The art of timing innovation – Part I

According to Gassmann et al. [1] timing is one of the main dimensions to consider when analyzing factors affecting a focal business model.

Why?

Giesen et al. [2] claim that during stable periods within the industry landscape it is satisfactory for organizations to do incremental adjustments to their focal business model, in essence keeping their contemporary one. However, we live in times of great change where the rate of technological development, changes in consumer behavior and civil unrest is unprecedented. Such changes force entire industries to adapt and during periods of industrial change an organization needs to do more radical changes to its business model .

Ok, so how does it work then?

Periods of stability and change can be explained through the innovation lifecycle theory which explains how a product, service or technology evolves through a set of stages over time [3][4][5]. Generally, the speed through which the lifecycle develops and declines is industry specific (Kaplan, 2014). Additionally, an S-curve diagram can be used to depict the lifecycle of a product, service or technology where the Y-axis represents “market adoption” in terms of cumulative sales performance while the X-axis represents the flow of time (see figure A) [4][5]. Consequently, the different stages of the lifecycle (introduction, growth, maturity and decline) are connected to the diffusion process of an innovation.

Early in a diffusion process of any product/technology, adoption rate is slow as technologically curious innovators (who only represent around 2,5% of the market) and early adopters (who represent around 14% of the market) are the ones that initially buy but they represent a small portion of the potential market [5][6]. The so called innovators and early adaptors ignore uncertainty regarding product performance as well as the initial high price of the product/technology due to curiosity, interest and/or the potential benefits of the product/technology [6]

Innovation life c

Figure A: Innovation lifecycle and diffusion rate 
Source: highered.mheducation.com, 2014

However, as time passes adoption rate increase as a dominant design emerges and the majority buyers, who are mainly risk adverse and shy away from new technology since they are unable to understand or appropriate its benefits or generally uninterested, become confident enough to buy the product/service/technology  [5][6][7]. The majority usually waits until the product/technology is perceived as reliable and trusted actors approve of its functionality [6]. Thus, the primary aim of a marketing strategy during the early phases of the lifecycle is to educate the market, create market awareness and prove the product performance.

As awareness of builds up, early majority buyers begin to purchase the product which act as a catalyst for other majority adopters [6]. When the lifecycle reaches maturity it can be extended through incremental improvements until decline. The decline stage is often broken through substitution for another sustaining technology or the occurance of disruption [4][5][8]. Consequently, the beginning of a curve relates to the creation of a market opportunity whereas the final part of the curve represents the decline of market adoption for the product, service, or technology [4].

Ummm…that is really theoretical.

Right! So stay tuned for the next part where I will give examples in order to make the stuff above more tangible and also dig into the theory revolving jumping S-curves.

References

[1] Bucherer, B., Eisert, U. and Gassmann, O. (2012) Towards Systematic Business Model Innovation: Lessons from Product Innovation Management. Creativity and innovation management, 21(2): 183-198.

[2] Giesen, E., Riddleberger, E., Christner, R. & Bell, R. (2009). Seizing the advantage – When and how to innovate your business mode. IBM Global Business Services: Executive Report.

[3] Birchenhall, C. & Windrum, P. (1998). Is product life cycle theory a special case? Dominant designs and the emergence of market niches through coevolutionnary-learning. Structural Change and Economic Dynamics 9: 109-134

[4] Kaplan, S. (2014). Innovation Lifecycles – Leveraging market, technology, and organizational S-curves to drive breakthrough growth. Innovation Point. Available at: http://www.innovation-point.com/Innovation_Lifecycles.pdf [acessed 2014-10-29 15:32]

[5] Rogers, E.M. (2003). Diffusion of innovations. (5. ed.) New York: Free press.

[6] Asthana, P. (1995). Jumping the technology S-curve. Available at: http://www.engr.mun.ca/~amyhsiao/Scurve.pdf [accessed: 2014-11-23 18:10]

[7] Teece, D.J. (1986). Profiting from technological innovation: Implications for integration, collaboration, licensing and public policy. Research Policy 15(6): 285–305

[8] Christensen, C.M. (2003 [2000]). The innovator’s dilemma: the revolutionary book that will change the way you do business. New York: HarperBusiness Essentials.

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