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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.
Consisting of three articles and a framework manuscript, this cumulative dissertation deals with sustainable compensation of chief executive officer (CEO) with a focus on climate-related aspects. Against the backdrop of the European action for sustainability and the EU Green Deal, the dissertation pays special attention to the consideration of climate-related aspects of corporate performance in CEO compensation. In this context, sustainable compensation is characterized by the consideration of long-term interests and sustainability of the company as well as by the inclusion of financial and non-financial aspects of environmental, social and governance performance (ESG) in compensation agreements. While this novel instrument of corporate governance aims to incentivize the implementation of sustainability-oriented corporate strategy, it is particularly important to unfold this incentive effect at the individual CEO level in view of their managerial discretion. The framework manuscript discusses the research objectives, the regulatory and theoretical background, the results of the dissertation and their implications in the context of regulation, research, and business practice. The essence of the dissertation are the three articles. The first article examines the current state of empirical research based on 37 articles that were published between 1992 and 2018. Based on a multidimensional research framework, the structured literature review compiles past research findings, identifies contentual and methodological foci in the research area, and derives questions for future research. The second article addresses the topic from a conceptual perspective. Taking the existing work as a starting point, a conceptual framework is derived, which organizes the determinants of carbon-related CEO compensation at societal, organizational, group and individual levels of analysis. On this basis, eight propositions are presented that seek to distinguish between the determinants which support and challenge the implementation of carbon-related CEO compensation. The third article focuses on the use of CO2-oriented performance indicators in CEO compensation. The empirical-qualitative study analyzes corporate disclosure of the 65 largest companies in the EU for the years 2018 and 2019. The study addresses the use of CO2-oriented performance indicators in corporate strategy and CEO compensation. It also examines which compensation components are determined with the help of CO2-oriented performance indicators, which type of performance indicators are used, and whether CO2-intensive and less CO2-intensive companies differ in this regard.
The dissertation consists of three scientific papers and a synopsis. The synopsis addresses the relevance of the dissertation and lists the key factors for the sustainability transition in the electricity system as a common denominator of the three papers. The relevance of the dissertation results, on the one hand, from the urgency of the sustainability transition in the electricity system and an insufficient transition willingness of the eastern European Member States. On the other hand, the Multi-Level-Perspective as one of the most important scientific frameworks to grasp transitions does not provide a sufficient explanation of its mechanisms. Moreover, Demand Response aggregators as new enterprises on the European electricity market and potential reform initiators are still under researched. The following key factors for the sustainability transition of the electricity system have been identified: supply security concerns, Europeanisation, policy making and the dominance of short-term oriented economic evaluation. Paper#1 sheds light on the roots of this problem in the context of Poland. It suggests that unfavorable regulation is symptomatic of the real, underlying barriers. In Poland, these barriers are coal dependence and political influence on energy enterprises. As main drivers, supply security concerns, EU regulatory pressure, and a positive cost-benefit profile of DR in comparison to alternatives, are revealed. A conceptual model of DR uptake in electricity systems is proposed. Applying a social mechanisms approach to the Multi-Level Perspective, paper#2 conceptualizes mechanisms of socio-technical transitions and of gaining legitimacy for transitions as co-evolutionary drivers and outcomes. Situational, action-formational, and transformational mechanisms that operate as drivers of change in a socio-technical transition require corresponding framing and framing contests to achieve legitimacy for that transition. The study illustrates the conceptual insight with the case of the coal dependent Polish electricity system. Paper #3, a qualitative study reveals Demand Response (DR) aggregators as institutional entrepreneurs that struggle to reform the still largely supply-oriented European electricity market. Unfavourable regulation, low value of flexibility, resource constraints, complexity, and customer acquisition are the key challenges DR aggregators face. To overcome them they apply a combination of strategies: lobbying, market education, technological proficiency, and upscaling the business. The study highlights DR aggregation as an architectural innovation that alters the interplay between key actors of the electricity system and provides policy recommendations including the necessity to assess the real value of DR in comparison to other flexibility sources by taking all externalities into account, a technology-neutral approach to market design and the need for simplification of DR programmes, and common standards to reduce complexity and uncertainty for DR providers.
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.
