Higher-Order Externalities in Multi-Platform Ecosystems

dc.contributor.author Schmidt, Rainer
dc.contributor.author Alt, Rainer
dc.contributor.author Zimmermann, Alfed
dc.date.accessioned 2023-12-26T18:42:39Z
dc.date.available 2023-12-26T18:42:39Z
dc.date.issued 2024-01-03
dc.identifier.isbn 978-0-9981331-7-1
dc.identifier.other 34673836-513e-42ff-9e54-733db76093f5
dc.identifier.uri https://hdl.handle.net/10125/106867
dc.language.iso eng
dc.relation.ispartof Proceedings of the 57th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Artificial Intelligence-based Assistants and Platforms
dc.subject complementarities
dc.subject complex ecosystems
dc.subject complex platforms
dc.subject eco-system modeling and analysis
dc.subject network effects
dc.subject network science
dc.title Higher-Order Externalities in Multi-Platform Ecosystems
dc.type Conference Paper
dc.type.dcmi Text
dcterms.abstract Platforms have become pivotal business models and involve a different logic than traditional pipeline business models. Important factors for understanding their emergence and growth are externalities such as network effects and complementarities. At present, these concepts are focused on the effects on a single platform, but with the diffusion of platforms and their maturity, platforms are increasingly linked to each other. This interconnection of multiple platforms towards multi-platform ecosystems poses two key challenges. First, their networked structure exceeds traditional analytical approaches that are based on dyadic relationships. Second, individual choices drive externalities in these ecosystems, giving rise to emergent structures. To address these issues, the present research proposes a network science-based methodology that augments existing approaches to understand and visualize ecosystems (“ecosystem intelligence”). It presents a network conceptualization that captures the structure of multi-platform ecosystems and proposes a method for data collection and detailed network modeling. Among the main findings are three new types of externalities referred to as higher-order externalities. These include remote externalities that indicate value creation across platforms, transitive externalities representing chains between platforms, and polyadic externalities capturing value creation in n-ary relationships. They contribute to the understanding and management of the intricacies of multi-platform ecosystems, which can open new avenues in ecosystem intelligence.
dcterms.extent 10 pages
prism.startingpage 3990
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