Divergent Innovation: Directing the Wisdom of Crowd to Tackle Societal Challenges

Chuhan Cao, Jiantao Zhu, Bingqing Xiong, Eric Tze Kuan Lim, Hefu Liu, Zhao Cai, Chee Wee Tan

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review


Crowdsourcing is acknowledged as a promising avenue for addressing societal challenges by drawing on the wisdom of the crowd to offer diverse solutions to complex problems. Advancing a new conceptual framework of ‘divergent innovation’ which delineates between topic and quality divergence as focal metrics of performance when crowdsourcing for solutions to societal challenges, this study investigates the impacts of four ideation stimuli on divergent innovation. These four stimuli include task description concreteness, resource richness, topic entropy, and judging criteria comprehensiveness. Empirical analysis based on data sourced from an online crowd-ideation platform reveals that task description concreteness negatively affects topic divergence but positively influences quality divergence, whereas resource richness positively affects topic divergence but negatively influences quality divergence. Additionally, the relationship between topic entropy and topic divergence is U-shaped, with no significant impact on quality divergence. These findings contribute to extant literature on crowdsourcing and offer invaluable insights for practitioners.
Original languageEnglish
Title of host publicationProceedings of the 44th International Conference on Information Systems (ICIS 2023)
Place of PublicationHyderabad, India
Publication statusPublished - Dec 2023
Externally publishedYes


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