When Readiness Doesn't Lead to Adoption

This post draws on my work using UTAUT and TOE–TAM to study AI integration in higher education, including the preprint “When Readiness Doesn’t Lead to Adoption.” Consider it a short companion to that research.

A lot of technology-acceptance research quietly assumes a straight line: measure readiness — awareness, attitudes, infrastructure, perceived usefulness — and adoption follows. In the higher-education settings I study, that line keeps breaking. Institutions score well on readiness instruments and then do not actually adopt the tools.

Why the gap opens

Readiness and adoption are measured at different levels and different moments. Readiness is often an individual, attitudinal snapshot; adoption is an organisational, behavioural outcome that unfolds over time. Between them sit factors the readiness survey never captured:

• Organisational fit — does the tool match how the institution actually makes decisions, allocates budgets, and rewards staff?
• Technology conditions — is there support, maintenance, and continuity, or a pilot that ends when the funding does?
• Perceived risk — what happens to an educator who adopts and it fails?

Frameworks like TOE (technology–organisation–environment) combined with TAM help name these, which is why I pair them rather than relying on acceptance models alone.

Why it matters for policy

If we treat readiness as a proxy for adoption, we will keep declaring success at the wrong moment — funding readiness-building while adoption stalls out of view. The more useful move is to measure adoption directly, over time, and to design for the organisational and environmental conditions that actually carry a tool from “ready” to “in use.” That is the thread my current research is following.