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Process Science (2024) AI Processed
Process science:the interdisciplinary study of socio-technical change
This editorial paper introduces and conceptualizes process science as an interdisciplinary field dedicated to the study of socio-technical processes over time. It examines how human actions and digital technologies interact dynamically, leveraging digital trace data and computational techniques to observe, explain, and guide change. The study establishes core tenets, taxonomy distinctions, and a research framework linking practical socio-technical processes with scientific knowledge creation.
Problem
Traditional research disciplines frequently prioritize an 'entity-first' view, focusing on static structures rather than ongoing, dynamic changes. However, contemporary phenomena—such as generative AI, platform economies, and societal transformations—are inherently socio-technical and continuously evolving, producing massive amounts of digital trace data that static analytical frameworks cannot adequately capture.
Outcome
- Defines process science as an interdisciplinary domain aimed at capturing, understanding, and intervening in socio-technical change across multiple levels of abstraction.
- Categorizes key activities of process science into discovery (detecting patterns via event data), explanation (identifying cause-effect relations), and intervention (shaping process trajectories).
- Advocates for shifting from an 'entity-first' to a 'process-first' perspective to identify analogies across diverse domains and support computationally intensive theory building.
- Introduces a research framework that integrates digital trace data from practice with theoretical foundations to solve real-world problems and drive societal impact.
- Categorizes key activities of process science into discovery (detecting patterns via event data), explanation (identifying cause-effect relations), and intervention (shaping process trajectories).
- Advocates for shifting from an 'entity-first' to a 'process-first' perspective to identify analogies across diverse domains and support computationally intensive theory building.
- Introduces a research framework that integrates digital trace data from practice with theoretical foundations to solve real-world problems and drive societal impact.
What it means for you
- CIO / IT Executive: On Monday morning, initiate a review of your organization's current digital infrastructure and data collection capabilities to identify which systems generate 'digital trace data' relevant to socio-technical processes. Prioritize understanding the flow of this data and its potential for analysis.
- IT Manager: On Monday morning, investigate your team's current methods for logging and tracking user interactions with key IT systems. Identify specific 'events' that could be captured and analyzed to reveal patterns of socio-technical change within your department.
- Business Strategist: On Monday morning, schedule a brainstorming session with your core team to identify one specific business process currently undergoing significant change (e.g., customer onboarding, product development). Brainstorm what digital 'trace data' this process generates and how understanding its dynamics could inform strategy.
- Researcher: On Monday morning, start cataloging existing datasets within your research domain that represent 'digital traces' of human-technology interaction. Begin exploring computational tools that can analyze event sequences rather than just static entities.
- Policymaker: On Monday morning, identify a specific public policy area (e.g., digital inclusion, citizen engagement) that is experiencing rapid socio-technical evolution. Begin researching existing digital platforms and services that generate trace data related to this policy area.