Analysis and quantification of technological cycles in high -technology industries
Abstract
New products are vital to the economic success of businesses. The speed and market dynamics of innovation in industry are important to understand when developing a business strategy from areas such as research funding to corporate structure. Developing a better understanding of the typical market dynamics will also aid in forecasting future new product cycles. Typical approaches to modeling the diffusion of new innovations in industry involve classification of the adopting populations into one or two specific categories with a nonlinear regressive fit to the respective models. Two flexible growth models developed for the biological sciences were adapted to analyze a cross section of high technology and product adoption curves. These models allowed for a multiplicity of adopting population influences rather than the specific categorizations used previously. One model developed by Schnute in 1981 was found to be particularly useful in the very early forecasting of a new product's growth. The analyses indicate that while the adopting population appears to be homogeneous within a single product's life cycle, the influence of the adopting populations of successive product generations are often significantly different. Dynamic random access memories (DRAMs) had fairly consistent adoption influences across product generations, while the adoption influences of successive generations of microprocessors were distinctly different. These adoption influences will drive the sales and marketing strategy and structure and could require changes in corporate strategy as new product generations are released. The growth times for the microprocessors appears not to be following any specific trend across product generations. Alternatively, the growth times for DRAMs has dramatically declined with newer generations.
Degree
Ph.D.
Advisors
Compton, Purdue University.
Subject Area
Industrial engineering|Marketing
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