AI-human interactions in healthcare: exploring users’ post-adoption behaviors of AI mental health chatbots
Bibliographic record
Abstract
The rise in mental health challenges has spurred innovative technological solutions, including AI-based chatbots for mental care management. This study explores how individuals engage with these chatbots for health decision-making, examining relationships between utilitarian and hedonic values, satisfaction with well-being outcomes, IT identity, social norms, and post-adoption behaviours. Using IT identity theory and a quantitative approach, we surveyed 309 current users of AI-based chatbots in the US. Structural equation modeling (SEM) was employed to analyse the data. Results show that both utilitarian and hedonic values positively influence user satisfaction with well-being outcomes. Satisfaction, in turn, shapes IT identity, reflecting personalised engagement with technology. IT identity positively influences behavioural intentions, including exploring new features, word of mouth, and continued use of AI chatbots. The study reveals mixed moderating effects of social norms, with significant moderation for word of mouth but not for behavioural intentions. Mediation effects of satisfaction in the relationships between values and IT identity provide insights into how technology becomes integral to individual identities and well-being routines. This research contributes to both theoretical understanding and practical applications of AI chatbots in mental health and well-being. It offers a robust framework for future research and interventions in AI-enabled healthcare.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".