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Artifact interactions retard technological improvement: An empirical study

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  • Subarna Basnet
  • Christopher L Magee

Abstract

Empirical research has shown performance improvement of many different technological domains occurs exponentially but with widely varying improvement rates. What causes some technologies to improve faster than others do? Previous quantitative modeling research has identified artifact interactions, where a design change in one component influences others, as an important determinant of improvement rates. The models predict that improvement rate for a domain is proportional to the inverse of the domain’s interaction parameter. However, no empirical research has previously studied and tested the dependence of improvement rates on artifact interactions. A challenge to testing the dependence is that any method for measuring interactions has to be applicable to a wide variety of technologies. Here we propose a novel patent-based method that is both technology domain-agnostic and less costly than alternative methods. We use textual content from patent sets in 27 domains to find the influence of interactions on improvement rates. Qualitative analysis identified six specific keywords that signal artifact interactions. Patent sets from each domain were then examined to determine the total count of these 6 keywords in each domain, giving an estimate of artifact interactions in each domain. It is found that improvement rates are positively correlated with the inverse of the total count of keywords with Pearson correlation coefficient of +0.56 with a p-value of 0.002. The results agree with model predictions, and provide, for the first time, empirical evidence that artifact interactions have a retarding effect on improvement rates of technological domains.

Suggested Citation

  • Subarna Basnet & Christopher L Magee, 2017. "Artifact interactions retard technological improvement: An empirical study," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-17, August.
    • Handle: RePEc:plo:pone00:0179596
    DOI: 10.1371/journal.pone.0179596
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    References listed on IDEAS

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    1. Christopher L. Benson & Christopher L. Magee, 2015. "Technology structural implications from the extension of a patent search method," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(3), pages 1965-1985, March.
    2. Eppinger, Steven D. & Browning, Tyson R., 2012. "Design Structure Matrix Methods and Applications," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262017520, December.
    3. Magee, C.L. & Basnet, S. & Funk, J.L. & Benson, C.L., 2016. "Quantitative empirical trends in technical performance," Technological Forecasting and Social Change, Elsevier, vol. 104(C), pages 237-246.
    4. William D. Nordhaus, 1996. "Do Real-Output and Real-Wage Measures Capture Reality? The History of Lighting Suggests Not," NBER Chapters, in: The Economics of New Goods, pages 27-70, National Bureau of Economic Research, Inc.
    5. Christopher L. Benson & Christopher L. Magee, 2013. "A hybrid keyword and patent class methodology for selecting relevant sets of patents for a technological field," Scientometrics, Springer;Akadémiai Kiadó, vol. 96(1), pages 69-82, July.
    6. Christopher L. Benson & Christopher L. Magee, 2013. "Erratum to: A hybrid keyword and patent class methodology for selecting relevant sets of patents for a technological field," Scientometrics, Springer;Akadémiai Kiadó, vol. 96(1), pages 83-83, July.
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    Cited by:

    1. Changbae Mun & Sejun Yoon & Hyunseok Park, 2019. "Structural decomposition of technological domain using patent co-classification and classification hierarchy," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(2), pages 633-652, November.
    2. Fang Han & Christopher L. Magee, 2018. "Testing the science/technology relationship by analysis of patent citations of scientific papers after decomposition of both science and technology," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(2), pages 767-796, August.

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