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From Agriscience to Agribusiness pp 465–481Cite as

A Bayesian Measure of Research Productivity

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Part of the book series: Innovation, Technology, and Knowledge Management ((ITKM))

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.

This chapter is an abbreviated and edited version of the article entitled “Knowledge Measurement and Productivity in a Research Program” published in the American Journal of Agricultural Economics 99 (4): 932–951.

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Notes

  1. 1.

    Basic research cannot as easily be divided into Fig. 1’s steps or into any regular steps at all. We note below the important differences between basic research and the applied research that motivates our present approach.

  2. 2.

    Basic research in contrast could be argued to lack any concrete outcomes or probabilities, being a matter more of discrete realizations than incremental steps. Probit models might in future be useful in representing that kind of discrete space.

  3. 3.

    Feed-the-Future Innovation Lab for Collaborative Research on Aquaculture and Fisheries, Oregon State University, sponsored by US Agency for International Development. The countries are Bangladesh, Cambodia, China, Ghana, Guyana, Indonesia, Kenya, Mexico, Nepal, Nicaragua, the Philippines, South Africa, Tanzania, Thailand, Uganda, and Vietnam.

  4. 4.

    If laboratory expansion did impair precision, we would be unlikely to observe any expansion unless the mean-surprise advantage more than compensated for the precision loss.

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Author information

Authors and Affiliations

  1. comScore, Inc., Reston, VA, USA

    Lin Qin

  2. Oregon State University, Corvallis, OR, USA

    Steven T. Buccola

Authors
  1. Lin Qin
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  2. Steven T. Buccola
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Correspondence to Steven T. Buccola .

Editor information

Editors and Affiliations

  1. Department of Agricultural and Applied Economics, University of Missouri, Columbia, Missouri, USA

    Nicholas Kalaitzandonakes

  2. Department of Information Systems and Technology Management, GWU School of Business, Washington, DC, USA

    Elias G. Carayannis

  3. School of Production Engineering & Management, Technical University of Crete, Chania, Crete, Greece

    Evangelos Grigoroudis

  4. Institute of Soil Science and Plant Cultivation, IUNG-PIB, Pulawy, Poland

    Stelios Rozakis

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Qin, L., Buccola, S.T. (2018). A Bayesian Measure of Research Productivity. In: Kalaitzandonakes, N., Carayannis, E., Grigoroudis, E., Rozakis, S. (eds) From Agriscience to Agribusiness. Innovation, Technology, and Knowledge Management. Springer, Cham. https://doi.org/10.1007/978-3-319-67958-7_23

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