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Technological improvement rates vary due to artifact interactions. A new patent analysis method confirms that more interactions slow down technology performance improvements, supporting prior models.

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Area of Science:

  • Technology Management
  • Innovation Studies
  • Quantitative Science

Background:

  • Technological progress often exhibits exponential performance improvements, but rates vary significantly across domains.
  • Quantitative models suggest artifact interactions, where changes in one component affect others, are key determinants of these varying improvement rates.
  • Previous research lacked empirical validation for the link between artifact interactions and technology improvement rates.

Purpose of the Study:

  • To empirically test the hypothesis that artifact interactions impede technological improvement rates.
  • To develop and validate a novel, cost-effective, and technology-domain-agnostic method for measuring artifact interactions.

Main Methods:

  • A novel patent-based methodology was developed to quantify artifact interactions across diverse technological domains.
  • Textual analysis of patent sets from 27 technology domains identified six keywords indicative of artifact interactions.
  • The frequency of these keywords was used to estimate the level of artifact interactions within each domain.

Main Results:

  • A significant positive correlation (Pearson's r = +0.56, p = 0.002) was found between technology improvement rates and the inverse of artifact interaction estimates.
  • This indicates that higher levels of artifact interactions are associated with slower rates of technological improvement.
  • The findings align with predictions from quantitative modeling research on technological progress.

Conclusions:

  • This study provides the first empirical evidence that artifact interactions exert a retarding influence on the improvement rates of technological domains.
  • The developed patent-based method offers a scalable and cost-effective approach to measuring artifact interactions across various technologies.
  • Understanding artifact interactions is crucial for predicting and potentially accelerating technological advancement.