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Institute
- Fakultät Wirtschaftswissenschaften (66) (remove)
With this dissertation, I present a human resources approach to entrepreneurship through selection and training of small-business owners in developing countries. Entrepreneurship is an important source of employment, innovation, and general economic prosperity (Autio, 2005; Walter et al., 2005; Reynolds et al., 2005; Kuratko, 2003). In developing countries, job creation through business ownership is especially important because job opportunities are limited (Walter et al., 2005; Mead & Liedholm, 1998). Strengthening the small business sector is one of the best ways to reduce poverty and increase economic growth (Birch, 1987). Thus, this dissertation adds to the scientific literature in taking a human resources approach to entrepreneurship: selecting and training entrepreneurs. Selection has widely been researched on in various scientific fields like human resource management, industrial-, work-, and organizational psychology, but only partly focusing on selection of entrepreneurs. Regarding training, there exists a fair amount of studies that focus on entrepreneurship education, but a lot of them suffer from substantial heterogeneity and methodological flaws (Glaub & Frese (2011); McKenzie & Woodruff (2013)). The dissertation combines the ideas of using selection procedures for entrepreneurs with the idea of teaching entrepreneurial skills.
In this dissertation, advanced nonlinear control strategies and nonlinear minimum-variance observation are combined, in order to improve the estimation and/or tracking quality within control and fault detection tasks, for several types of systems from the fields of electromobility and conventional drivetrain technology that have some potential for sustainability or performance improvements. The application-specific innovations in terms of nonlinear Kalman filter methods are: (1) Improved state of charge estimation for Lithium-ion battery cells, powered by a novel self-adaptive EKF that uses a high-order polynomial curve fit as a decomposition of the uncertain nonlinear output equation with intentionally redundant bases, and with a reduced number of polynomial parameters that are adapted online by the EKF itself. (2) Online estimation of the time delay between two periodic signals of roughly the same shape that have pronounced uncorrelated noise, based on a fractional-order approximation of the transcendent transfer function of the time delay which is used as a model in a novel kind of EKF. (3) Using two (E)KFs (one for the linear subsystem and one for the nonlinear subsystem of a new kind of multi-stage piezo-hydraulic actuator) in a cascaded loop structure in order to reduce the computation load of the estimation, by appropriate 'interfacing' between the two observers (using one shared system model equation, among other aspects). - The innovations in terms of nonlinear control methods are powered by observation, as well: (1) Sliding mode velocity control of a DC drive that is subject to nonlinear friction and unknown load torques, enhanced by an equivalent control law, and with a new intelligent switching gain adaptation scheme (for reduced control chattering and, thus, less energy consumption and actuator wear), which is powered by Taylor-linearized model predictive control, which in turn requires observer-based disturbance compensation (by a KF with a double-integrator disturbance model) for model-matching purposes in order to function correctly. (2) Direct speed control of permanent-magnet three-phase synchronous motors that have a high power-to-volume ratio, based on sliding mode control in a rotating d,q coordinate system, with a new equivalent control method that exploits both system inputs and with a secondary sliding surface to ensure compliance with the current-trajectory of maximum efficiency for the required torque, and which works without measurement of the rotor angle (thanks to a new kind of EKF that estimates all states in the stationary α,β coordinate system, as well as the disturbance/load torque and its derivative). In all instances, improvements (compared to methods existing in the literature) in terms of control and estimation performance have been achieved and confirmed using simulation studies or real experiments.
