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Artificial intelligence, most prominently in the form of machine learning, is shaping up to be one of the most transformational technologies of the 21st century. Auditors are among the professions forecasted to be the most affected by artificial intelligence, as the profession encompasses many highly structured and repetitive tasks. Automating such tasks would naturally increase the efficiency of financial statement audits. By allowing auditors to focus on higher value-added tasks, and the capability to analyze large volumes of data at a fracture of the time a human would need, artificial intelligence would also benefit the effectiveness of auditing. Despite these benefits, to this day, the actual adoption of artificial intelligence in the audit domain remains rather limited. The audit profession is highly regulated and has to consider requirements regarding, e.g. the application of professional standards, codes of conduct, and data protection obligations. Hence, the question arises of how audit firms can be supported in their efforts to adopt artificial intelligence and how machine learning systems can be designed to comply with the specific demands of the audit domain. The goal of this dissertation is to better understand the adoption of artificial intelligence in the audit domain and to actively support the adoption of artificial intelligence in auditing based on this understanding. To this end, we employ a mixture of research methods. On the one hand, the research presented here adopts a qualitative approach, examining the adoption of artificial intelligence and other advanced analytical technologies of the audit domain through taxonomy development and grounded theory. The findings of these studies inspire the second stream of work within this dissertation, which adopts a quantitative and design-oriented approach: It focuses on using machine learning to extract information from invoices for tests of details. Tests of details are essential substantive audit procedures used in nearly every audit. This dissertation proposes a new machine learning model architecture for information extraction from invoices, compares different machine learning models, and proposes design principles for machine learning pipelines for an audit application addressing the test of details through action design research.
Maximizing the value from data has become a key challenge for companies as it helps improve operations and decision making, enhances products and services, and, ultimately, leads to new business models. While enterprise architecture (EA) management and modeling have proven their value for IT-related projects, the support of enterprise architecture for data-driven business models (DDBMs) is a rather new and unexplored field. The research group argues that the current understanding of the intersection of data-driven business model innovation and enterprise architecture is incomplete because of five challenges that have not been addressed in existing research: (1) lack of knowledge of how companies design and realize data-driven business models from a process perspective, (2) lack of knowledge on the implementation phase of data-driven business models, (3) lack of knowledge on the potential support enterprise architecture modeling and management can provide to data-driven business model endeavors, (4) lack of knowledge on how enterprise architecture modeling and management support data-driven business model design and realization in practice, (5) lack of knowledge on how to deploy data-driven business models. The researchers address these challenges by examining how enterprise architecture modeling and management can benefit data-driven business model innovation. The mixed-method approach of this thesis draws on a systematic literature review, qualitative empirical research as well as the design science research paradigm. The investigators conducted a systematic literature search on data-driven business models and enterprise architecture. Considering the novelty of data-driven business models for academia and practice, they conducted explorative qualitative research to explain "why" and "how" companies embark on realizing data-driven business models. Throughout these studies, the primary data source was semi-structured interviews. In order to provide an artifact for DDBM innovation, the researchers developed a theory for design and action. The data-driven business model innovation artifact was inductively developed in two design iterations based on the design science paradigm and the design science research framework.
Analysis of User Behavior
(2020)
Online behaviors analysis consists of extracting patterns from server-logs. The works presented here were carried out within the "mBook" project which aimed to develop indicators of the quantity and quality of the learning process of pupils from their usage of an eponymous electronic textbook for History. In this thesis, the research group investigates several models that adopt different points of view on the data. The studied methods are either well established in the field of pattern mining or transferred from other fields of machine learning and data mining. The authors improve the performance of archetypal analysis in large dimensions and apply it to unveil correlations between visibility time of particular objects in the e-textbook and pupils' motivation. They present next two models based on mixtures of Markov chains. The first extracts users' weekly browsing patterns. The second is designed to process essions at a fine resolution, which is sine qua non to reveal the significance of scrolling behaviors. The authors also propose a new paradigm for online behaviors analysis that interprets sessions as trajectories within the page-graph. In this respect, they establish a general framework for the study of similarity measures between spatio-temporal trajectories, for which the study of sessions is a particular case. Finally, they construct two centroid-based clustering methods using neural networks and thus lay the foundations for unsupervised behaviors analysis using neural networks.
