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Attracting solutions in crowdsourcing contests: The role of knowledge distance, identity disclosure, and seeker status

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  • Pollok, Patrick
  • Lüttgens, Dirk
  • Piller, Frank T.

Abstract

We investigate in the context of crowdsourcing how seekers can increase open innovation performance, measured as received solver attention, by making two strategic decisions: selecting innovation tasks that are well suited for crowdsourcing and choosing between the potentials of status signalling through identity disclosure versus enjoying the benefits of anonymity. Drawing on uncertainty reduction theory, we suggest that a well-articulated problem statement reduces uncertainties of potential solvers and increases their willingness to participate. We argue that the ability of seekers to draft high-quality problem statements depends on the distance between the problem domain and their current knowledge stock. An analysis of 637 crowdsourcing projects finds that problem-seeker knowledge distance and received solver attention are curvilinear related such that moderate levels of knowledge distance maximize solver participation. However, high-status seekers who engage in identity-based status signalling are able to benefit from crowdsourcing across all levels of problem-seeker knowledge distance.

Suggested Citation

  • Pollok, Patrick & Lüttgens, Dirk & Piller, Frank T., 2019. "Attracting solutions in crowdsourcing contests: The role of knowledge distance, identity disclosure, and seeker status," Research Policy, Elsevier, vol. 48(1), pages 98-114.
    • Handle: RePEc:eee:respol:v:48:y:2019:i:1:p:98-114
    DOI: 10.1016/j.respol.2018.07.022
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    13. Feng, Yuanyue & Yi, Zihui & Yang, Congcong & Chen, Ruoyi & Feng, Ye, 2022. "How do gamification mechanics drive solvers’ Knowledge contribution? A study of collaborative knowledge crowdsourcing," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
    14. Francesco Cappa & Federica Rosso & Darren Hayes, 2019. "Monetary and Social Rewards for Crowdsourcing," Sustainability, MDPI, vol. 11(10), pages 1-14, May.
    15. Jesse Bockstedt & Cheryl Druehl & Anant Mishra, 2022. "Incentives and Stars: Competition in Innovation Contests with Participant and Submission Visibility," Production and Operations Management, Production and Operations Management Society, vol. 31(3), pages 1372-1393, March.
    16. Kathleen Diener & Dirk Luettgens & Frank Thomas Piller, 2019. "Intermediation For Open Innovation: Comparing Direct Versus Delegated Search Strategies Of Innovation Intermediaries," International Journal of Innovation Management (ijim), World Scientific Publishing Co. Pte. Ltd., vol. 24(04), pages 1-20, June.
    17. Franzò, Simone & Doppio, Nicola & Natalicchio, Angelo & Frattini, Federico & Mion, Luca, 2023. "Designing innovation contests to support external knowledge search in small and medium-sized enterprises," Technovation, Elsevier, vol. 121(C).
    18. Shi, Xiaoxiao & Evans, Richard & Shan, Wei, 2022. "Solver engagement in online crowdsourcing communities: The roles of perceived interactivity, relationship quality and psychological ownership," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    19. Hamed Tajedin & Hamed Tajedin & Mohammad Keyhani, 2019. "A Theory of Digital Firm-Designed Markets: Defying Knowledge Constraints with Crowds and Marketplaces," Strategy Science, INFORMS, vol. 4(4), pages 323-342, December.
    20. Tat Koon Koh & Muller Y. M. Cheung, 2022. "Seeker Exemplars and Quantitative Ideation Outcomes in Crowdsourcing Contests," Information Systems Research, INFORMS, vol. 33(1), pages 265-284, March.
    21. Liao, Junyun & Chen, Jiawen & Mou, Jian, 2021. "Examining the antecedents of idea contribution in online innovation communities: A perspective of creative self-efficacy," Technology in Society, Elsevier, vol. 66(C).
    22. Patel, Chirag & Ahmad Husairi, Mariyani & Haon, Christophe & Oberoi, Poonam, 2023. "Monetary rewards and self-selection in design crowdsourcing contests: Managing participation, contribution appropriateness, and winning trade-offs," Technological Forecasting and Social Change, Elsevier, vol. 191(C).

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    More about this item

    Keywords

    Open innovation; Crowdsourcing; Innovation intermediaries; Status; Identity revealing; Uncertainty reduction theory;
    All these keywords.

    JEL classification:

    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • L20 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - General
    • L60 - Industrial Organization - - Industry Studies: Manufacturing - - - General
    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design

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