Related Experiment Video
Updated: Jul 28, 2026
14:14
The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
Published on: May 13, 2022
Capturing 'R&D excellence': indicators, international statistics, and innovative universities
Robert J W Tijssen1,2, Jos J Winnink1
11Center for Science and Technology Studies (CWTS), Leiden University, Leiden, The Netherlands.
Scientometrics
|February 17, 2018
Summary
Research and Development (R&D) excellence, measured by patent citations to scientific publications, shows significant global disparities. Switzerland and Israel lead per capita, driven by human resources and science quality, not just R&D spending.
Area of Science:
- Bibliometrics and Innovation Studies
- Science and Technology Policy
- Research & Development (R&D) Performance Measurement
Background:
- Successful science-based technological innovation relies on excellent research.
- Defining and measuring 'R&D excellence' is crucial for understanding innovation drivers.
- Existing metrics often focus on R&D expenditure, potentially overlooking research impact.
Purpose of the Study:
- To define and quantify 'R&D excellence' using patent citations to research publications.
- To analyze global and national disparities in R&D excellence.
- To identify key explanatory factors for performance differentials in R&D excellence.
Main Methods:
- Quantified R&D excellence by analyzing citation frequencies from patents to research publications over 15 years.
- Defined 'excellent' patents as the top 10% most cited globally.
- Analyzed millions of citing patents and cited publications across 70 countries, controlling for national science system size.
Main Results:
- Significant global variations exist in domestic science contribution to patented technologies.
- USA leads in publication volume, but Switzerland and Israel show superior per capita performance.
- Human resources and science system quality are key drivers of R&D excellence, especially in medium-income countries.
Conclusions:
- R&D expenditure and university-industry cooperation correlate with publication volume but not necessarily per capita excellence.
- Top-ranking research universities significantly contribute to advanced science-based technologies.
- The study provides a novel, impact-oriented metric for R&D performance, complementing expenditure-based statistics.
Related Concept Videos
01:02Ranks
Unlike parametric methods, nonparametric statistics are ideal for nominal and ordinal data, requiring fewer assumptions about the population's nature or distribution. This makes nonparametric methods easier to apply and interpret, as they do not depend on parameters like mean or standard deviation. One common approach in nonparametric analysis is to sort data according to a specific criterion. For instance, we might arrange weather data from hottest to coldest days in a month or rank cities...
01:16Response Surface Methodology
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
The process of RSM involves several key steps:
01:11Introduction to R
R is a powerful software environment for statistical computing and graphics. Originating as an implementation of the S language, developed at Bell Laboratories, R has evolved into a robust, open-source statistical software favored by statisticians and data scientists worldwide. Its comprehensive suite includes data manipulation, calculation, and graphical display capabilities, making it versatile for data analysis and visualization. Its programming language is at the core of R's functionality,...
01:10Statgraphics
Statgraphics is a comprehensive statistical software suite designed for both basic and advanced data analysis. Originating in 1980 at Princeton University under Dr. Neil W. Polhemus, it was one of the pioneering tools for statistical computing on personal computers, with its public release in 1982 marking an early milestone in data science software. Over the years, it has evolved into a robust platform for data science, offering tools for regression analysis, ANOVA, multivariate statistics,...
01:02The R Chart
In statistical process control, control charts, particularly R charts, are instrumental in monitoring process variations and identifying non-random patterns that run charts might miss. R charts track the variability within process subgroups, which is crucial when standard deviation use is impractical or unknown process variations exist.
R charts are pivotal for pinpointing shifts in process variability. Stability is indicated when all data points remain within the defined upper and lower...
R charts are pivotal for pinpointing shifts in process variability. Stability is indicated when all data points remain within the defined upper and lower...
01:22Interpreting R Charts
R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum values—of a sample...
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum values—of a sample...