IDEAS home Printed from https://ideas.repec.org/a/eee/respol/v44y2015i3p777-788.html
   My bibliography  Save this article

Rapid improvements with no commercial production: How do the improvements occur?

Author

Listed:
  • Funk, Jeffrey L.
  • Magee, Christopher L.

Abstract

This paper empirically examines 13 technologies in which significant cost and performance improvements occurred even while no commercial production occurred. Since the literature emphasizes cost reductions through increases in cumulative production, this paper explores cost and performance improvements from a new perspective. The results demonstrate that learning in these pre-commercial production cases arises through mechanisms utilized in deliberate R&D efforts. We identity three mechanisms – materials creation, process changes, and reductions in feature scale – that enable these improvements to occur and use them to extend models of learning and invention. These mechanisms can also apply during post-commercial time periods and further research is needed to quantify the relative contributions of these three mechanisms and those of production-based learning in a variety of technologies.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:respol:v:44:y:2015:i:3:p:777-788
    DOI: 10.1016/j.respol.2014.11.005
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0048733314001966
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.respol.2014.11.005?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Mathews, John A. & Cho, Dong-Sung, 1999. "Combinative capabilities and organizational learning in latecomer firms: the case of the Korean semiconductor industry," Journal of World Business, Elsevier, vol. 34(2), pages 139-156, July.
    2. Natarajan Balasubramanian & Marvin B. Lieberman, 2010. "Industry learning environments and the heterogeneity of firm performance," Strategic Management Journal, Wiley Blackwell, vol. 31(4), pages 390-412, April.
    3. Dale W. Jorgenson & Mun S. Ho & Kevin J. Stiroh, 2008. "A Retrospective Look at the U.S. Productivity Growth Resurgence," Journal of Economic Perspectives, American Economic Association, vol. 22(1), pages 3-24, Winter.
    4. Murray, Fiona, 2004. "The role of academic inventors in entrepreneurial firms: sharing the laboratory life," Research Policy, Elsevier, vol. 33(4), pages 643-659, May.
    5. Murray, Fiona, 2002. "Innovation as co-evolution of scientific and technological networks: exploring tissue engineering," Research Policy, Elsevier, vol. 31(8-9), pages 1389-1403, December.
    6. Nordhaus, William D., 2007. "Two Centuries of Productivity Growth in Computing," The Journal of Economic History, Cambridge University Press, vol. 67(1), pages 128-159, March.
    7. Argote, L. & Epple, D., 1990. "Learning Curves In Manufacturing," GSIA Working Papers 89-90-02, Carnegie Mellon University, Tepper School of Business.
    8. Sidney G. Winter, 2008. "Scaling heuristics shape technology! Should economic theory take notice?," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 17(3), pages 513-531, June.
    9. Gavin Sinclair & Steven Klepper & Wesley Cohen, 2000. "What's Experience Got to Do With It? Sources of Cost Reduction in a Large Specialty Chemicals Producer," Management Science, INFORMS, vol. 46(1), pages 28-45, January.
    10. Fleming, Lee & Sorenson, Olav, 2001. "Technology as a complex adaptive system: evidence from patent data," Research Policy, Elsevier, vol. 30(7), pages 1019-1039, August.
    11. Katharine Sanderson, 2009. "Quantum dots go large," Nature, Nature, vol. 459(7248), pages 760-761, June.
    12. Dosi, Giovanni & Nelson, Richard R., 2010. "Technical Change and Industrial Dynamics as Evolutionary Processes," Handbook of the Economics of Innovation, in: Bronwyn H. Hall & Nathan Rosenberg (ed.), Handbook of the Economics of Innovation, edition 1, volume 1, chapter 0, pages 51-127, Elsevier.
    13. Klepper, Steven, 1996. "Entry, Exit, Growth, and Innovation over the Product Life Cycle," American Economic Review, American Economic Association, vol. 86(3), pages 562-583, June.
    14. Peter Thompson, 2012. "The Relationship between Unit Cost and Cumulative Quantity and the Evidence for Organizational Learning-by-Doing," Journal of Economic Perspectives, American Economic Association, vol. 26(3), pages 203-224, Summer.
