TY - THES A1 - Faltermaier, Stefan T1 - A Sociotechnical Perspective on Digital Transformation of Work in Organizations, on Platforms, and in Academia N2 - The findings of this dissertation reveal that digital transformation in the workplace accelerates ongoing changes in work practices, leading to transformations in organizations on both social and technological levels (study one). A key challenge identified in our case from study one is that highly virtual and digital work environments can undermine the integration of new employees and weaken organizational cohesion, resulting in more isolated work that tends to occur either individually or in small groups. In completely detached work settings (study two), our findings emphasize the prevalence of negative work experiences - frustration in our case – and the central role of both social and technical antecedents. Work settings devoid of any organizational framework, such as microtask crowd work, show an even greater detachment from shared values and structures compared to study one, posing significant challenges for the workers involved. These challenging conditions for individuals and organizations highlight the necessity for further research and the development of innovative theories to better comprehend these dynamics, and to devise strategies to manage these challenges effectively. Studies three and four build on this premise. In these studies, within the context of the digital transformation of academic work, we demonstrated how ML combined with XAI applications can be employed in theory development. KW - Digital Transformation of Work KW - Hybrid Work KW - Online Labor Platforms KW - Artificial Intelligence KW - Explainable Artificial Intelligence Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:739-opus4-15233 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 -