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Good to Go First? Position Effects in Expert Evaluation of Early-Stage Ventures

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  • Jiang Bian

    (University of Hong Kong, Pokfulam, Hong Kong)

  • Jason Greenberg

    (University of Pennsylvania, Philadelphia, Pennsylvania 19104)

  • Jizhen Li

    (Tsinghua University, Beijing 100084, China)

  • Yanbo Wang

    (University of Hong Kong, Pokfulam, Hong Kong)

Abstract

There is often considerable anxiety and conflicting advice concerning the benefits of presenting/being evaluated first. We thus investigate how expert evaluators vary in their evaluations of entrepreneurial proposals based upon the order in which they are evaluated. Our research setting is a premiere innovation fund competition in Beijing, China, where the prize money at stake is economically meaningful, and evaluators are quasi-randomly assigned to evaluate written grant proposals without the possibility of peer influence. This enables us to credibly recover a causal position effect. We also theorize and test how heterogeneity in evaluators’ prior (context-specific) judging experience moderates position effects. Overall, we find that a proposal evaluated first requires total assets in the top 10th percentile to merely equal the evaluation of a proposal in the bottom 10th percentile that is not evaluated first. Firm and evaluator fixed-effects models yield consistent findings. We consider evaluation design elements that may mollify these position effects in the discussion section.

Suggested Citation

  • Jiang Bian & Jason Greenberg & Jizhen Li & Yanbo Wang, 2022. "Good to Go First? Position Effects in Expert Evaluation of Early-Stage Ventures," Management Science, INFORMS, vol. 68(1), pages 300-315, January.
    • Handle: RePEc:inm:ormnsc:v:68:y:2022:i:1:p:300-315
    DOI: 10.1287/mnsc.2021.4132
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    References listed on IDEAS

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    Cited by:

    1. Hunt, Richard A. & Townsend, David M. & Manocha, Parul & Simpson, Joseph J., 2023. "Knowledge problem diagnosis and the fate of corporate entrepreneurship initiatives," Journal of Business Venturing Insights, Elsevier, vol. 19(C).

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