Technology forecasting: A new “step and wait” model claims to outperform industry rules of thumb in predictive power; advances in performance are often followed by a waiting period before the next step forward

Predicting the Path of Technological Innovation: SAW Versus Moore, Bass, Gompertz, and Kryder

Ashish Sood, Gareth M. James, Gerard J. Tellis, and Ji Zhu*

Marketing Science

Abstract

Competition is intense among rival technologies and success depends on predicting their future trajectory of performance. To resolve this challenge, managers often follow popular heuristics, generalizations, or “laws” like the Moore’s Law. We propose a model, Step And Wait (SAW), for predicting the path of technological innovation and compare its performance against eight models for 25 technologies and 804 technologies-years across six markets. The estimates of the model provide four important results. First, Moore’s Law and Kryder’s law do not generalize across markets; none holds for all technologies even in a single market. Second, SAW produces superior predictions over traditional methods, such as the Bass model or Gompertz law, and can form predictions for a completely new technology, by incorporating information from other categories on time varying covariates. Third, analysis of the model parameters suggests that: i) recent technologies improve at a faster rate than old technologies; ii) as the number of competitors increases, performance improves in smaller steps and longer waits; iii) later entrants and technologies that have a number of prior steps tend to have smaller steps and shorter waits; but iv) technologies with long average wait time continue to have large steps. Fourth, technologies cluster in their performance by market.

The law and the profits

Technology forecasting: A new “step and wait” model claims to outperform industry rules of thumb in predictive power

Mar 9th 2013 |From the print edition

PREDICTING the course of technological progress can be a risky business. Scorn the latest advances and you risk being left behind, as when Sony kept investing in flat-screen versions of cathode-ray televisions in the 1990s while Samsung piled into liquid-crystal displays (LCDs), and eventually replaced Sony as market leader. Embrace new ideas too early, though, and you may be left with egg on your face, as when General Motors spent more than $1 billion developing hydrogen fuel cells a decade ago, only to see them overtaken by lithium-ion batteries as the preferred power source for electric and hybrid vehicles.

To determine when to proceed with a new technology many managers and engineers employ popular heuristics, some of which are seen as “laws”. The best known is Moore’s law, proposed in 1965 by Gordon Moore, a co-founder of Intel. At first it stated that as more transistors are crammed onto the surface of silicon chips, the devices double in performance every year. This law was later revised to two years, and chip performance is now usually reckoned to double every 18 months. Other laws use “S” curves and various other calculations to predict how technologies will evolve.

Many of these laws have become widely accepted and are now applied when drawing conclusions about a broad range of technologies. Some have become self-fulfilling. Chipmakers, for example, use Moore’s law to co-ordinate their research and development (R&D) activity and plan their capital investment. In reality, however, such laws are unreliable because progress is rarely smooth. So Ashish Sood of the Goizueta School of Business at Emory University, Atlanta, and his colleagues have come up with their own law, which is explicitly based on the tendency of technology to progress in stops and starts.

Their “step and wait” (SAW) model, recently published in Marketing Science, notes that advances in performance are often followed by a waiting period before the next step forward. The steps can be big or small, and the waiting periods long or short. The researchers also hypothesise that greater support for innovation means new technologies improve in larger and more frequent steps than old technologies did. This is the result of higher R&D spending, the existence of better tools and the fact that more countries are undertaking research. But as the number of competitors in a new field increases, both the size of the steps and the length of the wait for the next step can change.

To look at the factors involved, Dr Sood and his colleagues studied 25 technologies in six markets: external lighting, desktop printers, display monitors, desktop memory, data transfer and car batteries. This provided more than a century of diverse technological evolution for them to get their teeth into. Using historical records, they tracked performance steps and waiting periods, in order to obtain averages for each technology.

They found, for instance, that in lighting the predicted step size as a percentage improvement in performance for light-emitting diode lamps was 0.34%, with a mean waiting time between steps of 3.6 years. For traditional incandescent lighting, an older technology, the step was 0.11% with waits of almost 20 years. Optical fibres used in networking produced some of the biggest step improvements, at 2.19% per step, with a waiting period between steps of less than two years.

Using these data to compile their SAW model, Dr Sood and his colleagues said they were able to produce superior predictions to those obtained for the technologies in question with the traditional laws. In the case of magnetic storage, for example, they found that it took 28 months for systems to double in performance, which is ten months more than the figure commonly used in predictions.

The researchers say SAW can also be used to predict the nature of the threat posed by a competing technology, by more accurately classifying the steps and waiting periods involved. Had it used their model, they reckon, Sony might have switched more quickly to LCDs.

About bambooinnovator
Kee Koon Boon (“KB”) is the co-founder and director of HERO Investment Management which provides specialized fund management and investment advisory services to the ARCHEA Asia HERO Innovators Fund (www.heroinnovator.com), the only Asian SMID-cap tech-focused fund in the industry. KB is an internationally featured investor rooted in the principles of value investing for over a decade as a fund manager and analyst in the Asian capital markets who started his career at a boutique hedge fund in Singapore where he was with the firm since 2002 and was also part of the core investment committee in significantly outperforming the index in the 10-year-plus-old flagship Asian fund. He was also the portfolio manager for Asia-Pacific equities at Korea’s largest mutual fund company. Prior to setting up the H.E.R.O. Innovators Fund, KB was the Chief Investment Officer & CEO of a Singapore Registered Fund Management Company (RFMC) where he is responsible for listed Asian equity investments. KB had taught accounting at the Singapore Management University (SMU) as a faculty member and also pioneered the 15-week course on Accounting Fraud in Asia as an official module at SMU. KB remains grateful and honored to be invited by Singapore’s financial regulator Monetary Authority of Singapore (MAS) to present to their top management team about implementing a world’s first fact-based forward-looking fraud detection framework to bring about benefits for the capital markets in Singapore and for the public and investment community. KB also served the community in sharing his insights in writing articles about value investing and corporate governance in the media that include Business Times, Straits Times, Jakarta Post, Manual of Ideas, Investopedia, TedXWallStreet. He had also presented in top investment, banking and finance conferences in America, Italy, Sydney, Cape Town, HK, China. He has trained CEOs, entrepreneurs, CFOs, management executives in business strategy & business model innovation in Singapore, HK and China.

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