@phdthesis{Dannenmann2023,
  author    = {Dannenmann, Barbara},
  title     = {K{\"o}nnen technologiegest{\"u}tzte Verhandlungstrainings unter Einsatz von K{\"u}nstlicher Intelligenz und Virtueller Realit{\"a}t das Vertriebstraining verbessern?},
  doi       = {10.25932/publishup-57737},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-577378},
  school      = {Universit{\"a}t Potsdam},
  pages     = {245},
  year      = {2023},
  abstract  = {Digitale und gesellschaftliche Entwicklungen fordern kontinuierliche Weiterbildung f{\"u}r Mitarbeiter im Vertrieb. Es halten sich in dieser Berufssparte aber immer noch einige Mythen zum Training von Vertriebsmitarbeitern. Unter anderem deshalb wurde in der Vergangenheit der Trainingsbedarf im Vertrieb stark vernachl{\"a}ssigt. Die Arbeit befasst sich deshalb zun{\"a}chst mit der Frage, wie der Vertrieb in Deutschland aktuell geschult wird (unter Einbezug der Corona-Pandemie) und ob sich aus den Trainingsgewohnheiten erste Hinweise zur Erlangung eines strategischen Wettbewerbsvorteils ergeben k{\"o}nnten. Dabei greift die Arbeit auf, dass Investitionen in das Training von Vertriebsmitarbeitern eine Anlage in die Wettbewerbsf{\"a}higkeit des Unternehmens sein k{\"o}nnten. Automatisierte Trainings, beispielsweise basierend auf Virtual Reality (VR) und K{\"u}nstlicher Intelligenz (KI), k{\"o}nnten in der Aus- und Weiterbildung des Vertriebs einen effizienten Beitrag in der Sicherstellung eines strategischen Wettbewerbsvorteils leisten. Durch weitere Forschungsfragen befasst sich die Arbeit anschließend damit, wie ein automatisiertes Vertriebstraining mit KI- und VR-Inhalten unter Einbeziehung der Nutzer gestaltet werden muss, um Vertriebsmitarbeiter in einem daf{\"u}r ausgew{\"a}hlten Verhandlungskontext zu trainieren. Dazu wird eine Anwendung mit Hilfe von Virtual Reality und K{\"u}nstlicher Intelligenz in einem Verhandlungsdialog entwickelt, getestet und evaluiert. Die vorliegende Arbeit liefert eine Basis f{\"u}r die Automatisierung von Vertriebstrainings und im erweiterten Sinne f{\"u}r Trainings im Allgemeinen.},
  language  = {de}
}
@phdthesis{FreitasdaCruz2021,
  author    = {Freitas da Cruz, Harry},
  title     = {Standardizing clinical predictive modeling},
  doi       = {10.25932/publishup-51496},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-514960},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xiii, 133},
  year      = {2021},
  abstract  = {An ever-increasing number of prediction models is published every year in different medical specialties. Prognostic or diagnostic in nature, these models support medical decision making by utilizing one or more items of patient data to predict outcomes of interest, such as mortality or disease progression. While different computer tools exist that support clinical predictive modeling, I observed that the state of the art is lacking in the extent to which the needs of research clinicians are addressed. When it comes to model development, current support tools either 1) target specialist data engineers, requiring advanced coding skills, or 2) cater to a general-purpose audience, therefore not addressing the specific needs of clinical researchers. Furthermore, barriers to data access across institutional silos, cumbersome model reproducibility and extended experiment-to-result times significantly hampers validation of existing models. Similarly, without access to interpretable explanations, which allow a given model to be fully scrutinized, acceptance of machine learning approaches will remain limited. Adequate tool support, i.e., a software artifact more targeted at the needs of clinical modeling, can help mitigate the challenges identified with respect to model development, validation and interpretation. To this end, I conducted interviews with modeling practitioners in health care to better understand the modeling process itself and ascertain in what aspects adequate tool support could advance the state of the art. The functional and non-functional requirements identified served as the foundation for a software artifact that can be used for modeling outcome and risk prediction in health research. To establish the appropriateness of this approach, I implemented a use case study in the Nephrology domain for acute kidney injury, which was validated in two different hospitals. Furthermore, I conducted user evaluation to ascertain whether such an approach provides benefits compared to the state of the art and the extent to which clinical practitioners could benefit from it. Finally, when updating models for external validation, practitioners need to apply feature selection approaches to pinpoint the most relevant features, since electronic health records tend to contain several candidate predictors. Building upon interpretability methods, I developed an explanation-driven recursive feature elimination approach. This method was comprehensively evaluated against state-of-the art feature selection methods. Therefore, this thesis' main contributions are three-fold, namely, 1) designing and developing a software artifact tailored to the specific needs of the clinical modeling domain, 2) demonstrating its application in a concrete case in the Nephrology context and 3) development and evaluation of a new feature selection approach applicable in a validation context that builds upon interpretability methods. In conclusion, I argue that appropriate tooling, which relies on standardization and parametrization, can support rapid model prototyping and collaboration between clinicians and data scientists in clinical predictive modeling.},
