Paper Number
ECIS2025-1236
Paper Type
SP
Abstract
Artificial intelligence (AI) is increasingly integrated into modern service encounters, shifting from traditional dyadic customer-provider interactions to polyadic interactions between multiple actors in digital service ecosystems. These ecosystems, compromising human and non-human actors (e.g., AI chatbots or service robots), fundamentally alter the nature of value co-creation. Although existing literature examines human-to-human interactions, non-human-to-human, and non-human-to-non-human interactions still need to be explored. Especially, wisdom on AI's roles in service ecosystem interaction is still scarce. To address this research gap, we develop a taxonomy that explores key AI actor attributes, laying the foundation for identifying future AI actor archetypes within digital service ecosystems. Thus, our findings contribute to service science by offering insights into (1) the data that AI relies on, (2) how AI can be integrated into digital service ecosystems, (3) its perceived behavior, and (4) the value it can deliver across digital service ecosystems.
Recommended Citation
Hansmeier, Philipp and Schäfer, Jannika Marie, "ARTIFICIAL INTELLIGENCE IN DIGITAL SERVICE ECOSYSTEMS – A TAXONOMY APPROACH" (2025). ECIS 2025 Proceedings. 2.
https://aisel.aisnet.org/ecis2025/intelserv/intelserv/2
ARTIFICIAL INTELLIGENCE IN DIGITAL SERVICE ECOSYSTEMS – A TAXONOMY APPROACH
Artificial intelligence (AI) is increasingly integrated into modern service encounters, shifting from traditional dyadic customer-provider interactions to polyadic interactions between multiple actors in digital service ecosystems. These ecosystems, compromising human and non-human actors (e.g., AI chatbots or service robots), fundamentally alter the nature of value co-creation. Although existing literature examines human-to-human interactions, non-human-to-human, and non-human-to-non-human interactions still need to be explored. Especially, wisdom on AI's roles in service ecosystem interaction is still scarce. To address this research gap, we develop a taxonomy that explores key AI actor attributes, laying the foundation for identifying future AI actor archetypes within digital service ecosystems. Thus, our findings contribute to service science by offering insights into (1) the data that AI relies on, (2) how AI can be integrated into digital service ecosystems, (3) its perceived behavior, and (4) the value it can deliver across digital service ecosystems.
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