Domain knowledge for AI adoption measurement, psychometric instrument development, and appropriate reliance research
Domain knowledge for AI adoption measurement, psychometric instrument development, and appropriate reliance research
This skill contains knowledge about the AIRS-16 validated instrument, the proposed AIRS-18 extension with Appropriate Reliance (AR), and research methodologies for studying AI adoption and human-AI collaboration.
| File | Purpose |
|---|---|
article/APPROPRIATE-RELIANCE-TECHNICAL-BRIEF.md | Full technical brief with AR implementation |
article/HOFMAN-MEETING-BRIEF.md |
| Research meeting preparation template |
alex_docs/AR-TELEMETRY-DESIGN.md | Behavioral telemetry design for hypothesis validation |
Source: Correa, F. (2025). Doctoral dissertation, Touro University Worldwide.
Production: airs.correax.com | Time: 5 minutes | Built by: Alex Cognitive Architecture
Validation: N=523, CFI=.975, TLI=.960, RMSEA=.053, R²=.852
| Link | Purpose |
|---|---|
| Take Assessment | Start the 16-item survey |
| View History | Review past results |
| Register Org | Enterprise organization setup |
| GitHub (Platform) | AIRS Enterprise source code |
| GitHub (Research) | Validation data & analysis |
| Role | Access |
|---|---|
| 👤 Participant | Take assessments, view personal results, download PDF reports |
| ✨ Founder | Organization creator, can be promoted to Admin |
| 🛡️ Admin | Dashboard analytics, member management, invitations |
| 👑 Super Admin | Platform-wide access, all orgs, AI prompts configuration |
| Construct | Code | Description |
|---|---|---|
| Performance Expectancy | PE | Belief that AI will help achieve job performance gains |
| Effort Expectancy | EE | Perceived ease of use of AI systems |
| Social Influence | SI | Degree to which colleagues/leadership encourage adoption |
| Facilitating Conditions | FC | Availability of organizational resources and training |
| Hedonic Motivation | HM | Enjoyment and curiosity when exploring AI capabilities |
| Price Value | PV | Perceived benefit relative to effort invested (β=.505 — strongest predictor) |
| Habit | HB | Extent to which AI use has become automatic and routine |
| Trust in AI | TR | Confidence in AI reliability, accuracy, and data handling |
| Predictor | β | p | Status |
|---|---|---|---|
| Price Value (PV) | .505 | <.001 | ✅ STRONGEST |
| Hedonic Motivation (HM) | .217 | .014 | ✅ Significant |
| Social Influence (SI) | .136 | .024 | ✅ Significant |
| Trust in AI (TR) | .106 | .064 | ⚠️ Marginal |
| Performance Expectancy (PE) | -.028 | .791 | ❌ Not significant |
| Effort Expectancy (EE) | -.008 | .875 | ❌ Not significant |
| Facilitating Conditions (FC) | .059 | .338 | ❌ Not significant |
| Habit (HB) | .023 | .631 | ❌ Not significant |
Insight: Traditional UTAUT2 predictors (PE, EE, FC, HB) do NOT predict AI adoption. Value perception, enjoyment, and social influence matter.
# AIRS Score = sum of 8 construct means (range: 8-40)
AIRS = PE + EE + SI + FC + HM + PV + HB + TR
# Typology (94.5% accuracy)
if AIRS <= 20: "AI Skeptic" # 17% of sample
elif AIRS <= 30: "Moderate User" # 67% of sample