An analysis of reward mechanism and knowledge sharing for crowdsourcing-based open innovation contest

Jhih Hua Jhang-Li, I. Robert Chiang

Research output: Contribution to journalArticlepeer-review


Purpose: The purpose of this paper is to investigate both the impact of different reward types and the adoption of knowledge-sharing practice on a crowdsourcing-based open innovation contest. Despite the benefit of knowledge sharing, contestants could struggle to find a balance between knowledge sharing and knowledge protection in open innovation. Design/methodology/approach: The authors' approach follows a stylised contest model in a game-theoretical setting in which contestants first decide on their efforts and then the contest sponsor chooses the winner. Moreover, the outcome of an open innovation contest is delineated as either intermediate goods that require further refinement and risk-taking versus a market-ready end product for the contest sponsor. The authors also investigate how knowledge sharing among contestants would be influenced by reward types such as fixed-monetary prizes vs performance-contingent awards. Findings: The contest sponsor will lower the prize level after adopting knowledge sharing. Therefore, the total effort will decline regardless of the reward type. Moreover, the choice of reward types depends on the contest sponsor's characteristics because the performance-contingent award is suitable for a large market size but the fixed-monetary prize can more efficiently raise the quantity of contestant inputs. Originality/value: Prior studies have tested the connection between contest performance and knowledge sharing in crowdsourcing-based contests; however, there is not an integrated framework to best design the operation of a contest when considering different reward types and knowledge-sharing practices.

Original languageEnglish
StateAccepted/In press - 2023


  • Decision making
  • Game theory
  • Information systems
  • Knowledge management
  • Mathematical modelling


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