Transparency, citations, and speed
How design nudges drive adoption in AI health consultations
Bibliographic Data
| ID | 19480419 |
|---|---|
| Authors | Rukun Zhang (0000-0002-6987-9311, Shenzhen University), Qingqing Xie (0000-0003-0505-6102, Shenzhen University), Bo Li (0000-0002-7801-0319, Shenzhen University, corresponding author), Bingcan Li (0000-0001-6899-5276) |
| Year | 2026 |
| Pages | 1-25 |
| Publication date | 2026-06-15 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Chinese Journal of Communication (JOURNAL) |
| Journal identifiers | ISSN: 1754-4769 • E-ISSN: 1754-4750 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/17544750.2026.2684626 |
| OpenAlex | W7164843230 |
| Language | EN |
| References cited | 64 |
As explainable AI gains prominence, the question of how its communication design influences user behavior in online health platforms remains underexplored. This study introduces the Human–AI Communication Pathway Optimization Model (HACPOM). This model integrates AI design nudges to explain how transparency of thinking (opaque vs. transparent), source referencing (no source vs. journal), and response latency (fast vs. slow) can influence adoption intention. In a 2 × 2 × 2 between-subjects experiment (N = 723), transparency of thinking and source referencing significantly increased users’ adoption intention. This outcome thus validated the direct route. Response latency showed no direct effect. However, it interacted with transparency, with the strongest advantage occurring under fast response conditions. Structural equation modeling supported HACPOM’s theorized indirect pathway. AI design nudges shaped the perceived risks and benefits, which influenced trust and thereby also influenced adoption intention. A supplementary qualitative interview with Shenzhen residents further corroborated the underlying mechanisms of the HACPOM framework. The implications for designing trustworthy AI health consultations are also discussed herein
Choice architecture · Digital health · Nudge theory · Public health · AI in Service Interactions · Artificial Intelligence in Healthcare and Education · Ethics and Social Impacts of AI
How does communication heal? Pathways linking clinician–patient communication to health outcomes
Perceived Information Quality in Data Exchanges
Construction and Validation of a Scale to Measure Celebrity Endorsers' Perceived Expertise, Trustworthiness, and Attractiveness
Predicting e-services adoption
Explainable Artificial Intelligence (XAI)
Trust and Satisfaction, Two Stepping Stones for Successful E-Commerce Relationships
ChatGPT
Optimising Nudges in Public Space
Responsible nudging for social good
Who needs explanation and when? Juggling explainable AI and user epistemic uncertainty
Mobile Patient-Provider Communication and Lifestyle Improvement
Following Medical Advice of an AI or a Human Doctor? Experimental Evidence Based on Clinician-Patient Communication Pathway Model
What Encourages Patients to Recommend Their Doctor After an Online Medical Consultation? The Influence of Patient-Centered Communication, Trust, and Negative Health Information Seeking Experiences
Linking Nonverbal Rapport to Health Outcome
Seeking medical advice in mobile applications
Understanding adoption intention of virtual medical consultation systems
Towards more trusted virtual physicians
Anthropomorphic chatbots' for future healthcare services
Exploring the drivers of XAI-enhanced clinical decision support systems adoption
Health-related fake news on social media platforms
Fair, Transparent, and Accountable Algorithmic Decision-making Processes
The effects of response time on older and young adults’ interaction experience with Chatbot
Artificial intelligence in medicine
When AI moderates online content
Credibility Assessments of Online Health Information
Mapping the challenges of Artificial Intelligence in the public sector
Using thematic analysis in psychology
Central and peripheral routes to persuasion
Resistance to Medical Artificial Intelligence
| Highly cited | No |
|---|