How big is the early majority segment of the technology adoption life cycle?

The technology adoption lifecycle is a sociological model that 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 majority and late majority, and the last group to eventually adopt a product are called "Laggards" or "phobics." For example, a phobic may only use a cloud service when it is the only remaining method of performing a required task, but the phobic may not have an in-depth technical knowledge of how to use the service.

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) by agricultural researchers Beal and Bohlen in 1957.[1]

The report summarized the categories as:[1]

• 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 neighbors
• late majority – older, less educated, fairly conservative and less socially active
• laggards – very conservative, had small farms and capital, oldest and least educated

The model has subsequently been adapted for many areas of technology adoption in the late 20th century.

Adaptations of the model[edit]

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 innovations, which may result in a Foster disruption based on an s-curve,[2] there is a gap or chasm between the first two adopter groups (innovators/early adopters), and the vertical markets.

Disruption as it is used today are of the Clayton M. Christensen variety. These disruptions are not s-curve based.

In educational technology, Lindy McKeown has provided a similar model (a pencil metaphor[3]) describing the Information and Communications Technology 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 organization.

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.[4]

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.[5]

Stephen L. Parente (1995) implemented a Markov Chain to model economic growth across different countries given different technological barriers.[6]

In Product marketing, Warren Schirtzinger proposed an expansion of the original lifecycle (the Customer Alignment Lifecycle[7]) which describes the configuration of five different business disciplines that follow the sequence of technology adoption.

Examples[edit]

One way to model product adoption[8] 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.

History[edit]

The technology adoption lifecycle is a sociological model that is an extension of an earlier model called the diffusion process, which was originally published in 1957 by Joe M. Bohlen, George M. Beal and 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.[9][10][11] Their original purpose was to track the purchase patterns of hybrid seed corn by farmers.

Bohlen, Beal and Rogers together developed a model called the diffusion process[12] and later, Rogers generalized the use of it in his widely acclaimed 1962 book Diffusion of Innovations[13] (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.[14]

What is the first segment of the technology adoption life cycle called?

What is the early adopters percentage?

What is early adoption of new technology?

What are the 5 segments of technology adaptation?