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Many dynamics are reshaping the global macroeconomics and finance. This cumulative dissertation empirically examines the impacts of two major global dynamics, the disaster risks and the China's rise, on the global economy. Chapter 1 introduces the motivation and summarizes the dissertation. Chapter 2 investigates how geopolitical risks affect financial stress in the whole financial system and its sub-sectors (banking, stock, foreign exchange, bond) of major emerging economies. Chapter 3 shows how different disaster risks (financial, geopolitical, natural-technological) can explain the returns and risk premiums of stock and housing in advanced economies between 1870 and 2015. Chapter 4 examines how the rise of China is contributing to higher economic growth in emerging economies, especially after the Global financial crisis of 2007-2008. Chapter 5 illustrates how a close trade and investment relation with China has helped African countries to reduce poverty and to improve their income distribution.
Corporate Social Responsibility (CSR) has been established in recent years as an essential component of the economic system, demanded and promoted by a wide variety of stakeholder groups. The present dissertation shows that organizations face major communicative challenges with regard to CSR. CSR is not only determined by organizations themselves, but rather arises in the interplay with economic and social discourses. It is assumed that boundarys of organizational action are under constant change, so that CSR actors inevitably initiate constitutive communication processes. The resulting polyphony requires an understanding of the underlying communication processes. Hence, the performative character of CSR communication is taken up by this dissertation and thus the constitution of both the communicating actors and their relationships in the network is illustrated. The presented scientific papers are united by the overarching assumption that communication does not accompany and describe organizational action, but unfolds its own power.
Detecting and Assessing Road Damages for Autonomous Driving Utilizing Conventional Vehicle Sensors
(2021)
Environmental perception is one of the biggest challenges in autonomous driving to move inside complex traffic situations properly. Perceiving the road's condition is necessary to calculate the drivable space; in manual driving, this is realized by the human visual cortex. Enabling the vehicle to detect road conditions is a critical and complex task from many perspectives. The complexity lies on the one hand in the development of tools for detecting damage, ideally using sensors already installed in the vehicle, and on the other hand, in integrating detected damages into the autonomous driving task and thus into the subsystems of autonomous driving. High-Definition Feature Maps, for instance, should be prepared for mapping road damages, which includes online and in-vehicle implementation. Furthermore, the motion planning system should react based on the detected damages to increase driving comfort and safety actively. Road damage detection is essential, especially in areas with poor infrastructure, and should be integrated as early as possible to enable even less developed countries to reap the benefits of autonomous driving systems. Besides the application in autonomous driving, an up-to-date solution on assessing road conditions is likewise desirable for the infrastructure planning of municipalities and federal states to make optimal use of the limited resources available for maintaining infrastructure quality. Addressing the challenges mentioned above, the research approach of this work is pragmatic and problem-solving. In designing technical solutions for road damage detection, the researchers conduct applied research methods in engineering, including modeling, prototyping, and field studies. They utilize design science research to integrate road damages in an end-to-end concept for autonomous driving while drawing on previous knowledge, the application domain requirements, and expert workshops. This thesis provides various contributions to theory and practice. The investigators design two individual solutions to assess road conditions with existing vehicle sensor technology. The first solution is based on calculating the quarter-vehicle model utilizing the vehicle level sensor and an acceleration sensor. The novel model-based calculation measures the road elevation under the tires, enabling common vehicles to assess road conditions with standard hardware. The second solution utilizes images from front-facing vehicle cameras to detect road damages with deep neural networks. Despite other research in this area, the algorithms are designed to be applicable on edge devices in autonomous vehicles with limited computational resources while still delivering cutting-edge performance. In addition, the analyses of deep learning tools and the introduction of new data into training provide valuable opportunities for researchers in other application areas to develop deep learning algorithms to optimize detection performance and runtime. Besides detecting road damages, the authors provide novel algorithms for classifying the severity of road damages to deliver additional information for improved motion planning. Alongside the technical solutions, they address the lack of an end-to-end solution for road damages in autonomous driving by providing a concept that starts from data generation and ends with servicing the vehicle motion planning. This includes solutions for detecting road damages, assessing their severity, aggregating the data in the vehicle and a cloud platform, and making the data available via that platform to other vehicles. Fundamental limitations in this dissertation are due to boundaries in modeling. The pragmatic approach simplifies reality, which always distorts the degree of truth in the result.
