèles spécifiques d’évolution des systèmes.

Concernant les contributions pratiques, la principale concerne le pilotage de l’évolution. Pour un gestionnaire de système d’information, l’application du cadre conceptuel permet de connaître précisément l’allocation réelle des ressources pour une évolution ainsi que la localisation du système dans son cycle de vie. Le cadre conceptuel peut donc aider les gestionnaires dans la planification et la stratégie d’évolution du système. Les modèles d’évolution, identifiés suite à l’application du cadre conceptuel, sont également une aide précieuse pour définir la stratégie de pilotage et les activités à entreprendre lors de la planification des évolutions.

Finalement, le cadre conceptuel a fourni les bases nécessaires à l’élaboration d’un tableau de bord pour le suivi du cycle de vie et le pilotage de l’évolution des systèmes d’information.

Post-industrial societies depend on efficiency and sustainability of their industrial production which is controlled by specialized industrial automation computer systems. Industrial automation is dominated by global companies with proprietary solutions and relies on technologies largely replaced in other computer markets. Companies operating in this mature market constantly improve their operations and manage technology disruptions. Related decisions are based on a combination of facts, emotions and personal agendas. We study three trends in industrial automation with two research projects observing competitiveness improvements through outsourcing and process improvement and one exploring the management outlook concerning rapid technology developments in adjacent high volume markets.

Globalization moves industrial facilities between continents and creates larger units. Responding to the changing environment requires companies to focus on core competences, process improvements and customer experience. Core competence focus leads to outsourcing or insourcing of selected activities. We research captive outsourcing, a novel outsourcing model, where the outsourced unit located in an emerging country is an integral part of the company’s operations, not an external supplier. It is not merely a low cost engineering pool but has responsibility for complete subsystems. Since employee commitment and low attrition rate are key for success of this model, the company focuses on employee satisfaction and develop a brand as an good local employer. Next we research support process improvement from the customer perspective and implement a new support process based on an end-to-end lead-time measurement system for reduction and faster resolution of customer issues.

New System-on-Chip technologies are disrupting Information and Communications Technology (ICT) markets. Shipment volumes of smartphones and tablets exceed all earlier computing technologies. As the last trend we research management views on the future from the perspectives of customers, incumbents and newcomers. To benefit from the new technologies a disruption management function is considered necessary and a new quantitative model for disruption assessment is proposed.

The emergence of powerful new technologies, the existence of large quantities of data, and increasing demands for the extraction of added value from these technologies and data have created a number of significant challenges for those charged with both corporate and information technology management. The possibilities are great, the expectations high, and the risks significant. Organisations seeking to employ cloud technologies and exploit the value of the data to which they have access, be this in the form of “Big Data” available from different external sources or data held within the organisation, in structured or unstructured formats, need to understand the risks involved in such activities. Data owners have responsibilities towards the subjects of the data and must also, frequently, demonstrate that they are in compliance with current standards, laws and regulations.

This thesis sets out to explore the nature of the technologies that organisations might utilise, identify the most pertinent constraints and risks, and propose a framework for the management of data from discovery to external hosting that will allow the most significant risks to be managed through the definition, implementation, and performance of appropriate internal control activities.

"Contribution of systemic science in the improvement of understanding of risk management system" offers a holistic view of enterprise wise risk management.

Risk management is often assessed through linear methods which stress positioning and causal logical frameworks: to such events correspond such consequences and such risks accordingly. Consideration of the interrelationships between risks is often overlooked and risks are rarely analyzed in their dynamic and nonlinear components.

This work shows what systemic methods, including the study of complex systems, are likely to bring to knowledge, management, anticipation of business risks, both on the conceptual and the practical sides. Based on the definitions of systems and risks in various areas, as well as methods used to manage risk, this work confronts these concepts with approaches of complex systems analysis and modeling.

This work highlights the reducing effects of some business risk analysis methods as well as limitations of risk universes caused in particular by unsuitable definitions. As a result this work also provides chief officers with a range of different tools and approaches which allows them a better understanding of complexity and as such a gain in efficiency in their risk management practices. It results in a better fit between strategy and risk management. Ultimately the firm gains in its maturity of risk management.

We are currently witnessing a distribution of Information and Communication Technologies (ICT) on a global scale. Yet, this distribution is carried out in different rhythms within each nation (and even among regions in a given country), which creates a “digital” gap, in addition to multiple inequalities already present. This computing and technological revolution engenders many changes in social relationships and permits numerous applications that are destined to simplify our lives.

Amine Bekkouche takes a closer look at the issue of e-government as an important consequence of ICTs, following the example of electronic commerce. First, he presents a synthesis of the main concepts in e-government as well as a panoramic view of the global situation in this domain.

