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Predicting Technology Success: Identifying Key Predictors and Assessing Expert Evaluation for Advanced Technologies

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  • Craig Galbraith
  • Sanford Ehrlich
  • Alex DeNoble

Abstract

This study investigates a set of precursor factors that appear related to future technology success, and whether or not expert evaluators can a priori provide useful information during the technology review process. Sixty-nine highly advanced post 9–11 technologies are tracked over time. Based upon the results of this study, we conclude that a reasonably good predictive model can be constructed from organizational and technology factors, such as firm size, stage of development, and strategic partnerships. The results also indicate that the incremental value of expert reviewers and technology evaluators to predict future technology success is relatively small. Reviewers that provided the greatest predicative power, however, had current scientific responsibilities. These results raise important issues regarding the capability of developing predictive models of technology success. Copyright Springer Science+Business Media, LLC 2006

Suggested Citation

  • Craig Galbraith & Sanford Ehrlich & Alex DeNoble, 2006. "Predicting Technology Success: Identifying Key Predictors and Assessing Expert Evaluation for Advanced Technologies," The Journal of Technology Transfer, Springer, vol. 31(6), pages 673-684, November.
    • Handle: RePEc:kap:jtecht:v:31:y:2006:i:6:p:673-684
    DOI: 10.1007/s10961-006-0022-8
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    References listed on IDEAS

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    1. Heslop, Louise A & McGregor, Eileen & Griffith, May, 2001. "Development of a Technology Readiness Assessment Measure: The Cloverleaf Model of Technology Transfer," The Journal of Technology Transfer, Springer, vol. 26(4), pages 369-384, October.
    2. Elton, Edwin J & Gruber, Martin J & Blake, Christopher R, 1996. "Survivorship Bias and Mutual Fund Performance," The Review of Financial Studies, Society for Financial Studies, vol. 9(4), pages 1097-1120.
    3. Davidsson, Per & Honig, Benson, 2003. "The role of social and human capital among nascent entrepreneurs," Journal of Business Venturing, Elsevier, vol. 18(3), pages 301-331, May.
    4. Zacharakis, Andrew L. & Meyer, G. Dale, 2000. "The potential of actuarial decision models: Can they improve the venture capital investment decision?," Journal of Business Venturing, Elsevier, vol. 15(4), pages 323-346, July.
    5. Scott Shane & Toby Stuart, 2002. "Organizational Endowments and the Performance of University Start-ups," Management Science, INFORMS, vol. 48(1), pages 154-170, January.
    6. Craig S. Galbraith & Alex F. DeNoble & Sanford B. Ehrlich, 2004. ""Spin-In" Technology Transfer for Small R&D Bio-Technology Firms: The Case of Bio-Defense," The Journal of Technology Transfer, Springer, vol. 29(3_4), pages 377-382, August.
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    Citations

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    Cited by:

    1. Daniel Nepelski & Giuseppe Piroli, 2018. "Organizational diversity and innovation potential of EU-funded research projects," The Journal of Technology Transfer, Springer, vol. 43(3), pages 615-639, June.
    2. Gabriela Menet & Marek Szarucki, 2020. "Impact of Value Co-Creation on International Customer Satisfaction in the Airsoft Industry: Does Country of Origin Matter?," JRFM, MDPI, vol. 13(10), pages 1-23, September.
    3. Daniel Nepelski & Vincent Roy & Annarosa Pesole, 2019. "The organisational and geographic diversity and innovation potential of EU-funded research networks," The Journal of Technology Transfer, Springer, vol. 44(2), pages 359-380, April.
    4. Miller, Fiona Alice & Sanders, Carrie B. & Lehoux, Pascale, 2009. "Imagining value, imagining users: Academic technology transfer for health innovation," Social Science & Medicine, Elsevier, vol. 68(8), pages 1481-1488, April.
    5. Gicheva, Dora & Link, Albert N., 2016. "On the economic performance of nascent entrepreneurs," European Economic Review, Elsevier, vol. 86(C), pages 109-117.
    6. Daniel Nepelski & Vincent Roy, 2021. "Innovation and innovator assessment in R&I ecosystems: the case of the EU Framework Programme," The Journal of Technology Transfer, Springer, vol. 46(3), pages 792-827, June.
    7. Stephan Hess & Roland Siegwart, 2013. "R&D Venture: proposition of a technology transfer concept for breakthrough technologies with R&D cooperation: A case study in the energy sector," The Journal of Technology Transfer, Springer, vol. 38(2), pages 153-179, April.
    8. Juan Jesus Arenas & Domingo González, 2018. "Technology Transfer Models and Elements in the University-Industry Collaboration," Administrative Sciences, MDPI, vol. 8(2), pages 1-17, June.
    9. Markus A. Kirchberger & Larissa Pohl, 2016. "Technology commercialization: a literature review of success factors and antecedents across different contexts," The Journal of Technology Transfer, Springer, vol. 41(5), pages 1077-1112, October.
    10. Craig Galbraith & Alex DeNoble & Sanford Ehrlich & Jessica Mesmer-Magnus, 2010. "Review panel consensus and post-decision commercial performance: a study of early stage technologies," The Journal of Technology Transfer, Springer, vol. 35(2), pages 253-281, April.

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    More about this item

    Keywords

    technology commercialization; prediction model; technology transfer; O32;
    All these keywords.

    JEL classification:

    • 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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