AI Capabilities Scale – AICAP
Measures the capabilities of artificial intelligence (AI) technologies within organizational contexts across business model innovation, information management, and computer science.
Scale Development & Technical Details
Scale Overview
The AI Capabilities Scale – AICAP was developed by Mohamad Abou-Foul, Jose L. Ruiz-Alba, & Pablo J. López-Tenorio (2023). It is designed to measure Artificial Intelligence Capabilities. The scale is intended for use with Organizations / Manufacturing firms.
Scale Structure
This instrument consists of 17 items organized into 4 factors/subscales: AI customer value proposition, AI key processes optimization, AI key resources optimization, AI societal good.
| Factor / Subscale | Items | N |
|---|---|---|
| AI customer value proposition | 1,2,3,4 | 4 |
| AI key processes optimization | 5,6,7,8,9,10 | 6 |
| AI key resources optimization | 11,12,13,14 | 4 |
| AI societal good | 15,16,17 | 3 |
Response Format
Respondents rate each item using a 5-point Likert scale (1 = Strongly Disagree to 5 = Strongly Agree).
Scoring
Items are scored by subscale, with each subscale sum representing a distinct dimension.
Total scores range from 17 to 85. Interpretation guidelines:
- Low AI Capabilities: 17 – 34
- Moderate AI Capabilities: 35 – 68
- High AI Capabilities: 69 – 85
Psychometric Properties
Internal Consistency: The scale has demonstrated good to excellent internal consistency with reported Cronbach’s alpha values of Cronbach's alpha > 0.70 for all sub-dimensions.
Administration
The scale is self-administered and typically takes approximately 5 minutes to complete. It can be administered individually or in group settings. No special training is required for administration.