AI Creativity Attribution
Information SystemsReliability: alpha 0.863 sample items
About this scale
A multi-item measure of how users attribute creative credit when working with AI — to themselves, the AI, or shared. Captures the new question of authorship in human–AI collaboration.
Sample item(s)
“When I use AI to help create something, the credit is mostly mine”
“AI tools deserve some recognition for the creative output we produce together”
“Without AI, the creative output would not exist as it is”
Reliability
Reported internal consistency: alpha 0.86. As a rule of thumb, Cronbach’s α ≥ .70 is considered acceptable, ≥ .80 good, and ≥ .90 excellent for established measures.
Notes
Captures the novel construct of distributed authorship in human–AI work.
Source & citation
Magni, F., Park, J., & Chao, M. M. (2024): Humans as creativity gatekeepers: Are we biased against AI creativity? Journal of Business and Psychology, Vol. 39, No. 3, pp. 643–656.
Magni, Park & Chao (2024): AI creativity bias. JBP, 39(3), 643–656.
Source. Scale details are summarised from the publication cited above. Always consult the original source for the full item set, scoring, and permission to use.
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