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Institut
- Fakultät Wirtschaftswissenschaften (66) (entfernen)
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.
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.
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.
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.
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.
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.