Can experts really assess future technology success? A neural network and Bayesian analysis of early stage technology proposals
This paper compares experts' assessments to a set of structural variables to determine whether each effectively predicts technology success. Using 69 homeland security and defense-related technologies, expert reviewers scored each technology on various dimensions as part of a government grant f...
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| Veröffentlicht in: | Journal of high technology management research 2007, Vol.17 (2), p.125-137 |
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| container_end_page | 137 |
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| container_issue | 2 |
| container_start_page | 125 |
| container_title | Journal of high technology management research |
| container_volume | 17 |
| creator | Galbraith, Craig S. DeNoble, Alex F. Ehrlich, Sanford B. Kline, Doug M. |
| description | This paper compares experts' assessments to a set of structural variables to determine whether each effectively predicts technology success. Using 69 homeland security and defense-related technologies, expert reviewers scored each technology on various dimensions as part of a government grant funding process. These technologies were tracked over 3 years and degrees of success recorded. Different predictive models were estimated using an artificial neural network technique, the Bayesian Data Reduction Algorithm, and two regression equations. The results suggest that experts provide little predictive power, and that a reasonable technology success model can be estimated using a limited set of structural variables. |
| doi_str_mv | 10.1016/j.hitech.2006.11.002 |
| format | Article |
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A neural network and Bayesian analysis of early stage technology proposals</title><title>Journal of high technology management research</title><description>This paper compares experts' assessments to a set of structural variables to determine whether each effectively predicts technology success. Using 69 homeland security and defense-related technologies, expert reviewers scored each technology on various dimensions as part of a government grant funding process. These technologies were tracked over 3 years and degrees of success recorded. Different predictive models were estimated using an artificial neural network technique, the Bayesian Data Reduction Algorithm, and two regression equations. 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A neural network and Bayesian analysis of early stage technology proposals</atitle><jtitle>Journal of high technology management research</jtitle><date>2007</date><risdate>2007</risdate><volume>17</volume><issue>2</issue><spage>125</spage><epage>137</epage><pages>125-137</pages><issn>1047-8310</issn><eissn>1879-1638</eissn><abstract>This paper compares experts' assessments to a set of structural variables to determine whether each effectively predicts technology success. Using 69 homeland security and defense-related technologies, expert reviewers scored each technology on various dimensions as part of a government grant funding process. These technologies were tracked over 3 years and degrees of success recorded. Different predictive models were estimated using an artificial neural network technique, the Bayesian Data Reduction Algorithm, and two regression equations. 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| ispartof | Journal of high technology management research, 2007, Vol.17 (2), p.125-137 |
| issn | 1047-8310 1879-1638 |
| language | eng |
| recordid | cdi_proquest_journals_200527788 |
| source | Elsevier ScienceDirect Journals |
| subjects | Alliances Bayesian analysis Expert assessment Forecasting Neural networks Predictions Studies Success Technology Technology commercialization |
| title | Can experts really assess future technology success? A neural network and Bayesian analysis of early stage technology proposals |
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