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Technology adoption lifecycle

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Title: Technology adoption lifecycle  
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Subject: Technology evangelist, Technology acceptance model, Technology life cycle, Innovation, Chemical engineering
Collection: Demographics, Innovation, Technological Change
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Technology adoption lifecycle

The technology adoption lifecycle is a Everett M. Rogers at Iowa State University and which was originally published only for its application to agriculture and home economics).[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 model called the diffusion process[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]

Rogers' bell curve

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:

  • innovators – had larger farms, were more educated, more prosperous and more risk-oriented
  • early adopters – younger, more educated, tended to be community leaders, less prosperous
  • early majority – more conservative but open to new ideas, active in community and influence to neighbours
  • late majority – older, less educated, fairly conservative and less socially active
  • laggards – very conservative, had small farms and capital, oldest and least educated


  • Adaptations of the model 1
  • Examples 2
  • See also 3
  • Notes 4

Adaptations of the model

The model has spawned a range of adaptations that extend the concept or apply it to specific domains of interest.

In his 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.

Wenger, White and Smith, in their book Digital habitats: Stewarding technology for communities, talk of technology stewards: people with sufficient understanding of the technology available and the technological needs of a community to steward the community through the technology adoption process.[9]

Rayna and Striukova (2009) propose that the choice of initial market segment has crucial importance for crossing the chasm, as adoption in this segment can lead to a cascade of adoption in the other segments. This initial market segment has, at the same time, to contain a large proportion of visionaries, to be small enough for adoption to be observed from within the segment and from other segment and be sufficiently connected with other segments. If this is the case, the adoption in the first segment will progressively cascade into the adjacent segments, thereby triggering the adoption by the mass-market. [10]


One way to model product adoption[11] 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.

A threshold can be set 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.

See also


  1. ^
  2. ^ Gross, Neal C. (1942) The diffusion of a culture trait in two Iowa townships. M.S. Thesis, Iowa State College, Ames.
  3. ^ Ryan, Bryce, and Neal C. Gross (1943) “The diffusion of hybrid seed corn in two Iowa communities.” Rural Sociology 8: 15–24. RS(E)
  4. ^ Ryan, Bryce, and Neal C. Gross (1950) Acceptance and diffusion of hybrid corn seed in two Iowa communities. Research Bulletin 372, Agricultural Experiment Station, Ames, Iowa.
  5. ^ Beal, George M., Everett M. Rogers, and Joe M. Bohlen (1957) "Validity of the concept of stages in the adoption process." Rural Sociology 22(2):166–168.
  6. ^ Rogers, Everett M. (1962). Diffusion of Innovations, Glencoe: Free Press.
  7. ^ Savage, Robert L. (1985). "Diffusion Research Traditions and the Spread of Policy Innovations in a Federal System" Publius 15 (Fall): 1–27.
  8. ^ Pencil metaphor
  9. ^
  10. ^ Rayna, Thierry, Striukova, Ludmila and Landau, Samuel, Crossing the Chasm or Being Crossed Out: The Case of Digital Audio Players (March 7, 2009). International Journal of Actor-Network Theory and Technological Innovation, Vol. 1, No. 3, pp. 36-54, July-September 2009. Available at SSRN:
  11. ^ Von Ahn, Luis. (2008) Science of the Web lectures at Carnegie Mellon University.
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