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Institut
- Fakultät Wirtschaftswissenschaften (66) (entfernen)
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.
This thesis analyses how European merger control law is applied to the energy sector and to which extent its application may facilitate the liberalisation of the electricity, natural gas and petroleum industries so that only these concentrations will be cleared that honour the principles of the liberalisation directives. After having discussed the complex micro- and macro-economic considerations which accompany any concentration of business activities, this thesis discusses the merger control regime of the European Community (EC) so as to establish whether the merger control under either Art. 66 Treaty Establishing the European Coal and Steal Community (ECSCT), the case law under Art. 101 and 102 Treaty on the functioning of the European Union (TFEU) and (Art. 81 and Art. 82 Treaty Establishing the European Economic Community (ECT), as it was introduced by the Commission and reviewed by the CJEU, the original Merger Regulation (MR1989) or the amended Merger Regulation of 1997 (MR1997) or the amended Merger Regulation of 2004 (MR2004) facilitate the liberalisation of European electricity and gas markets. Said liberalisation was introduced by the Internal Electricity Market Directive (IEMD), the Hydrocarbons Licensing Directive and the Internal Gas Market Directive (IGMD). The paper focuses on the contestable idea that regulatory amendments - especially the introduction of third party access by means of the directives - only form a first necessary condition for attaining economic alterations whereas pro-active conduct of the marketers is the second and decisive one in order to increase the competitive performance of the European energy supply industries. The analysis is supported by a second argument which relates closely to the ambivalent nature of concentrations: A concentration may be used to increase the process of market opening and the expansion into new markets by pooling of scarce resources. It may also be used as a retro -active means so as to create national champions, increase barriers to market entry of new competitors, enable cross-subsidisation so as to expand dominant positions on heretofore competitive up- and downstream markets.
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.
Analysis of User Behavior
(2020)
Online behaviors analysis consists of extracting patterns from server-logs. The works presented here were carried out within the "mBook" project which aimed to develop indicators of the quantity and quality of the learning process of pupils from their usage of an eponymous electronic textbook for History. In this thesis, the research group investigates several models that adopt different points of view on the data. The studied methods are either well established in the field of pattern mining or transferred from other fields of machine learning and data mining. The authors improve the performance of archetypal analysis in large dimensions and apply it to unveil correlations between visibility time of particular objects in the e-textbook and pupils' motivation. They present next two models based on mixtures of Markov chains. The first extracts users' weekly browsing patterns. The second is designed to process essions at a fine resolution, which is sine qua non to reveal the significance of scrolling behaviors. The authors also propose a new paradigm for online behaviors analysis that interprets sessions as trajectories within the page-graph. In this respect, they establish a general framework for the study of similarity measures between spatio-temporal trajectories, for which the study of sessions is a particular case. Finally, they construct two centroid-based clustering methods using neural networks and thus lay the foundations for unsupervised behaviors analysis using neural networks.
This cumulative dissertation embraces four empirical papers addressing socio-economic issues relevant to policy-makers and society as a whole. These papers cover important aspects of human life including health at birth, life satisfaction, unemployment periods and retirement decisions. The analyses are carried out applying advanced econometric methods and are based on data sets consisting of survey data as well as administrative records. The first joint paper investigates the causal impact of prenatal exposure to air pollution on neonatal health in Italy in the 2000s combining detailed information on mother's residential location from birth certificates with PM10 concentrations from air pollution monitors. Variation in local weekly rainfall is exploited as an instrumental variable for non-random air pollution exposure. Using quasi-experimental variation in rainfall shocks allows to identify the effect of PM10, ruling out potential bias due to confounder pollutants. The paper estimates the effect of exposure for both the entire pregnancy period and separately for each trimester to test whether the neonatal health effects are driven by pollution exposure during a particular gestation period. This information enhances our understanding of the mechanisms at work and help prevent pregnant mothers from most dangerous exposure periods. Additionally, the effects of prenatal exposure to PM10 are estimated by maternal labor market status and maternal education level to understand how the pollution burden is shared across different population groups. This decomposition allows to identify possible mechanisms through which environmental inequality reinforces the negative impact of early-life exposure to air pollution. This study finds that average PM10 and days with PM10 level above the hazard limit reduce birth weight, gestational age, and measures of overall newborn health. Effects are largest for third trimester exposure and for low-income and less educated mothers. The second joint paper updates previous findings on the total East-West gap in overall life satisfaction and its trend by using data from the German Socio-Economic Panel for the years 1992 to 2013. Additionally, the effects are separately analyzed for men and women as well as for four birth cohorts. The results indicate that reported life satisfaction is, on average, significantly lower in East than in West German federal states and that part of