Digital Innovation in Data-Poor and Unstructured Environments: Envisioning Autonomous Systems in Forestry

While IS research has increasingly turned its attention to autonomous systems, empirical investigations are predominantly conducted in artificial settings such as offices or factories. As a result, we know relatively little about what shapes autonomy in natural settings, where there is poor access to data on vegetation and terrain. To address this knowledge gap, we report a qualitative case study on the development of autonomous forestry machines. Applying the Technology-Organization-Environment (TOE) framework, our analysis reveals that autonomy is inherently shaped by its surroundings, including technological limitations, dynamic organizational processes, and intricate environmental factors. This study furthers IS research on autonomous systems by highlighting their embedded, emergent nature in data-poor and unstructured settings.