Location
Hilton Waikoloa Village, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
7-1-2025 12:00 AM
End Date
10-1-2025 12:00 AM
Description
In an era of data-driven decision-making, organizations increasingly build data ecosystems to share and capitalize on data within their ecosystem. With the advent of various initiatives with different goals, architectures, and governance structures, understanding the possible configurations that lead to vibrant data ecosystems is crucial for enhancing innovation and collaboration. This study investigates these configurations, focusing on the interplay between technical and social boundary resources, centralization, domain specialization, and the number of developing partners. Based on data from 26 data ecosystem initiatives, we use fuzzy-set Qualitative Comparative Analysis to identify three configurations of vibrant data ecosystems and derive two configurations associated with less success. Our findings contribute to understanding how different elements' combinations impact data ecosystems' performance, offering insights for practitioners aiming to enhance data sharing, innovation, and collaboration within their ecosystems.
Recommended Citation
Kernstock, Philipp; Altenkamp, Pascal; Böttcher, Timo; Hein, Andreas; and Krcmar, Helmut, "A Configurational Approach to Understanding Data Ecosystems" (2025). Hawaii International Conference on System Sciences 2025 (HICSS-58). 3.
https://aisel.aisnet.org/hicss-58/in/data_ecosystems/3
A Configurational Approach to Understanding Data Ecosystems
Hilton Waikoloa Village, Hawaii
In an era of data-driven decision-making, organizations increasingly build data ecosystems to share and capitalize on data within their ecosystem. With the advent of various initiatives with different goals, architectures, and governance structures, understanding the possible configurations that lead to vibrant data ecosystems is crucial for enhancing innovation and collaboration. This study investigates these configurations, focusing on the interplay between technical and social boundary resources, centralization, domain specialization, and the number of developing partners. Based on data from 26 data ecosystem initiatives, we use fuzzy-set Qualitative Comparative Analysis to identify three configurations of vibrant data ecosystems and derive two configurations associated with less success. Our findings contribute to understanding how different elements' combinations impact data ecosystems' performance, offering insights for practitioners aiming to enhance data sharing, innovation, and collaboration within their ecosystems.
https://aisel.aisnet.org/hicss-58/in/data_ecosystems/3