This dissertation includes an introduction and five empirical papers focusing on the educational and career decision-making process of individuals in Germany. The five papers embrace different determinants of educational and career decisions including school performance, social background, leisure activities as well as professional expectations, and contribute to the existing literature in this research area. Chapter 2 of this dissertation begins by analysing the nexus between students’ time allocation and school performance in terms of grades and satisfaction with their own performance in mathematics, the German language and a first foreign language, as well as overall achievement. This chapter looks at the heterogeneity of three important extracurricular activities: student jobs, sports and participation in music. Moreover, the heterogeneity of each activity is addressed by accounting for different types of the particular activity and differences in the number of years the activity has been pursued. For this purpose, data from the German SOEP, as a representative panel survey of private households and people in Germany, in particular cross-sectional survey data of 3388 students who are about 17 years old and enrolled in a German secondary school, were used. The main findings are that having a job as a student is negatively correlated with school performance, whereas participation in sports and music is positively correlated. However, the results reveal heterogeneity in each activity, especially with respect to intensity. Chapter 3 addresses the concrete post-school decision of school students, in particular whether to study or to enter the German VET system (Vocational Education and Training). It focuses on individual risk preferences and the social background of individuals and how these determinants affect the ultimate decision to enrol in university or to start an apprenticeship given the same level of qualification. For the empirical approach data from the German SOEP were used, in particular information on individuals' educational decisions between 2007 and 2013. The results indicate that (i) individual risk preferences do not have an overall effect on the real transition; (ii) privileged individuals are more likely to take up higher education; and (iii) compared to highly educated parents, parents without an academic background are less likely to guide their children into tertiary education, regardless of how much they support their children with their school work. Chapter 4 deals with the reconsideration of educational decisions in terms of early contract cancellations in VET. In particular, the effects of a second job on the intention to cancel a VET contract early are analysed for apprentices in Germany. For the empirical approach the representative German firm-level study "BIBB Survey Vocational Training from the Trainee's Point of View 2008", conducted by the Federal Institute for Vocational Education and Training (BIBB), is used. The survey contains 5901 apprentices that were interviewed during their second year of apprenticeship (205 schools, 340 classes, and 15 common occupations). Furthermore, it includes the design, procedures, basic conditions, and quality criteria of apprenticeships. The applied probit regressions show a higher intention to quit if apprentices require a secondary job to cover their living costs. In Chapter 5, new data on 191 apprentices from a vocational school, located in a northern German federal state, are used to validate the empirical results of Chapter 4. This chapter presents new insights into secondary-job-related burdens during apprenticeship. Due to limitations in the data, the applied empirical approach in Chapter 4 lacks to analyse how holding multiple jobs increases the intention to leave an apprenticeship early. Therefore, Chapter 5 includes the investigations of burdens related to the second job. The results indicate a lower intention to quit the apprenticeship if an apprentice holds a second job to cover living costs. However, secondary jobs are linked to lower quality of training, which, on the other hand, increases the intention to leave the apprenticeship early. Furthermore, the probability of secondary-job-related burdens increases with the number of working hours. Chapter 6 concludes the thesis by investigating subjective determinants of early contract cancellations in VET. It examines ten questions on what apprentices want to achieve and how unfulfilled expectations affect the intention to leave the apprenticeship early. The findings of this investigation contributes to the existing research on early contract cancellation. The questions considered include information on the performance, personal development, career development and prospects or position in society and their meaning to apprentices. For the research approach, the "BIBB Survey Vocational Training from the Trainee's Point of View 2008" is considered again. The probit and ordered probit regressions applied show significant effects of job characteristics that represent job security. The expectation of being retained after an apprenticeship and the encouragement to consistently train further decrease the intention to leave the apprenticeship early. Furthermore, women appear to be more affected by job security signals than men, but they also sort more often into occupations with lower retention probabilities. Consequently, this result may be an indication of occupational segregation rather than a sign of differences between sexes.
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.
Online marketing, especially Paid Search Advertising, has become one of the most important paid media channels for companies to sell their products and services online. Despite being under intensive examination by a number of researchers for several years, this topic still offers interesting opportunities to contribute to the community, particularly because of its large economic impact and practical relevance as well as the detailed and widely unfiltered view of consumer behavior that such marketing offers. To provide answers to some of the important questions from advertisers in this context, the author present four papers in his thesis, in which he extends previous works on optimization topics such as click and conversion prediction. He applies and extends methods from other fields of research to specific problems in Paid Search. After a short introduction, the dissertation starts with a paper in which the authors illustrates a new method that helps advertisers to predict conversion probabilities in Paid Search using sparse keyword-level data. They address one of the central problems in Paid search advertising, which is optimizing own investments in this channel by placing bids in keyword auctions. In many cases, evaluations and decisions are made with extremely sparse data, although anecdotal evidence suggests that online marketing is a typical "Big Data" topic. In the developed algorithm presented in this paper, the authors use information such as the average time that users spend on the advertiser's website and bounce rates for every given keyword. This previously unused data set is shared between all keywords and used as prior knowledge in the proposed model. A modified version of this algorithm is now the core prediction engine in a productive Paid Search Bid Optimization System that calculates and places millions of bids every day for some of the most recognized retailers and service providers in the German market. Next, the author illustrates the development of a non-reactive experimental method for A/B testing of Paid Search Advertising activities. In that paper, the authors provide an answer to the question of whether and under what circumstances it makes economic sense for brand owners to pay for Paid Search ads for their own brand keywords in Google AdWords auctions. Finally, the author presents two consecutive papers with the same theoretical foundation in which he applies Bayesian methods to evaluate the impact of specific text features in Paid Search Advertisements.
