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- 2023 (6) (entfernen)
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- Fakultät Management und Technologie (6) (entfernen)
This study examines the perspective of German venture capitalists on the success factors of digital startups and follows an explorative three-dimensional research approach that integrates the micro perspective on the entrepreneurial personality, the macro perspective on the entrepreneurial context, and the meso perspective on the business model. Thus, the study operates in a very young field of entrepreneurship research. One of the purposes of this research project is to work out the significance of particular characteristics at each research level for the economic success of a digital start-up from the perspective of German venture capitalists. Furthermore, the study sheds light on the view of this group of experts on the relevance of an entire group of characteristics. To answer the central research questions, qualitative research methods and a mixed-methods approach are pursued, with quantitative and qualitative primary data being collected by means of theory-driven semi-structured expert interviews. As a result, a total of four articles have been produced: three articles that focus on presenting the results of qualitative research from only one of the three aforementioned research perspectives each, and a fourth article that combines methods from qualitative and quantitative research and derives an integrated, evidence-based working model of the economic success of digital startups from the perspective of German venture capital (VC) investors.
This cumulative dissertation presents how commercial banks in Germany communicate their ambitions and commitment regarding corporate responsibility - i.e., CSR. The results of the first article show that the quality of mandatory non-financial reporting needs to be improved and that certain characteristics (e.g., previous reporting experience, reporting format and standard) have a positive influence on reporting quality. The second article shows that the CSR reporting scope on bank websites also has room for improvement and that various banking characteristics such as size, capital market orientation, media visibility or public ownership have an influence on communication. The third article illustrates that credit institutions in Germany are increasingly using social media for CSR communication, but that CSR communication strategies differ (Facebook vs. Twitter). The fourth article discusses CSR communication using advertisements and shows that the conceptual design of advertisements should be in line with the credit institution's business model and is therefore beneficial.
The requirements for the design of information and assistance systems in labour-intensive processes are interdisciplinary and have not yet been sufficiently addressed in research. This dissertation analyses, evaluates and describes possibilities for increasing the effectiveness and efficiency of labour-intensive processes through design-optimised socio-technical systems. The work thus contributes to further developing information and assistance systems for industrial applications and use in healthcare. The central dimensions of people, activity, context and technology are the focus of the scientific investigations following the Design Science Research paradigm. Design principles derived from this, a corresponding taxonomy, and a conceptual reference model for the design of socio-technical systems are the results of this dissertation.
The computational analysis and the optimization of transport and mixing processes in fluid flows are of ongoing scientific interest. Transfer operator methods are powerful tools for the study of these processes in dynamical systems. The focus in this context has been mostly on closed dynamical systems and the main applications have been geophysical flows. In this thesis, the authors consider transport and mixing in closed flow systems and in open flow systems that mimic technical mixing devices. Via transfer operator methods, They study the coherent behavior in closed example systems including a turbulent Rayleigh-Bénard convection flow and consider the finite-time mixing of two fluids. They extend the transfer operator framework to specific open flows. In particular, they study time-periodic open flow systems with constant inflow and outflow of fluid particles and consider several example systems. In this case, the transfer operator is represented by a transition matrix of a time-homogeneous absorbing Markov chain restricted to finite transient states. The chaotic saddle and its stable and unstable manifolds organize the transport processes in open systems. The authors extract these structures directly from leading eigenvectors of the transition matrix. For a constant source of two fluids in different colors, the mass distribution in the mixer and its outlet region converges to an invariant mixing pattern. In parameter studies, they quantify the degree of mixing of the resulting patterns by several mixing measures. More recently, network-based methods that construct graphs on trajectories of fluid particles have been developed to study coherent behavior in fluid flow. They use a method based on diffusion maps to extract organizing structures in open example systems directly from trajectories of fluid particles and extend this method to describe the mixing of two types of fluids.
In the study, predictive models for predicting therapy outcome are created using the dataset from E-COMPARED project, which belongs to the so-called type 3 models that use data from the intervention and preintervention phases to predict treatment outcomes, which can help to adapt intervention to maximize treatment. The predictive models aim to classify patients into two groups, improved and nonimproved. Since it is important to determine whether the models contribute to improvement of treatment, research questions that can contribute to the usage of type 3 models are established. The study focuses on the following three questions: (1) How accurately can the therapy outcome be predicted by various machine learning algorithms? Answering this question can let the people concerned obtain information about the reliability of contemporary predictive models. In addition, if the predictive power of the models is good, it is more likely to be used to assist therapists’ decisions. (2) Which kind of data is more important in predicting the therapy outcome? The answer to this question can show which dataset should be considered first to make better predictive models. Therefore, it can be helpful for researchers who want to make predictive models in the future and eventually help to facilitate personalized therapy. (3) What are the features with strong predictive power? The answer to this question can affect the people concerned, especially therapists. Therapists can use the most influential features revealed to adjust and improve future treatments.
In past digital health interventions, an issue has been that participants drop out over time which is referred to as the "law of attrition" (Eysenbach, 2005). Based on this, the study proposes that though initially, participants respond to the intervention, there is a hypothesized second diminishing effect of an intervention. However, the study suggests that on top, there is a third effect. Independent of the individual notification or nudge, people could build the knowledge, skills and practice needed to independently engage in the behavior themselves (schraefel and Hekler, 2020). Using behavioral theory and inspired by prior animal computational models of behavior, the thesis proposes a dynamical computational model to allow for a separation of intervention and internalization. It is targeted towards the specific case of the HeartSteps intervention that could not explain a diminishing immediate effect of the intervention, second hypothesized effect, while a person’s overall steps remained constant, third effect (Klasnja et al., 2019). The study incorporates a habituation mechanism from learning theory that can account for the immediate diminishing effect. At the same time, a reinforcement mechanism allows participants to internalize the message and engage in behavior independently. The simulation shows the importance of a participant’s responsiveness to the intervention and a sufficient recovery period after each notification. To estimate the model, the study uses data from the HeartSteps intervention (Klasnja et al., 2019; Liao et al., 2020), a just-in-time adaptive intervention that sent two to five walking suggestions per day. The study runs a Bayesian estimation with Stan in R. Additional validation tests are needed to estimate the accuracy of the model for different individuals. It could however serve as a template for future just-intime adaptive interventions due to its generic structure. In addition, this model is of high practical relevance as its derived dynamics can be used to improve future walking suggestions and ultimately optimize notification-based digital health interventions.