@phdthesis{Grum2021,
  author    = {Grum, Marcus},
  title     = {Construction of a concept of neuronal modeling},
  year      = {2021},
  abstract  = {The business problem of having inefficient processes, imprecise process analyses, and simulations as well as non-transparent artificial neuronal network models can be overcome by an easy-to-use modeling concept. With the aim of developing a flexible and efficient approach to modeling, simulating, and optimizing processes, this paper proposes a flexible Concept of Neuronal Modeling (CoNM). The modeling concept, which is described by the modeling language designed and its mathematical formulation and is connected to a technical substantiation, is based on a collection of novel sub-artifacts. As these have been implemented as a computational model, the set of CoNM tools carries out novel kinds of Neuronal Process Modeling (NPM), Neuronal Process Simulations (NPS), and Neuronal Process Optimizations (NPO). The efficacy of the designed artifacts was demonstrated rigorously by means of six experiments and a simulator of real industrial production processes.},
  language  = {en}
}
@phdthesis{ChujfiLaRoche2020,
  author    = {Chujfi-La-Roche, Salim},
  title     = {Human Cognition and natural Language Processing in the Digitally Mediated Environment},
  school      = {Universit{\"a}t Potsdam},
  pages     = {148},
  year      = {2020},
  abstract  = {Organizations continue to assemble and rely upon teams of remote workers as an essential element of their business strategy; however, knowledge processing is particular difficult in such isolated, largely digitally mediated settings. The great challenge for a knowledge-based organization lies not in how individuals should interact using technology but in how to achieve effective cooperation and knowledge exchange. Currently more attention has been paid to technology and the difficulties machines have processing natural language and less to studies of the human aspect—the influence of our own individual cognitive abilities and preferences on the processing of information when interacting online. This thesis draws on four scientific domains involved in the process of interpreting and processing massive, unstructured data—knowledge management, linguistics, cognitive science, and artificial intelligence—to build a model that offers a reliable way to address the ambiguous nature of language and improve workers' digitally mediated interactions. Human communication can be discouragingly imprecise and is characterized by a strong linguistic ambiguity; this represents an enormous challenge for the computer analysis of natural language. In this thesis, I propose and develop a new data interpretation layer for the processing of natural language based on the human cognitive preferences of the conversants themselves. Such a semantic analysis merges information derived both from the content and from the associated social and individual contexts, as well as the social dynamics that emerge online. At the same time, assessment taxonomies are used to analyze online comportment at the individual and community level in order to successfully identify characteristics leading to greater effectiveness of communication. Measurement patterns for identifying effective methods of individual interaction with regard to individual cognitive and learning preferences are also evaluated; a novel Cyber-Cognitive Identity (CCI)—a perceptual profile of an individual's cognitive and learning styles—is proposed. Accommodation of such cognitive preferences can greatly facilitate knowledge management in the geographically dispersed and collaborative digital environment. Use of the CCI is proposed for cognitively labeled Latent Dirichlet Allocation (CLLDA), a novel method for automatically labeling and clustering knowledge that does not rely solely on probabilistic methods, but rather on a fusion of machine learning algorithms and the cognitive identities of the associated individuals interacting in a digitally mediated environment. Advantages include: a greater perspicuity of dynamic and meaningful cognitive rules leading to greater tagging accuracy and a higher content portability at the sentence, document, and corpus level with respect to digital communication.},
  language  = {en}
}
@phdthesis{Fischer2020,
  author    = {Fischer, Caroline},
  title     = {Knowledge Sharing in the Public Sector},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xiii, 222},
  year      = {2020},
  abstract  = {This dissertation examines the activity of knowledge sharing by public employees in the workplace. Building on the Rubicon model of human behavior formation, I use a threefold approach to analyze the knowledge-sharing process: public employees' motivation to share knowledge, their intention to share, and knowledge sharing behavior as such. The first article maps the knowledge-sharing behavior of public employees. It builds a typology of behavioral patterns and shows that public employees mainly share their knowledge responsively and directly with a knowledge receiver rather than an information medium. The second article elaborates on the construct of knowledge-sharing motivation and develops a scale to measure this kind of work motivation in a selective and domain-specific way. Data from three studies indicate three dimensions of knowledge-sharing motivation, namely appreciation, growth and altruism, and tangible rewards. Based on these dimensions, the third article analyzes whether the satisfaction of public employees' underlying needs can foster ther knowledge-sharing intention. The study indicates that both tested treatments (appreciation by co-workers, benefits in a performance appraisal) positively affect knowledge-sharing intention if it is explicit knowledge that ought to be shared. However, no effects of either treatment can be found if implicit knowledge is shared. Hence, to foster sharing of explicit knowledge, the analyzed motivation-enhancing rewards can be used in public management practice. To enhance implicit knowledge sharing, ability- and opportunity-enhancing management instruments are discussed. All in all, this dissertation integrates a micro-level perspective on human knowledge sharing into a meso-level perspective on organizational knowledge management. It adds to the literature on workplace behaviors of public employees and knowledge management and aims to incorporate knowledge sharing and management into the public administration and management literature.},
  language  = {en}
}
@phdthesis{vonKaphengst2019,
  author    = {von Kaphengst, Dragana},
  title     = {Project's management quality in development cooperation},
  doi       = {10.25932/publishup-43099},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-430992},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xvii, 237},
  year      = {2019},
  abstract  = {In light of the debate on the consequences of competitive contracting out of traditionally public services, this research compares two mechanisms used to allocate funds in development cooperation—direct awarding and competitive contracting out—aiming to identify their potential advantages and disadvantages. The agency theory is applied within the framework of rational-choice institutionalism to study the institutional arrangements that surround two different money allocation mechanisms, identify the incentives they create for the behavior of individual actors in the field, and examine how these then transfer into measurable differences in managerial quality of development aid projects. In this work, project management quality is seen as an important determinant of the overall project success. For data-gathering purposes, the German development agency, the Gesellschaft f{\"u}r Internationale Zusammenarbeit (GIZ), is used due to its unique way of work. Whereas the majority of projects receive funds via direct-award mechanism, there is a commercial department, GIZ International Services (GIZ IS) that has to compete for project funds. The data concerning project management practices on the GIZ and GIZ IS projects was gathered via a web-based, self-administered survey of project team leaders. Principal component analysis was applied to reduce the dimensionality of the independent variable to total of five components of project management. Furthermore, multiple regression analysis identified the differences between the separate components on these two project types. Enriched by qualitative data gathered via interviews, this thesis offers insights into everyday managerial practices in development cooperation and identifies the advantages and disadvantages of the two allocation mechanisms. The thesis first reiterates the responsibility of donors and implementers for overall aid effectiveness. It shows that the mechanism of competitive contracting out leads to better oversight and control of implementers, fosters deeper cooperation between the implementers and beneficiaries, and has a potential to strengthen ownership of recipient countries. On the other hand, it shows that the evaluation quality does not tremendously benefit from the competitive allocation mechanism and that the quality of the component knowledge management and learning is better when direct-award mechanisms are used. This raises questions about the lacking possibilities of actors in the field to learn about past mistakes and incorporate the finings into the future interventions, which is one of the fundamental issues of aid effectiveness. Finally, the findings show immense deficiencies in regard to oversight and control of individual projects in German development cooperation.},
  language  = {en}
}