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How crowdsourcing improves prediction of market-oriented outcomes

Author

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  • Lang, Mark
  • Bharadwaj, Neeraj
  • Di Benedetto, C. Anthony

Abstract

Firms often struggle to be proficient in predicting uncertain market conditions and forecasting the outcomes of their business initiatives. This research introduces crowdsourcing as an innovative tool that can enhance market information processing, and in turn, improve prediction of market-oriented outcomes (e.g., sales). We field test a forecasting tournament with employees at a Fortune 100 consumer packaged goods firm, and examine the extent to which predictions based on the “wisdom of the crowd” outperform those generated by traditional forecasting approaches. We find that crowdsourcing produces results superior to the firm's incumbent approaches almost three-fourths of the time across a broad range of business decisions. Additionally, we conduct a survey with participants to open up the “black box” of crowdsourcing. We find that differences in information acquisition and interpretation are the underlying mechanisms that can explain the improved prediction accuracy found through crowdsourcing.

Suggested Citation

  • Lang, Mark & Bharadwaj, Neeraj & Di Benedetto, C. Anthony, 2016. "How crowdsourcing improves prediction of market-oriented outcomes," Journal of Business Research, Elsevier, vol. 69(10), pages 4168-4176.
    • Handle: RePEc:eee:jbrese:v:69:y:2016:i:10:p:4168-4176
    DOI: 10.1016/j.jbusres.2016.03.020
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    Cited by:

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    2. Carlos Devece & Daniel Palacios-Marqués & D. Enrique Ribeiro-Soriano, 2021. "IT-based strategy, capabilities, and practices: crowdsourcing implementation in market-oriented firms," Review of Managerial Science, Springer, vol. 15(1), pages 15-32, January.
    3. Xu, Hui & Wu, Yang & Hamari, Juho, 2022. "What determines the successfulness of a crowdsourcing campaign: A study on the relationships between indicators of trustworthiness, popularity, and success," Journal of Business Research, Elsevier, vol. 139(C), pages 484-495.
    4. Sommarberg, Matti & Mäkinen, Saku J., 2019. "A method for anticipating the disruptive nature of digitalization in the machine-building industry," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 808-819.
    5. Steils, Nadia & Hanine, Salwa, 2019. "Recruiting valuable participants in online IDEA generation: The role of brief instructions," Journal of Business Research, Elsevier, vol. 96(C), pages 14-25.
    6. Hongzhou Chen & Xiaolin Duan & Abdulmotaleb El Saddik & Wei Cai, 2024. "Political Leanings in Web3 Betting: Decoding the Interplay of Political and Profitable Motives," Papers 2407.14844, arXiv.org.

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