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dc.contributor.authorTriulzi, G.
dc.contributor.authorAlstott, J.
dc.contributor.authorMagee, C.
dc.date.accessioned2020-10-01T16:53:46Z
dc.date.available2020-10-01T16:53:46Z
dc.date.issued2020
dc.identifier.urihttp://hdl.handle.net/1992/47131
dc.description.abstractThe future direction of technology development depends on the relative yearly rate of functional performance improvement of different technologies. We use patent data to identify accurate and reliable predictors of this rate for 30 technologies. We illustrate how patent-based predictors should be normalized to correct for possible confounding factors introduced by changing patenting dynamics. We test the accuracy and reliability of various predictors by means of a Monte Carlo cross-validation exercise. We find that a measure of the centrality of domains¿ patented inventions in the overall US patent citation network is an accurate and highly reliable predictor of improvement rates.
dc.formatapplication/pdf
dc.language.isoeng
dc.sourceinstname:Universidad de los Andes
dc.sourcereponame:Repositorio Institucional Séneca
dc.titleEstimating technology performance improvement rates by mining patent data
dc.typeArtículo de revistaes_CO
dc.rights.accessRightsopenAccess
dc.subject.keywordTechnology performance
dc.subject.keywordExponential rates
dc.subject.keywordPatent citations
dc.subject.keywordCentrality
dc.subject.keywordTechnological trajectories
dc.identifier.urlhttps://www.sciencedirect.com/science/article/pii/S0040162520309264
dc.journal.titleTechnological Forecasting and Social Change
dc.issue.number158
dc.type.versionpublishedVersion
dc.provenance.unitFacultad de Administración


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