Artifact interactions retard technological improvement: An empirical study

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, w...

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Main Authors: Basnet, Subarna, Magee, Christopher L., Magee, Christopher L
Other Authors: Massachusetts Institute of Technology. Department of Mechanical Engineering
Format: Article
Published: Public Library of Science (PLoS) 2018
Online Access:http://hdl.handle.net/1721.1/113251
https://orcid.org/0000-0001-9622-6247
https://orcid.org/0000-0001-5316-8358
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author Basnet, Subarna
Magee, Christopher L.
Magee, Christopher L
author2 Massachusetts Institute of Technology. Department of Mechanical Engineering
author_facet Massachusetts Institute of Technology. Department of Mechanical Engineering
Basnet, Subarna
Magee, Christopher L.
Magee, Christopher L
author_sort Basnet, Subarna
collection MIT
description 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.
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spelling mit-1721.1/1132512022-09-28T11:55:33Z Artifact interactions retard technological improvement: An empirical study Basnet, Subarna Magee, Christopher L. Magee, Christopher L Massachusetts Institute of Technology. Department of Mechanical Engineering Massachusetts Institute of Technology. Institute for Data, Systems, and Society SUTD-MIT International Design Centre (IDC) Basnet, Subarna Magee, Christopher L 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. 2018-01-22T15:59:53Z 2018-01-22T15:59:53Z 2017-08 2016-11 2018-01-19T16:02:03Z Article http://purl.org/eprint/type/JournalArticle 1932-6203 http://hdl.handle.net/1721.1/113251 Basnet, Subarna, and Magee, Christopher L. “Artifact Interactions Retard Technological Improvement: An Empirical Study.” Edited by Zhong-Ke Gao. PLOS ONE 12, 8 (August 2017): e0179596 © 2017 Basnet and Magee https://orcid.org/0000-0001-9622-6247 https://orcid.org/0000-0001-5316-8358 http://dx.doi.org/10.1371/JOURNAL.PONE.0179596 PLOS ONE Creative Commons Attribution 4.0 International License http://creativecommons.org/licenses/by/4.0 application/pdf Public Library of Science (PLoS) PLoS
spellingShingle Basnet, Subarna
Magee, Christopher L.
Magee, Christopher L
Artifact interactions retard technological improvement: An empirical study
title Artifact interactions retard technological improvement: An empirical study
title_full Artifact interactions retard technological improvement: An empirical study
title_fullStr Artifact interactions retard technological improvement: An empirical study
title_full_unstemmed Artifact interactions retard technological improvement: An empirical study
title_short Artifact interactions retard technological improvement: An empirical study
title_sort artifact interactions retard technological improvement an empirical study
url http://hdl.handle.net/1721.1/113251
https://orcid.org/0000-0001-9622-6247
https://orcid.org/0000-0001-5316-8358
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