TY - THES A1 - Stoffels, Dominik T1 - Advancing Pattern Detection, Theory Development and Decision Making with Explainable AI N2 - The application of explainable artificial intelligence (XAI) methods in data-driven decision-making and computationally intensive theory development (CTD) is a subject of ongoing debate, particularly concerning how and whether these methods can be effectively employed, and how the reliability of their explanations can be ensured. This dissertation addresses these issues by systematically analyzing the usability of XAI for pattern detection, CTD, and decision-making, drawing on various real-world and synthetic datasets and employing different empirical methods and perspectives. The dissertation consists of four studies, each addressing distinct issues in the field of XAI application. KW - Explainable Artificial Intelligence KW - Machine Learning KW - Computationally Intensive Theory Development Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-15975 ER - TY - JOUR A1 - Stoffels, Dominik A1 - Faltermaier, Stefan A1 - Strunk, Kim Simon A1 - Fiedler, Marina T1 - Guiding computationally intensive theory development with explainable artificial intelligence: The case of shapley additive explanations JF - Journal of Information Technology (ISSN: 1466-4437) N2 - This study advances the field of Computationally Intensive Theory Development (CTD) by examining the capabilities of Explainable Artificial Intelligence (XAI), in particular SHapley Additive exPlanations (SHAP), for theory development, while providing guidelines for this process. We evaluate SHAP’s methodological abilities and develop a structured approach for using SHAP to harness insights from black-box predictive models. For this purpose, we leverage a dual-methodological approach. First, to assess SHAP’s capabilities in uncovering patterns that shape a phenomenon, we conduct a Monte-Carlo simulation study. Second, to illustrate and guide the theory development process with SHAP for CTD, we apply SHAP in a use-case using real-world data. Based on these analyses, we propose a stepwise uniform and replicable approach giving guidance that can benefit rigorous theory development and increase the traceability of the theorizing process. With our structured approach, we contribute to the use of XAI approaches in research and, by uncovering patterns in black-box prediction models, add to the ongoing search for next-generation theorizing methods in the field of Information Systems (IS). KW - - KW - computationally intensive theory development KW - next-generation theory development KW - machine learning patterns KW - IS research methods KW - explainable AI KW - black-box algorithms Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-16762 SN - 0268-3962 SN - 1466-4437 VL - 40 IS - 2 SP - 180 EP - 213 PB - SAGE Publications CY - London, England ER -