2016/01/11 by Subarna Basnet, Basnet, Subarna, Christopher L. Magee +1
Business, Management and Accounting · Decision Sciences · Engineering · #FOS: Economics and business #FOS: Physical sciences #General Economics (econ.GN) #Innovation Diffusion and Forecasting #Intellectual Property and Patents #Physics and Society (physics.soc-ph) #Technology Assessment and Management
paper · pdf · doi:10.48550/arxiv.1601.02677
openalex publication_date 2016/01/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 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 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 correlation coefficient of +0.56 with a p-value of 0.002. The empirical results agree with model predictions and support the suggestion that domains with higher number of artifacts interactions (higher complexity) will improve at a slower pace.