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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 this cumulative thesis, the author presents four manuscripts and two appendixes. In the manuscripts he discusses mindsets and their relation to the effectiveness of negotiation training. His general claim is that mindsets promise to be relevant for training effectiveness. Still, more research needs to be done and chapter 3 presents the Scale for the Integrative Mindset of Negotiators (SIM) that can be used for some of that research. In the appendixes, the author presents two negotiation training exercises. The first addresses an international refugee policy summit and the second a negotiation over the sale of a large solar pv park in Thailand.
This cumulative thesis extends the econometric literature on testing for cointegration in nonstationary panel data with cross-sectional dependence. Its self-contained chapters consist of two publications and two publication manuscripts which present three new panel tests for the cointegrating rank and an empirical study of the exchange rate pass-through to import prices in Europe. The first chapter introduces a new cointegrating rank test for panel data where the dependence is assumed to be driven by unobserved common factors. The common factors are first estimated and subtracted from the observations. Then an existing likelihood-ratio panel cointegration test is applied to the defactored data. The distribution of the test statistic, computed from defactored data, is shown to be asymptotically equivalent to that of a test statistic computed from cross-sectionally independent data. The second chapter proposes a new panel cointegrating rank test based on a multiple testing procedure, which is robust to positive dependence between the individual units' test statistics. The assumption of a certain type of positive dependence is shown by simulations not to be violated in panels with dependence structures commonly assumed in practice. The new test is applied to find empirical support of the monetary exchange rate model in a panel of eight OECD countries. The third chapter puts forward a new panel cointegration test allowing for both cross-sectional dependence and structural breaks. It employs known individual likelihood-ratio test statistics accounting for breaks in the deterministic trend and combines their p-values by a novel modification of the Inverse Normal method. The average correlation between the probits is inferred from the average cross-sectional correlation between the residuals of the individual VAR models in first differences. The fourth chapter studies the exchange rate pass-through to import prices in a panel of nineteen European countries through the prism of panel cointegration. Empirical evidence supporting a theoretical long-run equilibrium relationship between the model's variables is found by the newly proposed panel cointegration tests. Two different panel regression models, which take both cointegration and cross-sectional dependence into account, provide most recent estimates of the exchange rate pass-through elasticities.
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.
Internet- and mobile technologies are increasingly used to deliver mental health care. E-Mental Health is promising for the prevention and treatment of mental disorders. However, while E-Mental Health was shown to be an effective treatment tool, fewer studies investigated the prevention of mental health problems with E-Mental Health approaches. In a series of three studies, this dissertation examines internet- and mobile-based approaches for the early monitoring and supporting of mental health. First, a pilot study investigates the use of smartphone data as collected by daily self-reports and sensor information for the self-monitoring of bipolar disorder symptoms. It was found that some, but not all smartphone measurements predicted clinical symptoms of mania and depression, indicating that smartphones could be used as an earlywarning system for patients with bipolar disorder. Second, a randomized controlled trial evaluates the effectiveness of an internet-based intervention among persons with depression and sickness absence. The intervention was found to be effective in reducing depressive symptoms compared to a control group, suggesting that the internet can provide effective support for people with sickness absence due to depression. Third, a study protocol proposes to combine self-monitoring with a mobile intervention to support mental health in daily life. Supportive self-monitoring will be evaluated in a fully mobile randomized controlled trial among a sample of smartphone users with psychological distress. If supportive self-monitoring on the basis of a smartphone application is effective, it could be widely distributed to monitor and support mental health on a population level. Finally, the contribution of the presented studies to current research topics in E-Mental Health is discussed.
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.
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.
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.
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.
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.
The increasing perils of connectivity technologies in the context of large satellite constellations come alongside with legal aspects concerning the protection of the space environment. The interplay of connectivity and sustainability must be regulated. To analyse the legal measures and tools regulating the risks, both sides of the problem are taken into consideration. The technological side of large satellite constellations is summarized under the term cybersecurity. Cyber is a code-based system, i.e. at first sight it requires a specialized field of law. This holds true on space sustainability as well. Large satellite constellations raise the discussion on space debris and junk. The consensus on the LTS guidelines by COPUOS at UNISPACE+50 in 2018 constitutes a milestone in Space Law. Space sustainability requires a particular adoption of legal norms: the idea is very similar to the subject of cybersecurity. Since both areas of issue are internationally driven and have multilateral impact, self-regulation proves ineffective. The genesis of reliable and uniform legal rules requires a different approach considering the multilevel systems of obligations with different binding authority. This thesis evaluates the balance between the future of connectivity and space sustainability in the context of large satellite constellations by considering the impact of legal rules with different binding authority.
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.
