Crowdsourcing for Open Innovation

Crowdsourcing for open innovation harnesses distributed knowledge and creative potential by inviting external participants to contribute ideas, solutions and feedback across organisational boundaries. This model draws on digital platforms to pose challenges or contests to a heterogeneous crowd, enabling firms and institutions to tap into novel perspectives and specialised expertise. Key drivers include the ability to access a broader pool of problem solvers, accelerate ideation cycles, and reduce development costs. At the same time, effective crowdsourcing requires careful design of task structures, incentive mechanisms, and evaluation processes to ensure idea quality and sustained engagement. Interdisciplinary advances have explored how competition intensity, task framing and solver characteristics shape participation and success. Across sectors—from product development and logistics to cultural heritage and public administration—crowdsourcing has demonstrated global significance by fostering collaborative innovation, democratising research and driving social as well as economic impact.

Research from Nature Portfolio

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Crowdsourcing for Open Innovation publication trend

The graph below shows the total number of articles in crowdsourcing for open innovation across all publications each year (not limited to Nature Index journals).

Technical terms

Crowdsourcing: A method of sourcing ideas, solutions or labour by soliciting contributions from a large, undefined group of individuals via digital platforms.

Open innovation: A paradigm in which organisations use external as well as internal ideas and pathways to advance technology, products or services.

Tournament theory: An economic framework that models competition among participants vying for rewards, used to analyse incentive structures in contests.

Knowledge recombination: The process by which novel ideas emerge from combining distinct pieces of prior knowledge or expertise.

Knowledge distance: The cognitive or domain gap between a problem’s context and an individual’s existing knowledge base, affecting participation and performance.

Social capital: The resources and advantages individuals gain through their social networks, influencing idea quality and engagement in collaborative platforms.

References

1. Crowdsourcing for innovation: Effects of idea content and competition intensity on idea success. The Journal of Strategic Information Systems (2024).
2. Attracting solutions in crowdsourcing contests: The role of knowledge distance, identity disclosure, and seeker status. Research Policy (2019).
3. Crowdsourcing and open innovation: a systematic literature review, an integrated framework and a research agenda. Review of Managerial Science (2021).
4. Understanding crowdsourcing projects: A review on the key design elements of a crowdsourcing initiative. Creativity and Innovation Management (2021).
5. Crowdsourcing Logistics Pricing Optimization Model Based on DBSCAN Clustering Algorithm. IEEE Access (2020).
6. Visitor-Sensing: Involving the Crowd in Cultural Heritage Organizations. Sustainability (2020).
7. Citizen participation in public administration: investigating open government for social innovation. R and D Management (2019).