ECIS 2023 - STRUCTURING FEDERATED LEARNING APPLICATIONS – A LITERATURE ANALYSIS AND TAXONOMY

   Ensuring data privacy is an essential objective competing with the ever-rising capabilities of machine learning approaches fueled by vast amounts of centralized data. Federated learning addresses this conflict by moving the model to the data and ensuring the data itself does not leave a client's device. However, maintaining privacy impels new challenges concerning algorithm performance or fairness of the algorithm's results that remain uncovered from a sociotechnical perspective. We tackle this research gap by conducting a structured literature review and analyzing 152 articles to develop a taxonomy of federated learning applications with nine dimensions and 24 characteristics. Our taxonomy illustrates how different attributes of federated learning may affect the trade-off between an algorithm's privacy, performance, and fairness. Despite an increasing interest in the technical implementation of federated learning, our work is one of the first to emphasize an information systems perspective on this emerging and promising topic.</p

A Means to an End of the Other - Research Avenues at the Intersection of Organizational Digital Transformation and Digital Business Ecosystems

Digital technologies pose challenges and opportunities to individual and ecosystems of organizations. To date, two mostly isolated research streams study these related phenomena: Organizational digital transformation (ODT), focusing on the digital improvement process of individual incumbents and digital business ecosystems (DBEs), focusing on digitally-enabled value co-creation among organizations. Joining the forces of both research streams, our work aims to assess what empirical evidence and theory exist at their intersection. After conducting an assessing review, a theorizing review, and extracting assumptions in isolation, we derive four convergent assumptions for building future theory at their intersection along four topic areas: resources, coopetition, evolution, and control. We propose that ODT and DBEs can be a means to an end of the other connected in a cyclical relationship to meet digitally induced challenges. By presenting avenues for further research, our work builds a foundation for future theory at the intersection of ODT and DBEs