Space-related science and technologies affect our daily life. Many countries have already formulated national space regulations to regulate their space activities. China, as one space-faring country, has obtained several achievements in space science and technologies. In recent years, Chinese private space companies have sprung up quickly, which requires a stable and foreseeable legal framework to ensure development. However, compared to the other space powers, China is the only one that has not enacted any formal national space laws. Against the background of strengthening the rule of law in China, research on China's domestic space legislation is valuable and significant. The purpose of this thesis is two-fold. First, to find the legal basis and necessity of national space legislation and to extract the basic content of the existing national space legislation, simultaneously, to identify the new developments in the content of other States´ legislative practices. Second, based on the study of national space legislation, to propose the essential content of China's space legislation.
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.
In sub-Saharan Africa, women own or partly own one third of all businesses, thereby having a large potential to contribute to the economic development and societal well-being in this region. However, women-owned businesses tend to lag behind men-owned businesses in that they make lower profits, grow more slowly, and create fewer jobs. To identify reasons for this gap and effective means to promote women entrepreneurs, large parts of the entrepreneurship literature have compared male and female entrepreneurs with regard to individual characteristics, paying only limited attention to the underlying environmental conditions. This is problematic as women entrepreneurs operate under different conditions than men, with particularly pronounced differences in sub-Saharan Africa. Against this backdrop, the goal of this dissertation is to contribute to a more profound understanding of women entrepreneurship in sub-Saharan Africa and its promotion through training by examining critical context factors. Specifically, the author analyzes two context factors that influence women's entrepreneurial performance and the success of training interventions: 1) women entrepreneurs' husbands and 2) the entrepreneurship trainer. These analyses are embedded in considerations of the cultural, social, and economic conditions women entrepreneurs in sub-Saharan Africa are facing. In Chapter 2, the author conducts a systematic literature review on spousal influence in entrepreneurship and identifies six recurrent types of influence. Complementing the literature originating from Western settings, she develops propositions on how the sub-Saharan context affects husbands' influence on women entrepreneurship in this region. In Chapter 3, she builds on a cultural theory and an economic theory of the household to develop and empirically test a theoretical model of husbands' constraining and supportive influences on women entrepreneurship in sub-Saharan Africa. The empirical results point to three distinct types of husbands that differ significantly in their impact on women entrepreneurs' business success. In Chapter 4, the author explores the influence of the trainer on the effectiveness of entrepreneurship training in sub-Saharan Africa by drawing on an unsuccessful training implementation. Qualitative analyses indicate that the use of adequate teaching methods is critical towards training success.
This work investigates how managers/consultants (practitioners) of different ranks are engaged in patterns of behavior (practices) in socially situated contexts (practice) attempting to shape preferred shared interpretations of reality to achieve their goals. Following this line of inquiry, the work aims at (1) advancing our understanding of the role of practitioners in shaping managerial realities and (2) investigating how practitioners actually shape managerial realities, particularly focusing on "reality-shaping" practices and their content. The dissertation comprises a set of four complementary articles investigating these research questions empirically based on in-depth, empirical case studies and theoretically within various managerial contexts (client-consultant relationship, CEO post-succession strategic change process, evolutionary initiative development) and considering different actor perspectives (top managers, middle managers, consultants and clients). Resulting from this variety, the articles rely on and contribute to different, at times distant, research fields and therewith scholarly discussions. However, the literature on sensemaking and sensegiving offers a suitable overarching theoretical frame which is used in this work to synthesize the key contributions of the four articles.
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.
Micro- and small enterprises are of great importance for the economic growth in developing countries, as they contribute to employment creation and innovation. In light of their economic relevance, several approaches to support micro- and small enterprises have emerged, including building human capital through business trainings. However, the effects of existing business trainings on entrepreneurial success have so far been limited. One promising alternative training approach that has emerged in the last years is personal initiative training, which teaches self-starting, future-oriented, and persistent entrepreneurial behavior. This dissertation helps to improve the understanding of personal initiative training by shedding light on the mechanisms through which it affects business success, on supporting factors, and on its long-term impacts. Chapter 1 provides an overview on the topic of personal initiative training for entrepreneurs in developing countries. Chapter 2 introduces personal initiative training and other proactive behavior trainings in various contexts of work, including entrepreneurship. The chapter presents action regulation theory and the theory on personal initiative as the theoretical foundation of the training. In addition, the chapter provides insights into training and evaluation methods and makes recommendations for the successful implementation of personal initiative training. Chapter 3 offers a first answer to the question how personal initiative after training can be maintained over time. The chapter introduces training participants' need for cognition as beneficial factor for post-training personal initiative maintenance. Chapter 4 explains how action regulation trainings like personal initiative training contribute to poverty reduction in developing countries by supporting entrepreneurial success. Chapter 5 enlarges upon the topic of personal initiative training for entrepreneurial success in developing countries. The chapter focuses on how personal initiative training supports female entrepreneurs in developing countries by helping them to overcome the uncertainty involved in entrepreneurial actions. Chapter 6 summarizes the overall findings and illustrates the theoretical and practical implications that result from this dissertation. In sum, this dissertation makes a contribution to the better understanding of personal initiative training and its effects on entrepreneurship in developing countries and thereby helps to create effective interventions to combat poverty in developing countries.