The process perspective provides a unifying framework that has substantially contributed to our understanding of entrepreneurship. However, much of the research up to now has neglected this process oriented conception of entrepreneurship. There is therefore a need for studies that take the inherent dynamic processes into account and analyze the underlying mechanisms when researching entrepreneurship. This dissertation aims to improve our understanding of the entrepreneurial process. Specifically, this dissertation focuses on new venture creation and the processes of sustainable opportunity identification and opportunity deviation. Chapter 1 provides a general introduction that highlights the theoretical contributions of this dissertation and gives an overview over the conducted studies. Chapter 2 argues for a process model of entrepreneurship that places entrepreneurs and their actions center stage. The model combines different perspectives and levels of analysis and provides an integrative framework for researching new venture creation. In chapter 3 we establish and test a theoretical model of sustainable opportunity identification. The chapter explains how younger generations identify sustainable opportunities. The findings indicate that sustainable opportunity identification is a process with two transitions from problem to solution identification and from solution identification to sustainable opportunity identification. These transitions are contingent on awareness of consequences and entrepreneurial attitude. Chapter 4 offers insights into how deviation from the original opportunity increases the performance of entrepreneurial teams. The findings indicate that entrepreneurial teams with a high level of error orientation set themselves higher goals when deviating from their original opportunity. Higher goals then lead to higher team performance. Chapter 5 summarizes the overall findings and outlines the general theoretical and practical implications. Each chapter thus contributes to the process perspective by focusing on how different phases of the entrepreneurial process unfold and develop over time. Thereby, this dissertation advances our understanding of entrepreneurship as a process.
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 2013, the European Commission adopted the so called "Entrepreneurship 2020 Action Plan" to ease the creation of new ventures and to support the takeover of existing firms. The goal is to create a supportive environment for entrepreneurs to thrive and grow (European Commission 2013). This shows that the European Union puts its efforts to support small firms as they are seen as means for Europe's sustainable economic growth. However, the successful processes of growth and investment are complex and depend on different determinants. The present thesis focuses on the firm level and analyzes in three independent articles: how small firms invest over time, how new ventures grow and which variables influence growth, how small firms grow after business takeover and which variables influence growth. The framework that connects these articles forms the content-related focus on the early stage of development of small firms and the methodological and analytical approaches that comply with up-to-date and adequate statistical analysis techniques. Supported by an extensive dataset, which is the foundation of all three articles, it is possible to investigate empirically different open research questions using bivariate and multivariate analysis techniques. Thus, this thesis also serves the research needs for more multivariate analyses for small firms, for which so far mainly cross-sectional studies have been conducted.
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.
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.
Intelligent Product Design
(2012)
The aim of this thesis is to generate reality-based hypotheses about the opportunities and obstacles that create the implementation of Cradle to Cradle for the companies Jules Clarysse NV and Steelcase Inc. It discusses further which marketing-mix is appropriate for Cradle to Cradle products. Therefore exploratory expert interviews have been conducted with both companies. The empirical part is introduced by a literature study. From marketing perspective, the Cradle to Cradle approach for product design is investigated while taking into account that academic literature categorizes the concept on the one hand as consistent sustainability strategy, on the other hand as sustainable design. Moreover, the broad use of the expression design, within the literature of the Cradle to Cradle founders, is analyzed. Here, Cradle to Cradle design is holding out the prospect of Triple Top Line growth, rather than meeting only the economic bottom line. In regard of aesthetics, Cradle to Cradle aspires diversity in contrast to prevailing principles of Functionalism and universal design solutions. The ‘hidden‘ design assignment of Cradle to Cradle, service design, is highlighted as sphere that should be progressed. All these considerations form the interview guideline. The interviews serve as reality check whether there result Triple Top Lines and new service models for the companies and explore how aesthetics and tools of the marketing-mix are handled in Cradle to Cradle practice.
The concept of empowerment has gained considerable attention in the field of international development. Institutions such as the World Bank and the United Nations invest considerable funds and efforts trying to facilitate empowerment in developing countries. Thus, empowerment becomes important when people need to take action and be innovative in overcoming scarcity and fighting against poverty. Research shows the positive effects of empowerment on entrepreneurship-related behavior and outcomes such as proactive behavior, goal achievement, and innovation. Yet, there is a dearth of research addressing the phenomenon of empowerment in entrepreneurship. This dissertation aims to contribute to the understanding of the role of empowerment in entrepreneurship and its effects. Particularly, this dissertation targets the interplay between empowerment and entrepreneurship in the context of developing countries. Chapter 1 provides a general overview of the different topics of this dissertation. Chapter 2, introduces the construct of psychological empowerment at work as the theoretical foundation to advocate for the importance of empowerment in entrepreneurship. The chapter takes initial steps in drawing the rationale and identifying empirical evidence for the relationship between empowerment and entrepreneurial behavior and outcomes. Specifically, the chapter links the components of psychological empowerment to concrete action characteristics in entrepreneurship such as effectuation and experimentation. Chapter 3 establishes a first empirical link between empowerment and entrepreneurship. The chapter provides the construct of entrepreneurial empowerment and develops a multidimensional measure to measure its dimensions. By means of a nomological network, the chapter reveals the relations of entrepreneurial empowerment with relevant constructs and outcomes derived from entrepreneurship and empowerment research such as innovation, self-reliance, and decision-making. Chapter 4 posits entrepreneurship training, particularly personal initiative training and business literacy training, as effective means to facilitate entrepreneurial empowerment and its effect on business performance. The chapter uncovers the mechanisms accounting for the relationship between entrepreneurship training and entrepreneurial empowerment. Chapter 5 provides general theoretical and practical contributions and finishes with a general conclusion.