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Artificial intelligence in digital service ecosystems—Towards a taxonomy and archetypes

Philipp Hansmeier et al.

Electronic Markets2026https://doi.org/10.1007/s12525-026-00879-yarticle
AJG 2ABDC A
Weight
0.50

What the paper says

Traditional dyadic customer-provider interactions are being shifted to polyadic interactions involving diverse participants in digital service ecosystems. Especially, artificial intelligence (AI) is increasingly integrated into these ecosystems, so that they comprise non-human participants (e.g., AI-based chatbots)—fundamentally altering the nature of value (co-)creation. While existing literature examines human-to-human interactions, knowledge of service interactions between human actors and AI-based systems is still underexplored. To address this research gap, we develop a taxonomy, comprising six iterations, that explores the peculiarities of AI as either a resource or a (non-human) agent in digital service ecosystems. We evaluate our taxonomy using a multiple case study and derive the four archetypes of AI in digital service ecosystems: (1) discriminative experience enhancer, (2) protective ecosystem orchestrator, (3) ecosystem innovation companion, and (4) personalized service composer. Our results extend the knowledge on service science by showing how AI-based systems—discriminative or generative, and focusing on the interaction in the ecosystem or the individual service encounter—assume the role of resources and non-human agents. Researchers and practitioners can utilize our results to augment their ecosystems with AI.

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https://doi.org/https://doi.org/10.1007/s12525-026-00879-y

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@article{philipp2026,
  title        = {{Artificial intelligence in digital service ecosystems—Towards a taxonomy and archetypes}},
  author       = {Philipp Hansmeier et al.},
  journal      = {Electronic Markets},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1007/s12525-026-00879-y},
}

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Artificial intelligence in digital service ecosystems—Towards a taxonomy and archetypes

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.