The Pattern of Knowledge Flows Between Technology Fields

Caminati, Mauro; Stabile, Arsenio

doi:10.1111/j.1467-999x.2009.04073.x

“…With the rise of interest in innovation itself, many studies have used existing patent classifications to study spillovers across technology domains, generally considering classification as static. For instance, Kutz (2004) studied the growth and distribution of patent classes since 1976; Leydesdorff (2008), Antonelli et al (2010), and Strumsky et al (2012) and Youn et al (2015) studied co-classification patterns; and Caminati and Stabile (2010) and Acemoglu et al (2016) studied the patterns of citations across USPCS or NBER technology classes. Similarly, technological classification systems are used to estimate technological distance, typically between firms or inventors in the "technology space" based on the classification of their patent portfolio (Breschi et al 2003;Nooteboom et al 2007;Aharonson and Schilling 2016;Alstott et al 2016).…”

Section: Why Is Studying Classification Systems Important?mentioning

confidence: 99%

Long-run dynamics of the U.S. patent classification system

Lafond

¹

,

Kim

²

2019

View full text Add to dashboard Cite

Almost by definition, radical innovations create a need to revise existing classification systems. In this paper, we argue that classification system changes and patent reclassification are common and reveal interesting information about technological evolution. To support our argument, we present three sets of findings regarding classification volatility in the U.S. patent classification system. First, we study the evolution of the number of distinct classes. Reconstructed time series based on the current classification scheme are very different from historical data. This suggests that using the current classification to analyze the past produces a distorted view of the evolution of the system. Second, we study the relative sizes of classes. The size distribution is exponential so classes are of quite different sizes, but the largest classes are not necessarily the oldest. To explain this pattern with a simple stochastic growth model, we introduce the assumption that classes have a regular chance to be split. Third, we study reclassification. The share of patents that are in a different class now than they were at birth can be quite high. Reclassification mostly occurs across classes belonging to the same 1-digit NBER category, but not always. We also document that reclassified patents tend to be more cited than non-reclassified ones, even after controlling for grant year and class of origin.

show abstract

“…In this context, ICT for governance and policy modelling has recently materialized. Hence, taxonomy is defined for the research areas and sub areas that challenge the domain in order to deal with its diversity and complexity [23].…”

Section: B Review On Procuring Publication Dataset In the Field Of Ictdmentioning

confidence: 99%

Towards Interdisciplinary Research: A Bibliometric View of Information Communication Technology for Development in Different Disciplines

Kayani

¹

,

Hassan

²

,

Aljohani

³

et al. 2019

2019 Third World Conference on Smart Trends in Systems Security and Sustainablity (WorldS4)

0

View full text Add to dashboard Cite

We present a novel bibliometric view to create a taxonomy of the interdisciplinary field of Information Communication Technology for Development (ICTD or ICT4D), using scientific documents published in the fields related to information communication technologies that were indexed in the Scopus database from 2001 to 2015. Our research approach utilizes a set of relevant terms and venues (journals and conferences) to procure publications on ICTD topics, and further to analyze them statistically to identify emerging or emerged sub-disciplines. The sub-disciplines prominently include Economic Growth/E-Government/Political Economy, Cloud Enterprises/Entrepreneurship/ICT Innovation, and Smart Energy/Smart Grid. We identify the relevance and uniqueness of all ICTD topics, generated by the algorithm, by employing well-known indices of the coherence coefficient and Kullback-Leibler divergence, indicating the qualifications of a given topic as an established sub-discipline of ICTD. This paper attempts to discuss the interdisciplinary nature of the research field ICTD and recommends prospective directions for new subdisciplines based on the text analysis.

show abstract

“…With the rise of interest in innovation itself many studies have used existing patent classifications to study spillovers across technology domains, generally considering classification as static. For instance Kutz (2004) studied the growth and distribution of patent classes since 1976; Leydesdorff (2008), Antonelli et al (2010), Strumsky et al (2012) and Youn et al (2015) studied co-classification patterns; and Caminati & Stabile (2010) and Acemoglu et al (2016) studied the patterns of citations across USPCS or NBER technology classes. Similarly, technological classification systems are used to estimate technological distance, typically between firms or inventors in the "technology space" based on the classification of their patent portfolio (Breschi et al 2003, Nooteboom et al 2007, Aharonson & Schilling 2016, Alstott et al 2016.…”

Section: Introductionmentioning

confidence: 99%

Long-Run Dynamics of the U.S. Patent Classification System

Lafond

¹

,

Kim

²

2017

View full text Add to dashboard Cite

Almost by definition, radical innovations create a need to revise existing classification systems. In this paper, we argue that classification system changes and patent reclassification are common and reveal interesting information about technological evolution. To support our argument, we present three sets of findings regarding classification volatility in the U.S. patent classification system. First, we study the evolution of the number of distinct classes. Reconstructed time series based on the current classification scheme are very different from historical data. This suggests that using the current classification to analyze the past produces a distorted view of the evolution of the system. Second, we study the relative sizes of classes. The size distribution is exponential so classes are of quite different sizes, but the largest classes are not necessarily the oldest. To explain this pattern with a simple stochastic growth model, we introduce the assumption that classes have a regular chance to be split. Third, we study reclassification. The share of patents that are in a different class now than they were at birth can be quite high. Reclassification mostly occurs across classes belonging to the same 1-digit NBER category, but not always. We also document that reclassified patents tend to be more cited than non-reclassified ones, even after controlling for grant year and class of origin.

show abstract

The Pattern of Knowledge Flows Between Technology Fields

Cited by 7 publications

References 59 publications

Long-run dynamics of the U.S. patent classification system

Long-run dynamics of the U.S. patent classification system

Towards Interdisciplinary Research: A Bibliometric View of Information Communication Technology for Development in Different Disciplines

Long-Run Dynamics of the U.S. Patent Classification System

Contact Info

Product

Resources

About