Year: 2021
Type: dissertation
Abstract: Technology Readiness Levels (TRL) are used to assess technology maturity, an important factor in technology investment and onboarding decisions. Expertly assessed TRLs are time-consuming, financially demanding, and subject to bias. While quantitative TRL assessment tools have been suggested, their accuracy is yet to be confirmed. To address this, we explore an existing method that used prevailing bibliometric models of maturity to assess TRLs. We automated and replicated the method and investigated its reliability by estimating the TRLs of 23 technologies. We show that the results of the automated method do not agree with expertly assessed TRLs and we offer hypotheses as to why bibliometrics fail to provide accurate technology maturity estimates. We argue that the single indicator model commonly used to assess technology life cycle stages must be revised. Finally, we suggest machine learning and Natural Language Processing tools to replace outdated models of maturity and automate TRL assessments.
Source: TSpace (University of Toronto)
Author: S.E. Faidi
Language: English
Subfield: Management of Technology and Innovation
Domain: Social Sciences
Open Access status: green
Funder: University of Toronto
OpenAlex ID: W3183836862