Perceived usefulness of generative AI-based digital therapeutics and women's intention to adopt: A cross-sectional PLS-SEM study

초록

Objective This study examined the structural relationship between perceived usefulness of generative artificial intelligence (AI)-based digital therapeutics (DTx) and the formation of DTx acceptance intentions among women in their 20s, with a focus on how purpose-specific perceptions are integrated into concrete acceptance judgments.Methods In this cross-sectional online survey study, data were collected from 117 women in their 20s in the Republic of Korea and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). A two-stage reflective-reflective Higher-Order Component Model (HCM) was applied to integrate acceptance intentions across preventive and management, therapeutic, and medication optimization purposes.Results Perceived usefulness of DTx (DTx Usefulness) was the only significant predictor of the higher-order acceptance structure (HAS) (beta = 0.593, p < 0.001), and HAS showed a strong association with the final acceptance intention for medication optimization-oriented DTx use (beta = 0.846, p < 0.001). Approximately 72% of the variance in acceptance intention was explained by HAS. A sensitivity re-estimation excluding MO-DTx from HAS yielded HAS' -> FR beta = 0.664 (p < 0.001), indicating that the central association is attenuated by, but not solely attributable to, the medication-optimization content shared by the two constructs.Conclusion Among the women in their 20s represented in the sample, DTx acceptance intention was associated primarily with a higher-order structure integrating purpose-specific acceptance perceptions rather than with isolated technological features or individual background factors; the findings concern stated intention, not observed adoption. The findings highlight the importance of strategies emphasizing practical therapeutic utility over technological sophistication alone and offer implications for discussions on women's healthcare accessibility and gender-related disparities.

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