JOURNAL ARTICLE · EDITORIAL BRIEF
Development and Validation of the Large Language Model Privacy Concern Assessment Questionnaire: Psychometric Evaluation Study on a Brief Instrument for Assessing User Privacy Concerns With Large Language Model Applications
Leming Zhou; Hongtao Wang; Yi Xu; I Made Agus Setiawan; Haomin Hu; Andi Saptono; Yong K Choi; Bambang Parmanto
JMIR Human Factors · 21 October 2026
The finding or development
A brief questionnaire showed high internal consistency for AI privacy concerns. Transparency and control were closely related to concerns about data use; the authors recommend a total score.
What supports it
Journal psychometric study: 361 analysed responses; exploratory and holdout confirmatory factor analyses of a six-item scale.
Reviewed: Publisher abstract, publication metadata and limitations section; supplements and raw data not reviewed.
Why it matters for design
A starting point for investigating privacy perceptions alongside interviews and observed behaviour, rather than treating adoption as evidence of trust.
Read with this in mind
Predominantly White, highly educated sample; no lay cognitive pretesting or test–retest assessment. Mixed response anchors assume equivalence. Preliminary validation does not establish actual privacy protection or generalisability.
Original source
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DOI: 10.2196/102910
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