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

Author

Listed:
  • Sallie Keller

    (Social and Decision Analytics Laboratory, Biocomplexity Institute of Virginia Tech, Arlington, VA 22203)

  • Gizem Korkmaz

    (Social and Decision Analytics Laboratory, Biocomplexity Institute of Virginia Tech, Arlington, VA 22203)

  • Carol Robbins

    (The National Center for Science and Engineering Statistics, National Science Foundation, Alexandria, VA 22314)

  • Stephanie Shipp

    (Social and Decision Analytics Laboratory, Biocomplexity Institute of Virginia Tech, Arlington, VA 22203)

Abstract

Measuring the value of intangibles is not easy, because they are critical but usually invisible components of the innovation process. Today, access to nonsurvey data sources, such as administrative data and repositories captured on web pages, opens opportunities to create intangibles based on new sources of information and capture intangible innovations in new ways. Intangibles include ownership of innovative property and human resources that make a company unique but are currently unmeasured. For example, intangibles represent the value of a company’s databases and software, the tacit knowledge of their workers, and the investments in research and development (R&D) and design. Through two case studies, the challenges and processes to both create and measure intangibles are presented using a data science framework that outlines processes to discover, acquire, profile, clean, link, explore the fitness-for-use, and statistically analyze the data. The first case study shows that creating organizational innovation is possible by linking administrative data across business processes in a Fortune 500 company. The motivation for this research is to develop company processes capable of synchronizing their supply chain end to end while capturing dynamics that can alter the inventory, profits, and service balance. The second example shows the feasibility of measurement of innovation related to the characteristics of open source software through data scraped from software repositories that provide this information. The ultimate goal is to develop accurate and repeatable measures to estimate the value of nonbusiness sector open source software to the economy. This early work shows the feasibility of these approaches.

Suggested Citation

  • Sallie Keller & Gizem Korkmaz & Carol Robbins & Stephanie Shipp, 2018. "Opportunities to observe and measure intangible inputs to innovation: Definitions, operationalization, and examples," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 115(50), pages 12638-12645, December.
    • Handle: RePEc:nas:journl:v:115:y:2018:p:12638-12645
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    Citations

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    Cited by:

    1. Murciano-Goroff, Raviv & Zhuo, Ran & Greenstein, Shane, 2021. "Hidden software and veiled value creation: Illustrations from server software usage," Research Policy, Elsevier, vol. 50(9).
    2. J Bayoán Santiago Calderón & Carol Robbins & Ledia Guci & Gizem Korkmaz & Brandon L. Kramer, 2022. "Measuring the Cost of Open Source Software Innovation on GitHub," BEA Working Papers 0200, Bureau of Economic Analysis.
    3. repec:nbr:nberch:14271 is not listed on IDEAS
    4. David Noble & Michael B. Charles & Robyn Keast, 2023. "Valuing intangible outcomes from the Cooperative Research Centres‐Projects program," Australian Economic Papers, Wiley Blackwell, vol. 62(1), pages 47-62, March.
    5. Wright, Nataliya Langburd & Nagle, Frank & Greenstein, Shane, 2023. "Open source software and global entrepreneurship," Research Policy, Elsevier, vol. 52(9).

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