A decision-making Support system for Enterprise Architecture Modelling

Companies are increasingly conscious of the importance of Enterprise Architecture (EA) to represent and manage IT and business in a holistic way. EA modelling has become decisive to achieve models that accurately represents behaviour and assets of companies and lead them to make appropriate business decisions. Although EA representations can be manually modelled by experts, automatic EA modelling methods have been proposed to deal with drawbacks of manual modelling, such as error-proneness, time-consumption, slow and poor readaptation, and cost. However, automatic modelling is not effective for the most abstract concepts in EA like strategy or motivational aspects. Thus, companies are demanding hybrid approaches that combines automatic with manual modelling. In this context there are no clear relationships between the input artefacts (and mining techniques) and the target EA viewpoints to be automatically modelled, as well as relationships between the experts' roles and the viewpoints to which they might contribute in manual modelling. Consequently, companies cannot make informed decisions regarding expert assignments in EA modelling projects, nor can they choose appropriate mining techniques and their respective input artefacts. This research proposes a decision support system whose core is a genetic algorithm. The proposal first establishes (based on a previous literature review) the mentioned missing relationships and EA model specifications. Such information is then employed using a genetic algorithm to decide about automatic, manual or hybrid modelling by selecting the most appropriate input artefacts, mining techniques and experts. The genetic algorithm has been optimized so that the system aids EA architects to maximize the accurateness and completeness of EA models while cost (derived from expert assignments and unnecessary automatic generations) are kept under control

Exploring Blockchain Governance

Blockchain systems continue to attract significant interest from both practitioners and researchers. What is more, blockchain systems come in various types, such as cryptocurrencies or as inter-organizational systems in business networks. As an example of a cryptocurrency, Bitcoin, one of the most prominent blockchain systems to date and born at the time of a major financial crisis, spearheaded the promise of relying on code and computation instead of a central governing entity. Proponents would argue that Bitcoin stood the test of time, as Bitcoin continues to operate to date for over a decade. However, these proponents overlook the never-ending, heated debates “behind the scenes” caused by diverging goals of central actors, which led to numerous alternative systems (forks) of Bitcoin. To accommodate these actors’ interests in the pursuit of their common goal is a tightrope act, and this is where this dissertation commences: blockchain governance. Based on the empirical examples of various types and application domains of blockchain systems, it is the goal of this dissertation to 1) uncover governance patterns by showing, how blockchain systems are governed, 2) derive governance challenges faced or caused by blockchain systems, and, consequently, to 3) contribute to a better understanding to what blockchain governance is. This dissertation includes four parts, each of these covering different thematical areas: In the first part, this dissertation focuses on obtaining a better understanding of blockchain governance’s context of reference by studying blockchain systems from various application domains and system types, for example, led by inter-organizational networks, states, or an independent group of actors. The second part, then, focuses on a blockchain as an inter-organizational system called “cardossier”, a project I was involved in, and its governance as a frame of reference. Hereupon, for one, I report on learnings from my project involvement in the form of managerial guidelines, and, for two, I report on structural problems within cardossier, and problems caused by membership growth and how they can be resolved. The third part focuses on a wider study of blockchains as inter-organizational systems, where I summarize findings of an analysis of 19 blockchain consortia. The findings, for one, answer the question of why blockchain consortia adopt blockchain technology, and, for two, show internal and external challenges these systems faced to derive managerial recommendations. The fourth and last part studies blockchain governance’s evolution and contributes an analysis of blockchain’s governance features and its contrast to established modes of governance. These four parts, altogether, have scientific value as they increase our understanding on blockchain governance. Consequently, this dissertation contributes to the body of knowledge on modes of governance, distributed system governance, and blockchain governance in general. I do so, by grounding the concept of blockchain governance in empirical detail, showing how these systems are governed on various application domains and system types, and by studying empirical challenges faced or caused by these systems. This approach is relevant and necessary, as blockchain systems in general, but particularly outside of cryptocurrencies, mostly still are in pursuit of a sustainable blockchain governance. As blockchains can be expected to continue to mature, the upcoming years offer very fruitful ground for empirical research along the empirical insights and theoretical lines shown in this dissertation