Organizational culture is widely acknowledged to be a driver of organizational effectiveness. However, existing empirical research tends to focus on investigating the links between individual, isolated culture dimensions and effectiveness outcomes. This approach is at odds with the theoretical roots of organizational culture and does not do justice to the complex reality that most organizations face. This issue is addressed by this dissertation, which is comprised of four studies. Study 1 investigated the psychometric quality and cultural equivalence of three culture measures in a German context, based on a sample of 172 employees in a bank. The results suggested that the German versions of the Denison Organizational Culture Survey and the Organizational Culture Profile performed satisfactorily, while results regarding the GLOBE survey fell short of expectations. Study 2 reviewed the literature on the link between culture and effectiveness with a focus on studies that treat organizational culture as a holistic phenomenon. The review yielded four kinds of holistic approaches (aggregation-based, agreement-based, moderation- or mediation-based, and configuration-based). Study 3 investigated how a change in organizational culture induced by an M&A project impacts employee commitment. Based on a sample of 180 employees in a German organization, the findings suggest that individuals perceive cultural change differently, that cultural stability is positively related to employee commitment, and that group-level leader-member exchange and individual self-efficacy moderate this relationship. Study 4 introduced a new theoretical perspective (set theory) and a novel methodology (fuzzy set qualitative comparative analysis) to the field of organizational culture. Across two samples (1170 employees in a financial service provider and 998 employees in fashion retailer), results indicated that culture dimensions do not operate in isolation, but jointly work together in achieving different effectiveness outcomes.
This paper-based dissertation deals with capital structures and tax policies of German family businesses. Family firms as the predominant company form in Germany are mainly characterized by the overlapping of the two spheres family and business, both having different goal systems and preferences. This also has an impact on decision making with regard to corporate finance including the application of tax avoidance policies. In Germany, bank finance is the dominant financing source for family firms but there is a preference for internal finance since it comes along with more external independency. Extant research usually bases its results on samples of publicly listed companies. These studies come up with different results regarding family firms' actual financing preferences and capture their heterogeneity only to a very little extent. In this light, the present dissertation and its three papers examine different research questions in the context of capital structure decisions and tax avoidance in family firms. All the three papers apply a quantitative empirical research design. The first paper is a comparison between capital structures of family firms and non-family firms. The paper examines differences in bank debt and trade credit ratios. Overall, the findings show that family firms have significantly higher overall and long-term debt levels compared to their non-family counterparts. The identity as a family firm, which leads to a leap of faith by banks, can be a possible explanation for these results. The second paper is an in-depth examination of drivers of bank debt levels within the group of family firms. Further, it addresses heterogeneity amongst family firms and combines survey results and corresponding financial information. This represents a first attempt to capture family firm heterogeneity and its link to financial issues. The study shows that the more power in the company is exerted via management or supervisory board by the family, the less bank debt is used. Paper three is an extension of the previous two studies as it sheds light on tax avoidance, a significant instrument to strengthen the internal financing capability of a firm. This also takes up a research gap as there is very little research on taxation in family firms. Contrary to the expectation, the study reveals that private family firms might pay less tax than their non-family peers.
