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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.
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.
Mental health is an important factor in an individuals' life. Online-based interventions have been developed for the treatment of various mental disorders. During these interventions, a large amount of patient-specific data is gathered that can be utilized to increase treatment outcomes by informing decision-making processes of psychotherapists, experts in the field, and patients. The articles included in this dissertation focus on the analysis of such data collected in digital psychological treatments by using machine learning approaches. This dissertation utilizes various machine learning methods such as Bayesian models, regularization techniques, or decision trees to predict different psychological factors, such as mood or self-esteem, dropout of patients, or treatment outcomes and costs. These models are evaluated using a variety of performance metrics, for example, receiver operating characteristics curve, root mean square error, or specialized performance metrics for Bayesian inference. These types of analyses can support decision- making for psychologists and patients, which can, in turn, lead to better recommendations and subsequently to increased outcomes for patients and simultaneously more insight about the interplay between psychological factors. The analysis of user journey data has not yet been fully examined in the field of psychological research. A process for this endeavor is developed and a technical implementation is provided for the research community. The application of machine learning in this context is still in its infancy. Thus, another contribution is the exploration and application of machine learning techniques for the revelation of correlations between psychological factors or characteristics and treatment outcomes as well as their prediction. Additionally, economic factors are predicted to develop a process for treatment type recommendations. This approach can be utilized for finding the optimal treatment type for patients on an individual level considering predicted treatment outcomes and costs. By evaluating the predictive accuracy of multiple machine learning techniques based on various performance metrics, the importance of considering heterogeneity among patients' behavior and affect is highlighted in some articles. Furthermore, the potential of machine learning-based decision support systems in clinical practice has been examined from a psychotherapists' point of view.
Understanding that entrepreneurship can be better modeled from a systemic point of view is a primordial aspect that determines the important role of universities in entrepreneurial ecosystems. What makes the ecosystem approach a valuable tool for understanding social systems is that, from a holistic perspective, their behavior seems to have emerging characteristics. This dissertation presents a dual scientific account of the entrepreneurship phenomenon in universities. The work is divided into two equal parts, each of which is composed of two research papers. The narrative of the first half takes on a macro perspective view, consisting of one theoretical and one empirically-based conceptual case study. This part conceptually depicts a systematic approach to entrepreneurialism in higher education, namely an ecosystems perspective. The second half concentrates on the meso- and micro levels of study from the university's point of view, comprising of a case study as historical account for the emergence of the entrepreneurial university, and of a metasynthesis of empirical case studies in entrepreneurial universities, which serves as the basis for the development of entrepreneurial university archetypes. This doctoral work contributes to an in-depth understanding of Entrepreneurship in universities regarding its systemic qualities and archetypal characteristics of entrepreneurial universities. It argues for an ecosystem's perspective on the phenomenon of entrepreneurial activity, highlighting the fundamental role that universities play as the heart of entrepreneurial ecosystems. Furthermore, this research expands on the novel concept of the entrepreneurial university by using extensive case study literature to empirically identify distinct archetypes that better reflect the diverse reality of how universities engage as entrepreneurial actors by way of differentiated entrepreneurial structures, systems, and strategies.
Mobilität und Tourismus gehören untrennbar zusammen, denn ohne einen Ortswechsel gibt es keine Urlaubsreise. Der Tourismus aber verursacht ca. 5 % der anthropogenen Kohlendioxidemissionen, von denen etwa 75% auf den touristischen Verkehr entfallen. Neben dem Flugverkehr trägt insbesondere der motorisierte Individualverkehr einen hohen Anteil an den Emissionen. Angesichts des deutlichen Beitrags des touristischen Verkehrs zum Klimawandel erscheint es notwendig, sich mit Wegen zu einer ökologischen touristischen Mobilität zu beschäftigen. Zur Untersuchung der Einflussfaktoren auf die touristische Verkehrsmittelwahl wurde ein Erklärungsmodell basierend auf der Theorie des geplanten Verhaltens entwickelt. Neben den Basiskonstrukten der Einstellung, der subjektiven Norm und der wahrgenommenen Verhaltenskontrolle wurden als ergänzende Modellkonstrukte die persönliche Norm, das allgemeine Umweltbewusstsein sowie gewohnheitsmäßiges Handeln hinzugefügt. Eine empirische Untersuchung (N=738) ermittelte durch multiple lineare Regression wichtige Ansatzpunkte für die Gestaltung von Handlungsempfehlungen. Signifikante Ergebnisse konnten für die Konstrukte der Einstellung, der subjektiven Norm, der wahrgenommenen Verhaltenskontrolle, der persönlichen Norm, der Gewohnheit sowie der Kontrollvariablen Alter und Einkommen erreicht werden. An diesen Einflussfaktoren auf die Intention, zukünftig ein umweltfreundlicheres Verkehrsmittel zur Reise in den nächsten Städte-Kurzurlaub zu wählen, setzen die Implikationen für die Praxis an und zeigen Möglichkeiten auf, die touristische Mobilität ökologischer zu gestalten.
