Decision support for sustainable energy systems, energy economics, urban mobility, and emerging methods in information systems research

Given the challenges posed by climate change and ongoing urbanization, sustainable development has become a global priority. This dissertation explores the pivotal role of information systems in promoting sustainability by developing advanced decision support systems that harness data-driven methodologies to enhance decision-making in the energy and urban mobility sectors. The research is anchored in design science research principles, emphasizing the iterative creation and evaluation of innovative artifacts to solve real-world problems. The dissertation encompasses twelve research articles that span four main thematic areas: sustainable energy systems, energy economics, sustainable urban micromobility, and emerging methods in IS research. The studies employ a multidisciplinary approach, utilizing a diverse array of methodologies, including techno-economic simulations, morphological analyses, regression analyses, clustering techniques, real options valuation, graph network analyses, and natural language processing. A key contribution of this work is the development and enhancement of the Nano Energy System Simulator (NESSI), an artifact for analyzing energy systems and supporting decision-making processes in the building sector. In addition, the dissertation provides comprehensive analyses of photovoltaic system pricing and adoption, wind turbine economic assessments, and the integration of urban micromobility solutions. These studies collectively contribute to fostering market transparency, economic evaluations, and informed decision-making processes. Furthermore, the dissertation explores emerging methods and tools in IS research, such as the clustering of taxonomy-based data and the analysis of social media and academic discourse surrounding the conversational AI technologies ChatGPT and GPTZero. These contributions underscore the importance of methodological rigor and innovation in enhancing the relevance and impact of IS research. By aligning with the principles of Green IS, this dissertation not only addresses pressing environmental and societal issues but also contributes to the broader IS field by developing practical, sustainability-focused solutions. The integration of data science and data-driven methods throughout the research further enhances its impact, providing valuable tools and insights for stakeholders across various domains. The findings and methodologies presented offer a comprehensive framework for future research and practice in sustainable IS, driving advancements in both academic domain and real-world application.