<p>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.</p>

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

  • Philipp Hansmeier,
  • Jannika Marie Schäfer,
  • Philipp zur Heiden

摘要

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.