Technology Adoption Curve (Diffusion of Innovation)

The technology adoption curve is the bell-curve model from Everett Rogers’ Diffusion of Innovations (1962) that segments adopters of a new technology by innovativeness into five groups, each a slice of the normal distribution:

• Innovators (~2.5%) — risk-takers, resourced, first to try.
• Early adopters (~13.5%) — opinion leaders, high status, judicious choices.
• Early majority (~34%) — deliberate; adopt once it’s proven.
• Late majority (~34%) — skeptical; adopt after the majority has.
• Laggards (~16%) — tradition-bound; last to change.

The founding concept of this wiki’s cluster F (the diffusion & adoption of ideas/technologies), and the hub the cluster’s frameworks hang off.

Rogers’ full model (per diffusion-of-innovations-wikipedia)

Rogers defines diffusion as “the process by which an innovation is communicated through certain channels over time among the participants in a social system” — four elements: the innovation, communication channels, time, and the social system. Adoption over time traces an S-curve (cumulative), whose bell-curve derivative is the adopter split above; it becomes self-sustaining at critical mass. Five attributes predict how fast something diffuses: relative advantage, compatibility, complexity (simpler = faster), trialability, observability. Key critique: pro-innovation bias — the model assumes adoption is always good.

The two refinements that complete cluster F

• crossing-the-chasm (geoffrey-moore) — argues a chasm sits between early adopters (visionaries) and the early majority (pragmatists), a discontinuity Rogers’ continuous curve doesn’t show. (Rogers disputed this — see the tension in synthesis.)
• gartner-hype-cycle (Jackie Fenn, Gartner) — a maturity/expectations lens (trigger → peak → trough → slope → plateau) that pairs with the adoption curve but is descriptive of hype, not adoption share; heavily critiqued as non-universal.

The missing variable: who decides (2026-07-28)

Every predictor in the model above belongs to the innovation or to how word of it travels. There is no term for power — no state, no regulator, no organised opposition. brief-history-of-luddism (The Economist, paywalled past the first paragraph) puts exactly that in its standfirst: “States ultimately decide how fast technology is adopted.” If that holds, Rogers describes diffusion inside limits set politically rather than explaining the pace itself.

The historical case is the one the column opens on. The Luddites were not slow adopters waiting for the S-curve to reach them; they were textile workers destroying specific machines at night because those machines “stole jobs from humans.” Rogers’ own acknowledged pro-innovation bias — the assumption that adopting is the right move — is what the category “laggards” hides: sometimes the people at the tail have read their interests correctly and are acting on them.

Recorded as an open tension, not a correction. The argument behind that standfirst is unread.

Why it lives in this wiki

1. It is itself a “tool for thinking” about technological change — a conceptual framework for organizing one’s read of where ideas stand.
2. Reflexive / trend-spotting. The wiki is an LLM-maintained instrument for tracking ideas as they emerge; tech-adoption-curve-twenty-years argues good trend-calling comes from staying close to practitioners — what’s getting harder, not what’s getting hyped — the editorial cousin of this wiki’s own synthesis/lint discipline and the llm-wiki/gbrain continuous-curation bet.

Locating yourself on the curve, live

The models above are fitted after the fact. adoption-curve-measurement is the cluster’s measurement side: what an instrument would have to do, one candidate examined (search-demand signals, from search-marketing-wiki’s emerging-category-search-signals), and the four ways it fails — interest is not adoption, there is no denominator, it cannot tell hype from uptake, and it has no false-positive rate. It also reads directly on the power gap named above: searches for standards and statutes are the institutional layer moving first.

Related

diffusion-of-innovations-wikipedia · everett-rogers · crossing-the-chasm · gartner-hype-cycle · tech-adoption-curve-twenty-years · brief-history-of-luddism · tools-for-thought · llm-wiki