Literature DB >> 30530693

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

Sallie Keller1, Gizem Korkmaz2, Carol Robbins3, Stephanie Shipp2.   

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.

Entities:  

Keywords:  data science; intangibles; measurement; nonsurvey data; open source software

Year:  2018        PMID: 30530693      PMCID: PMC6294952          DOI: 10.1073/pnas.1800467115

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   11.205


  2 in total

1.  Forecasting innovations in science, technology, and education.

Authors:  Katy Börner; William B Rouse; Paul Trunfio; H Eugene Stanley
Journal:  Proc Natl Acad Sci U S A       Date:  2018-12-11       Impact factor: 11.205

2.  Creating a platform for costless personalization in clothing.

Authors:  Shane Greenstein
Journal:  Front Res Metr Anal       Date:  2022-09-06
  2 in total

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