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.
    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. 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.
    9. 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.
    10. Diego A. Comin & Bart Hobijn, 2009. "The CHAT Dataset," Harvard Business School Working Papers 10-035, Harvard Business School.
    11. 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.
    12. 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.
    13. 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. Coria, Jessica & Villegas-Palacio, Clara, 2010. "Targeted Enforcement and Aggregate Emissions With Uniform Emission Taxes," Working Papers in Economics 455, University of Gothenburg, Department of Economics.
    9. Alina Sorescu & Sorin M. Sorescu & Will J. Armstrong & Bart Devoldere, 2018. "Two Centuries of Innovations and Stock Market Bubbles," Marketing Science, INFORMS, vol. 37(4), pages 507-529, August.
    10. Isenhardt, Lars & Seifert, Stefan & Hüttel, Silke, 2021. "On the price effect of a right of first refusal in farmland auctions," 61st Annual Conference, Berlin, Germany, September 22-24, 2021 317062, German Association of Agricultural Economists (GEWISOLA).
    11. Jessica Coria & Xiao-Bing Zhang, 2015. "State-Dependent Enforcement to Foster the Adoption of New Technologies," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 62(2), pages 359-381, October.
    12. Theoharakis, Vasilis & Vakratsas, Demetrios & Wong, Veronica, 2007. "Market-level information and the diffusion of competing technologies: An exploratory analysis of the LAN industry," Research Policy, Elsevier, vol. 36(5), pages 742-757, June.
    13. Frambach, Ruud T. & Barkema, Harry G. & Nooteboom, Bart & Wedel, Michel, 1998. "Adoption of a service innovation in the business market: An empirical test of supply-side variables," Journal of Business Research, Elsevier, vol. 41(2), pages 161-174, February.
    14. Spyros Arvanitis & Marius Ley, 2013. "Factors Determining the Adoption of Energy-Saving Technologies in Swiss Firms: An Analysis Based on Micro Data," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 54(3), pages 389-417, March.
    15. Holwegler, Bernhard, 2000. "Implikationen der Technologiediffusion für technologische Arbeitslosigkeit," Violette Reihe: Schriftenreihe des Promotionsschwerpunkts "Globalisierung und Beschäftigung" 13/2000, University of Hohenheim, Carl von Ossietzky University Oldenburg, Evangelisches Studienwerk.
    16. BEN KHALIFA, Adel, 2017. "Territoire Appropriant Et Economie De La Connaissance Basee Sur Les Tic : Une Approche Eclectique [Appropriating Territory and ICT-Based Knowledge economy: An Eclectic Approach]," MPRA Paper 77535, University Library of Munich, Germany.
    17. J. G. Smythe, 2002. "Reputation, public information, and physician adoption of an innovation," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 3(2), pages 103-110, June.
    18. Garth Heutel & Erich Muehlegger, 2015. "Consumer Learning and Hybrid Vehicle Adoption," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 62(1), pages 125-161, September.
    19. Matthias Niggli & Christian Rutzer, 2023. "Digital technologies, technological improvement rates, and innovations “Made in Switzerland”," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 159(1), pages 1-31, December.
    20. Rui Baptista, 1999. "The Diffusion of Process Innovations: A Selective Review," International Journal of the Economics of Business, Taylor & Francis Journals, vol. 6(1), pages 107-129.

    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.