Opportunities to observe and measure intangible inputs to innovation: Definitions, operationalization, and examples

Area of Science:

• Innovation Management
• Data Science
• Intangible Asset Valuation

Background:

• Intangible assets like intellectual property and human capital are crucial for innovation but difficult to measure.
• Traditional methods often overlook the value of unique company resources and R&D investments.
• Emerging non-survey data sources offer new opportunities for capturing and valuing intangible innovations.

Purpose of the Study:

• To present a data science framework for creating and measuring intangible assets and innovations.
• To demonstrate the application of this framework through two distinct case studies.
• To advance the accurate and repeatable valuation of unmeasured economic contributions.

Main Methods:

• Utilized a data science framework encompassing data discovery, acquisition, profiling, cleaning, linking, fitness-for-use exploration, and statistical analysis.
• Case Study 1: Linked administrative data across business processes in a Fortune 500 company to foster organizational innovation.
• Case Study 2: Scraped data from software repositories to measure innovation in open-source software.

Main Results:

• Demonstrated the creation of organizational innovation by integrating disparate administrative data sources.
• Showcased the feasibility of measuring open-source software innovation using web-scraped data.
• Validated the data science approach for uncovering and quantifying previously unmeasured intangible assets.

Conclusions:

• The proposed data science framework effectively addresses the challenge of measuring intangible innovations.
• Linking diverse data sources can unlock significant organizational innovation and value.
• Accurate measurement of open-source software's economic value is achievable through data-driven methods.