Measuring public opinion towards artificial intelligence: development and validation of a general Al attitude short scale
A new 6-item scale reliably measures general AI attitudes, predicting AI acceptance across various risk contexts.
The rapid growth of AI necessitates reliable tools to gauge public sentiment. This study introduces a concise, psychometrically sound 6-item scale for measuring general AI attitudes, validated across German and US samples using both classical test theory (CTT) and item response theory (IRT). This scale is crucial for policymakers to understand public concerns, balance innovation, and ensure equitable AI governance.
Executive Impact
Our analysis reveals a robust general AI attitude short scale, demonstrating excellent internal consistency (α=0.95) and strong criterion validity. It predicts AI acceptance across low-, medium-, and high-risk scenarios, and correlates positively with digital competency. Demographic factors like age, gender, education, and AI familiarity significantly influence attitudes and acceptance, highlighting the need for nuanced policy interventions.
0.95
Internal Consistency (Cronbach's α)
0.991
CFA Fit (CFI)
0.59
AI Attitude R² for AI Acceptance (Low Risk)
0.48
AI Attitude R² for AI Acceptance (High Risk)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
The study rigorously validated a new 6-item general AI attitude short scale using both Classical Test Theory (CTT) and Item Response Theory (IRT). It demonstrated high reliability (Cronbach's α=0.95) and excellent model fit in Confirmatory Factor Analysis (CFA) across German and US samples. Item discrimination was high, and difficulty parameters showed broad coverage of the attitude spectrum. While some sensitivity was reduced at extreme ends, the scale provides a robust measure for general population attitudes.
The AI attitude scale showed strong criterion-related validity, positively correlating with digital competency and predicting AI acceptance across low-, medium-, and high-risk applications. General AI attitudes explained a substantial portion of variance in AI acceptance (R² 48–59%). Attitude strength (extremity) enhanced, while structural ambivalence (cognitive-affective inconsistency) weakened, the predictive power of AI attitudes on acceptance, consistent with established moderation theories.
Analysis revealed significant demographic predictors of AI attitude and acceptance in Germany. Younger individuals, those with prior AI experience, higher education, and greater digital competency generally held more positive attitudes. Older individuals and females tended to be more skeptical. The influence of these factors varied by risk context: digital competency and AI familiarity predicted acceptance in low-risk contexts but showed no or even reversed effects in high-risk scenarios.
0.95
Internal Consistency (Cronbach's α) across samples
Enterprise Process Flow
Concept Definition (Unidimensional, 3 Facets)
→
Item Adaptation (6 items from Stein et al., 2 non-reverse coded per facet)
→
Response Scale Adjustment (7-point, endpoint-labeled)
→
SQP Quality Estimation (Higher quality for new items)
→
Rigorous Validation (CTT & IRT across German & US samples)
→
Criterion Validity (Predicts AI acceptance, correlates with digital competency)
| Feature | Our Short Scale | Existing Scales (General Limitations) |
|---|---|---|
| Length | 6 items | Often too long (e.g., 20, 12 items) for multi-topic surveys |
| Facets Covered | Affective, Behavioral, Cognitive | Often focus only on negative aspects or miss facets |
| Internal Consistency | Excellent (α=0.95) | Sometimes subpar (α<0.74) |
| Factor Structure | Unidimensional, excellent fit | Often 2 opposing factors, or complex multidimensional |
| Psychometric Analysis | CTT & IRT used (first time for AI attitude scale) | Mostly CTT, IRT rarely or never used |
| AI Definition Provided | Yes | Often not provided, leading to ambiguity |
| Applicability | General, robust to evolving AI tech | May be limited by specific application focus or length |
Impact of Attitudes on AI Acceptance: A German Case Study
In our German sample, general AI attitudes were the strongest predictor of AI acceptance across all risk contexts (low, medium, high), explaining 48-59% of variance. This highlights how public sentiment directly translates into readiness to embrace AI applications. For instance, high AI attitude correlated with higher acceptance of AI for speech translation, legal document review, and even psychological counseling. However, demographic factors nuanced this: higher education and AI familiarity led to more skepticism in high-risk scenarios, suggesting well-informed individuals are more critical of sensitive AI applications.
Advanced ROI Calculator
Estimate the potential cost savings and efficiency gains by aligning your AI strategy with public sentiment and organizational readiness.
Potential Annual Savings
$0
Employee Hours Reclaimed Annually
0
Your Implementation Roadmap
A structured approach to integrate AI while considering public and employee sentiment for maximum success.
Phase 1: Initial Assessment & Strategy
Understand current AI perceptions, identify key stakeholders, and define clear objectives for AI integration. This phase involves deep dives into your organizational culture and existing technological infrastructure.
Phase 2: Scale Customization & Deployment
Tailor the AI attitude scale to specific organizational contexts if needed. Deploy the scale across target employee groups or customer segments, ensuring robust data collection methods.
Phase 3: Data Analysis & Insight Generation
Utilize advanced psychometric methods (CTT, IRT) to analyze collected data. Identify key drivers of AI attitudes, potential resistance points, and segments with high AI readiness. Develop actionable insights for communication and training.
Phase 4: Targeted Interventions & Monitoring
Implement communication campaigns and training programs tailored to address specific attitude barriers. Continuously monitor shifts in AI attitudes over time, adjusting strategies as AI technologies evolve.
Ready to Measure and Shape AI Attitudes in Your Organization?
Our expertise in psychometric scale development and AI attitude research can help you confidently navigate the complexities of AI integration. Let's discuss a tailored strategy for your enterprise.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat