IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0218370.html
   My bibliography  Save this article

A case against the trickle-down effect in technology ecosystems

Author

Listed:
  • Guanglu Zhang
  • Douglas Allaire
  • Venkatesh Shankar
  • Daniel A McAdams

Abstract

Technology evolution describes a change in a technology performance over time. The modeling of technology evolution is crucial for designers, entrepreneurs, and government officials to set reasonable R&D targets, invest in promising technology, and develop effective incentive policies. Scientists and engineers have developed several mathematical functions such as logistic function and exponential function (Moore’s Law) to model technology evolution. However, these models focus on how a technology evolves in isolation and do not consider how the technology interacts with other technologies. Here, we extend the Lotka-Volterra equations from community ecology to model a technology ecosystem with system, component, and fundamental layers. We model the technology ecosystem of passenger aircraft using the Lotka-Volterra equations. The results show limited trickle-down effect in the technology ecosystem, where we refer to the impact from an upper layer technology to a lower layer technology as a trickle-down effect. The limited trickle-down effect suggests that the advance of the system technology (passenger aircraft) is not able to automatically promote the performance of the component technology (turbofan aero-engine) and the fundamental technology (engine blade superalloy) that constitute the system. Our research warns that it may not be effective to maintain the prosperity of a technology ecosystem through government incentives on system technologies only. Decision makers should consider supporting the innovations of key component or fundamental technologies.

Suggested Citation

  • Guanglu Zhang & Douglas Allaire & Venkatesh Shankar & Daniel A McAdams, 2019. "A case against the trickle-down effect in technology ecosystems," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-7, June.
    • Handle: RePEc:plo:pone00:0218370
    DOI: 10.1371/journal.pone.0218370
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0218370
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0218370&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0218370?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
    ---><---

    References listed on IDEAS

    as
    1. Farmer, J. Doyne & Lafond, François, 2016. "How predictable is technological progress?," Research Policy, Elsevier, vol. 45(3), pages 647-665.
    2. Philippe Aghion & Patrick Bolton, 1997. "A Theory of Trickle-Down Growth and Development," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 64(2), pages 151-172.
    3. Marasco, Addolorata & Romano, Alessandro, 2018. "Inter-port interactions in the Le Havre-Hamburg range: A scenario analysis using a nonautonomous Lotka Volterra model," Journal of Transport Geography, Elsevier, vol. 69(C), pages 207-220.
    4. Pistorius, C. W. I. & Utterback, J. M., 1997. "Multi-mode interaction among technologies," Research Policy, Elsevier, vol. 26(1), pages 67-84, March.
    5. Riccardo Leoncini, 2001. "Segmentation and Increasing Returns in the Evolutionary Dynamics of Competing Techniques," Metroeconomica, Wiley Blackwell, vol. 52(2), pages 217-237, May.
    6. Carlsson, B & Stankiewicz, R, 1991. "On the Nature, Function and Composition of Technological Systems," Journal of Evolutionary Economics, Springer, vol. 1(2), pages 93-118, April.
    7. Nigel Meade & Towhidul Islam, 1998. "Technological Forecasting---Model Selection, Model Stability, and Combining Models," Management Science, INFORMS, vol. 44(8), pages 1115-1130, August.
    8. 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.
    9. Zhang, Guanglu & McAdams, Daniel A. & Shankar, Venkatesh & Darani, Milad Mohammadi, 2017. "Modeling the evolution of system technology performance when component and system technology performances interact: Commensalism and amensalism," Technological Forecasting and Social Change, Elsevier, vol. 125(C), pages 116-124.
    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. Marina V. Evseeva, 2020. "Technological differentiation in the development of the Ural macroregion’s subjects," Journal of New Economy, Ural State University of Economics, vol. 21(3), pages 132-157, October.
    2. Mirzadeh Phirouzabadi, Amir & Savage, David & Blackmore, Karen & Juniper, James, 2020. "The evolution of dynamic interactions between the knowledge development of powertrain systems," Transport Policy, Elsevier, vol. 93(C), pages 1-16.
    3. Shankar, Venkatesh & Kalyanam, Kirthi & Setia, Pankaj & Golmohammadi, Alireza & Tirunillai, Seshadri & Douglass, Tom & Hennessey, John & Bull, J.S. & Waddoups, Rand, 2021. "How Technology is Changing Retail," Journal of Retailing, Elsevier, vol. 97(1), pages 13-27.

