The technology adoption lifecycle is a sociological model developed by Joe M. Bohlen, George M. Beal and Everett M. Rogers at Iowa State University,[1] building on earlier research conducted there by Neal C. Gross and Bryce Ryan.[2][3][4] Their original purpose was to track the purchase patterns of hybrid seed corn by farmers.
Beal, Rogers and Bohlen together developed a technology diffusion model[5] and later Everett Rogers generalized the use of it in his widely acclaimed book, Diffusion of Innovations[6] (now in its fifth edition), describing how new ideas and technologies spread in different cultures. Others have since used the model to describe how innovations spread between states in the U.S.[7]
The technology adoption lifecycle model describes the adoption or acceptance of a new product or innovation, according to the demographic and psychological characteristics of defined adopter groups. The process of adoption over time is typically illustrated as a classical normal distribution or "bell curve." The model indicates that the first group of people to use a new product is called "innovators," followed by "early adopters." Next come the early and late majority, and the last group to eventually adopt a product are called "laggards."
The demographic and psychological (or "psychographic") profiles of each adoption group were originally specified by the North Central Rural Sociology Committee, Subcommittee for the Study of the Diffusion of Farm Practices (as cited by Beal and Bohlen in their study above).
The report summarised the categories as:
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The model has spawned a range of adaptations that extend the concept or apply it to specific domains of interest.
In this book, Crossing the Chasm, Geoffrey Moore proposes a variation of the original lifecycle. He suggests that for discontinuous or disruptive innovations, there is a gap or chasm between the first two adopter groups (innovators/early adopters), and the early majority.
In Educational technology, Lindy McKeown has provided a similar model (a pencil metaphor[8]) describing the ICT uptake in education. In medical sociology, Carl May has proposed Normalization Process Theory that shows how technologies become embedded and integrated in health care and other kinds of organisation.
One way to model product adoption[9] is to understand that people's behaviors are influenced by their peers and how widespread they think a particular action is. For many format-dependent technologies, people have a non-zero payoff for adopting the same technology as their closest friends or colleagues. If two users both adopt product A, they might get a payoff a > 0; if they adopt product B, they get b > 0. But if one adopts A and the other adopts B, they both get a payoff of 0.
We can set a threshold for each user to adopt a product. Say that a node v in a graph has d neighbors: then v will adopt product A if a fraction p of its neighbors is greater than or equal to some threshold. For example, if v's threshold is 2/3, and only one of its two neighbors adopts product A, then v will not adopt A. Using this model, we can deterministically model product adoption on sample networks.