Volltext-Downloads (blau) und Frontdoor-Views (grau)

Measuring Ecosystem Complexity - Decision-Making Based on Complementarity Graphs

  • Platforms feature increasingly complex architectures with regard to interconnecting with other digital platforms as well as with a variety of devices and services. This development also impacts the structure of digital platform ecosystems and forces providers of these services, devices, and services to incorporate this complexity in their decision-making. To contribute to the existing body of knowledge on measuring ecosystem complexity, the present research proposes two key artefacts based on ecosystem intelligence: On the one hand, complementarity graphs represent ecosystems with an ecosystem's functional modules as vertices and complementarities as edges. The nodes carry information about the category membership of the module. On the other hand, a process is suggested that can collect important information for ecosystem intelligence using proxies and web scraping. Our approach allows replacing data, which today is largely unavailable due to competitive reasons. We demonstrated the use of the artefacts in category-oriented complementarity maps that aggregate the information from complementarity graphs and support decision-making. They show which combination of module categories creates strong and weak complementarities. The paper evaluates complementarity maps and the data collection process by creating category-oriented complementarity graphs on the Alexa skill ecosystem and concludes with a call to pursue more research based on functional ecosystem intelligence.

Download full text files

Export metadata

Additional Services

Search Google Scholar


Erschienen in:Proceedings of the 56th Hawaii International Conference on System Sciences 2023
Publisher:University of Hawaii at Manoa
Place of publication:Honolulu
Document Type:Conference proceeding
Publication year:2023
Page Number:10
First Page:3464
Last Page:3473
DDC classes:004 Informatik
Open access?:Ja
Licence (German):License Logo  Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International