    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. Zhang, Guanglu & McAdams, Daniel A. & Shankar, Venkatesh & Darani, Milad Mohammadi, 2017. "Modeling the evolution of system technology performance when component and system technology performances interact: Commensalism and amensalism," Technological Forecasting and Social Change, Elsevier, vol. 125(C), pages 116-124.
    2. Marina V. Evseeva, 2020. "Technological differentiation in the development of the Ural macroregion’s subjects," Journal of New Economy, Ural State University of Economics, vol. 21(3), pages 132-157, October.
    3. Mario Coccia, 2019. "Technological Parasitism," Papers 1901.09073, arXiv.org.
    4. Coccia, Mario, 2019. "The theory of technological parasitism for the measurement of the evolution of technology and technological forecasting," Technological Forecasting and Social Change, Elsevier, vol. 141(C), pages 289-304.
    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. 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.
    8. Heinrich, Torsten, 2016. "The Narrow and the Broad Approach to Evolutionary Modeling in Economics," MPRA Paper 75797, University Library of Munich, Germany.
    9. Rubina Zadourian, 2024. "Model-based and empirical analyses of stochastic fluctuations in economy and finance," Papers 2408.16010, arXiv.org.
    10. J. Farmer & Cameron Hepburn & Penny Mealy & Alexander Teytelboym, 2015. "A Third Wave in the Economics of Climate Change," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 62(2), pages 329-357, October.
    11. Sandén, Björn A. & Hillman, Karl M., 2011. "A framework for analysis of multi-mode interaction among technologies with examples from the history of alternative transport fuels in Sweden," Research Policy, Elsevier, vol. 40(3), pages 403-414, April.
    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. Mirzadeh Phirouzabadi, Amir & Savage, David & Blackmore, Karen & Juniper, James, 2020. "The evolution of dynamic interactions between the knowledge development of powertrain systems," Transport Policy, Elsevier, vol. 93(C), pages 1-16.
    14. Xaviery N. Penisa & Michael T. Castro & Jethro Daniel A. Pascasio & Eugene A. Esparcia & Oliver Schmidt & Joey D. Ocon, 2020. "Projecting the Price of Lithium-Ion NMC Battery Packs Using a Multifactor Learning Curve Model," Energies, MDPI, vol. 13(20), pages 1-18, October.
    15. 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.
    16. Hedeler, Barbara & Hellsmark, Hans & Söderholm, Patrik, 2023. "Policy mixes and policy feedback: Implications for green industrial growth in the Swedish biofuels industry," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    17. Hann-Earl Kim & Yu-Sang Chang & Hee-Jin Kim, 2021. "Dynamic Electricity Intensity Trends in 91 Countries," Sustainability, MDPI, vol. 13(8), pages 1-26, April.
    18. Li, Yanan & Lin, Jun & Qian, Yanjun & Li, Dehong, 2023. "Feed-in tariff policy for biomass power generation: Incorporating the feedstock acquisition process," European Journal of Operational Research, Elsevier, vol. 304(3), pages 1113-1132.
    19. Foxon, T. J. & Gross, R. & Chase, A. & Howes, J. & Arnall, A. & Anderson, D., 2005. "UK innovation systems for new and renewable energy technologies: drivers, barriers and systems failures," Energy Policy, Elsevier, vol. 33(16), pages 2123-2137, November.
    20. Grossmann, Volker, 2008. "Risky human capital investment, income distribution, and macroeconomic dynamics," Journal of Macroeconomics, Elsevier, vol. 30(1), pages 19-42, March.

    More about this item

    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:plo:pone00:0218370. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.