In this dissertation the relation between time headway in car following and the subjective experience of a driver was researched. Three experiments were conducted in a driving simulator. Time headways in a range of 0.5 to 4.0 seconds were investigated at 50km/h, 100km/h, and 150km/h under varied visibility conditions and at differing levels of driver control over the car. The main research questions addressed the possible existence of a threshold effect for the subjective experience of time headways and the influence of vehicle speed, forward visibility, and vehicle control on the position of time headway thresholds. Furthermore, the validity of zero-risk driver behavior models was investigated. Results suggest that a threshold exists for the subjective experience of time headways in car following. This implies that the subjective experience of time headways stays constant for a range of time headways above a critical threshold. The subjective experience of a driver is only influenced by time headway once this critical time headway threshold is passed. Speed does not influence preferred time headway distances in self- and assisted-driving, i.e. time headway thresholds are constant for different speeds. However, in completely automated driving preferred time headways are influenced by vehicle speed. For higher speeds preferred time headways decrease. A reduction of forward visibility leads to a shift in preferred time headways towards larger time headways. Results of this dissertation give credence to zero-risk models of driver behavior.
The wide accessibility of the Internet and web-based programs enable an increased volume of online interventions for mental health treatment. In contrast to traditional face-to-face therapy, online treatment has the potential to overcome some of the barriers such as improved geographical accessibility, individual time planning, and reduced costs. The availability of clients' treatment data fuels research to analyze the collected data to obtain a better understanding of the relationship among symptoms in mental disorders and derive outcome and symptom predictions. This research leads to predictive models that can be integrated into the online treatment process to assist clinicians and clients. This dissertation discusses different aspects of the development of predictive modeling in online treatment: Categorization of predictive models, data analyses for predictive purposes, and model evaluation. Specifically, the categorization of predictive models and barriers against the uptake of mental health treatment are discussed in the first part of this dissertation. Data analysis and predictive modeling are emphasized in the second part by presenting methods for inference and prediction of mood as well as the prediction of treatment outcome and costs. Prediction of future and current mood can be beneficial in many aspects. Inference of users' mood levels based on unobtrusive measures or diary data can provide crucial information for intervention scheduling. Prediction of future mood can be used to assess clients' response to the treatment and expected treatment outcome. Prediction of the expected treatment costs and outcomes for different treatment types allows simultaneous optimization of these objectives and to increase the cost-effectiveness of the treatment. In the third part, a systematic predictive model evaluation incorporating simulation analyses is demonstrated and a method for model parameter estimation for computationally limited devices is presented. This dissertation aims to overcome the current challenges of predictive model development and its use in online treatment. The development of predictive models for varies data collected in online treatment is demonstrated and how these models can be applied in practice. The derived results contribute to computer science and mental health research with client individual data analysis, the development ofpredictive models, and their statistical evaluation.
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.
Corporate irresponsibility is often the result of intentionally irresponsible strategies, decisions, or actions, which negatively affect an identifiable stakeholder or environment. For instance, these range from the violation of the human rights and labor standards to environmental damages. Organizations enacting irresponsible practices rely on different factors upon multiple levels (field, organizational, individual) and its interrelations as well as processes evolving within the organization leading to such behavior. However, reasons for the occurrence of and explanations for corporate irresponsibility so far have been limited, leaving a fragmented understanding of this phenomenon. This dissertation helps to improve the understanding and explanation of corporate irresponsibility by identifying driving patterns of corporate irresponsibility and showing how the interactions across multiple levels add to this phenomenon. Chapter 1 provides an overview of the topic of corporate irresponsibility, the theoretical approaches of this dissertation and an introduction to the chapters. The second chapter offers a review and analysis of the corporate irresponsibility literature. The chapter presents a variance model outlining the concept, antecedents, moderators and outcomes of recent corporate irresponsibility literature as well as the different factors across levels (field, organizational, individual). Chapter 2 offers a critical analysis of what we know by referring to current literature and offers insights on what we don't know by deriving main implications for future research on corporate irresponsibility. Chapter 3 enlarges the understanding of corporate irresponsibility introducing a process approach to explain how corporate irresponsibility evolves over time and under which conditions. Based on a qualitative meta-analysis findings converge around two distinct process paths of corporate irresponsibility, the opportunistic-proactive, and, the emerging-reactive, subdivided into three phases. Chapter 3 sheds different lights upon the phases of corporate irresponsibility and its underlying mechanisms. The final chapter 4 focuses on different underlying mechanisms driving the final downfall or demise of organizations, organizational failure. Chapter 4 offers an alternative explanation to the competing extremism and inertia mechanisms driving organizational failure in recent studies by suggesting that these explanations are rather complementary. In addition, chapter 4 enlarges the explanation of organizational failure identifying the role of conflict mechanisms and its interplay with rigidity mechanisms. In sum, this dissertation contributes to a better understanding of what causes and increases corporate irresponsibility, and a better explanation of how and why corporate irresponsibility and organizational failure emerges, develops, grows or terminates over time.
