JOURNAL ARTICLE
Architectural Learning in the Professional 3D Printing Ecosystem: Proxies and the Complementarity Space.
Published In: Organization Science (INFORMS), 2026, v. 37, n. 1. P. 157 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Lehmann, Julian; Berends, Hans; Berente, Nicholas; Tumbas, Sanja 3 of 3
Abstract
This article examines how organizations engage in architectural learning to realize complementary value in ecosystems when complementarity is emergent—that is, when potential external complements exist but are not yet fully interoperable or understood. Through a longitudinal case study of PrintCo, a 3D printer manufacturer that transitioned from serving the maker market to the professional desktop fused deposition modeling (FDM) 3D printing ecosystem, the study reveals how PrintCo used proxies (stand-in components) to explore interdependencies with third-party materials and software complements. This learning informed the design of boundary resources—such as application programming interfaces (APIs), software development kits (SDKs), and material profiles—and adaptations to internal components, collectively shaping a "complementarity space." This complementarity space functions as a problem space that guides complementors in integrating their offerings with PrintCo’s technical architecture, enabling independent complement integration despite limited initial interoperability. The findings contribute to understanding architectural learning by distinguishing proxies from prototypes and highlighting how organizations can strategically position themselves in evolving ecosystems by constructing and refining complementarity spaces to facilitate complement integration when direct coordination with complementors is limited.
Additional Information
- Source:Organization Science (INFORMS). 2026/01, Vol. 37, Issue 1, p157
- Document Type:Article
- Subject Area:Engineering
- Publication Date:2026
- ISSN:1047-7039
- DOI:10.1287/orsc.2022.16779
- Accession Number:191081060
- Copyright Statement:Copyright of Organization Science (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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