Corporate Social Responsibility (CSR) has been established in recent years as an essential component of the economic system, demanded and promoted by a wide variety of stakeholder groups. The present dissertation shows that organizations face major communicative challenges with regard to CSR. CSR is not only determined by organizations themselves, but rather arises in the interplay with economic and social discourses. It is assumed that boundarys of organizational action are under constant change, so that CSR actors inevitably initiate constitutive communication processes. The resulting polyphony requires an understanding of the underlying communication processes. Hence, the performative character of CSR communication is taken up by this dissertation and thus the constitution of both the communicating actors and their relationships in the network is illustrated. The presented scientific papers are united by the overarching assumption that communication does not accompany and describe organizational action, but unfolds its own power.
This dissertation focused on the nature and role of organizational practices for the employment of older people and the extension of their working lives. The set of four articles is driven by the objective to further deepen our understanding of how organizations can facilitate ageing at work to the benefit of both, employees and employers. Findings are empirically based on qualitative expert interview data from Germany and the U.S. and several quantitative field studies among older employees in Germany. To bridge gaps in measurement of organizational practices related to aging at work, this dissertation proposes a new comprehensive, multifaceted, and thoroughly conceptualized measure of organizational practices related to aging at work, the Later Life Workplace Index (LLWI). Through the course of the four articles the LLWI is conceptually developed based on qualitative interview data, operationalized, validated based on multiple field studies among older workers, and applied in a multi-level study among older employees of 101 organizations. Results suggest that organizational practices are not uniform, but multifaceted in their presence within organizations and their effects for the employment of older workers. The LLWI distinguishes nine domains of practices including an age-friendly organizational climate, work design, individual development, and practices tailoring the retirement transition. Thus, it may lay the foundation for more granular organizational level research in the field. Further, this dissertation's fourth article applies the LLWI and argues based on person-environment fit and socio-emotional selectivity theory that organizational practices address different individual needs and, thus, affect employment depending on employees' individual characteristics. Results suggest that older employees' retirement intentions are effected by individual development, transition-to-retirement, and continued employment practices depending on their health resources. Application of the new measure in practice to improve organizations' response to the aging workforce and opportunities for future research based on the LLWI are discussed.
Derivatives are contracts between two parties, a buyer and a seller. The contract will be fulfilled in some point in the future at a predetermined price. The value of those contracts is based on an underlying entity which can be a traded asset or even the weather. Derivatives contain chances, but also risks, investor should be aware off. This thesis aims to deeply analyze two derivative products in the German market and one risk for each which influences the prices of those products. The first part of this thesis focuses on warrants and the issuer's credit risk involved. It finds evidence that the issuer's credit risk influences the connection between warrant characteristic and its prices. Over time this connection is unstable partly driven by the issuer's credit risk. The second paper of this thesis shows that issuers seem to use their credit risk systematically to influence warrant prices. Evidence is found that the changes in credit risk are not fully included in the prices directly, but that the adjustment to the new level of credit risk takes several days. In addition, the issuer's adjustment to changes in credit risk are different for credit risk increases than for credit risk decreases. Especially after financial crisis, in more stable times, evidence is found for such adverse pricing pattern. The third part of the thesis focuses on energy derivatives traded at the Europe Energy Exchange and analyses the influence of weather parameters on energy derivatives with different load profiles and time horizons. This part of the thesis finds that especially wind speed and sun hours have a strong influence on energy derivatives. However, not all products are influenced in the same manner. Products with a longer time horizon are influenced less than the product with a short horizon. Moreover, products comprising hours of the day where energy consumption is expected to be higher are influenced stronger than products comprising hours of a day with lower time consumption. The thesis shows that derivatives are not alike and that it is needed for future research to differentiate between products and the risks which are involved. Since even though we classify them all as derivatives the risks influencing the derivative´s prices do vary tremendously.
