Resources & Research
On this page you will find research papers and resources related to the STAIR Methodology. We’re still working on this site, so please stay tuned as we will upload more resources soon! Until then you are welcome to contact us at co*****@*********od.org.
Helping Leaders and Employees Navigate Generative AI Through Socio-Technical Reflection: The STAIR Method
By Louise Harder Fischer, Sanna-Maria Marttila
This paper presents STAIR (Sociotechnical AI Reflection), a leadership-oriented methodology for
enabling responsible and participatory integration of Generative AI (GenAI) in organizational settings.0
Developed through an Action Design Research (ADR) project within a Danish municipality’s Communications Department, STAIR supports organizations in balancing productivity goals with employee well-being. By grounding AI integration in localized sociotechnical principles and embedding structured reflection into everyday work practices, STAIR advances a human-centered model for
navigating the digital transformation of knowledge work.
Aaccepted for inclusion in ECIS 2025 TREOs by an authorized administrator of AIS Electronic Library (AISeL).
Crafting Meaningful Generative AI-Enabled Knowledge Work
By Louise Harder Fischer, Hanne Westh Nicolajsen, Sanna-Maria Marttila, and Sunniva Sandbukt
This 2024 research paper explores how Generative AI (GenAI) can be integrated into knowledge work without diminishing professional meaning and job satisfaction.
Based on an Action Design Research study within a Danish municipality’s Communication Department, the paper introduces sociotechnical principles designed to balance AI-driven efficiency with human-centric work design. The study emphasizes the need for transparency, organizational support, and continuous learning to ensure that GenAI enhances productivity without undermining professional autonomy and well-being.
The findings contribute to the development of structured approaches for AI adoption that align technology with human values and workplace dynamics.
Accepted for inclusion in ECIS 2024 Proceedings by an authorized administrator of AIS
Electronic Library (AISeL).
How sociotechnical reflection influence wellbeing and
productivity during GenAI integration.
By Louise Harder Fischer
This short paper from 2024 explores how sociotechnical reflection influences well-being and productivity during the integration of Generative AI (GenAI) in the workplace.
Based on an interventionist study in a Danish municipality, it presents eight sociotechnical principles designed to balance AI-driven efficiency with ethical considerations, professional autonomy, and employee well-being. The research forms the foundation of the STAIR Method, a structured approach to navigating AI adoption through continuous reflection and adaptation.
Presented at the hybrid conference with a workshop at University of Jöngköping, Sweden, August 16-17, 2024.
Artificial Intelligence and Digital Work: The Sociotechnical Reversal
By Louise Harder Fischer, Nico Wunderlich (IT-University of Copenhagen) & Richard Baskerville (Georgia State University)
This 2023 paper examines the shift in digital work environments, where Artificial Intelligence (AI) and automation increasingly dominate human decision-making. Introducing the concept of the ‘sociotechnical reversal,’ the authors highlight how technology is shaping the social aspects of work, rather than the other way around.
The paper proposes a recalibrated approach to AI integration, emphasizing sociotechnical principles that balance efficiency with human well-being, autonomy, and meaningful work.
Presented at the proceedings of the 56th Hawaii International Conference on System Sciences, january 2023.