Open Access

In this cumulative thesis, I present four manuscripts and two appendixes. In the manuscripts I discuss mindsets and their relation to the effectiveness of negotiation training. My general claim is that mindsets promise to be relevant for training effectiveness. Still, more research needs to be done and my co-authors of chapter 3 and I present the Scale for the Integrative Mindset of Negotiators (SIM) that can be used for some of that research. In the appendixes, I present two negotiation training exercises. The first addresses an international refugee policy summit and the second a negotiation over the sale of a large solar pv park in Thailand.

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, in particular due to its wide population access, a low threshold, the active role of the client, lower costs compared to traditional care, and the possibility to integrate interventions in real-world settings. 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.

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, we investigate 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. We 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. We 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. We also propose a new paradigm for online behaviors analysis that interprets sessions as trajectories within the page-graph. In this respect, we 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, we construct two centroid-based clustering methods using neural networks and thus lay the foundations for unsupervised behaviors analysis using neural networks. Keywords: online behaviors analysis, educational data mining, Markov models, archetypal analysis, spatio-temporal trajectories, neural network

Mit dem Thema „Kultur- und Kreativwirtschaft“ kam ich zum ersten Mal 2009 im Rahmen eines Praktikums in der Staatskanzlei des Bundeslandes Nordrhein-Westfalen in Kontakt. Damals waren in der Kulturverwaltung des Landes alle Weichen auf die Vorbereitung und die politische sowie administrative Begleitung der Kulturhauptstadt Europas RUHR.2010 gestellt. Dass mit dem Ruhrgebiet eine gesamte Region den Zuschlag für die Ausrichtung des Jahres erhielt, war ein Novum. Von der Entwicklung diverser Vorgängerprojekte und - veranstaltungen über die tatsächliche Ausgestaltung des Rahmenprogramms bis hin zur anschließenden Nachbereitung und Evaluation der einzelnen Programmpunkte und des gesamten Jahres: Bei allen Projektschritten nahmen Unternehmen und Akteure der Kultur-und Kreativwirtschaft eine essenzielle Rolle ein. Besonders reizvoll an einer eingehenden Beschäftigung mit dem Thema „Kultur- und Kreativwirtschaft“ war für mich dessen Interdisziplinarität. Neben Fragen, die aus der Interdependenz von Kultur und Ökonomie hervorgehen, entwickelte ich spezielles Interesse an der Ausgestaltung einer Policy für diese neu limitierte Wirtschaftsbranche. Nach meiner Bachelor-Arbeit zu einer kulturpolitischen Fragestellung schloss ich während meines Master-Studiums an der Leuphana Universität Lüneburg eine Praxisphase in der Abteilung „Kulturelle Angelegenheiten“ der Senatskanzlei Berlin an. Während dieses Praktikums leitete ich in der Berliner Kulturverwaltung die Ausschreibung einer empirischen Erhebung ein, die die wirtschaftlichen und sozialen Rahmenbedingungen für Kultur- und Kreativschaffende in Berlin und Brandenburg misst und die in besonderer Weise die spezifischen Standortfaktoren für die Kultur- und Kreativwirtschaft in den beiden Bundesländern untersucht. Ziel des Projektes war es, fortan eine regelmäßige Befragung ins Leben zu rufen, um mittels Zeitreihen Entwicklungen festzuhalten und konstant Handlungsempfehlungen für kultur- und wirtschaftspolitisch Verantwortliche bereitstellen zu können. Während der Mitarbeit in der Senatskanzlei Berlin und der fokussierten Analyse der umtriebigen Kultur- und Kreativwirtschaft in der Bundeshauptstadt entstand die Idee einer Untersuchung von kultur- und kreativwirtschaftlichen Strukturen im ländlichen Raum. Meine Schwerpunktsetzung auf ländliche Regionen erklärt sich folgendermaßen: Zum einen blieb eine wissenschaftliche Betrachtung von Kultur- und Kreativwirtschaft bisher in 5 erster Linie städtischen Ballungsräumen vorbehalten. Zum anderen zeigt sich mit Blick auf den veröffentlichten Report zur Kultur- und Kreativwirtschaft in Nordrhein-Westfalen, dass bei der Betrachtung des Wirtschaftssektors bis dato ein starker Fokus auf das Ruhrgebiet, das Rheinland und mit einigen Einschränkungen auf das Münsterland gelegt wurde (vgl. Ministerium für Wirtschaft, Energie, Industrie, Mittelstand und Handwerk 2012: 16). Gezielte Analysen zu Entwicklungen und Potenzialen der Kultur- und Kreativwirtschaft in ländlich geprägten Räumen, zum Beispiel in ostwestfälischen Verwaltungsbezirken, gibt es bis dato nicht. Hier setzt mein Forschungsvorhaben an. Hinweisen möchte ich an dieser Stelle noch darauf, dass aus Gründen der Lesbarkeit in der Thesis auf die weibliche Form verzichtet wurde, diese jedoch selbstverständlich immer mit gemeint ist.

