Me and MY Replika: Perceived Affordances and the Formation of Psychological Ownership of AI Companions
European Journal of Information Systems, Advance online publication.
In depth: the story behind this paperResearch
My research starts with how digital technologies can be designed to support people in context. I build and study artifacts, examine the interactions and relationships they enable, and develop knowledge that can inform future designs.
What changes when AI becomes a companion, collaborator, or agent?
This work connects the design of AI systems with the relationships people develop with them. It spans everyday assistance, virtual companionship, and collaborative work, asking how design choices shape interaction and behavior over time.
How can we learn from what we build, and build on what we learn?
My methodological research examines how insights from individual design studies can inform future artifacts. Current work addresses the construction and emergence of design principles, the accumulation of prescriptive knowledge, and human–AI approaches to extracting and synthesizing knowledge across studies.
How can human–AI collaboration be designed and organized in practice?
At the ERCIS Flow Factory, I lead the AI & Products flow field. Working with research and industry partners, I connect artifact design with the organizational conditions for human–AI collaboration, AI-supported services, and the integration of emerging AI systems.
Whose needs, values, and knowledge guide digital design?
This research includes virtual learning companions and participation companions, as well as an emerging focus on Indigenous-informed design for language revitalization. Across these settings, I examine how digital technologies can support meaningful participation and reflect the knowledge and priorities of the people involved.
Resources for researchers
A public platform where I share methodological knowledge and experiences for design-oriented research and doctoral education.
Co-developed at the University of Münster, LitFlow supports AI-augmented systematic literature reviews with an emphasis on researcher control, transparency, and traceability.
Currently in closed alpha