IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1704.03597.html
   My bibliography  Save this paper

Exploring the relationship between technological improvement and innovation diffusion: An empirical test

Author

Listed:
  • JongRoul Woo
  • Christopher L. Magee

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 improvement in performance during the diffusion process increases the desirability of the product diffusing. However, there has not been a broad empirical attempt to examine this effect and to clarify the underlying cause. Therefore, this paper reviews the theoretical basis and focuses upon empirical tests of this effect across multiple products and their underlying technologies. The results for 18 different diffusing products show the expected relationship-faster diffusion for products based on more rapidly improving technological domains- between technological improvement and diffusion with strong statistical significance. The empirical examination also demonstrates that technological improvement does not slow down in the latter parts of diffusion when penetration does slow down. This finding indicates that diffusion slow down in the latter stages is due to market saturation effects and is not due to slowdown of performance improvement.

Suggested Citation

  • JongRoul Woo & Christopher L. Magee, 2017. "Exploring the relationship between technological improvement and innovation diffusion: An empirical test," Papers 1704.03597, arXiv.org, revised May 2018.
  • Handle: RePEc:arx:papers:1704.03597
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1704.03597
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Cameron,A. Colin & Trivedi,Pravin K., 2013. "Regression Analysis of Count Data," Cambridge Books, Cambridge University Press, number 9781107667273, November.
    2. Geroski, P. A., 2000. "Models of technology diffusion," Research Policy, Elsevier, vol. 29(4-5), pages 603-625, April.
    3. Christopher L Benson & Christopher L Magee, 2015. "Quantitative Determination of Technological Improvement from Patent Data," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-23, April.
    4. Edward K. Y. Chen, 1983. "The Diffusion of Technology," Palgrave Macmillan Books, in: Multinational Corporations, Technology and Employment, chapter 4, pages 69-93, Palgrave Macmillan.
    5. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    6. Funk, Jeffrey L. & Magee, Christopher L., 2015. "Rapid improvements with no commercial production: How do the improvements occur?," Research Policy, Elsevier, vol. 44(3), pages 777-788.
    7. 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.
    8. Romer, Paul M, 1990. "Endogenous Technological Change," Journal of Political Economy, University of Chicago Press, vol. 98(5), pages 71-102, October.
    9. Stoneman, P & Ireland, N J, 1983. "The Role of Supply Factors in the Diffusion of New Process Technology," Economic Journal, Royal Economic Society, vol. 93(369a), pages 66-78, Supplemen.
    10. Olshavsky, Richard W, 1980. "Time and the Rate of Adoption of Innovations," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 6(4), pages 425-428, March.
    11. Diego A. Comin & Bart Hobijn, 2009. "The CHAT Dataset," Harvard Business School Working Papers 10-035, Harvard Business School.
    12. Paul Stoneman & Otto Toivanen, 1997. "The Diffusion Of Multiple Technologies: An Empirical Study," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 5(1), pages 1-17.
    13. Richard Alm & W. Michael Cox, 1997. "Time well spent: the declining real cost of living in America," Annual Report, Federal Reserve Bank of Dallas, pages 2-24.
    14. Rajshree Agarwal & Barry L. Bayus, 2002. "The Market Evolution and Sales Takeoff of Product Innovations," Management Science, INFORMS, vol. 48(8), pages 1024-1041, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Christopher L. Benson & Christopher L. Magee, 2018. "Data-Driven Investment Decision-Making: Applying Moore's Law and S-Curves to Business Strategies," Papers 1805.06339, arXiv.org.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Singh, Anuraag & Triulzi, Giorgio & Magee, Christopher L., 2021. "Technological improvement rate predictions for all technologies: Use of patent data and an extended domain description," Research Policy, Elsevier, vol. 50(9).