  language  = {en}
}
@phdthesis{Hecher2021,
  author    = {Hecher, Markus},
  title     = {Advanced tools and methods for treewidth-based problem solving},
  doi       = {10.25932/publishup-51251},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-512519},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xv, 184},
  year      = {2021},
  abstract  = {In the last decades, there was a notable progress in solving the well-known Boolean satisfiability (Sat) problem, which can be witnessed by powerful Sat solvers. One of the reasons why these solvers are so fast are structural properties of instances that are utilized by the solver's interna. This thesis deals with the well-studied structural property treewidth, which measures the closeness of an instance to being a tree. In fact, there are many problems parameterized by treewidth that are solvable in polynomial time in the instance size when parameterized by treewidth. In this work, we study advanced treewidth-based methods and tools for problems in knowledge representation and reasoning (KR). Thereby, we provide means to establish precise runtime results (upper bounds) for canonical problems relevant to KR. Then, we present a new type of problem reduction, which we call decomposition-guided (DG) that allows us to precisely monitor the treewidth when reducing from one problem to another problem. This new reduction type will be the basis for a long-open lower bound result for quantified Boolean formulas and allows us to design a new methodology for establishing runtime lower bounds for problems parameterized by treewidth. Finally, despite these lower bounds, we provide an efficient implementation of algorithms that adhere to treewidth. Our approach finds suitable abstractions of instances, which are subsequently refined in a recursive fashion, and it uses Sat solvers for solving subproblems. It turns out that our resulting solver is quite competitive for two canonical counting problems related to Sat.},
  language  = {en}
}
@phdthesis{Kunkel2023,
  author    = {Kunkel, Stefanie},
  title     = {Green industry through industry 4.0? Expected and observed effects of digitalisation in industry for environmental sustainability},
  doi       = {10.25932/publishup-61395},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-613954},
  school      = {Universit{\"a}t Potsdam},
  pages     = {vii, 168},
  year      = {2023},
  abstract  = {Digitalisation in industry - also called "Industry 4.0" - is seen by numerous actors as an opportunity to reduce the environmental impact of the industrial sector. The scientific assessments of the effects of digitalisation in industry on environmental sustainability, however, are ambivalent. This cumulative dissertation uses three empirical studies to examine the expected and observed effects of digitalisation in industry on environmental sustainability. The aim of this dissertation is to identify opportunities and risks of digitalisation at different system levels and to derive options for action in politics and industry for a more sustainable design of digitalisation in industry. I use an interdisciplinary, socio-technical approach and look at selected countries of the Global South (Study 1) and the example of China (all studies). In the first study (section 2, joint work with Marcel Matthess), I use qualitative content analysis to examine digital and industrial policies from seven different countries in Africa and Asia for expectations regarding the impact of digitalisation on sustainability and compare these with the potentials of digitalisation for sustainability in the respective country contexts. The analysis reveals that the documents express a wide range of vague expectations that relate more to positive indirect impacts of information and communication technology (ICT) use, such as improved energy efficiency and resource management, and less to negative direct impacts of ICT, such as electricity consumption through ICT. In the second study (section 3, joint work with Marcel Matthess, Grischa Beier and Bing Xue), I conduct and analyse interviews with 18 industry representatives of the electronics industry from Europe, Japan and China on digitalisation measures in supply chains using qualitative content analysis. I find that while there are positive expectations regarding the effects of digital technologies on supply chain sustainability, their actual use and observable effects are still limited. Interview partners can only provide few examples from their own companies which show that sustainability goals have already been pursued through digitalisation of the supply chain or where sustainability effects, such as resource savings, have been demonstrably achieved. In the