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Data-Driven Investment Decision-Making: Applying Moore's Law and S-Curves to Business Strategies

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  • Christopher L. Benson
  • Christopher L. Magee

Abstract

This paper introduces a method for linking technological improvement rates (i.e. Moore's Law) and technology adoption curves (i.e. S-Curves). There has been considerable research surrounding Moore's Law and the generalized versions applied to the time dependence of performance for other technologies. The prior work has culminated with methodology for quantitative estimation of technological improvement rates for nearly any technology. This paper examines the implications of such regular time dependence for performance upon the timing of key events in the technological adoption process. We propose a simple crossover point in performance which is based upon the technological improvement rates and current level differences for target and replacement technologies. The timing for the cross-over is hypothesized as corresponding to the first 'knee'? in the technology adoption "S-curve" and signals when the market for a given technology will start to be rewarding for innovators. This is also when potential entrants are likely to intensely experiment with product-market fit and when the competition to achieve a dominant design begins. This conceptual framework is then back-tested by examining two technological changes brought about by the internet, namely music and video transmission. The uncertainty analysis around the cases highlight opportunities for organizations to reduce future technological uncertainty. Overall, the results from the case studies support the reliability and utility of the conceptual framework in strategic business decision-making with the caveat that while technical uncertainty is reduced, it is not eliminated.

Suggested Citation

  • 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.
  • Handle: RePEc:arx:papers:1805.06339
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    References listed on IDEAS

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    1. Geroski, P. A., 2000. "Models of technology diffusion," Research Policy, Elsevier, vol. 29(4-5), pages 603-625, April.
    2. Farzin, Y. H. & Huisman, K. J. M. & Kort, P. M., 1998. "Optimal timing of technology adoption," Journal of Economic Dynamics and Control, Elsevier, vol. 22(5), pages 779-799, May.
    3. Farmer, J. Doyne & Lafond, François, 2016. "How predictable is technological progress?," Research Policy, Elsevier, vol. 45(3), pages 647-665.
    4. Raja Roy & MB Sarkar, 2016. "Knowledge, firm boundaries, and innovation: Mitigating the incumbent's curse during radical technological change," Strategic Management Journal, Wiley Blackwell, vol. 37(5), pages 835-854, May.
    5. 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.
    6. Bronwyn H. Hall & Adam Jaffe & Manuel Trajtenberg, 2005. "Market Value and Patent Citations," RAND Journal of Economics, The RAND Corporation, vol. 36(1), pages 16-38, Spring.
    7. 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.
    8. Ron Adner & Rahul Kapoor, 2016. "Innovation ecosystems and the pace of substitution: Re-examining technology S-curves," Strategic Management Journal, Wiley Blackwell, vol. 37(4), pages 625-648, April.
    9. Vikas A. Aggarwal & Hart E. Posen & Maciej Workiewicz, 2017. "Adaptive capacity to technological change: A microfoundational approach," Strategic Management Journal, Wiley Blackwell, vol. 38(6), pages 1212-1231, June.
    10. 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.
    11. Tianxu Chen & Lihong Qian & Vadake Narayanan, 2017. "Battle on the Wrong Field? Entrant Type, Dominant Designs, and Technology Exit," Strategic Management Journal, Wiley Blackwell, vol. 38(13), pages 2579-2598, December.
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