Relationship between technological improvement and innovation diffusion: an empirical test

ABSTRACT

Different technological domains have significantly different rates of performance improvement. Prior theory indicates that such differing rates should influence the relative speed of diffusion of the products embodying the different technologies, since the improvement in performance during the diffusion process increases the diffusing desirability of the product. However, there has been no broad empirical attempt to examine this effect and clarify its underlying cause. Therefore, this study reviews the theoretical basis and focuses upon empirical tests of this effect across multiple products and their underlying technologies. The results for 18 diffusing products show the expected relationship (faster diffusion for products based on more rapidly improving technological domains) between technological improvement and diffusion with vital statistical significance. The empirical examination also demonstrates that technological improvement does not slow down in the later stages of diffusion when penetration slows down. This finding indicates that the diffusion slowdown in the later stages is due to market saturation effects and not to a slowdown in performance improvement.

KEYWORDS:

• Innovation diffusion
• technological improvement
• technological change

Acknowledgments

The authors gratefully acknowledge the SUTD/MIT International Design Center’s support and the KU-KIST School Project.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 Previous studies are finding factors to explain variation in the diffusion speeds for different products used about 10–30 cases because of difficulties in collecting product adoption data. This study's empirical tests are based on 18 products. We were also able to find quantitative performance improvement data for the technological domains required to perform core functions of these products. The 18 products included home appliances, consumer electronics, automobiles, and medical imaging equipment. We aslo included various innovative products related to the diffusion process by which individuals and institutions in a society adopt new technologies or replace older technologies with newer ones. This includes technologies that have brought major advances and changes to the world. The 18 cases are numerous enough to reliably use linear regression models between two variables (diffusion speed and technological improvement rate). Such models generally require about 10–15 cases to obtain reliable estimates. Further, we conduct non-parametric statistical hypothesis tests for hypothesis H2, for which 18 cases are also sufficient.

2 According to Magee et al. (2006), the technological improvement rate is very similar in the cost-constrained performance metric and the other metric. They found out that substantial statistical significance exists for the differences between technological improvement rate in different functional categories (such as storage, transportation, transformation) but not between technological improvement rate in functional performance metrics in a given functional category.

Additional information

Notes on contributors

JongRoul Woo

JongRoul Woo is an Assistant Professor at the Graduate School of Energy and Environment (KU-KIST Green School), Korea University. He received his PhD from Seoul National University and BS from Korea Advanced Science and Technology. After the PhD degree, He worked as a Postdoctoral Associate at MIT in the Institute for Data, System, and Society (IDSS). His research focuses on innovation management, demand forecasting for new technologies, consumer behaviour analysis, and energy policy.

Christopher L. Magee

Christopher Lyman Magee is a Professor of the Practice at MIT in the Institute for Data, Society and Statistics (IDSS) and Mechanical Engineering and is co-director of the International Design Center which is simultaneously part of MIT and the Singapore University of Technology and Design. He is a member of the National Academy of Engineering, a fellow of ASM and SAE and a participant in major National Research Council Studies. A native of Pittsburgh, PA, Professor Magee received his BS and PhD from Carnegie-Mellon University in that city.