Smith, K. (2009). Measuring Innovation. In J. Fagerberg, D. C. Mowery, & R. R. Nelson (Eds.), The Oxford Handbook of Innovation (pp. 148-178). Oxford University Press. https://doi.org/10.1093/oxfordhb/9780199286805.003.0006

1. Challenge of Measuring Innovation: The paper emphasizes the inherent difficulty in quantifying innovation due to its novel nature. It notes that “Measurement implies commensurability,” yet innovation, by its definition of novelty, often defies such straightforward comparison and measurement.
2. Defining ‘New’ in Innovation: The paper raises a critical question about the nature of innovation: what qualifies as “new”? This encompasses whether innovation must be globally novel, new to a specific industry, or simply new to a particular firm. This question of novelty’s scope is central to understanding and measuring innovation.
3. Scope of Novelty in Innovation: The document suggests that novelty in innovation is not limited to groundbreaking inventions or products. It includes incremental changes which, over time, can lead to significant technological and economic impacts. This broader view of novelty acknowledges the importance of both radical and incremental innovation.
4. Multidimensional Nature of Innovation: Innovation is conceptualized as a complex process involving ideas, learning, knowledge creation, and the development of competences and capabilities. This multidimensionality makes many aspects of the innovation process challenging to measure directly.
5. Types of STI Indicators: The paper outlines three primary areas of indicators used in Science, Technology, and Innovation (STI) analysis: R&D data, patent data, and bibliometric data. It also mentions three additional classes: technometric indicators, synthetic indicators, and databases on specific topics.
   • R&D Data: This type of indicator includes metrics related to research and development activities, such as R&D spending, the number of R&D personnel, and R&D intensity (R&D expenditure as a percentage of GDP or sales). R&D data is often used to assess the input side of the innovation process, providing insights into how much effort and resources a country, industry, or firm is investing in research and development.
   • Patent Data: Patent indicators involve metrics on patent applications, grants, and citations. They are used to gauge the output of the innovation process, reflecting the generation of new, legally protected technological knowledge. The number of patents filed, granted, and cited provides an understanding of the inventive activity, its impact, and the technological domains where a firm or country is active.
   • Bibliometric Data: This refers to data on scientific publications and citations. Bibliometric indicators are useful for evaluating the scientific research output of organizations, regions, or countries. They include metrics like the number of publications in scientific journals, citations received, and the impact factor of journals. This data helps in assessing the contribution to and influence in the scientific community.
   • Technometric Indicators: These indicators are concerned with the technical performance characteristics of products. They measure and compare the specific technical properties and performance of innovations, such as efficiency, speed, durability, and other quantifiable features. Technometric indicators provide a detailed analysis of the technical progress and comparative advantage of new products or technologies.
   • Synthetic Indicators: Developed mainly for policy analysis and strategic planning, synthetic indicators are composite measures that integrate various innovation-related metrics. These might include a combination of R&D data, patent counts, technometric measures, and economic performance indicators. They are used to create rankings or scoreboards, like the Global Competitiveness Report, to assess and compare the overall innovation capability and performance of countries or industries.
   • Specialized Databases: These are detailed databases developed as research tools by individual researchers or groups. Examples include databases on specific topics like technological collaboration, industrial innovation patterns, or sector-specific innovation activities. Such databases allow for in-depth, specialized analysis and can include data sourced from surveys, administrative records, or other research efforts. They provide nuanced insights into particular aspects of innovation and are essential for academic and policy research.
6. GERD/GDP Ratio as an Indicator of Progressiveness: A high Gross Expenditure on R&D (GERD) to GDP ratio is often interpreted as a sign of a country’s technological progressiveness and commitment to knowledge creation. This metric is a widely-used indicator to assess and compare national innovation capacities.
7. Innovation Beyond R&D: The paper underscores that innovation is not confined to formal R&D activities. It involves a wide range of processes, including design, market exploration, and even organizational changes. This broader perspective is crucial for understanding the full scope of innovation activities and their impact.