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.
The dissertation contains four journal articles which are embedded within a framework manuscript that interconnects the individual articles and provides relevant background information. The dissertation's overall objective is to provide a multilayered and critical in-depth engagement with the timely phenomenon of integrated reporting (IR), a new reporting concept that is envisaged to revolutionize firms' present reporting infrastructure. While extant corporate reports (e.g., annual financial- and CSR report) often are criticized for being disconnected and to suffer from a lack of coherence, IR intends to provide all information that is material to a firm's short-, medium- und long-term value creation within one single, succinct document. To contribute to a set of previously defined relevant research gaps in literature, the dissertation makes use of a combined empirical-quantitative and explorative-qualitative research design. The first article entitled investigates a set of different IR-, corporate governance and financial accounting-specific factors that are expected to determine European and South African firms' materiality disclosure quality. To this purpose, an original, hand-collected materiality disclosure score was developed. The second article explores IR perceptions of SME managers that have not embarked on IR, but are potential candidates to do so in future. Based on a review of extant literature, the article develops a theoretical framework to subsequently discuss motives for and barriers to IR adoption. The critical discussion contributes to the academic debate on incentives for and barriers to voluntary IR adoption. The third article investigates whether voluntary IR adoption among European firms is associated with lower cost of public debt. While earlier studies suggest that IR leads to lower information asymmetries, increases analyst forecasts, and decreases cost of equity, corresponding evidence for the debt market is largely missing. Subsequent analyses test as to whether such an association is even more pronounced by a firm's environmental, social and governance (ESG) performance or its belonging to an environmentally sensitive industry. The fourth article uses an experimental design to investigate nonprofessional investors' reactions to an IR assurance. To this purpose, two separate experiments with two different groups of nonprofessional investors were carried out: one with Masters students and one with managers of large corporations. Results help to answer the question as to whether an IR assurance as well as its determinants, namely the assurance provider and the assurance level, affect nonprofessional investors' financial decision-making. In the second step, subsequent in-depth interviews reveal an IR assurance-critical attitude among managers, who draw upon their practical experience with assurance engagements.
In addition to a short introduction, this thesis contains five chapters that discuss various topics in the context of labor economics in general and the manufacturing sector in Egypt in particular. Chapter one presents the institutional framework of the Egyptian labor market and the different datasets that could be used by researchers and summarizes some previous empirical studies. Then, different microeconometric methods are applied in the subsequent four chapters, using the World Bank firm-level data for the manufacturing sector in Egypt to get an empirical evidence for the following issues: determinants of using fixed-term contracts in the Egyptian labor market in the manufacturing sector in chapter two, determinants of female employment in Egyptian manufacturing firms in chapter three, ownership structure and productivity in the Egyptian manufacturing firms in chapter four and, finally, exporting behavior of the Egyptian manufacturing firms is analyzed with a special focus on the impact of workforce skills-intensity in chapter five.