In the face of uncertainty, ecosystems can provide natural insurance to risk averse users of ecosystem services. We employ a conceptual ecological-economic model to analyze the allocation of (endogenous) risk and ecosystem quality by risk averse ecosystem managers who have access to financial insurance, and study the implications for individually and socially optimal ecosystem management, and policy design. We show that while an improved access to financial insurance leads to lower ecosystem quality, the effect on the free-rider problem and on welfare is determined by ecosystem properties. We derive conditions on ecosystem functioning under which, if financial insurance becomes more accessible, (i) the extent of optimal regulation increases or decreases; and (ii) welfare, in the absence of environmental regulation, increases or decreases.
Online advertising has become one of the most important dimension of corporate communications. In recent years, a new form of advertising on the Internet has emerged: real-time advertising. Among others, it allows companies to identify potential customers and target them with respect to their interests. In this way, real-time advertising can increase advertising effectiveness and it could, at the same time, improve user experience. With the emerge of this new form of advertising, statistical models have become even more important because they are now being increasingly used to predict online user behavior. The articles included in this dissertation analyze user-level clickstream data generated during multi-channel advertising campaigns (including TV advertising) and during real-time auctions. The goal of the analyses conducted here is to better understand advertising effects and to support decision-making in this context. Most of the analyses are based on Bayesian models. These models allow for a very flexible structure, which enables researchers to model, for instance, heterogeneity across different types of users or non-linear parameters such as users´ reaction times and exponential decay of advertising effects. In addition, these models allow for the inclusion of prior knowledge of parameter distributions, and, therefore, they are well suited for iterative analyses based on clickstream data. Bayesian models can be evaluated in different ways. Instead of only relying on statistical metrics, the articles included in this dissertation aim to estimate the economic value of these models based on their predictive performance. Although this measure can only approximate their true economic value, this approach can be used to compare and evaluate different models and to illustrate the impact of predictive analyses for companies in the context of big data. This dissertation contributes to both information systems research and marketing research and has many managerial implications. First, a process is developed to determine optimal sample sizes representing the best balance between computational costs and predictive accuracy in e-commerce in particular and big data contexts in general. In practice, this process can be used to reduce infrastructure and computational costs. Second, the articles included here describe models that can be used to measure the impact of television ads on users' online shopping behavior. The models can provide insights concerning the effectiveness of individual television ads, the interactions between different advertising channels and the difference in user behavior of TV-induced customers and their non-TV-induced counterparts. Thereby, the models could support decision-making with respect to future advertising campaigns and targeting. Third, the articles describe several possibilities to extend and improve decision support systems currently used in e-commerce and marketing. These improvements enable practitioners to predict users´ interests for arbitrary products and services by using corresponding websites as dependent variables. This approach can be used to improve the effectiveness of real-time advertising campaigns, especially those intended to raise brand awareness among customers.
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.
The present work introduces four theoretical papers, which primarily focus on R&D, interindustrial linkages, and their policy implications. All in all, three issues basically motivated conception and realization: At first, previous NEG models do not incorporate endogenous R&D activities of firms. Existing models include R&D only in a growth context, which increases the formal complexity and departs from the simple core-periphery formulation. Second, vertical linkages are extensively considered in the class of international models. In face of its formal simplicity, the majority of publications refer to the standard model of Krugman and Venables (1995) utilizing intra-industry trade in which the manufacturing sector produces its own intermediates. However, the results are similar to the core-periphery model, but the implications of vertical linkages, especially in terms of specialization, cannot be reproduced. In contrast, the more challenging version of Venables (1996), which considers an inter-industry framework of an explicit upstream and downstream sector, is often cited (143 citations according to IDEAS/RePEc), but only few papers were directly built on it: Puga and Venables (1996), Amiti (2005), Alonso-Villar (2005). The third issue concerns the calibration of real economies. Although, hundreds of numerical simulations have been done in order to display the modeling outcomes, an application to particular industries in terms of their spatial formation and evolution is still a neglected field of research. Against this background, the present work aims to make a contribution to these topics. For a summary, all four papers are briefly to be summarized at this point. The first paper, entitled 'Too Much R&D? – Vertical Differentiation and Monopolistic Competition,' discusses whether product R&D in developed economies tends to be too high compared with the socially desired level. In this context, a model of vertical and horizontal product differentiation within the Dixit-Stiglitz (1977) framework of monopolistic competition is set up where firms compete in horizontal attributes of their products, and also in quality that can be controlled by R&D investments. The paper reveals that in monopolistic-competitive industries, R&D intensity is positively correlated with