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Modeling the evolution of system technology performance when component and system technology performances interact: Commensalism and amensalism

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  • Zhang, Guanglu
  • McAdams, Daniel A.
  • Shankar, Venkatesh
  • Darani, Milad Mohammadi

Abstract

The interaction between technologies critically determines technology evolution. Commensalism and amensalism are two common relationships between component and system technologies but have attracted scant research attention. In both the relationships, the component technology performance is unaffected by the system technology performance. However, as the component technology performance improves, the system technology performance is enhanced in commensalism but inhibited in amensalism. We model commensalism and amensalism and predict the evolution of system technology performance using Lotka-Volterra equations. In these two scenarios, we decouple the equations and derive the general analytic solution for system technology performance. We also deduce the corresponding solutions for cases where the component technology performance follows a logistic function or simple exponential growth. The solutions consider the impact on system technology performance of changes in component technology performance and enable us to predict the future performance evolution of system technology. We demonstrate the prediction accuracy of our model through an empirical study of the concrete skyscraper technology. We also interpret the parameters in Lotka-Volterra equations and explore strategies to boost system technology performance. The analytic solutions and parameter interpretations allow practitioners and policy makers to use our model as a strategic management tool for their future work.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:tefoso:v:125:y:2017:i:c:p:116-124
    DOI: 10.1016/j.techfore.2017.08.004
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    References listed on IDEAS

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    2. 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.
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    Cited by:

    1. 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.
    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. 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.

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