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Can Experience be Trusted? Investigating the Effect of Experience on Decision Biases in Crowdworking Platforms

Author

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  • Thomas Görzen

    (University of Paderborn)

Abstract

Companies increasingly involve the crowd for collective decision making and, to aggregate the decisions, they commonly average the scores. By ignoring crowdworkers’ different levels of experience and decision biases, this method may not favor the best outcome. Alternatively, decisions can be weighted in favor of the more experienced judges in the crowd. However, previous research is inconclusive as to whether more experienced individuals are any better at avoiding decision biases. To answer this question, we conduct online crowd-based experiments with a range of treatments, comparing the anchoring effect of individuals with different levels of experience. Results indicate that not only does greater experience not protect crowdworkers from the anchoring effect but it increases their confidence in their decision, compared to less experienced individuals, even if they are wrong. Our findings provide valuable insights for both researchers and practitioners interested in improving the effectiveness of crowdworking decision-making.\\

Suggested Citation

  • Thomas Görzen, 2019. "Can Experience be Trusted? Investigating the Effect of Experience on Decision Biases in Crowdworking Platforms," Working Papers Dissertations 55, Paderborn University, Faculty of Business Administration and Economics.
    • Handle: RePEc:pdn:dispap:55
    as

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    File URL: http://groups.uni-paderborn.de/wp-wiwi/RePEc/pdf/dispap/DP55.pdf
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    References listed on IDEAS

    as
    1. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588.
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    4. Barry L. Bayus, 2013. "Crowdsourcing New Product Ideas over Time: An Analysis of the Dell IdeaStorm Community," Management Science, INFORMS, vol. 59(1), pages 226-244, June.
    5. Mingfeng Lin & Henry C. Lucas & Galit Shmueli, 2013. "Research Commentary ---Too Big to Fail: Large Samples and the p -Value Problem," Information Systems Research, INFORMS, vol. 24(4), pages 906-917, December.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    behavioral economics; digital nudging; decision bias; anchoring; crowdsourcing;
    All these keywords.

    JEL classification:

    • L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • M55 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Personnel Economics - - - Labor Contracting Devices
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D

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