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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.
The research presented here examines the ways the products and practices of digital game-based language learning (DGBLL) shape access to foreign language learning. Three different studies with different methodologies and foci were carried out to examine the affordances of various aspects of DGBLL. The emphasis in all three cases, two of which are empirical and one of which is a theoretical investigation, is on developing a better understanding of the affordances of DGBLL to derive implications for English Foreign Language (EFL) teacher education. In the first study, the focus is on constructing and implementing an evaluative framework to examine the pedagogical, linguistic, and ludic affordances of DGBLL tools. Analysis reveals that many dedicated DGBLL applications incorporate content, pedagogy, and game elements that are limited in their ability to reflect contemporary understandings of foreign language learning or generate motivation to pursue game-related goals. As such, they call into question existing typologies of DGBLL and emphasize the need for competent educators who can effectively align the selection of specific DGBLL tools with given language learning objectives. In order to understand the preexisting knowledge and attitudes that need to be addressed to develop such competence, the second study examines pre-service English foreign language (EFL) teachers’ beliefs and behaviors regarding DGBLL. The quantitative analysis reveals positive correlations between gameplaying and EFL skills and language learning strategies, and between gaming behaviors and beliefs about DGBLL. At the same time, low rates of gameplaying behaviors and negative correlations between prior digital media usage and attitudes towards DGBLL suggest the need for substantial theoretical and practical teacher preparation that takes into account underlying assumptions about gameplaying and foreign language learning. The third study examines the basis of these assumptions, relying on Bourdieu's notion of habitus to illuminate the foundation of these beliefs and his notion of linguistic capital to consider the potential impact of a non-gameplaying habitus on some language learners. Such differential acceptance of efficacious DGBLL in formal school settings may inhibit access to significant forms of capital, and requisite linguistic and digital competencies. While all three studies are limited in their scope, they hold important implications for teacher education. Given the nature of the applications analyzed, it becomes clear that, not only are particular applications appropriate for specific objectives; it must also be the role of teacher education to enhance pre-service teachers' (PST) abilities to understand these nuances and select media accordingly. This can only take place when PSTs' situated existing beliefs and behaviors, as illuminated by this research, are taken into account and addressed accordingly. Finally, this education must necessarily include initiatives to develop an understanding of issues of equity in access, participation, and outcomes as regards DGBLL.
Assessment of forest functionality and the effectiveness of forest management and certification
(2021)
Forest ecosystems are complex systems that develop inherent structures and processes relevant for their functioning and the provisioning of ecosystem services that contribute to human wellbeing. With increasing climate change impacts, especially regulating ecosystem services such as microclimate regulation are ever more relevant to maintain forest functions and services. A key question is how forest management supports or undermines the ecosystems’ capacity to maintain those functions and services. The main objective of this thesis is the development of a concept to assess the functionality of forests and to evaluate the effectiveness of forest ecosystem management including certification. An ecosystem-based and participatory methodology, named ECOSEFFECT, was developed. The method comprises a theoretical and an empirical plausibility analysis. It was applied to the Russian National FSC Standard in the Arkhangelsk Region of the Russian Federation - where boreal forests are exploited to meet Europe's demand for timber. In addition, the influence of forestry interventions on temperature regulation in Scots pine and European beech forests in Germany was assessed during two extreme hot and dry years in 2018 and 2019. Microclimate regulation is a suitable proxy for forest functionality and can be applied easily to evaluate the effectiveness of forest management in safeguarding regulating forest functions relevant under climate change. Thus, the assessment of forest microclimate regulation serves as convenient tool to illustrate forest functionality. In the boreal and temperate forests studied in the frame of this thesis, timber harvesting reduced the capacity to self-regulate forests’ microclimate and thus impair a crucial part of ecosystem functionality. Changes in structural forest characteristics influenced by forest management and silviculture significantly affect microclimatic conditions