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Algorithm Aversion / Appreciation

Information SystemsReliability: alpha 0.81–0.883 sample items

About this scale

A multi-item measure of the tendency to discount algorithmic advice in favour of human advice (aversion) or to overweight algorithmic advice (appreciation). Operationalized through preference items and behavioural weight-of-advice measures. Foundational for AI-in-decision-making research.

Sample item(s)

“I would rather take advice from a human expert than from an algorithm.”
“Algorithms are usually less accurate than human experts at tasks like this. (reverse-scored for appreciation)”
“I would feel more confident in a decision if it were supported by an algorithm.”

Reliability

Reported internal consistency: alpha 0.81–0.88. As a rule of thumb, Cronbach’s α ≥ .70 is considered acceptable, ≥ .80 good, and ≥ .90 excellent for established measures.

Notes

Logg, Minson & Moore (2019) document algorithm appreciation in the opposite direction; both phenomena coexist.

Source & citation

Dietvorst, B. J., Simmons, J. P., & Massey, C. (2015): Algorithm aversion: People erroneously avoid algorithms after seeing them err. Journal of Experimental Psychology: General, Vol. 144, No. 1, pp. 114–126.

Dietvorst, Simmons & Massey (2015): Algorithm aversion. JEP-G, 144(1), 114–126.

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.

Use it in your research

Need to check the reliability or psychometrics of this scale in your own data? Use the free DrAIStudio research tools:

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