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Recent Advances in R&D Benefit Measurement and Project Selection Methods


  • Norman Baker

    (Department of Quantitative Analysis, College of Business Administration, University of Cincinnati, Cincinnati, Ohio 45221)

  • James Freeland

    (Graduate School of Business, Stanford University, Stanford, California, 94305)


The purpose of this paper is to provide a current assessment of the literature addressed to quantitative models of the R and D project selection and resource allocation decision. In order to facilitate the critique and assessment the literature is somewhat arbitrarily divided into benefit measurement and resource allocation methods. The strengths and limitations of existing knowledge are identified with an emphasis on empirical investigations. Several research areas are identified and briefly described.

Suggested Citation

  • Norman Baker & James Freeland, 1975. "Recent Advances in R&D Benefit Measurement and Project Selection Methods," Management Science, INFORMS, vol. 21(10), pages 1164-1175, June.
  • Handle: RePEc:inm:ormnsc:v:21:y:1975:i:10:p:1164-1175

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    Cited by:

    1. Wang, Juite & Hwang, W.-L., 2007. "A fuzzy set approach for R&D portfolio selection using a real options valuation model," Omega, Elsevier, vol. 35(3), pages 247-257, June.
    2. Fernández Carazo, Ana & Gómez Núñez, Trinidad & Guerrero Casas, Flor M. & Caballero Fernández, Rafael, 2008. "Evaluación y clasificación de las técnicas utilizadas por las organizaciones, en las últimas décadas, para seleccionar proyectos = Evaluation and classification of the techniques used by organizations," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 5(1), pages 67-115, June.
    3. Chun, Dongphil & Hong, Sungjun & Chung, Yanghon & Woo, Chungwon & Seo, Hangyeol, 2016. "Influencing factors on hydrogen energy R&D projects: An ex-post performance evaluation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 1252-1258.
    4. Scobie, Grant M., 1984. "Investment in Agricultural Research: Some Economic Principles," Economics Working Papers 232447, CIMMYT: International Maize and Wheat Improvement Center.
    5. Eilat, Harel & Golany, Boaz & Shtub, Avraham, 2008. "R&D project evaluation: An integrated DEA and balanced scorecard approach," Omega, Elsevier, vol. 36(5), pages 895-912, October.
    6. Eilat, Harel & Golany, Boaz & Shtub, Avraham, 2006. "Constructing and evaluating balanced portfolios of R&D projects with interactions: A DEA based methodology," European Journal of Operational Research, Elsevier, vol. 172(3), pages 1018-1039, August.
    7. Scott A. Shane & Karl T. Ulrich, 2004. "50th Anniversary Article: Technological Innovation, Product Development, and Entrepreneurship in Management Science," Management Science, INFORMS, vol. 50(2), pages 133-144, February.
    8. Nicholas G. Hall & Daniel Zhuoyu Long & Jin Qi & Melvyn Sim, 2015. "Managing Underperformance Risk in Project Portfolio Selection," Operations Research, INFORMS, vol. 63(3), pages 660-675, June.
    9. Glickman, Theodore S., 2008. "Program portfolio selection for reducing prioritized security risks," European Journal of Operational Research, Elsevier, vol. 190(1), pages 268-276, October.
    10. repec:eee:appene:v:236:y:2019:i:c:p:444-464 is not listed on IDEAS
    11. Wang, Jue & Xu, Wei & Ma, Jian & Wang, Shouyang, 2013. "A vague set based decision support approach for evaluating research funding programs," European Journal of Operational Research, Elsevier, vol. 230(3), pages 656-665.
    12. Yu, Gun Jea & Hong, KiHoon, 2016. "Patents and R&D expenditure in explaining stock price movements," Finance Research Letters, Elsevier, vol. 19(C), pages 197-203.

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