Paper Type
Complete
Paper Number
PACIS2026-1162
Description
Knowledge transfer (KT) in non-university research institutions (NURIs) is often studied through mechanisms and relationships, while the concrete data objects crossing organizational boundaries remain underspecified. These transfer data (e.g., reports, publications, datasets) shape accountability, access control, reuse, and compliant handling. Yet conceptual clarity and a classification scheme for comparing them across transfer contexts are lacking. We propose a taxonomy of transfer data based on a systematic literature review, 12 expert interviews in German NURIs, and the classification of 234 empirically identified data objects. Building on this taxonomy, we derive three archetypes through exploratory hierarchical clustering: compliance and accountability information, operational coordination data, and scientific knowledge core. Our work advances KT research by shifting the unit of analysis from transfer mechanisms to transfer data and helps practitioners align governance rules with distinct data archetypes.
Recommended Citation
Gieß, Anna; Lovrekovic, Josip; Tavakoligargari, Masoumeh; Etzkorn, Manuel; Jürjens, Jan; von Korflesch, Harald F. O.; and Fettke, Peter, "The Hidden Backbone of Knowledge Transfer: A Taxonomy and Archetypes of Transfer Data in Non-University Research Institutions" (2026). PACIS 2026 Proceedings. 1.
https://aisel.aisnet.org/pacis2026/it_strategy/it_strategy/1
The Hidden Backbone of Knowledge Transfer: A Taxonomy and Archetypes of Transfer Data in Non-University Research Institutions
Knowledge transfer (KT) in non-university research institutions (NURIs) is often studied through mechanisms and relationships, while the concrete data objects crossing organizational boundaries remain underspecified. These transfer data (e.g., reports, publications, datasets) shape accountability, access control, reuse, and compliant handling. Yet conceptual clarity and a classification scheme for comparing them across transfer contexts are lacking. We propose a taxonomy of transfer data based on a systematic literature review, 12 expert interviews in German NURIs, and the classification of 234 empirically identified data objects. Building on this taxonomy, we derive three archetypes through exploratory hierarchical clustering: compliance and accountability information, operational coordination data, and scientific knowledge core. Our work advances KT research by shifting the unit of analysis from transfer mechanisms to transfer data and helps practitioners align governance rules with distinct data archetypes.
Comments
11-Strategy