Subsequently, he studies e-government in view of emerging countries, in particular through the illustration of a country in representative development. Then, he offers concrete solutions, which take the education sector as their starting point, to allow for a “computed digitalisation” of society that contribute to reduce the digital gap. Thereafter, he broadens these proposals to other domains and formulates recommendations that help their implementation. Finally, he concludes with perspectives that may constitute further research tracks and enable the elaboration of development projects, through the appropriation of ICTs, in order to improve the condition of the administered, and more generally, that of the citizen. 

Many everyday life problems involve finding an optimal solution among a finite set of possibilities, deemed the problem search space. In practice, enumerating all the possibilities becomes infeasible beyond a given problem size, but there exist approximate methods. In the most general case, these methods start with a candidate solution and gradually refine it through partial modifications until no improvement is possible. The variation operation, by connecting candidate solutions, induces a neighborhood structure in the search space, such that the search process can be described as a trajectory over this configuration space. Heuristic methods try to guide the search towards better solutions. Their performance, therefore, depends on the structure of the space being searched.

In this thesis, we analyze such structure by looking at the graph having as nodes solutions that are locally optimal and that act as attractors to the search trajectory, and as edges the possible transitions between those local optima. This allows us to employ methods from the science of complex networks in order to characterize in a novel way the search space of hard combinatorial problems; we argue that such network characterization can advance our understanding of the structural and dynamical properties of these spaces.

We investigate several methodologies to build the network of local optima and we apply our approach to prototypical problems such as the Quadratic Assignment Problem, the NK model of rugged landscapes, and the Permutation Flow-shop Scheduling Problem. We show that some network metrics can differentiate problem classes, correlate with problem non-linearity, and help to predict problem hardness as measured from the performances of trajectory-based search heuristics.

Enterprise-wide architecture has become a necessity for organizations to (re)align information technology (IT) to changing business requirements. Since a city planning metaphor inspired enterprise-wide architecture, this dissertation’s research axes can be outlined by similarities between cities and enterprises. Both are characterized as dynamic super-systems that need to address the evolving interest of various architecture stakeholders. Further, both should simultaneously adhere to a set of principles to guide the evolution of architecture towards the expected benefits. The extant literature on enterprise-wide architecture not only disregards architecture adoption’s complexities but also remains vague about how principles guide architecture evolution. To bridge this gap, this dissertation contains three interrelated research streams examining the principles and adoption of enterprise-wide architecture.

The first research stream investigates organizational intricacies inherent in architecture adoption. It characterizes architecture adoption as an ongoing organizational adaptation process. By analyzing organizational response behaviors in this adaptation process, it also identifies four archetypes that represent very diverse architecture approaches. The second research stream ontologically clarifies the nature of architecture principles along with outlining new avenues for theoretical contributions. This research stream also provides an empirically validated set of principles and proposes a research model illustrating how principles can be applied to generate expected architecture benefits. The third research stream examines architecture adoption in multinational corporations (MNCs). MNCs are specified by unique organizational characteristics that constantly strive for balancing global integration and local responsiveness. This research stream characterizes MNCs’ architecture adoption as a continuous endeavor. This endeavor tries to constantly synchronize architecture with stakeholders’ beliefs about how to balance global integration and local responsiveness.

To conclude, this dissertation provides a thorough explanation of a long-term journey in which organizations learn over time to adopt an effective architecture approach. It also clarifies the role of principles to purposefully guide the aforementioned learning process. 

There is a lack of dedicated tools for business model design at a strategic level. However, in today’s economic world the need to be able to quickly reinvent a company’s business model is essential to stay competitive. This research focused on identifying the functionalities that are necessary in a computer-aided design (CAD) tool for the design of business models in a strategic context. Using design science research methodology a series of techniques and prototypes have been designed and evaluated to offer solutions to the problem. The work is a collection of articles which can be grouped into three parts:

First establishing the context of how the Business Model Canvas (BMC) is used to design business models and explore the way in which CAD can contribute to the design activity.

The second part extends on this by proposing new technics and tools which support elicitation, evaluation (assessment) and evolution of business models design with CAD. This includes features such as multi-color tagging to easily connect elements, rules to validate coherence of business models and features that are adapted to the correct business model proficiency level of its users. A new way to describe and visualize multiple versions of a business model and thereby help in addressing the business model as a dynamic object was also researched.

The third part explores extensions to the business model canvas such as an intermediary model which helps IT alignment by connecting business model and enterprise architecture. And a business model pattern for privacy in a mobile environment, using privacy as a key value proposition.

The prototyped techniques and proposition for using CAD tools in business model modeling will allow commercial CAD developers to create tools that are better suited to the needs of practitioners.

