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The emission of anthropogenic trace substances into the aquatic environment continuously poses challenges to water suppliers. The contamination of raw waters with organic trace substances requires complex water treatment processes to secure drinking water quality. The routine monitoring of these raw waters as well as the behavior and fate of organic trace substances during different treatment processes is of great interest to recognize and counter potential dangers at an early stage. Non-target screening using liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) allows the detection of thousands of compounds within a single run and covers known as well as unknown substances. Compared to the established analytical techniques, this is a decisive advantage for the monitoring of raw and process waters during water treatment. While the analytical technique LC-HRMS has undergone significant developments in recent years, the algorithms for data processing reveal clear weaknesses. This dissertation therefore deals with reliable processing strategies for LC-HRMS data. The first part of this work seeks to highlight the problematics of false positive and false negative findings. Based on repeated measurements, various strategies of data processing were assessed with regard to the repeatability of the results. To ensure that real peaks were barely or not removed by the filtering procedure, samples were spiked with isotope-labeled standards. The results emphasize that the processing of sample triplicates results in sufficient repeatability and that the signal fluctuation across the triplicates emerged as a powerful filtering criteria. The number of false positives and false negatives could be significantly reduced by the developed strategies which consequently improve the validity of the data. The second part of this thesis addresses the development of processing strategies particularly aimed at assessing water treatment processes. The detected signals were tracked across the treatment process and classified based on their fold changes. A more reliable signal classification was achieved by implementing a recursive integration approach. Special integration algorithms allow a reliable signal classification even though the signal to be compared was below the intensity threshold. Different combinations of replicates of process influents and effluents were processed for evaluating the repeatability. The good repeatability was indicated by the results of both the plausibility checks and the ozonation process (ozonation of pretreated river water) and thus points to high reliability. The applicability of the developed strategies to real world applications is demonstrated in the last part of this work. Besides the prioritization of the generated results, the main focus was the identification of recognized compounds. The developed strategies clearly improve the validity of the underlying data. The combination of LC-HRMS analysis with reliable processing strategies opens up multiple possibilities for a more comprehensive monitoring of water resources and for the assessment of water treatment processes. The processing strategies and validation concepts may be easily transferred to other research fields.
Among all attenuation processes, biodegradation plays one of the most important role and is one of the most desirable processes in the environment. To assess biodegradation, a variety of biodegradation test procedures have been developed by several international organizations. OECD guidelines for ready biodegradability testing represent one of the most prominent group of internationally used screening biodegradation tests (series 301A-F). These tests are usually very simple in their designs and allow for the fast and cheap screening of biodegradability. However, because of their stringency, the test conditions are not close to simulating environmental conditions and may lead to unrealistic results. To overcome these limitations, OECD introduced simulation tests which are designed to investigate the behavior of chemicals in specified environmentally relevant compartments. Despite the fact that simulation tests give more insight into the fate of chemicals in the environment, they are not applied frequently as they are often tedious, time consuming and expensive. Consequently, there is a need to provide a new biodegradation testing method that would combine complex testing environment as in simulation tests, easiness in handling and good data repeatability as in screening biodegradation tests. Another challenge is an adaption of the existing biodegradation testing methods to new types of samples, i.e. mixtures of transformation products (TPs). The research on the presence of pharmaceuticals in the environment gained momentum in the 1990s; since then, it has been growing. Their presence in the environment is a wellestablished fact. A wide range of pharmaceuticals is continuously detected in many environmental compartments such as surface waters, soils, sediments, or ground waters. After pharmaceuticals reach the natural aquatic environment they may undergo a number of processes such as: photolysis (under direct sunlight), hydrolysis, oxidation and reduction reactions, sorption, biodegradation (by bacteria of fungi), and bioaccumulation. These processes, may cause their elimination from aquatic environment, if reaction is complete, or creation of new compounds i.e., transformation products (TPs). What is more, processes, like chlorination and advanced oxidation processes (AOPs), such as H2O2/UV, O3/UV, TiO2/UV, Fenton, and photo-Fenton, or UV treatment which might be applied in water or wastewater treatment, may also lead to the TPs introduction into aquatic environment. The research on the TPs brings many new challenges. From one side, there is a constant need for the the development of a sensitive and reliable analytical separation, detection, and structure elucidation methods. Additionally, there is a need for the preparation of appropriate assays for the investigation of properties of new compounds, especially those answering the question if TPs pose a higher risk to the aquatic ecosystems than their parent compounds. Among numerous groups of pharmaceuticals, two are of great importance: antibiotics since they might promote emergence and maintenance of antimicrobial resistance in the aquatic environment; and cytostatic drugs. Cytostatic drugs can exert carcinogenic, mutagenic and/or teratogenic effects in animals and humans. The challenges of biodegradation testing presented in this thesis, encompasses these different areas of interest and was divided into three objectives: 1) Identification of the knowledge gaps and data distribution of the two groups of pharmaceuticals antibiotics and cytostatic drugs (article I); 2) Increasing the knowledge on biodegradation of cytostatic drugs and their TPs (articles II, III, and IV) and 3) Establishment of a biodegradation test with closer to simulation tests conditions, that could be affordable and to support better understanding on processes in water sediment interface construction - screening water-sediment test. Further validation of the test with an insight into sorption and desorption processes (articles V and VI).