    2. Lafond, François & Bailey, Aimee Gotway & Bakker, Jan David & Rebois, Dylan & Zadourian, Rubina & McSharry, Patrick & Farmer, J. Doyne, 2018. "How well do experience curves predict technological progress? A method for making distributional forecasts," Technological Forecasting and Social Change, Elsevier, vol. 128(C), pages 104-117.
    3. Toshihiko Mukoyama, 2003. "A Theory of Technology Diffusion," Macroeconomics 0303010, University Library of Munich, Germany, revised 03 Jun 2003.
    4. Christopher L. Benson & Christopher L. Magee, 2018. "Data-Driven Investment Decision-Making: Applying Moore's Law and S-Curves to Business Strategies," Papers 1805.06339, arXiv.org.
    5. Paul A. David, "undated". "Zvi Griliches and the Economics of Technology Diffusion: Adoption of Innovations, Investment Lags, and Productivity Growth," Discussion Papers 09-016, Stanford Institute for Economic Policy Research, revised Mar 2010.
    6. Triulzi, Giorgio & Alstott, Jeff & Magee, Christopher L., 2020. "Estimating technology performance improvement rates by mining patent data," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    7. Rui Leite & Aurora Teixeira, 2012. "Innovation diffusion with heterogeneous networked agents: a computational model," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 7(2), pages 125-144, October.
    8. Dosi, Giovanni & Grazzi, Marco & Mathew, Nanditha, 2017. "The cost-quantity relations and the diverse patterns of “learning by doing”: Evidence from India," Research Policy, Elsevier, vol. 46(10), pages 1873-1886.
    9. George Korres & Emmanuel Marmaras & George Tsobanoglou, 2004. "Enterpreneurship and innovation activites in the schumpeterian lines," ERSA conference papers ersa04p169, European Regional Science Association.
    10. Anuraag Singh & Giorgio Triulzi & Christopher L. Magee, 2020. "Technological improvement rate estimates for all technologies: Use of patent data and an extended domain description," Papers 2004.13919, arXiv.org.
    11. Rossi, Federica, 2002. "An introductory overview of innovation studies," MPRA Paper 9106, University Library of Munich, Germany, revised Jun 2008.
    12. 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.
    13. repec:bla:jecsur:v:12:y:1998:i:2:p:131-76 is not listed on IDEAS
    14. Samaniego, Roberto M., 2013. "Knowledge spillovers and intellectual property rights," International Journal of Industrial Organization, Elsevier, vol. 31(1), pages 50-63.
    15. Milanez Ana, 2020. "Workforce Ageing and Labour Productivity Dynamics," Naše gospodarstvo/Our economy, Sciendo, vol. 66(3), pages 1-13, September.
    16. Henrik Braconier & Fredrik Sjöholm, 1998. "National and international spillovers from R&D: Comparing a neoclassical and an endogenous growth approach," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 134(4), pages 638-663, December.
    17. Gavazzoni, Federico & Santacreu, Ana Maria, 2020. "International R&D spillovers and asset prices," Journal of Financial Economics, Elsevier, vol. 136(2), pages 330-354.
    18. Brainerd, Elizabeth & Siegler, Mark V, 2003. "The Economic Effects of the 1918 Influenza Epidemic," CEPR Discussion Papers 3791, C.E.P.R. Discussion Papers.
    19. Karanfil, Fatih & Omgba, Luc Désiré, 2023. "The energy transition and export diversification in oil-dependent countries: The role of structural factors," Ecological Economics, Elsevier, vol. 204(PB).
    20. Falck, Oliver & Heimisch-Roecker, Alexandra & Wiederhold, Simon, 2021. "Returns to ICT skills," Research Policy, Elsevier, vol. 50(7).
    21. Luc Anselin & Attila Varga & Zoltan Acs, 2008. "Local Geographic Spillovers Between University Research and High Technology Innovations," Chapters, in: Entrepreneurship, Growth and Public Policy, chapter 9, pages 95-121, Edward Elgar Publishing.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:1704.03597. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.