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.

Since 2000, data generation has been growing rapidly from various sources, such as Internet usage, mobile devices and industrial sensors in manufacturing. As of 2011, these sources were responsible for a 1.4-fold annual data growth. This development influences practice and science equally and led to different notations, one of the most popular one is Big Data. Besides organization with a business model based solely on Big Data, companies have started to implement new technologies, methodologies and processes in order to deal with the influx of data from different sources and structures and benefit the most of it. As the progress of the implementation and the degree of professionalism regarding data analysis differs amongst industries and companies, latter ones are faced with a lack of orientation regarding their own stage of development and existing relevant capabilities in order to deal with the influx of data as only a few best practices exist. Therefore, this research project develops a maturity model for the assessment of companies capabilities in the field of data analysis with a focus on Big Data. Basis for the model development is a construction model, developed along the criteria of Design Science Research. The developed model contains the different levels of maturity and related measurements for the evaluation of a companies Big Data capabilities with a focus on topics along the dimensions data and organization. The developed model has been evaluated based an application to different companies in order to ensure the practical relevance. The structure of the thesis is the following: In a first step, a structured literature review is carried out, focussing on existing maturity models in the field of Big Data and nearby fields as Business Intelligence and Performance Management Systems. Based on the identified white spots, a design science research oriented construction model for the maturity model development is designed. This model is applied subsequently.

To be prepared for one´s own career is a major task during career development. However, existing research has primarily focused on adolescence in the transition from school to work while research on career preparation among university students, that are challenged by successfully transiting from university to work, are lacking so far. Thus, this cumulative dissertation studies career preparation in terms of career decidedness, planning, confidence, and career engagement using large samples of German university students and alumni as well as a variety of quantitative methods like latent state-trait analysis, cross-lagged analysis, and mediation analysis with multiple mediators. In the first paper, the stable component of career indecision is investigated with longitudinal data stemming from two samples with different time lags (Sample 1: N = 363, 7 weeks; Sample 2: N = 591, 6 months). Furthermore, the combined and unique effects of career indecisiveness and generalized indecisiveness on life satisfaction are examined using a sample consisting of 469 university students. Results indicate that career indecision is determined by a stable component (i.e., trait career indecisiveness) that is associated with lower core self-evaluations, lower occupational self-efficacy, and higher perception of career barriers. Additionally, results indicate that the stable career indecision component explains 5% of the variance in student life satisfaction beyond self-evaluated generalized indecisiveness. The second paper deals with the relationships of vocational interest characteristics - interest congruence, interest differentiation, and general interest level (elevation) - with several indicators of career preparedness (i.e., career planning, occupational self-efficacy beliefs, career decidedness, and career engagement) among a sample of 239 university students. Controlling for sociodemographic variables, multiple regression analyses revealed that differentiation is positively associated with career decidedness and career engagement and elevation is positively related to occupational self-efficacy beliefs and career engagement. The third paper investigates how protean career orientation (PCO) is related to vocational identity clarity and occupational self-efficacy. Study 1 reports a 1-year, three-wave cross-lagged study among 563 university students and established that PCO preceded changes in identity and self-efficacy - but not the other way around. Based on a 6-month longitudinal study of 202 employees, Study 2 shows that identity clarity and self-efficacy mediated the effects of PCO on career satisfaction and proactive career behaviors. PCO only possessed incremental predictive validity regarding proactive career behaviors. However, specific direct or mediated effects of PCO on job satisfaction could not be confirmed. The fourth paper explores the relationships between narcissism and two indicators of career success (i.e., salary and career satisfaction) among a group of young professionals (N = 314). A model proposing that the effect of narcissism on career success is mediated by increased occupational self-efficacy beliefs and career engagement was assessed. While correlations between narcissism and the two indicators of career success were minimal, the results show a significant indirect effect on salary via occupational self-efficacy and indirect effects on career satisfaction via self-efficacy and career engagement. Overall, the different studies corroborate the crucial role of career preparation for a successful start into working life. In sum, this dissertation contributes to literature on vocational psychology by providing novel insights in terms of facilitators and outcomes of career preparation among university students and graduates. Theoretical and practical implications are discussed, and promising directions for future research are identified.