and therefore forest ecosystems' vulnerability to climate change. Canopy coverage and the number of cut trees were most relevant for cooling maximum summer temperature in pine and beech forests in northern Germany. The Russian FSC standard has the potential to improve forest management and ecological outcomes, but there are shortcomings in the precision of targeting actual problems and ecological commitment. It is theoretically plausible that FSC prevents logging in high conservation value forests and intact forest landscapes, reduces the size and number of clearcuts, and prevents hydrological changes in the landscape. However, the standard was not sufficiently explicit and compulsory to generate a strong and positive influence on the identified problems and their drivers. Moreover, spatial data revealed, that the typical regular clearcut patterns of conventional timber harvesting continue to progress into the FSC-certified boreal forests, also if declared as "Intact Forest Landscape". This results in the need to verify the assumptions and postulates on the ground as it remains unclear and questionable if functions and services of boreal forests are maintained when FSC-certified clearcutting continues.The analysis of satellite-based data on tree cover loss showed that clearcutting causes secondary dieback in the surrounding of the cleared area. FSC-certification does not prevent the various negative impacts of clearcutting and thus fails to safeguard ecosystem functions. The postulated success in reducing identified environmental threats and stresses, e. g. through a smaller size of clearcuts, could not be verified on site. The empirical assessment does not support the hypothesis of effective improvements in the ecosystem. In practice, FSC-certification did not contribute to change clearcutting practices sufficiently to effectively improve the ecological performance. Sustainability standards that are unable to translate principles into effective outcomes fail in meeting the intended objectives of safeguarding ecosystem functioning. Clearcuts that carry sustainability labels are ecologically problematic and ineffective for the intended purpose of ecological sustainability.The overexploitation of provisioning services, i.e. timber extraction, diminishes the ecosystems' capacity to maintain other services of global significance. It also impairs ecosystem functions relevant to cope with and adapt to other stresses and disturbances that are rapidly increasing under climate change.
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.
This doctoral thesis deals with the topic of organizational misconduct and covers the three salient research streams in this area by addressing its performance outcomes, antecedents, and preventive measures. Specifically, it is concerned with the question of how different forms of misconduct are reflected in the stock performance of related organizations, thereby, covering the three pillars of corporate sustainability environmental, social, and governance (ESG). Furthermore, it aims to conceptualize how individual cognitive biases may lead to misconduct, therefore, potentially representing an antecedent and how existing management control systems can be enhanced to effectively address specific forms of misconduct, respectively. To these ends, the author first reviews the research stream of stock price reactions to environmental pollution events in terms of the underlying research samples, methodological specifications, and theoretical underpinnings. Based on the findings of the systematic literature review (SLR), he performs three stock-based event studies of the Volkswagen diesel emissions scandal (Dieselgate), workplace sexual harassment (#MeToo accusations), and the 2003 blackout in the US to cover the three ESG dimensions, respectively. In line with the SLR, his event studies reveal substantial stock losses to firms involved in misconduct that are eventually even accompanied by a spillover effect to uninvolved bystanders. Then, the author reviews the extant literature conceptually to develop a framework outlining how moral licensing as an individual cognitive bias might lead to a self-attribution of corporate sustainability, a consecutive accumulation of moral credit, and a later exchange of this credit by engaging in misconduct afterward. Finally, he assesses existing workplace sexual harassment management controls, such as awareness training and grievance procedures critically in another conceptual analysis. Based on the shortcomings stemming from management controls' focus on compliance and negligence of moral duties, he introduces five specific nudges firms should consider to enhance their existing management controls and eventually prevent occurrences of workplace sexual harassment. Based on the six distinct articles within this doctoral thesis, the author outlines its limitations and point at directions for future research. These mainly address providing further evidence on the long-term performance effects of organizational misconduct, enriching our knowledge on further cognitive biases eventually leading to misconduct, and conceptualizing nudging beyond the use-case of workplace sexual harassment.