In my dissertation I explore conceptual and economic aspects of resilience, i.e. a system’s ability to maintain its basic functions and controls under disturbances. I provide methodological considerations on the conceptual level and general insights derived from stylized ecological-economic models. In doing so, I demonstrate how to frame resilience so as to economically evaluate and investigate it as an important property of ecological-economic systems. Is conceptual vagueness an asset or a liability? In chapter 1 I address this question by weighing arguments from philosophy of science and applying them to the concept of resilience. I first sketch the wide spectrum of resilience concepts that ranges from concise concepts to the vague perspective of “resilience thinking”. Subsequently, I set out the methodological arguments in favor and against conceptual vagueness. While traditional philosophy of science emphasizes precision and conceptual clarity as precondition for empirical science, alternative views highlight vagueness as fuel for creative and pragmatic problem-solving. Reviewing this discussion, I argue that a trade-off between vagueness and precision exists, which is to be solved differently depending on the research context. In some contexts research benefits from conceptual vagueness while in others it depends on precision. Assessing the specific example of “resilience thinking” in detail, I propose a restructuring of the conceptual framework which explicitly distinguishes descriptive and normative knowledge. Chapter 2 investigates the common assumption that the optimization problem within a simple selfprotection problem (spp) is convex. It is shown that the condition given in the literature to legitimate this assumption may have implausible consequences. Via a simple functional specification we analyze the (non-)convexity of the spp more thoroughly and find that for reasonable parameter values strict convexity may not be justified. In particular, we demonstrate numerically that full self-protection is often optimal. Neglecting these boundary solutions and analyzing only the comparative statics of interior maxima may entail misleading policy implications such as underinvestment in self-protection. Thus, we highlight the relevance of full self-protection as a policy option even for non-extreme losses. Chapter 3 starts from the observation that ecosystem resilience is often interpreted as insurance: by decreasing the probability of future drops in the provision of ecosystem services, resilience insures risk-averse ecosystem users against potential welfare losses. Using a general and stringent definition of “insurance” and a simple ecological-economic model, we derive the economic insurance value of ecosystem resilience and study how it depends on ecosystem properties, economic context, and the ecosystem user’s risk preferences. We show that (i) the insurance value of resilience is negative (positive) for low (high) levels of resilience, (ii) it increases with the level of resilience, and (iii) it is one additive component of the total economic value of resilience. Chapter 4 performs a model analysis to study the origins of limited resilience in coupled ecologicaleconomic systems. We demonstrate that under open access to ecosystems for profit-maximizing harvesting forms, the resilience properties of the system are essentially determined by consumer preferences for ecosystem services. In particular, we show that complementarity and relative importance of ecosystem services in consumption may significantly decrease the resilience of (almost) any given state of the system. We conclude that the role of consumer preferences and management institutions is not just to facilitate adaptation to, or transformation of, some natural dynamics of ecosystems. Rather, consumer preferences and management institutions are themselves important determinants of the fundamental dynamic characteristics of coupled ecological-economic systems, such as limited resilience. Chapter 5 describes how real option techniques and resilience thinking can be integrated to better understand and inform decision making around environmental risks within complex systems. Resilience thinking offers a promising framework for framing environmental risks posed through the non-linear responses of complex systems to natural and human-induced disturbance pressures. Real options techniques offer the potential to directly model such systems including consideration of the prospect that the passage of time opens new options while closing others. Examples are provided which illustrate the potential for integrated resilience and real options approaches to contribute to understanding and managing environmental risk.
Since 2000, data generation has been growing rapidly from various sources, such as Internet usage, mobile devices and industrial sensors in manufacturing. As of 2011, these sources were responsible for a 1.4-fold annual data growth. This development influences practice and science equally and led to different notations, one of the most popular one is Big Data. Besides organization with a business model based solely on Big Data, companies have started to implement new technologies, methodologies and processes in order to deal with the influx of data from different sources and structures and benefit the most of it. As the progress of the implementation and the degree of professionalism regarding data analysis differs amongst industries and companies, latter ones are faced with a lack of orientation regarding their own stage of development and existing relevant capabilities in order to deal with the influx of data as only a few best practices exist. Therefore, this research project develops a maturity model for the assessment of companies capabilities in the field of data analysis with a focus on Big Data. Basis for the model development is a construction model, developed along the criteria of Design Science Research. The developed model contains the different levels of maturity and related measurements for the evaluation of a companies Big Data capabilities with a focus on topics along the dimensions data and organization. The developed model has been evaluated based an application to different companies in order to ensure the practical relevance. The structure of the thesis is the following: In a first step, a structured literature review is carried out, focussing on existing maturity models in the field of Big Data and nearby fields as Business Intelligence and Performance Management Systems. Based on the identified white spots, a design science research oriented construction model for the maturity model development is designed. This model is applied subsequently.