the raw East-West gap is due to differences in household income and unemployment status. The conditional East-West gap decreased in the first years after the German reunification and remained quite stable and sizable since the mid-nineties. The results further indicate that gender differences are small. Finally, the East-West gap is significantly smaller and shows a trend towards convergence for younger birth cohorts. The third joint paper explores the effects of a major reform of unemployment benefits in Germany on the labor market outcomes of individuals with some health impairment. The reform induced a substantial reduction in the potential duration of regular unemployment benefits for older workers. This work analyzes the reform in a wider framework of institutional interactions, which allows to distinguish between its intended and unintended effects. The results based on routine data collected by the German Statutory Pension Insurance and a Difference-in-Differences design provide causal evidence for a significant decrease in the number of days in unemployment benefits and increase in the number of days in employment. However, they also suggest a significant increase in the number of days in unemployment assistance, granted upon exhaustion of unemployment benefits. Transitions to unemployment assistance represent an unintended effect, limiting the success of a policy change that aims to increase labor supply via reductions in the generosity of the unemployment insurance system. The fourth, single-authored paper explores how an increase in the early retirement age affects labor force participation of older workers. The analysis is based on a social security reform in Germany, which raised the early retirement age over several birth cohorts to boost employment of older people and ultimately alleviate the burden on the public pension system. Detailed administrative data from the Federal Employment Agency allow to distinguish between employment and unemployment as well as disability pensions and retirement benefits claims. Using a Regression Kink design in a quasi-experimental framework, the author shows that the raised early retirement age had positive employment effects and negative effects on retirement benefits claims. The results also show that some population groups are more sensitive to a reduction in retirement options and more likely to seek benefits from other government programs. In this respect, the author finds that workers in manufacturing sector respond to the raised early retirement age by claiming benefits from the disability insurance program designed to compensate for reduced earnings capacity due to severe health problems. The treatment heterogeneity analysis further suggests that high-wage workers are more likely to delay exits from employment, which is in line with incentives but might also indicate an increased inequality within the affected birth cohorts induced by the reform. Finally, women seem to rely on alternative sources of income such as retirement benefits for women, or spouse's or partner's income not observed in the data. All things considered, workers did not adjust to the increased early retirement age by substituting early retirement with other government programs but rather responded to the reform in line with the policy intent. At the same time, the findings point to heterogeneous behavioral responses across different population groups. This implies that raising the early retirement age is an effective policy tool to increase employment only among older people who have the real choice to delay employment exits. Therefore, reforms that raise statutory ages should ensure social support for workers only marginally attached to the labor market or not able to work longer due to potential health problems or other circumstances.
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.
This dissertation investigates work ability as a concept that supports workers, employers, and societies in the extension of working lives, and how work ability is related to the level of self-determination in the transition to retirement, and ultimately life satisfaction. In the first study of this dissertation, the Work Ability Survey-R (WAS-R) was translated from English into German and then evaluated regarding its psychometric properties and construct validity. The WAS-R operationalizes work ability as the interplay of personal and organizational resources and thus allows companies to derive targeted interventions to maintain work ability. In the second study, the WAS-R was examined together with the questionnaire Work-Related Behavior and Experience Pattern (Arbeitsbezogenes Verhaltens- und Erlebensmuster, AVEM) regarding its construct validity. A striking feature of this study was the high number of participants with the answering pattern indicating low work-related ambitions and protection. Persons with this pattern are in danger of entering the risk pattern for burnout in the future. The findings support the validity of the WAS-R. In the third contribution, two studies examined the experience of control (i.e., autonomy) in the transition to retirement as a mediator between previous work ability, health, and financial well-being, and later life satisfaction in retirement. Control was found to partially mediate the relationship between work ability and later life satisfaction. Different mechanisms on later life satisfaction of work ability and health, and the subjective and objective financial situation were found. This dissertation contributes to research on and practice with aging workers in two ways: (1) The German translation of the WAS-R is presented as a useful instrument for measuring work ability, assessing individual and organizational aspects and therefore enabling employers to make targeted interventions to maintain and improve work ability, and eventually enable control during later work life, the retirement transition and even old age. (2) This dissertation corroborates the importance of good work ability and health, even in old age, as well as control in these phases of life. Work ability is indirectly related to life satisfaction in the long period of retirement, mediated by a sense of control in the transition to retirement. This emphasizes the importance of the need for control as postulated by the SDT also in the transition to retirement.
