KorLabs
Why people adopt AI — and why they don't.
KorLabs is the research home of Jason Kor, EdD. My doctoral work at Creighton University asked a narrow question with wide consequences: when organizations put artificial intelligence in front of people, what actually separates the tools they use from the ones they abandon? The answer turned out to be less about the technology than most adoption plans assume.
The study
Risk, Trust, and Utility: Unpacking Consumer Intentions to Adopt Artificial Intelligence — a quantitative correlational study of recent AI and machine learning users, submitted for the Doctor of Education in Interdisciplinary Leadership at Creighton University and published on ProQuest. It extends the Technology Acceptance Model with risk and trust constructs adapted for AI contexts.
Usefulness is what is left standing
Considered one at a time, risk awareness, trust, and ease of use all moved adoption intent. Considered together, only perceived usefulness still separated the adopters from the skeptics. Risk and trust did not disappear — they moved upstream, shaping whether people could see the value at all.
Risk is four concerns, not one
Risk never behaved as a single objection. Financial, psychological, privacy, and performance concerns each pulled differently, and each asks for a different response. A single trust-and-safety message answers only one of the four.
Trust is an enabling condition
Trust in AI developers scored below the scale midpoint — the lowest measure in the study — even among people who intended to adopt. Integrity-related trust mattered more than technical competence. People were not asking whether it could be built. They were asking whether they would be told the truth about it.
Usability is the floor, not the ceiling
Ease of use behaved as a hygiene factor rather than a differentiator. A clumsy tool keeps people from ever seeing its value; a polished one does not create any. Past the point where people can comfortably operate a system, further polish buys progressively less.
Five recommendations for practitioners
The study closes with a practitioner plan, ordered by strength of evidence: demonstrate and deliver usefulness early and often; build trust through integrity, transparency, and accountability; address each risk dimension with practical safeguards and plain-language communication; reduce friction through usability, onboarding, and support; and lead the organizational change that turns intent into sustained use.
Ongoing writing
I publish a weekly piece for industry leaders, each grounded in the study and one of its practitioner recommendations. Read them on LinkedIn under #KorLabsAI.