TY  - CONF
A1  - Stein, Hannah
A1  - Rix, Calvin
A1  - Effertz, Anna
A1  - Bergau, Sven
A1  - Maass, Wolfgang
T1  - Data Sharing in the German Food Industry - Empirical Insights
T2  - AMCIS 2022 Proceedings
N2  - Big data are collected along the entire food industry value chain, but remain mostly unused. Data sharing in data ecosystems could lead to efficiency gains and new revenue streams. We investigate data sharing within food industry and derive challenges and opportunities for data sharing in this context. We conducted interviews with ten qualified experts from the German food industry. The results reveal that mainly trust, usefulness and value influence users’ attitude towards data sharing. Our results confirm social exchange theory in conjunction with technology acceptance model as relevant underlying IS theories of data sharing.
KW  - data sharing
KW  - food industry
KW  - data value
KW  - data ecosystems
KW  - rev
Y1  - 2023
UR  - https://epub.fir.de/frontdoor/index/index/docId/3091
UR  - https://aisel.aisnet.org/amcis2022/DataEcoSys/DataEcoSys/1
ER  -