Apply the Technology Acceptance Model (Davis, 1989) and Unified Theory of Acceptance and Use of Technology (Venkatesh et al., 2003) to predict technology adoption.
SkillScore64Maintained
Automatic
◆Research pack
Last verified 8/28/2026
What it changes
Passed 14 of 22 test cases with the skill, and 12 of 22 without it.
Without skill55%
With skill64%
Checks it passed that the model alone missed
The markdown table header contains the column Score (1-7).
The markdown table header contains the column Key Drivers.
The markdown table header contains the column Key Barriers.
and 2 more in the full report
2 fixed12 passed either way8 still failing0 broke
Pass-rate lift
+9 pp
Tokens
+25%
Agent turns
0%
Response time
-9%
Model gemini-3.6-flashMeasured 8/28/2026v114 of 22 cases passed with skill
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 2 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 2
Prompt
We are rolling out an enterprise e-signature tool across legal departments. Some teams report high social influence from department heads. How does social influence interact with voluntariness and user experience during initial rollout?
What was checked, without and with the skill
Checked
Without skill
With skill
The analysis states that Social Influence has a stronger impact in mandatory adoption settings than voluntary ones.
Missed
Passed
The analysis states that Social Influence is most pronounced for early adopters.
Missed
Passed
case-11 · gemini-3.6-flash · 8/28/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md98 linessingle file
Technology Acceptance Model (TAM) and UTAUT
Overview
TAM posits that Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) determine behavioral intention to use technology. UTAUT synthesizes eight prior models into four core constructs — Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions — moderated by age, gender, experience, and voluntariness.
When to Use
Predicting user adoption of a new technology or system
Diagnosing why a technology rollout has low uptake
Designing interventions to improve acceptance rates
Organizational-level diffusion (use DOI or TOE framework)
When adoption is mandatory with no behavioral variance
Assumptions
IRON LAW: Technology adoption is driven by PERCEIVED value, not actual
capability. A superior system with poor perceived usefulness will be
rejected; an inferior system perceived as useful will be adopted.
Key assumptions:
Users are rational actors who form intentions before behavior
Perceptions can be measured via self-report instruments
External variables influence adoption only through the core constructs
Behavioral intention is the primary predictor of actual use
Methodology
Step 1 — Define the technology and user population
Specify the system under evaluation, target users, and usage context. Identify whether adoption is voluntary or mandatory.
Apply the Uppsala Internationalization Model to analyze gradual foreign market entry based on psychic distance and experiential learning. Use this skill when the user needs to plan a staged internationalization sequence, understand why firms enter culturally similar markets first, or evaluate whether a firm's international expansion follows the establishment chain from export to subsidiary.