Technological development made it possible to store and process data on a scale not imaginable decades ago — a development that also includes network data. A particular characteristic of network data is that, unlike standard data, the objects of interest, called nodes, have relationships to (possibly all) other objects in the network. Collecting empirical data is often complicated and cumbersome, hence, the observed data are typically incomplete and might also contain other types of errors. Because of the interdependent structure of network data, these errors have a severe impact on network analysis methods. This cumulative dissertation is about the impact of erroneous network data on centrality measures, which are methods to assess the position of an object, for example a person, with respect to all other objects in a network. Existing studies have shown that even small errors can substantially alter these positions. The impact of errors on centrality measures is typically quantified using a concept called robustness. The articles included in this dissertation contribute to a better understanding of the robustness of centrality measures in several aspects. It is argued why the robustness needs to be estimated and a new method is proposed. This method allows researchers to estimate the robustness of a centrality measure in a specific network and can be used as a basis for decision making. The relationship between network properties and the robustness of centrality measures is analyzed. Experimental and analytical approaches show that centrality measures are often more robust in networks with a larger average degree. The study of the impact of non-random errors on the robustness suggests that centrality measures are often more robust if missing nodes are more likely to belong to the same community compared to missingness completely at random. For the development of imputation procedures based on machine learning techniques, a process for the evaluation of node embedding methods is proposed.
Der Wandel des Energiesystems ist eine der zentralen Nachhaltigkeitstransformationen, denen sich die Forschung widmet. Wie für die Transition-Forschung verschiedentlich festgestellt, besteht allerdings eine gewisse Lücke bei der Frage, wie Nachhaltigkeitstransformationen organisiert und finanziert werden. Insbesondere fehlt es an einer Ausdifferenzierung und vertieften Analyse einzelner institutionell-organisatorischer Lösungen und an einer Darstellung im Zusammenhang der komplexen sozio-ökologisch-technischen Systeme, in die konkrete Organisationslösungen für eine nachhaltige Energieversorgung eingebunden sind. In der vorliegenden Arbeit werden mit genossenschaftlichen Ansätzen, also Organisationslösungen mit (Teil-)Eigentum der Bürger an den Anlagen, spezifische hybride finanzielle Arrangements im Energiesektor in den Fokus gerückt. Dem institutionenanalytischen Ansatz der Bloomington School folgend wird im Rahmenpapier und insgesamt sechs Fachartikeln der Frage nachgegangen, welche Formen genossenschaftlicher Ansätze im Globalen Norden und Globalen Süden anzutreffen sind und welche Rolle diesen in den Transformationsprozessen des jeweiligen Energiesystems zukommt. Für die Analyse wird auf das Social-Ecological Systems Framework zurückgegriffen, das für die einzelnen Untersuchungen modifiziert bzw. konkretisiert wird. Im Einzelnen wird in den Fachartikeln ein Überblick über die Erkenntnisse zu genossenschaftlichen Ansätzen im Globalen Süden gegeben, auf der Makroebene den wechselnden politischen Prozessen von Koordination und Contestation nachgegangen, auf der Mesoebene die Entwicklungen von Windenergiegenossenschaften in Belgien, Dänemark, Deutschland und dem Vereinigten Königreich vergleichend analysiert, der Zusammenhang von Finanz- und Energiesystem untersucht und für diesen Kontext Gerechtigkeitsnormen konkretisiert und schließlich auf der Mikroebene die Inklusivität von Bürgerenergieinitiativen näher betrachtet und Unterschiede in den Investitionsmotiven verschiedener Bürgerenergieakteure herausgearbeitet.
Nur verhältnismäßig kurze Zeit nach der Gründung der Plattform Airbnb, auf der Privatanbieter ihren Wohnraum an Touristen vermieten können, entscheiden sich auch in Deutschland jedes Jahr Millionen von Städtereisenden für eine Übernachtung in der Wohnung Fremder - und damit auch gegen die Hotellerie. Dass in großen Teilen der Hotellerie kaum Reaktionen auf den Trend der Sharing-Angebote festzustellen sind, ist unter anderem auf ein fehlendes Verständnis der Bedürfnisse und Motive der Nutzer der Plattformen zurückzuführen. In dieser Arbeit wird deshalb mit Hilfe einer umfassenden Online-Befragung zunächst eine Kundentypologie von Hotelkunden und Sharing-Nutzern erstellt, bevor auf der Grundlage von Experteninterviews Handlungsempfehlungen für die Hotellerie abgeleitet werden.
