Sharing Economy, Platforms and Crowds
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Paper Number
2343
Paper Type
short
Description
Platform operators invite complementors for their participation in value creation (e.g., platform sides such as application developers). While platform operators impose control on who gets to participate on each side of the market and how, the pursuit of growth due to competitive pressures leads to open-access policies. A consequence of providing open-access to a platform is that rivals can also act as complementors. In this study, we examine how platform operators acting as complementors can engage in a form of learning not available to outsiders and use this learning to achieve performance gains over the rivals. We find that the positive effects of complementor learning on performance deteriorate as market dynamism increases, unless platform users face high switching costs in a market. Furthermore, time to start learning plays a critical role, where learning can start “too early” due to negative implications of network effects in a nascent market.
Recommended Citation
Ozer, Gorkem Turgut; Anderson, Edward; and Figge, Patrick, "Organizational learning in multisided digital platforms: A multi-method simulation study" (2021). ICIS 2021 Proceedings. 19.
https://aisel.aisnet.org/icis2021/sharing_econ/sharing_econ/19
Organizational learning in multisided digital platforms: A multi-method simulation study
Platform operators invite complementors for their participation in value creation (e.g., platform sides such as application developers). While platform operators impose control on who gets to participate on each side of the market and how, the pursuit of growth due to competitive pressures leads to open-access policies. A consequence of providing open-access to a platform is that rivals can also act as complementors. In this study, we examine how platform operators acting as complementors can engage in a form of learning not available to outsiders and use this learning to achieve performance gains over the rivals. We find that the positive effects of complementor learning on performance deteriorate as market dynamism increases, unless platform users face high switching costs in a market. Furthermore, time to start learning plays a critical role, where learning can start “too early” due to negative implications of network effects in a nascent market.
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09-Crowds