The emergence of sustainability as a guiding principle for tourism development came along with needs to introduce instruments that can monitor the actual impacts of tourism. Sustainability assessments in tourism (SAT) have gained popularity in recent years with a range of measurement schemes being introduced for national and subnational tourism destinations. With the help of sustainability indicators these schemes intend to guide decision-makers in making better evidence-informed decisions and to improve the overall sustainability performance of tourism. With this dissertation, the author aims to contribute to a deeper understanding of the implementation and performance of sustainability assessments, by linking transformative needs of tourism with necessary assessment approaches that can serve as effective instruments for a shift towards a more sustainable tourism development. Thus, the research is part of recent efforts to establish profound and effective measurement approaches for sustainable tourism. The author employs a mixed-methods approach combining qualitative, quantitative, set-theoretic, and review methods, with the aim of maximising the validity of results. First, he explores the general progress and current state of research on sustainability assessments in tourism, with the intention to identify patterns, key elements and research gaps within assessment approaches This is followed by subsequent detailed analyses that examine specific environmental and socio-economic sustainability issues with the aim of providing conceptual, methodological and empirical solutions for assessing them in detail. The dissertation highlights that concrete assessment tools are needed for evidence-informed decision-making and the establishment of effective actions in destination management. The findings indicate that assessments will be more successful in terms of serving as tools for decision-making, if they tackle main drivers of change and encourage management or policymakers to take decisions that affect multiple sustainability issues. It also reviews different concepts and accounting principles and rises awareness of a cautious selection of methods and measurement approaches, as this may affect overall results. The thesis empirically evaluates and applies different measurement approaches in specific destinations, with the help of quantitative and qualitative data collection methodologies. In general, my thesis provides further clarification about key environmental and socio-economic measurement methodologies, which supports ongoing debates about sustainability impacts of tourism. Thus, the research contributes to knowledge, frameworks, methodologies and practical application for tourism governance and tourism sustainability science.
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.
This doctoral thesis contains four empirical studies analysing the personal accountability of prime ministers and the electoral presidentialisation of parliamentary elections in European democracies. It develops the concept of presidentialised prime ministerial accountability as a behavioural element in the chain of accountability in parliamentary systems. The ongoing presidentialisation of parliamentary elections, driven by changes in mass communication and erosion of societal cleavages, that fosters an increasing influence of prime ministers' and other leading candidates' personalities on vote choices, has called performance voting – and the resulting accountability mechanism of electoral punishment and reward of governing parties – into question. This thesis analyses whether performance voting can be extended to the personal level of parliamentary governments and asks whether voters hold prime ministers personally accountable for the performance of their government. Furthermore, it explores how voters change their opinion of prime ministers and how differences in party system stability and media freedom between Western and Central Eastern Europe contribute to higher electoral presidentialization in Central Eastern European parliamentary elections. This thesis relies on several national data sources: the "British Election Study", the "German Longitudinal Election Study" and other German election surveys, the "Danish Election Study", as well as, data from the "Forschungsgruppe Wahlen". In addition, it utilises cross-national data from the "Comparative Study of Electoral Systems".