Extracting meaningful representations of data is a fundamental problem in machine learning. Those representations can be viewed from two different perspectives. First, there is the representation of data in terms of the number of data points. Representative subsets that compactly summarize the data without superfluous redundancies help to reduce the data size. Those subsets allow for scaling existing learning algorithms up without approximating their solution. Second, there is the representation of every individual data point in terms of its dimensions. Often, not all dimensions carry meaningful information for the learning task, or the information is implicitly embedded in a low-dimensional subspace. A change of representation can also simplify important learning tasks such as density estimation and data generation. This thesis deals with the aforementioned views on data representation and contributes to them. The authors first focus on computing representative subsets for a matrix factorization technique called archetypal analysis and the setting of optimal experimental design. For these problems, they motivate and investigate the usability of the data boundary as a representative subset. The authors also present novel methods to efficiently compute the data boundary, even in kernel-induced feature spaces. Based on the coreset principle, they derive another representative subset for archetypal analysis, which provides additional theoretical guarantees on the approximation error. Empirical results confirm that all compact representations of data derived in this thesis perform significantly better than uniform subsets of data. In the second part of the thesis, the research group is concerned with efficient data representations for density estimation. The researchers analyze spatio-temporal problems, which arise, for example, in sports analytics, and demonstrate how to learn (contextual) probabilistic movement models of objects using trajectory data. Furthermore, they highlight issues of interpolating data in normalizing flows, a technique that changes the representation of data to follow a specific distribution. The authors show how to solve this issue and obtain more natural transitions on the example of image data.
Consisting of three articles and a framework manuscript, this cumulative dissertation deals with sustainable compensation of chief executive officer (CEO) with a focus on climate-related aspects. Against the backdrop of the European action for sustainability and the EU Green Deal, the dissertation pays special attention to the consideration of climate-related aspects of corporate performance in CEO compensation. In this context, sustainable compensation is characterized by the consideration of long-term interests and sustainability of the company as well as by the inclusion of financial and non-financial aspects of environmental, social and governance performance (ESG) in compensation agreements. While this novel instrument of corporate governance aims to incentivize the implementation of sustainability-oriented corporate strategy, it is particularly important to unfold this incentive effect at the individual CEO level in view of their managerial discretion. The framework manuscript discusses the research objectives, the regulatory and theoretical background, the results of the dissertation and their implications in the context of regulation, research, and business practice. The essence of the dissertation are the three articles. The first article examines the current state of empirical research based on 37 articles that were published between 1992 and 2018. Based on a multidimensional research framework, the structured literature review compiles past research findings, identifies contentual and methodological foci in the research area, and derives questions for future research. The second article addresses the topic from a conceptual perspective. Taking the existing work as a starting point, a conceptual framework is derived, which organizes the determinants of carbon-related CEO compensation at societal, organizational, group and individual levels of analysis. On this basis, eight propositions are presented that seek to distinguish between the determinants which support and challenge the implementation of carbon-related CEO compensation. The third article focuses on the use of CO2-oriented performance indicators in CEO compensation. The empirical-qualitative study analyzes corporate disclosure of the 65 largest companies in the EU for the years 2018 and 2019. The study addresses the use of CO2-oriented performance indicators in corporate strategy and CEO compensation. It also examines which compensation components are determined with the help of CO2-oriented performance indicators, which type of performance indicators are used, and whether CO2-intensive and less CO2-intensive companies differ in this regard.
Understanding that entrepreneurship can be better modeled from a systemic point of view is a primordial aspect that determines the important role of universities in entrepreneurial ecosystems. What makes the ecosystem approach a valuable tool for understanding social systems is that, from a holistic perspective, their behavior seems to have emerging characteristics. This dissertation presents a dual scientific account of the entrepreneurship phenomenon in universities. The work is divided into two equal parts, each of which is composed of two research papers. The narrative of the first half takes on a macro perspective view, consisting of one theoretical and one empirically-based conceptual case study. This part conceptually depicts a systematic approach to entrepreneurialism in higher education, namely an ecosystems perspective. The second half concentrates on the meso- and micro levels of study from the university's point of view, comprising of a case study as historical account for the emergence of the entrepreneurial university, and of a metasynthesis of empirical case studies in entrepreneurial universities, which serves as the basis for the development of entrepreneurial university archetypes. This doctoral work contributes to an in-depth understanding of Entrepreneurship in universities regarding its systemic qualities and archetypal characteristics of entrepreneurial universities. It argues for an ecosystem's perspective on the phenomenon of entrepreneurial activity, highlighting the fundamental role that universities play as the heart of entrepreneurial ecosystems. Furthermore, this research expands on the novel concept of the entrepreneurial university by using extensive case study literature to empirically identify distinct archetypes that better reflect the diverse reality of how universities engage as entrepreneurial actors by way of differentiated entrepreneurial structures, systems, and strategies.
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.
