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Institut
- Fakultät Wirtschaftswissenschaften (66) (entfernen)
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.