Glossary of Technology Adoption Terms
Every industry term used across this site, defined in plain English. No jargon without a definition.
These definitions cover technology adoption research, the Dagliyan Theory, AI governance, and enterprise systems practice.
Technology Adoption
The process by which people and organizations start using a new technology in their everyday work. Adoption is more than buying a tool — it means the tool is actually used, trusted, and kept.
Technology Acceptance
An individual's willingness to use a new technology. Research shows acceptance depends mostly on two beliefs: whether the tool seems useful and whether it seems easy to use.
Innovation Diffusion
How a new idea or technology spreads through a group over time — from a few early users to the majority. Different groups adopt at different speeds and for different reasons.
Early Adopter
Someone who tries a new technology soon after it appears, before most people do. Early adopters take more risk and often influence whether the majority follows.
The Chasm
The gap between early adopters, who will tolerate rough edges, and the mainstream majority, who expect a proven, reliable product. Many technologies fail while trying to cross this gap.
Adoption Facilitators
Conditions that make people more likely to use a new technology — for example clear benefits, good training, leadership support, and trust in the provider.
Adoption Inhibitors
Conditions that hold adoption back — for example fear of job loss, unclear benefits, poor fit with existing routines, or lack of trust in the technology.
The Dagliyan Theory
Dr. George Dagliyan's framework for technology adoption. It explains adoption as the interplay of adoption facilitators, adoption inhibitors, and brand influence — the trust a recognized provider adds to the decision.
Brand Influence
The effect a provider's reputation has on whether people adopt its technology. A trusted brand lowers perceived risk, which matters most when the technology itself is new or hard to evaluate.
AI Adoption
The process of putting artificial intelligence tools into real, routine use inside an organization — moving beyond pilots and demos to everyday work.
AI Governance
The rules, roles, and checks an organization puts around its use of AI — who approves AI systems, how they are monitored, and what happens when something goes wrong.
Responsible AI
Building and using AI in ways that are safe, fair, and accountable — including testing for errors and bias, protecting privacy, and being transparent about how the AI is used.
Enterprise System
Large software that runs an organization's core operations — such as finance, inventory, or customer records — and shares that data across departments.
Enterprise Risk Management (ERM)
A structured way for an organization to identify the risks that could hurt it, decide which matter most, and act on them — rather than handling each risk in isolation.
Digital Transformation
Changing how an organization works — its processes, decisions, and services — using digital technology. It is an organizational change effort, not just a software purchase.
Diagnostic Control Systems
Management tools that track measurable results against targets — like dashboards and performance reports — so leaders can spot problems and correct course early.
Change Management
The practice of helping people move from old ways of working to new ones — through communication, training, and support — so a change actually sticks.
Template Builder
An approach to enterprise software in which reusable, configurable templates replace one-off custom builds, making systems faster to deploy and easier to maintain.
AI Readiness
How prepared an organization is to use AI well — including the quality of its data, the skills of its people, its governance, and leadership commitment.
Pilot Purgatory
The common situation where an AI or technology project succeeds as a small trial but never reaches full production use, cycling through demos without delivering real value.
Technology Acceptance Model (TAM)
A widely used research model, introduced by Fred Davis in 1989, that predicts whether people will use a technology based on its perceived usefulness and perceived ease of use.
UTAUT
The Unified Theory of Acceptance and Use of Technology, published by Venkatesh and colleagues in 2003. It combines earlier models and adds factors like social influence and supporting conditions.