chapter 3 - Bentham Science

Forecasting with Innovation Diffusion Applications of Spreadsheets in Education The Amazing Power of a Simple Tool 161
4. The fourth or late majority stage makes up another 34% of the market. Late majority adopters
do not adopt products until after all risks have been identified and when the adoption becomes
an economic necessity or when there is social pressure to adopt.
5. The fifth and final stage, which accounts for the final 16% of the market are commonly
referred to as laggards. Laggards frequently wait to adopt a new technology until the next
technology generation has entered the market.
Fig. (9.2) provides a graphic representation of the adopter categories [2].
Fig. (9.2): Distribution of adopter categories.
The constant examination of products as they move through their life cycles is crucial to a firm’s
success. Managers are challenged with developing new strategies, examining ongoing strategies,
and eliminating non-competitive strategies for a product’s diffusion into markets. Many times it is
the primary job of the operations and marketing managers to forecast a product’s diffusion. Within
the business discipline the product life cycle and forecasting can be linked using what is called
innovation diffusion theory [3].
Innovation diffusion theory has received considerable attention since its inception in the 1960’s.
The diffusion of innovation can be defined as the process by which an innovation is imparted on
members of society through certain channels over time [4]. Seminal works in the area of technological
change and rates of imitation were completed by Fourt [5] and Mansfield [6]. A detailed
discussion of using diffusion models in product forecasting can be found in [7]. The work by
Fourt [5] and Mansfield [6] are the basis for research in diffusion modeling by Bass [8].
The Bass model for new product growth and innovation diffusion has been used to forecast
numerous products in many different industries [5, 6, 8–13]. A comprehensive review of the contributions
to this literature through the 1970’s is provided in [14] and on into the 1980’s in [15].
An excellent article comparing nine growth and innovation diffusion models, including Mansfield’s
and Bass’ models, can be found in [13]. The influence of information technology diffusion on large
and small businesses is discussed in [9, 10]. Additional insights are provided in [16–20].
This chapter extends the work by [21] to the original Mansfield model applied to the technology
industry where successive generations of the technology exist. The model is developed and then
tested using modem sales from 1994-2009. The diffusion model is applied to each successive
generation of modem innovation: 14.4k, 28.8k, 56k, broadband < 3.6Mbps, and broadband >
3.6Mbps. Two forecasting models for the > 3.6Mbps technology are developed, analyzed, and
reported on.