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arXiv · 1805.06339

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

Christopher L. Benson·Christopher L. Magee
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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.

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BibTeXRIS

Christopher L. Benson, Christopher L. Magee. 2018-05-16. Data-Driven Investment Decision-Making: Applying Moore's Law and S-Curves to Business Strategies. https://arxiv.org/abs/1805.06339

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