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Incentive Structures for the Adoption of Crowdsourcing in Public Policy: A Bureaucratic Politics Model

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  • Luciana Cingolani

    (Centre for Digital Governance, Hertie School, 10117 Berlin, Germany)

  • Tim Hildebrandt

    (Anstalt für Kommunale Datenverarbeitung in Bayern, 80686 Munich, Germany)

Abstract

Collective intelligence paradigms have been increasingly embraced by the public sector as a way to address complex policy challenges. The field is limited, however, by our little, comparative understanding of the political economy factors that create incentives around the adoption of certain types of collective intelligence over others in different organizational settings. This article uses a typology of citizen-sourced open innovation models based on the work of Daren Brabham as well as insights from Elinor Ostrom and Archon Fung, in order to produce a theoretical model of the determinants of crowdsourcing adoption decisions in the public sector. The model derives a political economy analysis that matches various administrative scenarios to different collective intelligence modalities and developments. The insights are illustrated through a number of crowdsourcing initiatives and provide important lessons to practitioners designing such collective challenges.

Suggested Citation

  • Luciana Cingolani & Tim Hildebrandt, 2022. "Incentive Structures for the Adoption of Crowdsourcing in Public Policy: A Bureaucratic Politics Model," Sustainability, MDPI, vol. 14(20), pages 1-16, October.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:20:p:12982-:d:938869
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    References listed on IDEAS

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    Cited by:

    1. Junjie Li & Guohui Zhan & Xin Dai & Meng Qi & Bangfan Liu, 2022. "Innovation and Optimization Logic of Grassroots Digital Governance in China under Digital Empowerment and Digital Sustainability," Sustainability, MDPI, vol. 14(24), pages 1-28, December.

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