Entrepreneurship is an important means for economic development and poverty alleviation . Due to the relevance of entrepreneurship, scholars call for research that contributes to the understanding of successful business creation. In order to best understand new venture creation, research needs to investigate barriers of entrepreneurship. A barrier that has received wide attention in the literature on new venture creation is capital requirements. Scholars argue that capital requirements are an entry barrier for new venture creation, as most people who start businesses have difficulties in acquiring the necessary amount of capital needed for starting the businesses. Particularly in developing countries, scholars and practitioners regard improvements in access to capital as a major solution to support new venture creation. However, besides improving access to capital, there are alternative solutions that help to deal with the problems of capital requirements and capital constraints in the process of new venture creation. In this dissertation, I argue that a possible means to master capital requirements and capital constraints in business creation is action-oriented entrepreneurship training. I draw on actionregulation theory (Frese & Zapf, 1994), theories supporting an interactionist approach (Endler & Edwards, 1986; Terborg, 1981) and on theories about career development (Arthur, 1994; Briscoe & Hall, 2006) to reason that action-oriented entrepreneurship training allows for handling capital requirements and capital constraints with regard to business creation. Specifically, I argue that action-oriented entrepreneurship training helps to deal with financial requirements and capital constraints in two ways: First, the training reduces the negative effect of capital constraints on business creation through the development of financial mental models. Second, the training supports finding employment and receiving employment income, which enable businesses creation.
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.
This cumulative dissertation embraces four empirical papers addressing socio-economic issues relevant to policy-makers and society as a whole. These papers cover important aspects of human life including health at birth, life satisfaction, unemployment periods and retirement decisions. The analyses are carried out applying advanced econometric methods and are based on data sets consisting of survey data as well as administrative records. The first joint paper investigates the causal impact of prenatal exposure to air pollution on neonatal health in Italy in the 2000s combining detailed information on mother's residential location from birth certificates with PM10 concentrations from air pollution monitors. Variation in local weekly rainfall is exploited as an instrumental variable for non-random air pollution exposure. Using quasi-experimental variation in rainfall shocks allows to identify the effect of PM10, ruling out potential bias due to confounder pollutants. The paper estimates the effect of exposure for both the entire pregnancy period and separately for each trimester to test whether the neonatal health effects are driven by pollution exposure during a particular gestation period. This information enhances our understanding of the mechanisms at work and help prevent pregnant mothers from most dangerous exposure periods. Additionally, the effects of prenatal exposure to PM10 are estimated by maternal labor market status and maternal education level to understand how the pollution burden is shared across different population groups. This decomposition allows to identify possible mechanisms through which environmental inequality reinforces the negative impact of early-life exposure to air pollution. This study finds that average PM10 and days with PM10 level above the hazard limit reduce birth weight, gestational age, and measures of overall newborn health. Effects are largest for third trimester exposure and for low-income and less educated mothers. The second joint paper updates previous findings on the total East-West gap in overall life satisfaction and its trend by using data from the German Socio-Economic Panel for the years 1992 to 2013. Additionally, the effects are separately analyzed for men and women as well as for four birth cohorts. The results indicate that reported life satisfaction is, on average, significantly lower in East than in West German federal states and that part of the raw East-West gap is due to differences in household income and unemployment status. The conditional East-West gap decreased in the first years after the German reunification and remained quite stable and sizable since the mid-nineties. The results further indicate that gender differences are small. Finally, the East-West gap is significantly smaller and shows a trend towards convergence for younger birth cohorts. The third joint paper explores the effects of a major reform of unemployment benefits in Germany on the labor market outcomes of individuals with some health impairment. The reform induced a substantial reduction in the potential duration of regular unemployment benefits for older workers. This work analyzes the reform in a wider framework of institutional interactions, which allows to distinguish between its intended and unintended effects. The results based on routine data collected by the German Statutory Pension Insurance and a Difference-in-Differences design provide causal evidence for a significant decrease in the number of days in unemployment benefits and increase in the number of days in employment. However, they also suggest a significant increase in the number of days in unemployment assistance, granted upon exhaustion of unemployment benefits. Transitions to unemployment assistance represent an unintended effect, limiting the success of a policy change that aims to increase labor supply via reductions in the generosity of the unemployment insurance