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What Matters in Funding: The Value of Research Coherence and Alignment in Evaluators’ Decisions

Author

Listed:
  • Charles Ayoubi
  • Sandra Barbosu
  • Michele Pezzoni

    (GREDEG - Groupe de Recherche en Droit, Economie et Gestion - UNS - Université Nice Sophia Antipolis (1965 - 2019) - CNRS - Centre National de la Recherche Scientifique - UniCA - Université Côte d'Azur)

  • Fabiana Visentin

Abstract

Entrepreneurs, managers, and scientists participate in competitive selection processes to obtain resources. The project they propose is a crucial aspect of their success. In this paper, we focus on the selection of scientists applying for academic funding by submitting a research proposal. We argue that two core dimensions of the research proposal affect the probability of funding success: its coherence with the applicant's previous work, and its alignment with subjects of general interest for the scientific community. Employing a neural network algorithm, we analyse the text of 2,494 research proposals for a prestigious fellowship awarded to promising early-stage North American researchers. We find field-specific heterogeneity in the committees' evaluations. In life sciences and chemistry, evaluators value the research proposal's coherence positively with the scientist's recent work and the proposals' alignment with the current subject of general interest for the scientific community. Conversely, in physics, evaluators give more weight to bibliometric indicators and less to the proposal coherence and alignment. Our results can be extended beyond the academic context to managerial implications in cases such as entrepreneurs and managers submitting project proposals to investors
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Charles Ayoubi & Sandra Barbosu & Michele Pezzoni & Fabiana Visentin, 2021. "What Matters in Funding: The Value of Research Coherence and Alignment in Evaluators’ Decisions," Post-Print halshs-03566468, HAL.
    • Handle: RePEc:hal:journl:halshs-03566468
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    Cited by:

    1. Ballester, Omar & Penner, Orion, 2022. "Robustness, replicability and scalability in topic modelling," Journal of Informetrics, Elsevier, vol. 16(1).

    More about this item

    JEL classification:

    • I23 - Health, Education, and Welfare - - Education - - - Higher Education; Research Institutions
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D
    • O38 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Government Policy

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