All of the papers contained in this thesis deal with some aspect of labor market inequality. The impact of September 11th, 2001 on the employment prospects of Arabs and Muslims in the German labor market (chapter 2) examines whether the attacks on the World Trade Center and the Pentagon on September 11th, 2001 have influenced the job prospects of persons from predominantly Muslim countries in the German labor market. Using a large, representative database of the German working population, evidence from regression-adjusted difference-in-differences-estimates indicates that 9/11 did not cause a severe decline in job prospects. This result, which is in line with prior evidence from Sweden and England, is robust over a wide range of control groups. Islamistic terror and the job prospects of Arab men in Britain: Does a country's direct involvement matter? (chapter 3) examines whether the labor market prospects of Arab men in England are influenced by recent Islamistic terrorist attacks. We use data from the British Labour Force Survey from Spring 1999 to Winter 2006 and treat the terrorist attacks on the USA on September 11th, 2001, the Madrid train bombings on March 11th, 2004 and the London bombings on July 7th, 2005 as quasi-experimental events that may have changed the attitudes towards Arab or Muslim men. Using treatment group definitions based on ethnicity, country of birth and religion, evidence from difference-in-differences-estimators combined with matching indicates that the real wages, hours worked and employment probabilities of Arab men were unchanged by the attacks. This finding is in line with prior evidence from Europe. Effects of the obligation to employ severely disabled workers - findings from the introduction of the Law to Combat Unemployment among Severely Disabled People'' (chapter 4) uses new administrative data from the German Federal Employment Agency -- the Integrated Employment Biographies Sample IEBS -- to assess the impact of a mandatory employment quota for disabled workers in Germany. We use an exogenous change, introduced through the Law to Combat Unemployment among Severely Disabled People'' (Gesetz zur Bekämpfung der Arbeitslosigkeit Schwerbehinderter''), as a natural experiment and measure the change in the reemployment probability of the unemployed disabled by means of regression-adjusted difference-in-differences estimators. Our results indicate that the change in the employment quota neither enhanced nor worsened the employment prospects of the disabled. Finally, Intra-firm wage inequality and firm performance -- First evidence from German linked employer-employee-data (chapter 6) deals with the impact of wage inequality on firm performance. Economic theory suggests both positive and negative relationships between intra-firm wage inequality and productivity. This paper contributes to the growing empirical literature on this subject. We combine German employer-employee-data for the years 1995-2005 with inequality measures using the whole wage distribution of a firm and rely on panel-instrumental variable estimators to control for unobserved heterogeneity and simultaneity problems. Our results indicate a relatively small impact of wage inequality on firm performance in West Germany, while there seems to be a relationship for some inequality measures in East Germany. Further analysis shows that the relationship varies strongly with industrial relations in East Germany.
A Matter of Connection: Competence Development in Teacher Education for Sustainable Development
(2021)
Based on a dual case study, this cumulative dissertation investigates how individual "education for sustainable development" (ESD) courses, as part of the teacher education programs at Leuphana University in Lüneburg/Germany and Arizona State University (ASU)/USA, actually foster students' ESD-specific professional action competence. Furthermore, this work sheds light on the link between learning processes and outcomes, to reveal which factors actually affect the achievement of ILOs and competence development. The findings of this study indicate that both courses under investigation eventually live up to their role and increased student teachers' competence and commitment to implement ESD in their future careers; yet, mainly due to their different thematic foci, to varying degrees. Additionally, the four Cs (personal, professional, social, and structural connections) were revealed as significant factors that support students' learning and should be considered when planning and designing course offerings in TESD, with the goal of developing students' knowledge, skills, and attitudes.
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.
When screening projects for potential investment placements, Venture Capitalists have to base their decision on the information provided in the business plan. The aim of this study is to make VCs aware of the influence of various factors which are discussed in business plans, such as the management team and risk minimising strategies. In order to do this, the business plans of four companies which received investment placements were analysed. The analysis revealed the two main success factors to be industrial experience and a filled product pipeline. The results also suggested that the business plan in its current form may not cover all the information needed for an optimal result. However, since this work is only a first approach further research needs to be carried out.