Essays on Say-on-Pay: theoretical analysis, literature review and empirical evidence from Germany
(2019)
The dissertation contains four journal articles together with a framework manuscript. The overall subject is the so-called Say-on-Pay (SOP) vote. SOP is a law that enables shareholders to vote on the appropriateness of executive compensation during the firms’ annual general meeting. The dissertation investigates SOP votes from different angles. While the framework provides a background for the relevance of the work, outlines existing research gaps, covers an in-depth discussion and concludes relevant research questions, the four articles present the essence of the dissertation. The first article is a theoretical paper on the recent advances of behavioural agency theory. It serves as a theoretical foundation for the empirical work of the dissertation. Although principal-agent theory has gained a prominent place in research, its negative image of self-serving managers is frequently criticized. Consequently, scholars advocate the utilization of positive management theories, such as stewardship theory. This paper reviews the literature of both theoretical concepts and describes how behavioural characteristics allow for a mutually beneficial symbiosis of the two theories. The second article establishes the foundation of the scholarly knowledge in the field by systematically reviewing the empirical literature. The review covers 71 empirical articles published between January 1995 and September 2017. The studies are reviewed within an empirical research framework that separates the reasons for shareholder activism and SOP voting dissent as input factor on the one hand and the consequences of shareholder pressure as output factor on the other. The implications are analysed, and new directions for further research are discussed by proposing 19 different research questions. Building on the research gaps defined in the literature review, the third article is an empirical manuscript. In this paper, a hand-selected sample of 1,676 annual general meetings with 268 management-sponsored SOP votes in 164 different companies between 2010 and 2015 in Germany is analysed. The analysis focused on the structure, rather than the level, of executive compensation by applying a sample-selection model and panel data regression. Finally, the fourth paper investigates the rare setting of voluntary SOP votes. Using 1,841 annual general meetings of listed firms in Germany between 2010 and 2016, the effects of financial and non-financial (sustainable) performance on SOP voting likelihood and voting results are tested.
This cumulative dissertation deals with the association between corporate governance, corporate finance and corporate tax avoidance in four scientific articles. The aim of this dissertation is to explain corporate tax avoidance by (a) focusing on corporate governance institutions as determinants of tax avoidance and (b) focusing on financial consequences of tax avoidance. Due to the close association between corporate governance and the concept of corporate social responsibility (CSR), the relationship between CSR and tax avoidance is also addressed. The first article using structured literature review methodology, analyzes extant research on the association between corporate governance and tax avoidance based on stakeholder-agency theory. However, also classical principal-agent theory is taken into account as its classical foundation. The first article identifies a number of open research questions and thereby serves as a theoretical basis for the subsequent articles. The second article also using structured literature review methodology, analyzes extant research on the association between CSR and tax avoidance. This article is also based on stakeholder-agency theory and identifies open research questions. The third article based on results of the first article, investigates tax avoidance by German private family firms as a specific variant of corporate governance, using an empirical quantitative approach. The article finds that (a) German private family firms avoid more tax than non-family firms, that (b) tax avoidance is positively associated with the capital stake of the family and that (c) tax avoidance is positively associated with the number of shareholders in both family and non-family firms. Results reinforce that corporate tax avoidance is associated to conflicts among the shareholders of private firms. The fourth article investigates the cost of debt of German public firms as a function of tax avoidance and tax risk. The article finds that (a) tax avoidance is negatively associated to the cost of debt, that (b) tax risk is positively associated to the cost of debt and that (c) the association between tax avoidance and the cost of debt becomes negative when a high level of tax risk is present.
In sub-Saharan Africa, women own or partly own one third of all businesses, thereby having a large potential to contribute to the economic development and societal well-being in this region. However, women-owned businesses tend to lag behind men-owned businesses in that they make lower profits, grow more slowly, and create fewer jobs. To identify reasons for this gap and effective means to promote women entrepreneurs, large parts of the entrepreneurship literature have compared male and female entrepreneurs with regard to individual characteristics, paying only limited attention to the underlying environmental conditions. This is problematic as women entrepreneurs operate under different conditions than men, with particularly pronounced differences in sub-Saharan Africa. Against this backdrop, the goal of this dissertation is to contribute to a more profound understanding of women entrepreneurship in sub-Saharan Africa and its promotion through training by examining critical context factors. Specifically, the author analyzes two context factors that influence women's entrepreneurial performance and the success of training interventions: 1) women entrepreneurs' husbands and 2) the entrepreneurship trainer. These analyses are embedded in considerations of the cultural, social, and economic conditions women entrepreneurs in sub-Saharan Africa are facing. In Chapter 2, the author conducts a systematic literature review on spousal influence in entrepreneurship and identifies six recurrent types of influence. Complementing the literature originating from Western settings, she develops propositions on how the sub-Saharan context affects husbands' influence on women entrepreneurship in this region. In Chapter 3, she builds on a cultural theory and an economic theory of the household to develop and empirically test a theoretical model of husbands' constraining and supportive influences on women entrepreneurship in sub-Saharan Africa. The empirical results point to three distinct types of husbands that differ significantly in their impact on women entrepreneurs' business success. In Chapter 4, the author explores the influence of the trainer on the effectiveness of entrepreneurship training in sub-Saharan Africa by drawing on an unsuccessful training implementation. Qualitative analyses indicate that the use of adequate teaching methods is critical towards training success.