Filtern
Erscheinungsjahr
- 2021 (32) (entfernen)
Dokumenttyp
- Dissertation (32) (entfernen)
Institut
- Fakultät Nachhaltigkeit (17)
- Fakultät Wirtschaftswissenschaften (7)
- Fakultät Bildung (5)
- Institut für Nachhaltigkeitssteuerung (INSUGO) (4)
- Fakultät Kulturwissenschaften (3)
- Institut für Wirtschaftsinformatik (IIS) (3)
- Institut für Bildungswissenschaft (IBIWI) (2)
- Institut für Ethik und Transdisziplinäre Nachhaltigkeitsforschung (IETSR) (2)
- Institut für Kultur und Ästhetik Digitaler Medien (ICAM) (2)
- Institut für Management und Organisation (IMO) (2)
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.
One of the Colombian strategies to diversify and decarbonize the energy sector is encouraging the use of non-conventional renewable resources (NCRR). This thesis measures the environmental rebound effect (ERE) when increasing the shares of wind power into the Colombian power grid in the residential (household) sector. For doing so, a process-based Life Cycle Assessment (P-LCA), an environmental extended input output (EEIO) model and re-spending models (almost ideal demand system AIDS) were applied. Direct rebound effect was measured thought the elasticity price of the electricity demand; furthermore, the environmental savings for increasing the shares of wind power into the grid were calculated via P-LCA. For doing so, a P-LCA for a wind farm in Colombia was performed, whereas the information for other energy resources (Hydro, Coal, Gas, Solar and Thermal) where collected from Ecoinvent 3.4 database. To calculate the environmental indirect rebound effect the monetary savings obtained for the environmental efficiency were calculated. For doing so, an AIDS was applied to obtain the marginal budget shares (MBS). Combining the MBS obtained with the EEIO model the monetary savings were translated into environmental indicators. The ERE is presented for ten impact categories (climate change (CC), acidification (A), ecotoxicity (E), marine eutrophication (MEUT), terrestrial eutrophication (TEUT), carcinogenic effects (CE), non-carcinogenic effects (NCE), ozone layer depletion (OD), photochemical ozone creation (POC), and respiratory effects, inorganics (RES)). Moreover, a sensitive analysis was conducted to measure the variability of the ERE to different values of the direct rebound effect and different percentages of price efficiency. The results show that the inclusion of the environmental rebound effect has generally a non-negligible impact on the overall environmental indicators across all studied years. Such impacts ranging across impact categories from 5% (eutrophication) and 6,109% (photochemical oxidant creation) for the combined model, whereas for the single model the values fall on the ranges of 1% (eutrophication) and 9,277% (photochemical oxidant creation). Further, a sensitivity analysis of the elasticity price of the electricity and the price of the electricity reveals that the ERE varies in different ways, specifically, changes in these parameters could vary the impacts, respectively, by up to about <1% and 38%. Backfire effects are present for 8 of the 10 environmental impacts studied in different magnitudes across the years, depending meanly of the savings available to re-invest.