Patent citation analysis with Google

This is an accepted manuscript of an article published by Wiley-Blackwell in Journal of the Association for Information Science and Technology on 23/09/2015, available online: https://doi.org/10.1002/asi.23608 The accepted version of the publication may differ from the final published version.Citations from patents to scientific publications provide useful evidence about the commercial impact of academic research, but automatically searchable databases are needed to exploit this connection for large-scale patent citation evaluations. Google covers multiple different international patent office databases but does not index patent citations or allow automatic searches. In response, this article introduces a semiautomatic indirect method via Bing to extract and filter patent citations from Google to academic papers with an overall precision of 98%. The method was evaluated with 322,192 science and engineering Scopus articles from every second year for the period 1996–2012. Although manual Google Patent searches give more results, especially for articles with many patent citations, the difference is not large enough to be a major problem. Within Biomedical Engineering, Biotechnology, and Pharmacology & Pharmaceutics, 7% to 10% of Scopus articles had at least one patent citation but other fields had far fewer, so patent citation analysis is only relevant for a minority of publications. Low but positive correlations between Google Patent citations and Scopus citations across all fields suggest that traditional citation counts cannot substitute for patent citations when evaluating research

The value of indirect ties in citation networks:SNA analysis with OWA operator weights

This paper seeks to advance the theory and practice of the dynamics of complex networks in relation to direct and indirect citations. It applies social network analysis (SNA) and the ordered weighted averaging operator (OWA) to study a patent citations network. So far the SNA studies investigating long chains of patents citations have rarely been undertaken and the importance of a node in a network has been associated mostly with its number of direct ties. In this research OWA is used to analyse complex networks, assess the role of indirect ties, and provide guidance to reduce complexity for decision makers and analysts. An empirical example of a set of European patents published in 2000 in the renewable energy industry is provided to show the usefulness of the proposed approach for the preference ranking of patent citations

Knowledge spillovers from clean and dirty technologies

How much should governments subsidize the development of new clean technologies? We use patent citation data to investigate the relative intensity of knowledge spillovers in clean and dirty technologies in two technological fields: energy production and transportation. We introduce a new methodology that takes into account the whole history of patent citations to capture the indirect knowledge spillovers generated by patents. We find that conditional on a wide range of potential confounding factors clean patents receive on average 43% more citations than dirty patents. Knowledge spillovers from clean technologies are comparable in scale to those observed in the IT sector. The radical novelty of clean technologies relative to more incremental dirty inventions seems to account for their superiority. Our results can support public support for clean R&D. They also suggest that green policies might be able to boost economic growth through induced knowledge spillovers

The relationship of policy induced R&D networks and inter-regional knowledge diffusion

Knowledge diffusion is argued to be strongly influenced by knowledge networks and spatial structures. However, empirical studies primarily apply an indirect approach in measuring their impact. Moreover, little is known about how policy can influence the spatial diffusion of knowledge. This paper seeks to fill this gap by testing empirically the effects of policy induced knowledge networks on the propensity of inter-regional patent citations. We use patent citation data for 141 labor market regions in Germany between 2000 to 2009, which is merged with information on subsidized joint R&D projects. Based on the latter, we construct a network of subsidized R&D collaboration. Its impact on inter-regional patent citations is evaluated with binomial and negative binomial regression models. Our findings do not indicate that inter-regional network links created by public R&D subsidies facilitate patent citations and, hence, inter-regional knowledge diffusion

Knowledge Flows, Patent Citations and the Impact of Science on Technology

Technological innovation depends on knowledge developed by scientific research. The num-ber of citations made in patents to the scientific literature has been suggested as an indicator of this process of transfer of knowledge from science to technology. We provide an intersec-toral insight into this indicator, by breaking down patent citations into a sector-to-sector ma-trix of knowledge flows. We then propose a method to analyze this matrix and construct vari-ous indicators of science intensity of sectors, and the pervasiveness of knowledge flows. Our results indicate that the traditional measure of the number of citations to science literature per patent captures important aspects intersectoral knowledge flows, but that other aspects are not captured. In particular, we show that high science intensity implies that sectors are net suppli-ers of knowledge in the economic sector, but that science intensity does not say much about pervasiveness of either knowledge use or knowledge supply by sectors. We argue that these results are related to the specific and specialized nature of knowledge.Knowledge, Input-Output Analysis, Knowledge Flow Matrices, Science-to-Technology Transfer, Patents

How Licensing Resolves Hold-Up: Evidence from a Dynamic Panel Data Model with Unobserved Heterogeneity

In a patent thicket licensing provides a mechanism to either avoid or resolve hold-up. Firms' R&D incentives will differ depending on how licensing is used. In this paper we study the choice between ex ante licensing to avoid hold-up and ex post licensing to resolve it. Building on a theoretical model of a patent portfolio race, firms' choices of licensing contracts are modelled. We derive several hypotheses from the model and find support for these using data from the semiconductor industry. The empirical results show that firms' relationships in product markets and technology space jointly determine the type of licensing contract chosen. Implications for the regulation of licensing are discussed. We estimate a dynamic panel data model with unobserved heterogeneity and a lagged dependent variable. A method suggested by Wooldridge (2005) is employed to estimate a random effects probit model using conditional maximum likelihood

Industry-Science Connections in Agriculture: Do public science collaborations and knowledge flows contribute to firm-level agricultural research productivity?

Prior research identifies a direct positive link between the stock of public scientific knowledge and agricultural productivity; however, an indirect contribution to agricultural productivity is also possible when this stock facilitates private sector invention. This study examines how “connectedness” between the stock of public scientific knowledge and private firms influences firm-level research productivity. Bibliographic information identifies the nature and degree to which firms use public agricultural science through citations and collaborations on scientific papers. Fixed effects models show that greater citations and collaborations with university researchers are associated with greater agricultural research productivity.public science, research productivity, patents, citations, collaboration, R&D, Productivity Analysis, Research and Development/Tech Change/Emerging Technologies, Q16, O31,