Human–AI Collaboration Quality
Information SystemsReliability: alpha 0.86–0.91 across subscales4 sample items
About this scale
A multi-item measure of the perceived quality of working with an AI agent or tool, including coordination, complementarity, transparency, and shared understanding. Captures the team-like quality of human–AI dyads in knowledge work.
Sample item(s)
“The AI tool and I complement each other well in completing tasks.”
“I understand how the AI tool arrives at its outputs.”
“I can rely on the AI tool to do its part of the task.”
“The AI tool and I produce better work together than either of us could alone.”
Reliability
Reported internal consistency: alpha 0.86–0.91 across subscales. As a rule of thumb, Cronbach’s α ≥ .70 is considered acceptable, ≥ .80 good, and ≥ .90 excellent for established measures.
Notes
Recent operationalizations: McNeese et al. (2018) HAT trust scale; Bansal et al. (2024) human–AI complementarity index.
Source & citation
Seeber, I., Bittner, E., Briggs, R. O., De Vreede, T., De Vreede, G.-J., Elkins, A., Maier, R., Merz, A. B., Oeste-Reiß, S., Randrup, N., Schwabe, G., & Söllner, M. (2020): Machines as teammates: A research agenda on AI in team collaboration. Information & Management, Vol. 57, No. 2, Article 103174.
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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