@phdthesis{Stoffels2025, author = {Stoffels, Dominik}, title = {Advancing Pattern Detection, Theory Development and Decision Making with Explainable AI}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:739-opus4-15975}, school = {Universit{\"a}t Passau}, pages = {ii, 145 Seiten}, year = {2025}, abstract = {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.}, language = {en} } @article{StoffelsFaltermaierStrunketal.2024, author = {Stoffels, Dominik and Faltermaier, Stefan and Strunk, Kim Simon and Fiedler, Marina}, title = {Guiding computationally intensive theory development with explainable artificial intelligence: The case of shapley additive explanations}, series = {Journal of Information Technology (ISSN: 1466-4437)}, volume = {40}, journal = {Journal of Information Technology (ISSN: 1466-4437)}, number = {2}, publisher = {SAGE Publications}, address = {London, England}, issn = {0268-3962}, doi = {10.1177/02683962241289597}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:739-opus4-16762}, pages = {180 -- 213}, year = {2024}, abstract = {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).}, subject = {-}, language = {en} }