Match!

Measuring technological novelty with patent-based indicators

Published on Apr 1, 2016in Research Policy5.42
· DOI :10.2139/ssrn.2382485
Dennis Verhoeven2
Estimated H-index: 2
(Katholieke Universiteit Leuven),
Jurriën Bakker3
Estimated H-index: 3
(Katholieke Universiteit Leuven),
Reinhilde Veugelers5
Estimated H-index: 5
Abstract
This study provides a new, more comprehensive measurement of technological novelty. Integrating insights from the existing economics and management literature, we characterize inventions ex ante along two dimensions of technological novelty: Novelty in Recombination and Novelty in Knowledge Origins. For the latter dimension we distinguish between Novel Technological and Novel Scientific Origins. For each dimension we propose an operationalization using patent classification and citation information. Results indicate that the proposed measures for the different dimensions of technological novelty are correlated, but each conveys different information. We perform a series of analyses to assess the validity of the proposed measures and compare them with other indicators used in the literature. Moreover, an analysis of the technological impact of inventions identified as novel shows that technological novelty increases the variance of technological impact and the likelihood of being among the positive outliers with respect to impact. This holds particularly for those inventions that combine Novelty in Recombination with Novelty in Technological and Scientific Origins. The results support our indicators as ex ante measures of technological novelty driving potentially radical impact.
  • References (81)
  • Citations (41)
References81
Newest
#1Jurriën Bakker (Katholieke Universiteit Leuven)H-Index: 3
#2Dennis Verhoeven (Katholieke Universiteit Leuven)H-Index: 2
Last.Bart Van Looy (Katholieke Universiteit Leuven)H-Index: 28
view all 4 authors...
#1Sam Arts (Katholieke Universiteit Leuven)H-Index: 5
#2Francesco Paolo Appio (Sant'Anna School of Advanced Studies)H-Index: 9
Last.Bart Van Looy (Katholieke Universiteit Leuven)H-Index: 28
view all 3 authors...
#1Marc Gruber (EPFL: École Polytechnique Fédérale de Lausanne)H-Index: 21
#2Dietmar Harhoff (LMU: Ludwig Maximilian University of Munich)H-Index: 43
Last.Karin Hoisl (LMU: Ludwig Maximilian University of Munich)H-Index: 13
view all 3 authors...
Cited By41
Newest
#1Nils Grashof (University of Bremen)H-Index: 1
#2Kolja Hesse (University of Bremen)H-Index: 1
Last.Dirk Fornahl (University of Bremen)H-Index: 15
view all 3 authors...
#1William Arant (University of Bremen)H-Index: 1
#2Dirk Fornahl (University of Bremen)H-Index: 15
Last.Cathrin Söllner (University of Bremen)H-Index: 1
view all 5 authors...
#1Uwe Cantner (FSU: University of Jena)H-Index: 23
#2Eva Dettmann (Halle Institute for Economic Research)H-Index: 3
Last.Maria Kristalova (FSU: University of Jena)H-Index: 1
view all 5 authors...
#1Warren Boeker (UW: University of Washington)H-Index: 20
#2Michael Howard (A&M: Texas A&M University)H-Index: 21
Last.Arvin Sahaym (WSU: Washington State University)H-Index: 9
view all 4 authors...
#1José Lobo (ASU: Arizona State University)H-Index: 18
#2Deborah Strumsky (ASU: Arizona State University)H-Index: 15
#1Taewon Kang (Sant'Anna School of Advanced Studies)H-Index: 2
#2Chulwoo Baek (Duksung Women's University)H-Index: 6
Last.Jeong-Dong Lee (SNU: Seoul National University)H-Index: 18
view all 3 authors...
#1Serhat Burmaoglu (Georgia Institute of Technology)
#2Olivier Sartenaer (University of Cologne)H-Index: 1
Last.Alan L. Porter (Georgia Institute of Technology)H-Index: 41
view all 3 authors...
View next paperRecombinant Uncertainty in Technological Search