The increasing perils of connectivity technologies in the context of large satellite constellations come alongside with legal aspects concerning the protection of the space environment. The interplay of connectivity and sustainability must be regulated. To analyse the legal measures and tools regulating the risks, both sides of the problem are taken into consideration. The technological side of large satellite constellations is summarized under the term cybersecurity. Cyber is a code-based system, i.e. at first sight it requires a specialized field of law. This holds true on space sustainability as well. Large satellite constellations raise the discussion on space debris and junk. The consensus on the LTS guidelines by COPUOS at UNISPACE+50 in 2018 constitutes a milestone in Space Law. Space sustainability requires a particular adoption of legal norms: the idea is very similar to the subject of cybersecurity. Since both areas of issue are internationally driven and have multilateral impact, self-regulation proves ineffective. The genesis of reliable and uniform legal rules requires a different approach considering the multilevel systems of obligations with different binding authority. This thesis evaluates the balance between the future of connectivity and space sustainability in the context of large satellite constellations by considering the impact of legal rules with different binding authority.
Entwicklungen und Potenziale der Kultur- und Kreativwirtschaft im ländlichen Raum - Der Kreis Höxter
(2014)
In der vorliegenden Thesis wird ein interdisziplinäres, exploratives und handlungsorientiertes Problemgerüst durchleuchtet. Als übergeordneter Forschungsgegenstand wird zum einen der Stadt-Land-Unterschied im Rahmen kultur- und kreativwirtschaftlicher Strukturentwicklung betrachtet. Zum anderen werden konkrete Handlungsoptionen zur Förderung der Wirtschaftsbranche für regionalpolitisch Verantwortliche in ländlichen Räumen entworfen und aufgezeigt. Folgende Leitfragen werden herangezogen: Welche standortfaktoriellen Vorteile bezüglich der wirtschaftlichen, sozialen und kulturellen Rahmenbedingungen bieten ländliche Räume für die Kultur- und Kreativwirtschaft (im Gegensatz zu städtischen Räumen)? Welche Handlungsspielräume haben regional- und wirtschaftspolitisch Verantwortliche im Hinblick auf die kulturelle Entwicklung von ländlichen Räumen? Wie lässt sich konkret eine im Sinne der Standortattraktivität agierende Wirtschaftsförderung mit einer Stärkung der Kulturlandschaft vereinbaren?
The process perspective provides a unifying framework that has substantially contributed to our understanding of entrepreneurship. However, much of the research up to now has neglected this process oriented conception of entrepreneurship. There is therefore a need for studies that take the inherent dynamic processes into account and analyze the underlying mechanisms when researching entrepreneurship. This dissertation aims to improve our understanding of the entrepreneurial process. Specifically, this dissertation focuses on new venture creation and the processes of sustainable opportunity identification and opportunity deviation. Chapter 1 provides a general introduction that highlights the theoretical contributions of this dissertation and gives an overview over the conducted studies. Chapter 2 argues for a process model of entrepreneurship that places entrepreneurs and their actions center stage. The model combines different perspectives and levels of analysis and provides an integrative framework for researching new venture creation. In chapter 3 we establish and test a theoretical model of sustainable opportunity identification. The chapter explains how younger generations identify sustainable opportunities. The findings indicate that sustainable opportunity identification is a process with two transitions from problem to solution identification and from solution identification to sustainable opportunity identification. These transitions are contingent on awareness of consequences and entrepreneurial attitude. Chapter 4 offers insights into how deviation from the original opportunity increases the performance of entrepreneurial teams. The findings indicate that entrepreneurial teams with a high level of error orientation set themselves higher goals when deviating from their original opportunity. Higher goals then lead to higher team performance. Chapter 5 summarizes the overall findings and outlines the general theoretical and practical implications. Each chapter thus contributes to the process perspective by focusing on how different phases of the entrepreneurial process unfold and develop over time. Thereby, this dissertation advances our understanding of entrepreneurship as a process.