While mobile technologies can provide great personalized services for mobile users, they also threaten their privacy. Such personalization-privacy paradox are particularly salient for context aware technology based mobile applications where user’s behaviors, movement and habits can be associated with a consumer’s personal identity.  
In this thesis, I studied the privacy issues in the mobile context, particularly focus on an adaptive privacy management system design for context-aware mobile devices, and explore the role of personalization and control over user’s personal data. This allowed me to make multiple contributions, both theoretical and practical. In the theoretical world, I propose and prototype an adaptive Single-Sign On solution that use user’s context information to protect user’s private information for smartphone. To validate this solution, I first proved that user’s context is a unique user identifier and context awareness technology can increase user’s perceived ease of use of the system and service provider’s authentication security. I then followed a design science research paradigm and implemented this solution into a mobile application called “Privacy Manager”. I evaluated the utility by several focus group interviews, and overall the proposed solution fulfilled the expected function and users expressed their intentions to use this application. To better understand the personalization-privacy paradox, I built on the theoretical foundations of privacy calculus and technology acceptance model to conceptualize the theory of users’ mobile privacy management. I also examined the role of personalization and control ability on my model and how these two elements interact with privacy calculus and mobile technology model. In the practical realm, this thesis contributes to the understanding of the tradeoff between the benefit of personalized services and user’s privacy concerns it may cause. By pointing out new opportunities to rethink how user’s context information can protect private data, it also suggests new elements for privacy related business models. 

Games are powerful and engaging. On average, one billion people spend at least 1 hour a day playing computer and videogames. This is even more true with the younger generations. Our students have become the « digital natives », the « gamers », the « virtual generation ». Research shows that those who are most at risk for failure in the traditional classroom setting, also spend more time than their counterparts, using video games. They might strive, given a different learning environment.  
Educators have the responsibility to align their teaching style to these younger generation learning styles. However, many academics resist the use of computer-assisted learning that has been “created elsewhere”. This can be extrapolated to game-based teaching: even if educational games were more widely authored, their adoption would still be limited to the educators who feel a match between the authored games and their own beliefs and practices. Consequently, game-based teaching would be much more widespread if teachers could develop their own games, or at least customize them. Yet, the development and customization of teaching games are complex and costly.  
This research uses a design science methodology, leveraging gamification techniques, active and cooperative learning theories, as well as immersive sandbox 3D virtual worlds, to develop a method which allows management instructors to transform any off-the-shelf case study into an engaging collaborative gamified experience. This method is applied to marketing case studies, and uses the sandbox virtual world of Second Life. 

There is no doubt about the necessity of protecting digital communication: Citizens are entrusting their most confidential and sensitive data to digital processing and communication, and so do governments, corporations, and armed forces. Digital communication networks are also an integral component of many critical infrastructures we are seriously depending on in our daily lives. Transportation services, financial services, energy grids, food production and distribution networks are only a few examples of such infrastructures. Protecting digital communication means protecting confidentiality and integrity by encrypting and authenticating its contents. But most digital communication is not secure today. Nevertheless, some of the most ardent problems could be solved with a more stringent use of current cryptographic technologies.
Quite surprisingly, a new cryptographic primitive emerges from the ap-plication of quantum mechanics to information and communication theory: Quantum Key Distribution. QKD is difficult to understand, it is complex, technically challenging, and costly-yet it enables two parties to share a secret key for use in any subsequent cryptographic task, with an unprecedented long-term security. It is disputed, whether technically and economically fea-sible applications can be found.
Our vision is, that despite technical difficulty and inherent limitations, Quantum Key Distribution has a great potential and fits well with other cryptographic primitives, enabling the development of highly secure new applications and services. In this thesis we take a structured approach to analyze the practical applicability of QKD and display several use cases of different complexity, for which it can be a technology of choice, either because of its unique forward security features, or because of its practicability.

A mobile ad hoc network (MANET) is a decentralized and infrastructure-less network. This thesis aims to provide support at the system-level for developers of applications or protocols in such networks. To do this, we propose contributions in both the algorithmic realm and in the practical realm. In the algorithmic realm, we contribute to the field by proposing different context-aware broadcast and multicast algorithms in MANETs, namely six-shot broadcast, six-shot multicast, PLAN-B and ageneric algorithmic approach to optimize the power consumption of existing algorithms. For each algorithm we propose, we compare it to existing algorithms that are either probabilistic or context-aware, and then we evaluate their performance based on simulations. We demonstrate that in some cases, context-aware information, such as location or signal-strength, can improve the effciency. In the practical realm, we propose a testbed framework, namely ManetLab, to implement and to deploy MANET-specific protocols, and to evaluate their performance. This testbed framework aims to increase the accuracy of performance evaluation compared to simulations, while keeping the ease of use offered by the simulators to reproduce a performance evaluation. By evaluating the performance of different probabilistic algorithms with ManetLab, we observe that both simulations and testbeds should be used in a complementary way. In addition to the above original contributions, we also provide two surveys about system-level support for ad hoc communications in order to establish a state of the art. The first is about existing broadcast algorithms and the second is about existing middleware solutions and the way they deal with privacy and especially with location privacy.

Thesis in joint-supervision with the Université Paris-Diderot