Understanding the Seeds of Growth: Technological Evolution and Product Innovation


Ashish Sood and Gerard J. Tellis (2005) ,"Understanding the Seeds of Growth: Technological Evolution and Product Innovation", in AP - Asia Pacific Advances in Consumer Research Volume 6, eds. Yong-Uon Ha and Youjae Yi, Duluth, MN : Association for Consumer Research, Pages: 56.

Asia Pacific Advances in Consumer Research Volume 6, 2005      Page 56


Ashish Sood, University of Southern California, U.S.A.

Gerard J. Tellis, University of Southern California, U.S.A.

Many marketers think market segmentation is the most important engine of growth. On the contrary, it is technological change that is perhaps the most powerful engine of growth. Numerous examples can be cited from the industry to support this claim. First, the growth of Microsoft from a fledging company to the colossus of the computer industry was enabled by technological change. Second, emergence of internet-enabled products, walkman, washers etc. suggest that technological creates new growth markets. Third, the meteoric rise of Amazon and Dell suggests that it is technological change that also propels small outsiders into market leaders.

However, firms can not gain from technological change if they do not understand it well. Specifically they need to understand how new technologies evolve, including any underlying patterns. It is also important to know the dimensions of competition between technologies, the process of transition between old and new, and the source of innovations. Currently, the main sources of answers to all these questions are limited findings in the technology management literature (e.g. Foster 1986; Utterback 1994; Christensen 1997). These sources promote a theory commonly known as the Theory of S curves. Our study seeks to confirm whether this commonly accepted model of technological evolution promoted by these sources holds.

One of the reasons for limited research in this area is the lack of readymade data. We had to painstakingly collect data over a large portfolio of categories using historical method (Golder 2000; Golder and Tellis 1993). We define three types of innovationsBplatform, design and component innovationsBstrictly based on the intrinsic characteristics of the technology. These definitions avoid the error of circular reasoning created if definitions based on market outcomes of innovations are used to predict market outcomes. A platform innovation is the emergence of an entirely new technology based on scientific principles distinctly different from those of the existing technologies e.g. the compact disk. Design and component innovations incorporate changes in materials and layout respectively within the same platform innovation.

We selected a mix of old and new categories allowing both comparison and validation. Existence of at least two platform-innovations in each category and ease of data availability were other criteria. Our sample includes 23 technologies in external lighting, data transfer, computer memory, desktop printers, display monitors, and analgesics. We first identified all the platform innovations and then recorded the maximum performance of products for each year from its year of introduction till 2001, and details of the firm that introduced it.

Prior literature (Foster 1986, Sahal 1981) suggests that technologies evolve through an initial period of slow growth, followed by one of fast growth culminating in a plateau. When plotted against time, the path resembles an S curve. To test these hypotheses, we plotted performance of technologies over time and also fitted the generalized logistic function using nonlinear regression techniques in SAS to estimate the model, but found little support for the hypothesis. In majority of technologies we found long periods of static performance interspersed with abrupt improvements in performance. Some technologies even showed no change in performance since introduction.

The theory of S curves suggests that sometime during the life of the first technology, a new one emerges and initially it also performs worse than the old technology. With time, it improves faster than the old technology and finally overtakes it in performance. Hence we expect a series of S-curves with single crossing, new attack always from below and each ending at higher level. However we find no support for these hypotheses. Most new technologies performed better than the old technology, right from the time they were introduced while others never improved. Also, some technologies exhibited multiple crossings as dominance shifted interchangeably between the two.

Past research suggests that competition occurs systematically and sequentially along four generic dimensions of inter-technological competition: functionality, reliability, convenience, and cost (Christensen 1999). On the contrary our results suggest a sequence of random, unpredictable secondary dimensions in each of the six categories e.g. brightnessBcolor indexBlifeBcompactness in lighting.

There is evidence of both increasing pace and constant pace of technological change in prior literature. However most of the studies employ indirect measures due to lack of data. Our rich data allows using three direct measures of the rate of technological changeBthe pace of introduction of new technologies, of technological improvements within each platform and the annual rate of improvement for each technology. Tests of all three measures support an increasing pace of technological change.

The conventional wisdom is that small outsiders are more likely to introduce new technologies. Although these small firms are ridiculed and ignored by incumbents in the beginning, they eventually become successful and large incumbents with more opportunity and resources for innovations. However, size and incumbency lead to complacency and technological inertia. Hence we expect innovations to come from small outsiders and large incumbents. We find strong support for both these hypotheses.

To summarize, we failed to find support for many prevailing beliefs about technological change. We conducted a series of robustness tests to validate our findings on shape, path, and crossing patterns. First we redid all analyses for two different reference pointsBfirst technology in each category, the dominant technology in each category in addition to the one just prior. Second, we examined the effect of using benefits per dollar as a metric instead of only benefits. Finally, we also tested the hypotheses using multiple dimensions of performance simultaneously.

This study has several implications for managers. First, using the S-curve to predict the performance of a technology is quite risky and may be misleading. Second, the continuous emergence of new technologies and the steady growth of most technologies suggest that relying on the status quo is deadly for any firm. Third, another threat to incumbents is the emergence of secondary dimensions of competition. Fourth, large firms are not doomed to extinction. More details and results are available in Sood, Ashish and Gerard J. Tellis (2005), "Technological Evolution and Radical Innovation," Journal of Marketing.



Ashish Sood, University of Southern California, U.S.A.
Gerard J. Tellis, University of Southern California, U.S.A.


AP - Asia Pacific Advances in Consumer Research Volume 6 | 2005

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