When Acceptance Doesn’t Travel: Measurement (Non-)Invariance in Acceptance Models of AI Assistants for Privacy Management

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Abstract
Personalized privacy assistants (PPAs) are emerging AI assistants that hold the promise to manage privacy on users’ behalf. Prior to the design decisions and deployments of a PPA in different regions, it is important to understand prospective user acceptance and factors contributing to users’ behavioral intention to adopt the assistant. In this paper, we report a cross-regional survey study with participants from China, Germany and California analyzing a UTAUT2-informed acceptance model for PPA acceptance. The results suggest that cross-regional comparability of PPA acceptance cannot be assumed. While partial measurement invariance supports meaningful structural comparisons between California and Germany, compositional invariance was not established for several constructs when comparing the Chinese sample with Californian and German samples. Together, we raise three provocations based on these findings: 1) Cross-regional acceptance comparisons might often be invalid by design; 2) it is time to reconsider acceptance models for AI assistants for privacy management, and 3) some thoughts on the future of PPAs.

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