The dissertation consists of three scientific papers and a synopsis. The synopsis addresses the relevance of the dissertation and lists the key factors for the sustainability transition in the electricity system as a common denominator of the three papers. The relevance of the dissertation results, on the one hand, from the urgency of the sustainability transition in the electricity system and an insufficient transition willingness of the eastern European Member States. On the other hand, the Multi-Level-Perspective as one of the most important scientific frameworks to grasp transitions does not provide a sufficient explanation of its mechanisms. Moreover, Demand Response aggregators as new enterprises on the European electricity market and potential reform initiators are still under researched. The following key factors for the sustainability transition of the electricity system have been identified: supply security concerns, Europeanisation, policy making and the dominance of short-term oriented economic evaluation. Paper#1 sheds light on the roots of this problem in the context of Poland. It suggests that unfavorable regulation is symptomatic of the real, underlying barriers. In Poland, these barriers are coal dependence and political influence on energy enterprises. As main drivers, supply security concerns, EU regulatory pressure, and a positive cost-benefit profile of DR in comparison to alternatives, are revealed. A conceptual model of DR uptake in electricity systems is proposed. Applying a social mechanisms approach to the Multi-Level Perspective, paper#2 conceptualizes mechanisms of socio-technical transitions and of gaining legitimacy for transitions as co-evolutionary drivers and outcomes. Situational, action-formational, and transformational mechanisms that operate as drivers of change in a socio-technical transition require corresponding framing and framing contests to achieve legitimacy for that transition. The study illustrates the conceptual insight with the case of the coal dependent Polish electricity system. Paper #3, a qualitative study reveals Demand Response (DR) aggregators as institutional entrepreneurs that struggle to reform the still largely supply-oriented European electricity market. Unfavourable regulation, low value of flexibility, resource constraints, complexity, and customer acquisition are the key challenges DR aggregators face. To overcome them they apply a combination of strategies: lobbying, market education, technological proficiency, and upscaling the business. The study highlights DR aggregation as an architectural innovation that alters the interplay between key actors of the electricity system and provides policy recommendations including the necessity to assess the real value of DR in comparison to other flexibility sources by taking all externalities into account, a technology-neutral approach to market design and the need for simplification of DR programmes, and common standards to reduce complexity and uncertainty for DR providers.
Mental health is an important factor in an individuals' life. Online-based interventions have been developed for the treatment of various mental disorders. During these interventions, a large amount of patient-specific data is gathered that can be utilized to increase treatment outcomes by informing decision-making processes of psychotherapists, experts in the field, and patients. The articles included in this dissertation focus on the analysis of such data collected in digital psychological treatments by using machine learning approaches. This dissertation utilizes various machine learning methods such as Bayesian models, regularization techniques, or decision trees to predict different psychological factors, such as mood or self-esteem, dropout of patients, or treatment outcomes and costs. These models are evaluated using a variety of performance metrics, for example, receiver operating characteristics curve, root mean square error, or specialized performance metrics for Bayesian inference. These types of analyses can support decision- making for psychologists and patients, which can, in turn, lead to better recommendations and subsequently to increased outcomes for patients and simultaneously more insight about the interplay between psychological factors. The analysis of user journey data has not yet been fully examined in the field of psychological research. A process for this endeavor is developed and a technical implementation is provided for the research community. The application of machine learning in this context is still in its infancy. Thus, another contribution is the exploration and application of machine learning techniques for the revelation of correlations between psychological factors or characteristics and treatment outcomes as well as their prediction. Additionally, economic factors are predicted to develop a process for treatment type recommendations. This approach can be utilized for finding the optimal treatment type for patients on an individual level considering predicted treatment outcomes and costs. By evaluating the predictive accuracy of multiple machine learning techniques based on various performance metrics, the importance of considering heterogeneity among patients' behavior and affect is highlighted in some articles. Furthermore, the potential of machine learning-based decision support systems in clinical practice has been examined from a psychotherapists' point of view.
This dissertation focused on the nature and role of organizational practices for the employment of older people and the extension of their working lives. The set of four articles is driven by the objective to further deepen our understanding of how organizations can facilitate ageing at work to the benefit of both, employees and employers. Findings are empirically based on qualitative expert interview data from Germany and the U.S. and several quantitative field studies among older employees in Germany. To bridge gaps in measurement of organizational practices related to aging at work, this dissertation proposes a new comprehensive, multifaceted, and thoroughly conceptualized measure of organizational practices related to aging at work, the Later Life Workplace Index (LLWI). Through the course of the four articles the LLWI is conceptually developed based on qualitative interview data, operationalized, validated based on multiple field studies among older workers, and applied in a multi-level study among older employees of 101 organizations. Results suggest that organizational practices are not uniform, but multifaceted in their presence within organizations and their effects for the employment of older workers. The LLWI distinguishes nine domains of practices including an age-friendly organizational climate, work design, individual development, and practices tailoring the retirement transition. Thus, it may lay the foundation for more granular organizational level research in the field. Further, this dissertation's fourth article applies the LLWI and argues based on person-environment fit and socio-emotional selectivity theory that organizational practices address different individual needs and, thus, affect employment depending on employees' individual characteristics. Results suggest that older employees' retirement intentions are effected by individual development, transition-to-retirement, and continued employment practices depending on their health resources. Application of the new measure in practice to improve organizations' response to the aging workforce and opportunities for future research based on the LLWI are discussed.