Collaborative governance is a promising approach to address the difficult challenges of sustainability through global public and private partnerships between diverse actors of state, market and civil society. The textile and clothing industry is an excellent example where a variety of such initiatives have evolved to address the wicked sustainability challenges. However, the question arises whether collaborative governance actually leads to transformation. In this dissertation, the author therefore questions whether and how collaborative governance in the textile sector provides space for, or pathways to, sustainability transformation. In three scientific articles and this framework paper, the author uses a mixed-methods research approach and follows scholars of sustainability science towards transformation research. First, he conducts a systematic literature review on inter-organizational and governance partnerships before diving into a critical case study on an interactive collaborative governance initiative, the German Partnership for Sustainable Textiles (Textiles Partnership). The multi-stakeholder initiative (MSIs) was initiated by the German government in 2015 and brings together more than 130 organizations and companies from seven stakeholder groups. It aims at improving working conditions and reducing environmental impacts in global textile and clothing supply chains. In two empirical articles, the author then explores learning spaces in the partnership and the ways in which governance actors navigate the complex governance landscape. For the former, he uses a quantitative and qualitative social network analysis based on annual reports and qualitative interviews with diverse actors from the partnership. Then, he uses qualitative content analysis of the interviews, policy documents and conducts a focus group discussion to validate assumptions about the broader empirical governance landscape and the social interactions within. Finally, in this framework paper, he uses theories of transformation to distinguish forms of change and personal, political and practical spheres of transformation, and reflects on the findings of the three articles in this cumulative dissertation.
Viable communication systems
(2020)
Society has come to the point that it requires a "Great Transformation" towards sustainability to ensure the viability of the planet for a vital society. The energy transition plays a central role for this transformation. For transforming the patterns of energy production and use in an energy transition as part of the "Great Transformation", this process of change now needs to strengthen its focus on information, communication, and knowledge systems. Human society needs to establish a knowledge system that has the potential to create usable knowledge for sustainability solutions. This requires organizing a communication system that is sufficiently complex, interconnected, and, at the same time, efficient for integrating reflexive, open-ended, inter- and transdisciplinary learning, evaluation, and knowledge co-production processes across multiple levels. This cumulative dissertation contributes to research in this direction by applying a systemic sustainability perspective on the content and organization of communication in the field of research on sustainable energy and the operational level of municipal climate action as part of the energy transition. Regarding sustainability, this thesis uses strong sustainability and its principles as a frame for evaluating the content of communication. Regarding the systemic perspective, the thesis particularly relies on the following theories: (i) the human-environment system model by R. Scholz as an overarching framework regarding interactions between humans and nature, (ii) social systems theory by N. Luhmann to reflect the complexity of society, (iii) knowledge management to consider the human character of knowledge and a practice-oriented perspective, and (iv) management cybernetics, in particular, the Viable System Model by S. Beer as a framework to analyze and assess organizational structures. Furthermore, the thesis leverages the potential of text mining as a method to identify and visualize patterns in texts that reflect prevalent paradigms in communication. The thesis applies the above conceptual and methodological basis in three case studies. Case Study 1 investigates the measures proposed in 16 municipal climate action plans of regional centers in Lower Saxony, Germany. It uses a text mining approach in the form of an Summary interpretation network analysis. It analyzes how different societal subsystems are connected at the semantic level and to what extent sustainability principles can be recognized. Case Study 2 analyzes and reflects paradigms and discursive network structures in international scientific publications on sustainable energy. The study investigates 26533 abstracts published from 1990 to 2016 using a text mining approach, in particular topic modeling via latent Dirichlet allocation. Case Study 3 turns again to the cases of municipal climate action in Lower Saxony examined in Case Study 1. It examines the involvement of climate action managers of these cities in multilevel knowledge processes. Using design principles for knowledge systems, it evaluates to what extent knowledge is managed in this field across levels for supporting the energy transition and to what extent local innovation potential is leveraged or supported. The three case studies show that international research on sustainable energy and municipal climate action in Germany provide promising contributions to achieve a transformation towards sustainability but do not fully reflect the complexity of society and still support a growth paradigm, in contrast to a holistic sustainability paradigm. Further, the case studies show that research and local action are actively engaging with the diversity of energy technologies but are lagging in dealing with the socio-epistemic (communication) system, especially with regard to achieving cohesion. Using the example of German municipalities, Case Studies 1 and 3 highlight the challenges of achieving coherent local action for sustainability and bottom-up organizational learning due to incomplete or uncoordinated multilevel knowledge exchange.