system. The fourth, single-authored paper explores how an increase in the early retirement age affects labor force participation of older workers. The analysis is based on a social security reform in Germany, which raised the early retirement age over several birth cohorts to boost employment of older people and ultimately alleviate the burden on the public pension system. Detailed administrative data from the Federal Employment Agency allow to distinguish between employment and unemployment as well as disability pensions and retirement benefits claims. Using a Regression Kink design in a quasi-experimental framework, the author shows that the raised early retirement age had positive employment effects and negative effects on retirement benefits claims. The results also show that some population groups are more sensitive to a reduction in retirement options and more likely to seek benefits from other government programs. In this respect, the author finds that workers in manufacturing sector respond to the raised early retirement age by claiming benefits from the disability insurance program designed to compensate for reduced earnings capacity due to severe health problems. The treatment heterogeneity analysis further suggests that high-wage workers are more likely to delay exits from employment, which is in line with incentives but might also indicate an increased inequality within the affected birth cohorts induced by the reform. Finally, women seem to rely on alternative sources of income such as retirement benefits for women, or spouse's or partner's income not observed in the data. All things considered, workers did not adjust to the increased early retirement age by substituting early retirement with other government programs but rather responded to the reform in line with the policy intent. At the same time, the findings point to heterogeneous behavioral responses across different population groups. This implies that raising the early retirement age is an effective policy tool to increase employment only among older people who have the real choice to delay employment exits. Therefore, reforms that raise statutory ages should ensure social support for workers only marginally attached to the labor market or not able to work longer due to potential health problems or other circumstances.
In this dissertation, the author focuses on the link between (internal) corporate governance structures and processes and firms financial reporting quality. Specifically, the dissertation aims to provide insights into the following general research question: What is the effect of different corporate governance stakeholders on the financial reporting quality of a firm? The author provides insights into this question through three different articles. Paper #1 explores the relationship between family firm status and earnings management and synthesizes and explains previous research findings with the help of meta-analytic methods that are still uncommon in financial accounting research. The authors find a negative relationship between family firms and earnings management on average across 37 primary studies (and 305 effect sizes in total). Furthermore, they show that the considerable variation in size and direction of primary effect sizes can be explained by researchers choice of study design, earnings management proxy and different institutional settings. The second paper explores institutional owners as a different set of shareholders and their impact on financial reporting quality. The study enables the authors to compare the results against the backdrop of the previous chapter and to see different rationales that managers in institutionally-owned companies might have to engage in earnings management. Here, the authors study 511 effect sizes from a total of 87 primary studies and find that the average effect is slightly negative, meaning institutional owners on average can get more transparent earnings figures from the companies they invest in. Similar to the work they did on family firms, they find considerable heterogeneity between results from primary studies. Specifically, their multivariate meta-regression models can explain 26% of the variability in effect sizes, mainly attributable to study design choices. The third paper is concerned with managers and how managerial personality drives the propensity to engage in fraudulent accounting activities. The author uses a primary sample of 956 professionals, who work in accounting and finance departments, and ask them to rate their immediate superior on dark triad personality traits, as well as common actions taken by management to obscure and manipulate earnings figures. He finds that managers with high ratings for dark triad personality traits engage to a greater extent in fraudulent accounting practices, than managers scoring low on the dark triad scale. Moreover, the author can show that traditional risk management mechanisms, like internal audit departments, are only partially effective. Specifically, he finds that only internal audit departments that are fully staffed by external personnel can curb the adverse effect of dark triad managers on financial reporting quality. This suggests that managers with dark personalities can take advantage of mixed or entirely in-house internal audit departments. Overall, this dissertation contributes significantly to both literature streams of corporate governance and financial reporting quality. This work can explain a significant degree of heterogeneity in previous findings on the link between different kinds of ownership and earnings management. Further, it stresses that the considerable variation in current findings is not mainly attributable to cross-country differences, as previously suggested, but in no small part attributable to study design features. Finally, the author can provide additional evidence on current research linking executive personality traits and financial reporting practices.