Over the last decades corporate irresponsibility has gained increasing interest among practitioners and researchers. Corporate irresponsibility is often the result of intentionally irresponsible strategies, decisions, or actions, which negatively affect an identifiable stakeholder or environment. For instance, these range from the violation of the human rights and labor standards to environmental damages. Organizations enacting irresponsible practices rely on different factors upon multiple levels (field, organizational, individual) and its interrelations as well as processes evolving within the organization leading to such behavior. However, reasons for the occurrence of and explanations for corporate irresponsibility so far have been limited, leaving a fragmented understanding of this phenomenon. This dissertation helps to improve the understanding and explanation of corporate irresponsibility by identifying driving patterns of corporate irresponsibility and showing how the interactions across multiple levels add to this phenomenon. Chapter 1 provides an overview of the topic of corporate irresponsibility, the theoretical approaches of this dissertation and an introduction to the chapters. The second chapter offers a review and analysis of the corporate irresponsibility literature. The chapter presents a variance model outlining the concept, antecedents, moderators and outcomes of recent corporate irresponsibility literature as well as the different factors across levels (field, organizational, individual). Chapter 2 offers a critical analysis of what we know by referring to current literature and offers insights on what we don´t know by deriving main implications for future research on corporate irresponsibility. Chapter 3 enlarges the understanding of corporate irresponsibility introducing a process approach to explain how corporate irresponsibility evolves over time and under which conditions. Based on a qualitative meta-analysis findings converge around two distinct process paths of corporate irresponsibility, the opportunistic-proactive, and, the emerging-reactive, subdivided into three phases. Chapter 3 sheds different lights upon the phases of corporate irresponsibility and its underlying mechanisms. The final chapter 4 focuses on different underlying mechanisms driving the final downfall or demise of organizations, organizational failure. Chapter 4 offers an alternative explanation to the competing extremism and inertia mechanisms driving organizational failure in recent studies by suggesting that these explanations are rather complementary. In addition, chapter 4 enlarges the explanation of organizational failure identifying the role of conflict mechanisms and its interplay with rigidity mechanisms. In sum, this dissertation contributes to a better understanding of what causes and increases corporate irresponsibility, and a better explanation of how and why corporate irresponsibility and organizational failure emerges, develops, grows or terminates over time. Hopefully all three articles motivate more research on this important topic to prevent such behavior in advance. 4

Research on motivational and cognitive processes in entrepreneurship has commonly relied on a static approach, investigating entrepreneurs´ motivation and cognition at only one point in time. However, entrepreneurs´ motivation and cognition are dynamic processes that considerably change over time. The goal of this dissertation is thus to adopt a dynamic perspective on motivational and cognitive processes in entrepreneurship. In three different chapters, I examine dynamic changes in the level and impact of three different processes, i.e., creativity, entrepreneurial passion, and opportunity identification. In Chapter 2, I develop a theoretical model on the alternating role of creativity in the course of the entrepreneurial process. The model emphasizes that the effects of two components underlying creativity, i.e., divergent and convergent thinking, considerably change both in magnitude and in direction throughout the entrepreneurial process. In Chapter 3, I establish and empirically test a theoretical model on entrepreneurial passion. The theoretical analysis and empirical results show that the relationships between feelings of entrepreneurial passion, entrepreneurial self-efficacy, and entrepreneurial success are dynamic and reciprocal rather than static and unidirectional. In Chapter 4, I develop and test a theoretical model on the effect of entrepreneurship training on opportunity identification over time. The theoretical and empirical investigation indicates that entrepreneurship training effects systematically decay over time and that action planning and entrepreneurial action sustain the effects in the long term. Altogether, the research reported in this dissertation provides novel insights into entrepreneurs´ motivation and cognition which more static approaches would have obscured. Moreover, the theoretical and empirical results of each chapter resolve apparent contradictions in past research and integrate hitherto fragmented theoretical perspectives into more inclusive theoretical frameworks. Thereby, this dissertation represents an important step toward a more integrated understanding of motivational and cognitive mechanisms underlying successful entrepreneurship.

Network analysis methods have long been used in the social sciences. About 25 years ago, these methods gained popularity in various other domains and many real-world phenomena have been modeled using networks. Well-known examples include (online) social networks, economic networks, web graphs, metabolic networks, infrastructure networks, and many more. Technological development made it possible to store and process data on a scale not imaginable decades ago — a development that also includes network data. A particular characteristic of network data is that, unlike standard data, the objects of interest, called nodes, have relationships to (possibly all) other objects in the network. Collecting empirical data is often complicated and cumbersome, hence, the observed data are typically incomplete and might also contain other types of errors. Because of the interdependent structure of network data, these errors have a severe impact on network analysis methods. This cumulative dissertation is about the impact of erroneous network data on centrality measures, which are methods to assess the position of an object, for example a person, with respect to all other objects in a network. Existing studies have shown that even small errors can substantially alter these positions. The impact of errors on centrality measures is typically quantified using a concept called robustness. The articles included in this dissertation contribute to a better understanding of the robustness of centrality measures in several aspects. It is argued why the robustness needs to be estimated and a new method is proposed. This method allows researchers to estimate the robustness of a centrality measure in a specific network and can be used as a basis for decision making. The relationship between network properties and the robustness of centrality measures is analyzed. Experimental and analytical approaches show that centrality measures are often more robust in networks with a larger average degree. The study of the impact of non-random errors on the robustness suggests that centrality measures are often more robust if missing nodes are more likely to belong to the same community compared to missingness completely at random. For the development of imputation procedures based on machine learning techniques, a process for the evaluation of node embedding methods is proposed.