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A case against the trickle-down effect in technology ecosystems

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  • 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
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    References listed on IDEAS

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    3. Nigel Meade & Towhidul Islam, 1998. "Technological Forecasting---Model Selection, Model Stability, and Combining Models," Management Science, INFORMS, vol. 44(8), pages 1115-1130, August.
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