Narrow Results

Technological change is argued to be taking place along ordered and selective patterns, shaped jointly by technological and scientific principles, and economic and other societal factors. Historical, descriptive analysis is often used to analyze these "trajectories". Recently, quantitative methods have been proposed to map these trajectories. It is argued that such methods have, so far, not been able to illuminate the engineering side of technological trajectories. In order to fill this gap, a methodology proposed by Hummon and Doreian (1989) is used and extended to undertake a citation analysis of patents in the field of fuel cells.

In this paper our aim is to assess the potential of using patent statistics in predicting the future sales anticipations and present value of a company active in the science-based industry. Instead of using conventional patent and patent application counts as indicators, we constructed patent citation weighted portfolios for each company. This way the heterogeneity of patents can be taken into account. Our data covers biotechnology patents held by Finnish biotechnology companies. Empirical results imply that particularly backward citations are related to present value estimations of the companies. In contrast to some previous studies, forward citations do not seem to predict the present value estimations. The findings provide some important implications on interpreting the significance of patent citations regarding valuation of science-based companies, and planning technology and innovation policies.

In recent years, business practitioners are seen valuing patents on the basis of the market price that the patent can attract. Researchers have also looked into various patent latent variables and firm variables that influence the price of a patent. Forward citations of a patent are shown to play a role in determining price. Using patent auction price data (of Ocean Tomo now ICAP patent brokerage), we delve deeper into the role of forward citations. The successfully sold 167 singleton patents form the sample of our study. We found that, it is mainly the right tail of the citation distribution that explains the high prices of the patents falling on the right tail of the price distribution. There is consistency in the literature on the positive correlation between patent prices and forward citations. In this paper, we go deeper to understand this linear relationship through case studies. Case studies of patents with high and low citations are described in this paper to understand why some patents attracted high prices. We look into the role of additional patent latent variables like age, technology discipline, class and breadth of the patent in influencing citations that a patent receives.

The aim of this paper is to analyse the pattern of knowledge flows as evidenced by the patent citations in three economic areas: USA, Japan and Europe. In each economic area, we exploit information from two international patent offices data, the United States Patent and Trademarks Office (USPTO) data and the European Patent Office (EPO) data. In this way, we can investigate the link between the technological proximity and knowledge spillovers for 240 international firms. In particular, the contribution to the existing literature is twofold: First, we use an international sample so that we can compare the empirical results among different economic markets; second, we explore the robustness of results with respect to patent system features. In order to compute the technological proximity, we consider both the symmetrical measure and asymmetrical one. The empirical results indicate that there is a statistically significant correlation between technological proximity and knowledge spillovers measured by patent citations and that these results are robust with respect to patent office data used in the analysis.