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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.
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.
Diese Studie untersucht die Wirkung einer verpflichtenden externen Begutachtung von Gründungsvorhaben im Rahmen der Ich-AG-Förderung der Bundesagentur für Arbeit. Unter Verwendung von prozessproduzierten Daten zu den Gründern und ihren Vorhaben wird geprüft, inwieweit sich Unterschiede zwischen Gründern im Arbeitsagenturbezirk Lüneburg, die unter diese Regelung fallen und solchen, die dies nicht tun, ergeben. Die Ergebnisse der Studie deuten darauf hin, dass keine Unterschiede in beobachtbaren Merkmalen zwischen diesen Gründern bestehen, was ein Hinweis auf die Wirkungslosigkeit der externen Begutachtung sein kann.
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.