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Algorithmic HRM Acceptance

HR & WorkReliability: alpha 0.85–0.90 across subdimensions4 sample items

About this scale

A multi-item measure of employees' acceptance of HR decisions made by algorithms or AI (e.g., hiring, promotion, performance evaluation, scheduling, compensation). Captures perceived appropriateness, fairness, and willingness to comply with algorithmic HR decisions.

Sample item(s)

“I would accept a hiring decision made by an AI system.”
“I am comfortable with my performance being evaluated by an algorithm.”
“Algorithmic HR decisions are appropriate in my workplace.”
“I trust algorithmic decisions in HR as much as decisions made by human managers.”

Reliability

Reported internal consistency: alpha 0.85–0.90 across subdimensions. As a rule of thumb, Cronbach’s α ≥ .70 is considered acceptable, ≥ .80 good, and ≥ .90 excellent for established measures.

Notes

Decision domain (hiring vs. firing vs. evaluation) strongly moderates acceptance — anchor items to the specific decision context.

Source & citation

Meijerink, J., Boons, M., Keegan, A., & Marler, J. (2021): Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM. International Journal of Human Resource Management, Vol. 32, No. 12, pp. 2545–2562.

Meijerink, Boons, Keegan & Marler (2021): Algorithmic HRM. IJHRM, 32(12), 2545–2562.

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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