Porting learning from interdependencies back home: Performance implications of multihoming for complementors in platform ecosystems

Abstract

Abstract Research Summary Recognizing the role of complementors in creating value in interdependent platform ecosystems, strategy research has recently started to examine performance heterogeneity across complementors. However, research has thus far focused on the performance implications of dynamics unfolding within a particular ecosystem. We take a step toward exploring influences that arise beyond the focal ecosystem by focusing conceptually on multihoming. We argue that multihoming to another platform produces learning benefits that enhance a complementor's performance on the home platform, especially when dealing with a high level of interdependencies and having greater similarity to other complements. We find supportive evidence in our analysis of open‐source software platforms between 2012 and 2018 and discuss implications for research on platform ecosystems, multihoming, and open‐source software. Managerial Summary Prior studies viewed multihoming as an important strategy for complementors in platform ecosystems. However, little is known about the extent to which such expansion affects the performance of complementors on their home platforms. This study investigates this issue using data on software package complementors in a variety of platforms housed in GitHub, the world's largest repository of open‐source software. The findings show that following multihoming, a complementor experiences a performance improvement in the home platform even when compared to the performance change observed during the same period for another complementor with similar attributes but that remains in single‐homing. These findings underscore the strategic implications of multihoming as a significant driver of performance heterogeneity across complementors in platform ecosystems.

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