Paper Number
ICIS2025-1126
Paper Type
Complete
Abstract
In today’s digital economy, data sharing is crucial for organizations to fully exploit innovation and value creation potential. However, systematic information asymmetries regarding benefit structures across actor roles create coordination challenges and hinder purposeful engagement in data sharing solutions. This study addresses this gap by analyzing the potential benefits of data sharing from a multi-actor perspective, focusing on the key actor roles within data ecosystems: data provider, data consumer, and intermediary. Through a structured literature review complemented by analysis of eight expert interviews, we developed a classification of 22 direct and indirect benefits. For information systems research, our work contributes transparency to value creation opportunities from data sharing and provides a theoretically grounded foundation for designing mutually beneficial data sharing solutions and effective incentive mechanisms in ecosystems.
Recommended Citation
Fassnacht, Marcel; Müller, Jannes; Benz, Carina; Holstein, Joshua; and Satzger, Gerhard, "A Multi-Actor Benefit Classification for Inter-Organizational Data Sharing in Ecosystems" (2025). ICIS 2025 Proceedings. 2.
https://aisel.aisnet.org/icis2025/digitstrategy/digitstrategy/2
A Multi-Actor Benefit Classification for Inter-Organizational Data Sharing in Ecosystems
In today’s digital economy, data sharing is crucial for organizations to fully exploit innovation and value creation potential. However, systematic information asymmetries regarding benefit structures across actor roles create coordination challenges and hinder purposeful engagement in data sharing solutions. This study addresses this gap by analyzing the potential benefits of data sharing from a multi-actor perspective, focusing on the key actor roles within data ecosystems: data provider, data consumer, and intermediary. Through a structured literature review complemented by analysis of eight expert interviews, we developed a classification of 22 direct and indirect benefits. For information systems research, our work contributes transparency to value creation opportunities from data sharing and provides a theoretically grounded foundation for designing mutually beneficial data sharing solutions and effective incentive mechanisms in ecosystems.
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