The Process of Process Science – Generating Actionable Insights from Digital Trace Data
Abstract
Process science is an interdisciplinary field that concerns the study of socio-technical processes and their dynamics. While the field offers a compelling conceptual foundation outlining what process science is, concrete methodological guidance on how to conduct process science studies remains underdeveloped. In this paper, we present the process of process science, which conceptualizes critical activities on how to leverage the use of computational techniques, such as process mining, and digital trace data to generate meaningful insights. We frame process science studies as iterative inquiries guided by three core principles: (a) contextual grounding (b) iterative reasoning, and (c) the integration of computational and human sense-making. Building on these principles, we articulate three recursive cycles that constitute the process of process science: (1) a descriptive cycle, (2) an explanatory cycle and (3) a prescriptive cycle. For each cycle, we outline key activities that ensure rigor, relevance, and methodological transparency and illustrate them with the case of a financial institution. Together, these contributions offer methodological guidance for planning, conducting and communicating process science studies. This foundation enables future work to refine the recursive cycles of inquiry, develop tools that better support iterative sense-making, and extend process science across diverse organizational domains.
Keywords
Process Science; Methodology; Iteration; Process Mining; Digital Trace Data