market concentration. Furthermore, welfare and policy analysis demonstrate an overinvestment in R&D with the result that vertical differentiation is too high and horizontal differentiation is too low. The only effective policy instrument in order to contain welfare losses turns out to be a price control of R&D services. The main contribution of this closed economy model in the course of the present work is a modeling framework, which can easily be adapted to the New Economic Geography. This has been approached in the second paper: ‘R&D and the Agglomeration of Industries' in which the seminal core-periphery model of Krugman (1991) is extended by endogenous research activities. Beyond the common ‘anonymous' consideration of R&D expenditures within fixed costs, this model introduces vertical product differentiation, which requires services provided by an additional R&D sector. In the context of international factor mobility, the destabilizing effects of a mobile scientific workforce are analyzed. In combination with a welfare analysis and a consideration of R&D promoting policy instruments and their spatial implications, this paper also makes a contribution to the brain-drain debate. In contrast to this migration based approach, the third paper 'Agglomeration, Vertical Specialization, and the Strength of Industrial Linkages' focuses on vertical linkages in their capacity as an additional agglomeration force. The paper picks up the seminal model of Venables (1996) and provides a quantifying concept for the sectoral coherence in vertical-linkage models of the New Economic Geography. Based upon an alternative approach to solve the model and to determine critical trade cost values, this paper focuses on the interdependencies between agglomeration, specialization and the strength of vertical linkages. A central concern is the idea of an 'industrial base,' which is attracting linked industries but is persistent to relocation. As a main finding, the intermediate cost share and substitution elasticity basically determine the strength of linkages. Thus, these parameters affect how strong the industrial base responds to changes in trade costs, relative wages and market size. The fourth paper 'The Spatial Dynamics of the European Biotech Industry' presents a simulation study of the R&D intensive biotech industry using the standard Venables model. Thus, it connects all three preceding papers and puts them into the real economic context of the European integration. The paper reviews the potential development of the European biotech industry with respect to its spatial structure. On the first stage, the present industrial situation as object of investigation is described and evaluated with respect to a further model implementation. In this context, the article introduces the findings of an online survey concerning international trade, conducted with German biotech firms in 2006. On the second stage, the results are completed by the outcomes of a numerical simulation within the New Economic Geography (NEG), considering vertical linkages between the biotech and pharmaceutical industries as an agglomerative force. The analysis reveals only a slight relocation tendency to the European periphery, constrained by market size, infrastructure and factor supply. In the final conclusions, central results of all four papers are summarized with respect to economic policy. Against the background of general legitimization and the impact of political intervention, Chapter 6 draws the main conclusions for location and innovation policies. In this regard, the industrial-base concept as well as the mobility of R&D play a central role during this discussion.
In this cumulative thesis, the author presents four manuscripts and two appendixes. In the manuscripts he discusses mindsets and their relation to the effectiveness of negotiation training. His general claim is that mindsets promise to be relevant for training effectiveness. Still, more research needs to be done and chapter 3 presents the Scale for the Integrative Mindset of Negotiators (SIM) that can be used for some of that research. In the appendixes, the author presents 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.
Maximizing the value from data has become a key challenge for companies as it helps improve operations and decision making, enhances products and services, and, ultimately, leads to new business models. While enterprise architecture (EA) management and modeling have proven their value for IT-related projects, the support of enterprise architecture for data-driven business models (DDBMs) is a rather new and unexplored field. The research group argues that the current understanding of the intersection of data-driven business model innovation and enterprise architecture is incomplete because of five challenges that have not been addressed in existing research: (1) lack of knowledge of how companies design and realize data-driven business models from a process perspective, (2) lack of knowledge on the implementation phase of data-driven business models, (3) lack of knowledge on the potential support enterprise architecture modeling and management can provide to data-driven business model endeavors, (4) lack of knowledge on how enterprise architecture modeling and management support data-driven business model design and realization in practice, (5) lack of knowledge on how to deploy data-driven business models. The researchers address these challenges by examining how enterprise architecture modeling and management can benefit data-driven business model innovation. The mixed-method approach of this thesis draws on a systematic literature review, qualitative empirical research as well as the design science research paradigm. The investigators conducted a systematic literature search on data-driven business models and enterprise architecture. Considering the novelty of data-driven business models for academia and practice, they conducted explorative qualitative research to explain "why" and "how" companies embark on realizing data-driven business models. Throughout these studies, the primary data source was semi-structured interviews. In order to provide an artifact for DDBM innovation, the researchers developed a theory for design and action. The data-driven business model innovation artifact was inductively developed in two design iterations based on the design science paradigm and the design science research framework.