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Exploring the effects of reward and competition intensity on participation in crowdsourcing contests

Author

Listed:
  • Dan Li

    (Harbin Institute of Technology)

  • Longying Hu

    (Harbin Institute of Technology)

Abstract

With the growth of web 2.0 technology, crowdsourcing contests now offer task-seekers an opportunity to access expertise and resources at a lower cost. Many studies have investigated the effects of influencing factors on solvers’ participation behaviors. However, there has been scant research on the effect of competition intensity and on the relationships between competition and reward. We collect data from the Taskcn website in China to build a two-equation model based on expectation-value theory to explore the effects of task reward and competition intensity on solvers’ registration and submission behaviors. The research results verify that task reward is positively associated with the number of registrations and submissions, and competition intensity is negatively associated with solvers’ submissions. In addition, the empirical results show that competition intensity moderates the relationship between task reward and submissions. These findings provide valuable contributions to the literature on crowdsourcing contests.

Suggested Citation

  • Dan Li & Longying Hu, 2017. "Exploring the effects of reward and competition intensity on participation in crowdsourcing contests," Electronic Markets, Springer;IIM University of St. Gallen, vol. 27(3), pages 199-210, August.
    • Handle: RePEc:spr:elmark:v:27:y:2017:i:3:d:10.1007_s12525-017-0252-7
    DOI: 10.1007/s12525-017-0252-7
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    References listed on IDEAS

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    1. Tracy Xiao Liu & Jiang Yang & Lada A. Adamic & Yan Chen, 2014. "Crowdsourcing with All-Pay Auctions: A Field Experiment on Taskcn," Management Science, INFORMS, vol. 60(8), pages 2020-2037, August.
    2. Christian Terwiesch & Yi Xu, 2008. "Innovation Contests, Open Innovation, and Multiagent Problem Solving," Management Science, INFORMS, vol. 54(9), pages 1529-1543, September.
    3. Yuxiang Zhao & Qinghua Zhu, 2014. "Evaluation on crowdsourcing research: Current status and future direction," Information Systems Frontiers, Springer, vol. 16(3), pages 417-434, July.
    4. Yan Chen & Teck‐Hua Ho & Yong‐Mi Kim, 2010. "Knowledge Market Design: A Field Experiment at Google Answers," Journal of Public Economic Theory, Association for Public Economic Theory, vol. 12(4), pages 641-664, August.
    5. Kevin J. Boudreau & Nicola Lacetera & Karim R. Lakhani, 2011. "Incentives and Problem Uncertainty in Innovation Contests: An Empirical Analysis," Management Science, INFORMS, vol. 57(5), pages 843-863, May.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Keng Yang, 2019. "Research on Factors Affecting Solvers’ Participation Time in Online Crowdsourcing Contests," Future Internet, MDPI, vol. 11(8), pages 1-13, August.
    2. 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.
    3. Moghaddam, Ehsan Noorzad & Aliahmadi, Alireza & Bagherzadeh, Mehdi & Markovic, Stefan & Micevski, Milena & Saghafi, Fatemeh, 2023. "Let me choose what I want: The influence of incentive choice flexibility on the quality of crowdsourcing solutions to innovation problems," Technovation, Elsevier, vol. 120(C).
    4. Christofer F. Daiberl & Sascha Julian Oks & Angela Roth & Kathrin M. Möslein & Steven Alter, 2019. "Design principles for establishing a multi-sided open innovation platform: lessons learned from an action research study in the medical technology industry," Electronic Markets, Springer;IIM University of St. Gallen, vol. 29(4), pages 711-728, December.
    5. Meng-Meng Wang, 2022. "Encouraging solvers to sustain participation intention on crowdsourcing platforms: an investigation of social beliefs," Information Technology and Management, Springer, vol. 23(1), pages 39-50, March.
    6. Zhou, Fei & Lin, Youhai & Mou, Jian & Cohen, Jason & Chen, Sihua, 2023. "Understanding the dark side of gamified interactions on short-form video platforms: Through a lens of expectations violations theory," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).
    7. Dargahi, Rambod & Namin, Aidin & Ketron, Seth C. & Saint Clair, Julian K., 2021. "Is self-knowledge the ultimate prize? A quantitative analysis of participation choice in online ideation crowdsourcing contests," Journal of Retailing and Consumer Services, Elsevier, vol. 62(C).
    8. Hyeon Jo & Youngsok Bang, 2023. "Factors influencing continuance intention of participants in crowdsourcing," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-13, December.
    9. 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

    Crowdsourcing contest; Task reward; Competition intensity; Registration; Submission; Two-equation model;
    All these keywords.

    JEL classification:

    • M - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics

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