    15. Michael A. Lapré & Amit Shankar Mukherjee & Luk N. Van Wassenhove, 2000. "Behind the Learning Curve: Linking Learning Activities to Waste Reduction," Management Science, INFORMS, vol. 46(5), pages 597-611, May.
    16. Aaron D. Franklin, 2013. "The road to carbon nanotube transistors," Nature, Nature, vol. 498(7455), pages 443-444, June.
    17. K. J. Arrow, 1971. "The Economic Implications of Learning by Doing," Palgrave Macmillan Books, in: F. H. Hahn (ed.), Readings in the Theory of Growth, chapter 11, pages 131-149, Palgrave Macmillan.
    18. Zeeya Merali, 2011. "First sale for quantum computing," Nature, Nature, vol. 474(7349), pages 18-18, June.
    19. Arthur, W. Brian, 2007. "The structure of invention," Research Policy, Elsevier, vol. 36(2), pages 274-287, March.
    20. Ron Adner & Daniel Levinthal, 2001. "Demand Heterogeneity and Technology Evolution: Implications for Product and Process Innovation," Management Science, INFORMS, vol. 47(5), pages 611-628, May.
    21. C. Lanier Benkard, 2000. "Learning and Forgetting: The Dynamics of Aircraft Production," American Economic Review, American Economic Association, vol. 90(4), pages 1034-1054, September.
    22. Lee Fleming, 2001. "Recombinant Uncertainty in Technological Search," Management Science, INFORMS, vol. 47(1), pages 117-132, January.
    23. Béla Nagy & J Doyne Farmer & Quan M Bui & Jessika E Trancik, 2013. "Statistical Basis for Predicting Technological Progress," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-7, February.
    24. Pavitt, Keith, 1984. "Sectoral patterns of technical change: Towards a taxonomy and a theory," Research Policy, Elsevier, vol. 13(6), pages 343-373, December.
    25. Nile W. Hatch & David C. Mowery, 1998. "Process Innovation and Learning by Doing in Semiconductor Manufacturing," Management Science, INFORMS, vol. 44(11-Part-1), pages 1461-1477, November.
    26. Gold, Bela, 1974. "Evaluating Scale Economies: The Case of Japanese Blast Furnaces," Journal of Industrial Economics, Wiley Blackwell, vol. 23(1), pages 1-18, September.
    27. Ron Adner & Peter Zemsky, 2005. "Disruptive Technologies and the Emergence of Competition," RAND Journal of Economics, The RAND Corporation, vol. 36(2), pages 229-254, Summer.
    28. Paul S. Adler & Kim B. Clark, 1991. "Behind the Learning Curve: A Sketch of the Learning Process," Management Science, INFORMS, vol. 37(3), pages 267-281, March.
    29. Nemet, Gregory F., 2006. "Beyond the learning curve: factors influencing cost reductions in photovoltaics," Energy Policy, Elsevier, vol. 34(17), pages 3218-3232, November.
    30. William D. Nordhaus, 2014. "The Perils of the Learning Model for Modeling Endogenous Technological Change," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1).
    31. Nemet, Gregory F., 2009. "Demand-pull, technology-push, and government-led incentives for non-incremental technical change," Research Policy, Elsevier, vol. 38(5), pages 700-709, June.
    32. Rosenberg, Nathan, 1969. "The Direction of Technological Change: Inducement Mechanisms and Focusing Devices," Economic Development and Cultural Change, University of Chicago Press, vol. 18(1), pages 1-24, Part I Oc.
    33. Marvin B. Lieberman, 1984. "The Learning Curve and Pricing in the Chemical Processing Industries," RAND Journal of Economics, The RAND Corporation, vol. 15(2), pages 213-228, Summer.
    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. Nandakumar, Karthik & Funk, Jeffrey L., 2015. "Understanding the timing of economic feasibility: The case of input interfaces for human-computer interaction," Technology in Society, Elsevier, vol. 43(C), pages 33-49.
    2. 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).
    3. 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.
    4. 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.
    5. 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).
    6. Ben Klemens, 2021. "Attributing Value to Patents and Trademarks in Complex Production Chains," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 12(2), pages 842-875, June.
    7. Jeffrey Funk, 2018. "Technology change, economic feasibility, and creative destruction: the case of new electronic products and services," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 27(1), pages 65-82.
    8. 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.
    9. 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.
    10. 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.

