Enterprise AI Analysis
Investigating the factors influencing users' adoption of artificial intelligence health assistants based on an extended UTAUT model
This study delves into the factors influencing the adoption of AI health assistants by ordinary users, extending the UTAUT model with perceived trust and risk. Our findings confirm the robustness of UTAUT's core constructs and highlight the significant roles of perceived trust and risk in shaping behavioral intention, providing valuable insights for AI health assistant developers and operators.
Executive Impact at a Glance
Key metrics derived from this research highlight the critical drivers of AI health assistant adoption and their potential impact on user engagement.
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Variance in Behavioral Intention Explained
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Trust's Influence on Performance Expectancy
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Trust's Influence on Perceived Risk
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
The original UTAUT model, including Performance Expectancy (PE), Effort Expectancy (EE), and Social Influence (SI), was found to be a robust predictor of Behavioral Intention (BI). This confirms its applicability in the context of AI health assistants, supporting its use as a foundational framework.
Perceived Trust (PT) significantly impacts Behavioral Intention (BI), Performance Expectancy (PE), and Effort Expectancy (EE). Higher trust correlates with greater expectations of efficacy and ease of use, as well as a stronger intention to adopt AI health assistants.
Perceived Risk (PR) negatively influences Behavioral Intention (BI), indicating that concerns about privacy, security, and potential errors deter users from adopting AI health assistants. Mitigating these risks is crucial for enhancing user acceptance.
Unexpectedly, Facilitating Conditions (FC) did not significantly affect Behavioral Intention (BI). This suggests that while external support resources are present, they may not directly translate into a stronger intent to use in the early adoption stages for AI health assistants.
88.7%
of Behavioral Intention Explained by Model
Enterprise Process Flow
Users perceive AI health assistant effectiveness
→
Users perceive AI health assistant ease of use
→
Social endorsement of AI health assistants
→
Users develop high trust in AI health assistants
→
Users perceive low risk with AI health assistants
→
Increased behavioral intention to use AI health assistants
| Factor | Impact on Behavioral Intention | Practical Implication |
|---|---|---|
| Performance Expectancy (PE) | Significant positive (β=0.693) |
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| Effort Expectancy (EE) | Significant positive (β=0.582) |
|
| Social Influence (SI) | Significant positive (β=0.247) |
|
| Perceived Trust (PT) | Significant positive (β=0.583) |
|
| Perceived Risk (PR) | Significant negative (β=-0.127) |
|
| Facilitating Conditions (FC) | Not significant (β=0.01) |
|
IFlytek Healthcare App: A Real-World Success
The IFLY Healthcare app, serving as the experimental material, demonstrates the practical impact of AI health assistants. With over 12 million downloads and a 98.8% positive rating since its October 2023 launch, it exemplifies successful user acceptance and effective health management. Its features, including disease self-examination and medical information searches, align directly with the study's findings on performance and effort expectancy, showcasing how robust AI implementation can drive widespread adoption and positive user outcomes.
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