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A Bayesian Measure of Research Productivity

In: From Agriscience to Agribusiness

Author

Listed:
  • Lin Qin

    (comScore, Inc.)

  • Steven T. Buccola

    (Oregon State University)

Abstract

We use Bayesian probability theory to develop a new way of measuring research productivity. The metric accommodates a wide variety of project types and productivity sources and accounts for the contributions of “failed” as well as “successful” investigations. Employing a mean-absolute-deviation loss functional form with this new metric allows decomposition of knowledge gain into an outcome probability shift (mean surprise) and outcome variance reduction (statistical precision), a useful distinction, because projects scoring well on one often score poorly on the other. In an international aquacultural research program, we find laboratory size to moderately boost mean surprise but have no effect on precision, while scientist education improves precision but has no effect on mean surprise. Returns to research scale are decreasing in the size dimension but increasing when size and education are taken together, suggesting the importance of measuring human capital at both the quantitative and qualitative margin.

Suggested Citation

  • Lin Qin & Steven T. Buccola, 2018. "A Bayesian Measure of Research Productivity," Innovation, Technology, and Knowledge Management, in: Nicholas Kalaitzandonakes & Elias G. Carayannis & Evangelos Grigoroudis & Stelios Rozakis (ed.), From Agriscience to Agribusiness, pages 465-481, Springer.
    • Handle: RePEc:spr:innchp:978-3-319-67958-7_23
    DOI: 10.1007/978-3-319-67958-7_23
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