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A Prediction Market-Based Gamified Approach to Enhance Knowledge Sharing in Organizations

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  • Hajime Mizuyama
  • Seiyu Yamaguchi
  • Mizuho Sato

Abstract

Background . Knowledge sharing among the members of an organization is crucial for enhancing the organization’s performance. However, knowing how to motivate and direct members to effectively and efficiently share their relevant private knowledge concerning the organization’s activities is not entirely a straightforward matter. Aim . This study aims to propose a gamified approach not only for motivating truthful sharing and collective evaluation of knowledge among the members of an organization but also for properly directing those actions so as to maximize the usefulness of the shared knowledge. A case study is also conducted to understand how the proposed approach works in a live business scenario. Method . A prediction market game on a binary event on whether the specified activity will be completed successfully is devised. The game utilizes an original comment aggregation and evaluation system through which relevant knowledge can be shared verbally and evaluated collectively by the players themselves. Players’ behavior is driven toward a desirable direction with the associated incentive framework realized by three game scores. Results . The proposed gamified approach was implemented as a web application and verified with a laboratory experiment. The game was also played by four participants who deliberated on an actual sales proposal in a real company. It was observed that the various valuable knowledge elements that were successfully collected from the participants could be utilized for refining the sales proposal. Conclusions . The game induced motivation through gamification, and some of the designed game scores worked in directing the players’ behavior as desired. The players learned from others’ comments, which brought about a snowball effect and enriched collective knowledge. Future research directions include how to transform this knowledge into an easy-to-comprehend representation.

Suggested Citation

  • Hajime Mizuyama & Seiyu Yamaguchi & Mizuho Sato, 2019. "A Prediction Market-Based Gamified Approach to Enhance Knowledge Sharing in Organizations," Simulation & Gaming, , vol. 50(5), pages 572-597, October.
  • Handle: RePEc:sae:simgam:v:50:y:2019:i:5:p:572-597
    DOI: 10.1177/1046878119867382
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    References listed on IDEAS

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    1. Justin Wolfers & Eric Zitzewitz, 2004. "Prediction Markets," Journal of Economic Perspectives, American Economic Association, vol. 18(2), pages 107-126, Spring.
    2. Renzl, Birgit, 2008. "Trust in management and knowledge sharing: The mediating effects of fear and knowledge documentation," Omega, Elsevier, vol. 36(2), pages 206-220, April.
    3. Li, Yung-Ming & Jhang-Li, Jhih-Hua, 2010. "Knowledge sharing in communities of practice: A game theoretic analysis," European Journal of Operational Research, Elsevier, vol. 207(2), pages 1052-1064, December.
    4. Robin Hanson, 2003. "Combinatorial Information Market Design," Information Systems Frontiers, Springer, vol. 5(1), pages 107-119, January.
    5. Robin Hanson, 2007. "Logarithmic Market Scoring Rules for Modular Combinatorial Information Aggregation," Journal of Prediction Markets, University of Buckingham Press, vol. 1(1), pages 3-15, February.
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    Cited by:

    1. Toshiko Kikkawa & Susumu Ohnuma, 2019. "From Then to Now: Transformation in Simulation and Gaming in Japan," Simulation & Gaming, , vol. 50(5), pages 491-493, October.
    2. Lima Nasrin Eni, 2022. "Mediating Role of Knowledge Sharing in the Nexus among Human Capital, IT Capability, Transactional Leadership and Innovation Performance: Empirical Evidence from Bangladeshi Telecommunication Sector," International Journal of Science and Business, IJSAB International, vol. 9(1), pages 1-17.

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