    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. Jeffrey Funk, 2018. "Technology change, economic feasibility, and creative destruction: the case of new electronic products and services," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 27(1), pages 65-82.
    2. 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.
    3. Farmer, J. Doyne & Lafond, François, 2016. "How predictable is technological progress?," Research Policy, Elsevier, vol. 45(3), pages 647-665.
    4. 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.
    5. 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).
    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. 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.
    8. 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.
    9. Lafond, François & Greenwald, Diana & Farmer, J. Doyne, 2022. "Can Stimulating Demand Drive Costs Down? World War II as a Natural Experiment," The Journal of Economic History, Cambridge University Press, vol. 82(3), pages 727-764, September.
    10. 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.
    11. Dosi, Giovanni & Nelson, Richard R., 2010. "Technical Change and Industrial Dynamics as Evolutionary Processes," Handbook of the Economics of Innovation, in: Bronwyn H. Hall & Nathan Rosenberg (ed.), Handbook of the Economics of Innovation, edition 1, volume 1, chapter 0, pages 51-127, Elsevier.
    12. Benson, Christopher L. & Magee, Christopher L., 2014. "On improvement rates for renewable energy technologies: Solar PV, wind turbines, capacitors, and batteries," Renewable Energy, Elsevier, vol. 68(C), pages 745-751.
    13. Lee, You-Na & Walsh, John P., 2016. "Inventing while you work: Knowledge, non-R&D learning and innovation," Research Policy, Elsevier, vol. 45(1), pages 345-359.
    14. Grafström, Jonas & Poudineh, Rahmat, 2021. "A review of problems associated with learning curves for solar and wind power technologies," Ratio Working Papers 347, The Ratio Institute.
    15. Rui, Huaichuan & Cuervo-Cazurra, Alvaro & Annique Un, C., 2016. "Learning-by-doing in emerging market multinationals: Integration, trial and error, repetition, and extension," Journal of World Business, Elsevier, vol. 51(5), pages 686-699.
    16. Sáenz-Royo, Carlos & Salas-Fumás, Vicente, 2013. "Learning to learn and productivity growth: Evidence from a new car-assembly plant," Omega, Elsevier, vol. 41(2), pages 336-344.
    17. Guido Fioretti, 2007. "A connectionist model of the organizational learning curve," Computational and Mathematical Organization Theory, Springer, vol. 13(1), pages 1-16, March.
    18. Funk, Jeffery, 2009. "Components, systems and discontinuities: The case of magnetic recording and playback equipment," Research Policy, Elsevier, vol. 38(7), pages 1192-1202, September.
    19. Tat Y. Chan & Jia Li & Lamar Pierce, 2014. "Learning from Peers: Knowledge Transfer and Sales Force Productivity Growth," Marketing Science, INFORMS, vol. 33(4), pages 463-484, July.
    20. Cantner, Uwe & Vannuccini, Simone, 2021. "Pervasive technologies and industrial linkages: Modeling acquired purposes," Structural Change and Economic Dynamics, Elsevier, vol. 56(C), pages 386-399.

    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:eee:respol:v:44:y:2015:i:3:p:777-788. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/respol .

    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.