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Platform Ecosystems as Complex Adaptive Systems: Exploring Emergence in India’s Aadhaar Project

Addo, Atta and Masiero, Silvia (2019) Platform Ecosystems as Complex Adaptive Systems: Exploring Emergence in India’s Aadhaar Project Academy of Management Proceedings, 2019 (1).

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We advance a view of digital platforms as generative of innovation phenomena that emerge from causal mechanisms and complex reinforcing interactions that are adaptive. Drawing from literature on complex adaptive systems (CAS), we develop our arguments with an exploratory case study of the Aadhaar platform ecosystem – the world’s largest biometric identification system that has emerged in less than a decade to transform service provision to over 1.2 billion Indian residents. We trace mechanisms endogenous to the platform artefacts – a control mechanism, a generativity mechanism, and an innovation mechanism – that interact with each other and with political and economic forces to underpin a range of emergent phenomena associated with the platform such as its scale, scope and hard-to-imitate integration into the Indian context. We show that platform ecosystems are complex adaptive systems whose emergent outcomes are better explained by overall dynamics across the platform and its embedded context rather than by standalone mechanisms. We contribute to the digital innovation literature by illuminating emergence across socially embedded digital ecosystems.

Item Type: Article
Divisions : Faculty of Arts and Social Sciences > Surrey Business School
Authors :
Masiero, Silvia
Date : 1 August 2019
DOI : 10.5465/AMBPP.2019.12049abstract
Copyright Disclaimer : Copyright of Academy of Management Journal is the property of Academy of Management and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder’s express written permission. However, users may print, download, or email articles for individual use.
Depositing User : Diane Maxfield
Date Deposited : 09 Jan 2020 17:37
Last Modified : 09 Jan 2020 17:37

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