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A typology of principal investigators based on their human capital: an exploratory analysis

Author

Listed:
  • María José Foncubierta-Rodríguez

    (University of Cádiz)

  • Fernando Martín-Alcázar

    (University of Cádiz)

  • José Luis Perea-Vicente

    (University of Cádiz)

Abstract

There is burgeoning literature on principal investigators (PIs) and their influential role in science, technology transfer and research commercialisation. However, there is yet no analysis of this actor from the perspective of their human capital (HC), i.e., the combination of knowledge, abilities and skills that they possess. Consequently, the purpose of this paper is to fill this gap by identifying whether a range of different PI profiles exists, based on their different HC. A cluster analysis was developed using a database comprised of 224 PIs of research teams, from a wide range of scientific fields. Three different PI profiles were identified, research-oriented PIs, accomplished PIs, management-focused PIs. The relationship between each of these profiles and their performance was analysed at both individual and research team level, and our findings reinforce the idea that there is not a size that fits all. Indeed, contrary to the ‘more is better’ statement, higher levels of HC are not necessarily connected to better results, our findings suggested an adequate combination of HC as the best option for PIs. Results of the relationship between PI gender, performance and the three different PI profiles have been examined, as well.

Suggested Citation

  • María José Foncubierta-Rodríguez & Fernando Martín-Alcázar & José Luis Perea-Vicente, 2023. "A typology of principal investigators based on their human capital: an exploratory analysis," The Journal of Technology Transfer, Springer, vol. 48(3), pages 932-954, June.
    • Handle: RePEc:kap:jtecht:v:48:y:2023:i:3:d:10.1007_s10961-022-09935-z
    DOI: 10.1007/s10961-022-09935-z
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    More about this item

    Keywords

    Principal investigator; Profiles; Human capital; Cluster analysis; Research team; Gender; Performance; Academic entrepreneurship;
    All these keywords.

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • J16 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Gender; Non-labor Discrimination
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • L9 - Industrial Organization - - Industry Studies: Transportation and Utilities
    • M1 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration
    • O30 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - General
    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives
    • 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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