third study (section 4, joint work with Peter Neuh{\"a}usler, Melissa Dachrodt and Marcel Matthess), I conduct an econometric panel data analysis. I examine the relationship between the degree of Industry 4.0, energy consumption and energy intensity in ten manufacturing sectors in China between 2006 and 2019. The results suggest that overall, there is no significant relationship between the degree of Industry 4.0 and energy consumption or energy intensity in manufacturing sectors in China. However, differences can be found in subgroups of sectors. I find a negative correlation of Industry 4.0 and energy intensity in highly digitalised sectors, indicating an efficiency-enhancing effect of Industry 4.0 in these sectors. On the other hand, there is a positive correlation of Industry 4.0 and energy consumption for sectors with low energy consumption, which could be explained by the fact that digitalisation, such as the automation of previously mainly labour-intensive sectors, requires energy and also induces growth effects. In the discussion section (section 6) of this dissertation, I use the classification scheme of the three levels macro, meso and micro, as well as of direct and indirect environmental effects to classify the empirical observations into opportunities and risks, for example, with regard to the probability of rebound effects of digitalisation at the three levels. I link the investigated actor perspectives (policy makers, industry representatives), statistical data and additional literature across the system levels and consider political economy aspects to suggest fields of action for more sustainable (digitalised) industries. The dissertation thus makes two overarching contributions to the academic and societal discourse. First, my three empirical studies expand the limited state of research at the interface between digitalisation in industry and sustainability, especially by considering selected countries in the Global South and the example of China. Secondly, exploring the topic through data and methods from different disciplinary contexts and taking a socio-technical point of view, enables an analysis of (path) dependencies, uncertainties, and interactions in the socio-technical system across different system levels, which have often not been sufficiently considered in previous studies. The dissertation thus aims to create a scientifically and practically relevant knowledge basis for a value-guided, sustainability-oriented design of digitalisation in industry.},
  language  = {en}
}
@phdthesis{Schmeiss2019,
  author    = {Schmeiss, Jessica},
  title     = {Designing value architectures for emerging technologies},
  school      = {Universit{\"a}t Potsdam},
  pages     = {135},
  year      = {2019},
  abstract  = {The business model has emerged as a construct to understand how firms drive innovation through emerging technologies. It is defined as the 'architecture of the firm's value creation, delivery and appropriation mechanisms' (Foss \& Saebi, 2018, p. 5). The architecture is characterized by complex functional interrelations between activities that are conducted by various actors, some within and some outside of the firm. In other words, a firm's value architecture is embedded within a wider system of actors that all contribute to the output of the value architecture. The question of what drives innovation within this system and how the firm can shape and navigate this innovation is an essential question within innova- tion management research. This dissertation is a compendium of four individual research articles that examine how the design of a firm's value architecture can fa- cilitate system-wide innovation in the context of Artificial Intelligence and Block- chain Technology. The first article studies how firms use Blockchain Technology to design a governance infrastructure that enables innovation within a platform ecosystem. The findings propose a framework for blockchain-enabled platform ecosystems that address the essential problem of opening the platform to allow for innovation while also ensuring that all actors get to capture their share of the value. The second article analyzes how German Artificial Intelligence startups design their business models. It identifies three distinct types of startup with dif- ferent underlying business models. The third article aims to understand the role of a firm's value architecture during the socio-technical transition process of Arti- ficial Intelligence. It identifies three distinct ways in which Artificial Intelligence startups create a shared understanding of the technology. The last article exam- ines how corporate venture capital units configure value-adding services for their venture portfolios. It derives a taxonomy of different corporate venture capital types, driven by different strategic motivations. Ultimately, this dissertation provides novel empirical insights into how a firm's value architecture determines it's role within a wider system of actors and how that role enables the firm to facilitate innovation. In that way, it contributes to both business model and innovation management literature.},
  language  = {en}
}