Location
Hilton Hawaiian Village, Honolulu, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2024 12:00 AM
End Date
6-1-2024 12:00 AM
Description
Data spaces have gained increasing attention, as they allow federated data sharing among and within participants of interoperable data spaces, for the benefit of all. However, data space initiatives are few in number; moreover, data space adoption among organizations is low. Research thus far has mainly focused on technical factors but lacks a more holistic approach that clarifies what drives data space adoption and federated data sharing as main functions. This exploratory study aims to fill this research gap; it identifies 12 drivers developed by 28 interviewed experts, discussing the coding techniques that are most frequently used in grounded theory. The identified drivers contribute to the current knowledge, while also potentially informing data space projects and organizations’ decisions regarding data space adoption.
Recommended Citation
Hutterer, Andreas and Krumay, Barbara, "The adoption of data spaces: Drivers toward federated data sharing" (2024). Hawaii International Conference on System Sciences 2024 (HICSS-57). 3.
https://aisel.aisnet.org/hicss-57/in/platform_ecosystems/3
The adoption of data spaces: Drivers toward federated data sharing
Hilton Hawaiian Village, Honolulu, Hawaii
Data spaces have gained increasing attention, as they allow federated data sharing among and within participants of interoperable data spaces, for the benefit of all. However, data space initiatives are few in number; moreover, data space adoption among organizations is low. Research thus far has mainly focused on technical factors but lacks a more holistic approach that clarifies what drives data space adoption and federated data sharing as main functions. This exploratory study aims to fill this research gap; it identifies 12 drivers developed by 28 interviewed experts, discussing the coding techniques that are most frequently used in grounded theory. The identified drivers contribute to the current knowledge, while also potentially informing data space projects and organizations’ decisions regarding data space adoption.
https://aisel.aisnet.org/hicss-57/in/platform_ecosystems/3