Air quality models are important tools which are utilized for a large field of application. When combined with data from observations, models can be employed to create a comprehensive estimation of the past and current distribution of pollutants in the atmosphere. Moreover, projections of future concentration changes due to changing emissions serve as an important decision basis for policymakers. For the determination of atmospheric concentrations of air pollutants by means of numerical modelling it is essential to possess a model which is able to create anthropogenic and biogenic emissions with a temporally and spatially high resolution. The emission data is needed as input for a chemistry transport model which calculates transport, deposition, and degradation of air pollutants. To evaluate the impact of changing emissions on the environment a flexible emission model with the capability to create diverse emission scenarios is needed. Further, it is important to always take into account a variety of different species to properly represent the major chemical reactions in the atmosphere (e.g. ozone chemistry, aerosol formation). Currently there are only a few high resolution emission datasets available for Europe. The amount of substances included in these datasets, however, is limited. Moreover, they can not be used as basis for the creation of new emission scenarios. To enable the creation of emission scenarios in the course of this doctoral thesis the American emission model SMOKE was adopted and modified. On the basis of a multitude of different georeferenced datasets, official statistics, and further model results the newly created emission model “SMOKE for Europe” is capable of creating hourly emission data for the European continent with a spatial resolution of up to 5x5km2. In order to demonstrate the universal applicability of the emission model the carcinogenic species benzo[a]pyrene (BaP) was exemplarily implemented into the model. BaP belongs to the group of polycyclic aromatic hydrocarbons. Because of its high toxicity the European Union introduced an annual target value of 1 ng/m3 in January 2010. SMOKE for Europe was used to create a variety of emission scenarios for the years 1980, 2000, and 2020. These emission scenarios were then used to determine the impact of emission changes on atmospheric concentrations of BaP and to identify regions which exceed the European target value. Additionally the impact of different legislation and fuel use scenarios on the projected atmospheric concentrations in 2020 was investigated. Furthermore, additional use cases for a flexible emission model are pointed out. The SMOKE for Europe model was used to simulate the transport of volcanic ash after the eruption of the Icelandic volcano Eyjafjallajokull in March 2010. By comparison of modelled concentrations for different emission scenarios with observations from remote sensing and air plane flights distribution and concentration of the volcanic ash over Europe was estimated. The results of this thesis have been presented in four scientific papers published in international peerreviewed journals. The papers are reprinted at the end of this thesis.
This study aims to answer four main research questions regarding the roles, strategies, barriers, and representation of the media and environmental nongovernmental organisations (ENGOs) in environmental communication in Malaysia. From a theoretical lens, this study has incorporated the essential concepts of media, ENGOs, and environmental communication from both Western and Asian, particularly Malaysian perspectives as primary points of reference. For the purpose of this study, a total of 13 interviewees from Media A and Media B and 11 interviewees from ENGO A and ENGO B were chosen for the qualitative interview while 2,050 environmental articles were collected as samples from Media A´s and Media B´s newspapers along with ENGO A´s and ENGO B´s newsletters from the period 2012 to 2014 for the quantitative content analysis. Specifically, the findings from interview confirmed that both the Malaysian media and ENGOs have shared quite similar roles in environmental communication, particularly in environmental legitimacy (creating trust, credibility, and relationships with the public), in democracy (acting as a watchdog and mobilising the public sphere), and in constructing public mind about environmental problems. Pictures undoubtedly were one of the most vital tools in social construction, especially for presenting the reality of the environmental problems to the public. This was in harmony with the results of the quantitative content analysis, where more than 60% of pictures were found on environmental articles in media newspapers and ENGOs newsletters. Malaysian media and ENGOs have shared two common strategies in environmental communication, namely campaigning and collaboration with other stakeholders, while the ENGOs have two extra strategies: advocacy and lobbying strategies. Malaysian media and ENGOs also have collaborated with each other and the level of collaboration between them was at the coordination (medium) level. Both social actors especially the media were also relied heavily on their sources for environmental articles and the result of quantitative content analysis showed that the government was the main source for media newspapers, whereas other ENGOs and laypersons were the main sources for ENGOs´ newsletters. There are also colossal barriers faced by both Malaysian media and ENGOs throughout the process of environmental communication and some of the barriers faced by both media and ENGOs include the problem with limited knowledge of the environment, while some other barriers, like media laws and ownership, were only faced by the media; other barriers such as funding problems were specifically faced by the ENGOs. In terms of representation of environmental information, the Malaysian media make more presentations on environmental problems, especially on topics like floods, wildlife and water crises in their newspapers, while ENGOs have given more attention to environmental effort topics such as conservation and sustainable living in their newsletters. Surprisingly, not only the media but also the ENGOs used the same (news) values like timeliness, proximity, and impact as criteria for the selection of environmental issues for their publications. Other factors such as the background of the organisation and the interest of journalists or editors also influence the selection of environmental issues. It is hoped that the proposed theoretical framework of this study can serve as a crucial guideline for the development of environmental communication studies, especially among the media and ENGOs not only in Malaysia but also in other (Southeast) Asian regions that share a similar background.
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.