In this dissertation, advanced nonlinear control strategies and nonlinear minimum-variance observation are combined, in order to improve the estimation and/or tracking quality within control and fault detection tasks, for several types of systems from the fields of electromobility and conventional drivetrain technology that have some potential for sustainability or performance improvements. The application-specific innovations in terms of nonlinear Kalman filter methods are: (1) Improved state of charge estimation for Lithium-ion battery cells, powered by a novel self-adaptive EKF that uses a high-order polynomial curve fit as a decomposition of the uncertain nonlinear output equation with intentionally redundant bases, and with a reduced number of polynomial parameters that are adapted online by the EKF itself. (2) Online estimation of the time delay between two periodic signals of roughly the same shape that have pronounced uncorrelated noise, based on a fractional-order approximation of the transcendent transfer function of the time delay which is used as a model in a novel kind of EKF. (3) Using two (E)KFs (one for the linear subsystem and one for the nonlinear subsystem of a new kind of multi-stage piezo-hydraulic actuator) in a cascaded loop structure in order to reduce the computation load of the estimation, by appropriate 'interfacing' between the two observers (using one shared system model equation, among other aspects). - The innovations in terms of nonlinear control methods are powered by observation, as well: (1) Sliding mode velocity control of a DC drive that is subject to nonlinear friction and unknown load torques, enhanced by an equivalent control law, and with a new intelligent switching gain adaptation scheme (for reduced control chattering and, thus, less energy consumption and actuator wear), which is powered by Taylor-linearized model predictive control, which in turn requires observer-based disturbance compensation (by a KF with a double-integrator disturbance model) for model-matching purposes in order to function correctly. (2) Direct speed control of permanent-magnet three-phase synchronous motors that have a high power-to-volume ratio, based on sliding mode control in a rotating d,q coordinate system, with a new equivalent control method that exploits both system inputs and with a secondary sliding surface to ensure compliance with the current-trajectory of maximum efficiency for the required torque, and which works without measurement of the rotor angle (thanks to a new kind of EKF that estimates all states in the stationary α,β coordinate system, as well as the disturbance/load torque and its derivative). In all instances, improvements (compared to methods existing in the literature) in terms of control and estimation performance have been achieved and confirmed using simulation studies or real experiments.
The dissertation contains four journal articles which are embedded within a framework manuscript that interconnects the individual articles and provides relevant background information. The dissertation's overall objective is to provide a multilayered and critical in-depth engagement with the timely phenomenon of integrated reporting (IR), a new reporting concept that is envisaged to revolutionize firms' present reporting infrastructure. While extant corporate reports (e.g., annual financial- and CSR report) often are criticized for being disconnected and to suffer from a lack of coherence, IR intends to provide all information that is material to a firm's short-, medium- und long-term value creation within one single, succinct document. To contribute to a set of previously defined relevant research gaps in literature, the dissertation makes use of a combined empirical-quantitative and explorative-qualitative research design. The first article entitled investigates a set of different IR-, corporate governance and financial accounting-specific factors that are expected to determine European and South African firms' materiality disclosure quality. To this purpose, an original, hand-collected materiality disclosure score was developed. The second article explores IR perceptions of SME managers that have not embarked on IR, but are potential candidates to do so in future. Based on a review of extant literature, the article develops a theoretical framework to subsequently discuss motives for and barriers to IR adoption. The critical discussion contributes to the academic debate on incentives for and barriers to voluntary IR adoption. The third article investigates whether voluntary IR adoption among European firms is associated with lower cost of public debt. While earlier studies suggest that IR leads to lower information asymmetries, increases analyst forecasts, and decreases cost of equity, corresponding evidence for the debt market is largely missing. Subsequent analyses test as to whether such an association is even more pronounced by a firm's environmental, social and governance (ESG) performance or its belonging to an environmentally sensitive industry. The fourth article uses an experimental design to investigate nonprofessional investors' reactions to an IR assurance. To this purpose, two separate experiments with two different groups of nonprofessional investors were carried out: one with Masters students and one with managers of large corporations. Results help to answer the question as to whether an IR assurance as well as its determinants, namely the assurance provider and the assurance level, affect nonprofessional investors' financial decision-making. In the second step, subsequent in-depth interviews reveal an IR assurance-critical attitude among managers, who draw upon their practical experience with assurance engagements.
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.