Abstract
User resistance has been a significant topic in healthcare information technology research. However, existing studies mainly explored user resistance from a singular perspective, either focusing on healthcare providers or recipients, overlooking the potential transfer effects among healthcare subjects. Our research attempts to address this research gap by investigating the influence of physician stance toward healthcare
AI (resistance vs. acceptance) on patient resistance to healthcare AI. Drawing upon negativity bias and confirmation bias theories, we develop a conceptual framework. We suggest that compared to a positive stance (i.e., acceptance), a physician’s negative stance (i.e., resistance) exerts a more significant effect on changing patient resistance to AI. However, this effect diminishes when patients receive a high-level (vs.
low-level) confirmatory stance. To evaluate these assumptions, we undertake a pilot experimental study. The preliminary results contribute to user resistance and social influence literature, offering practical insights for healthcare AI company managers.
AI (resistance vs. acceptance) on patient resistance to healthcare AI. Drawing upon negativity bias and confirmation bias theories, we develop a conceptual framework. We suggest that compared to a positive stance (i.e., acceptance), a physician’s negative stance (i.e., resistance) exerts a more significant effect on changing patient resistance to AI. However, this effect diminishes when patients receive a high-level (vs.
low-level) confirmatory stance. To evaluate these assumptions, we undertake a pilot experimental study. The preliminary results contribute to user resistance and social influence literature, offering practical insights for healthcare AI company managers.
| Original language | English |
|---|---|
| Title of host publication | AMCIS 2024 Proceedings |
| Publisher | Association for Information Systems |
| ISBN (Electronic) | 978-1-958200-11-7 |
| Publication status | Published - Aug 2024 |
Keywords
- User resistance
- negativity bias
- confirmation bias
- social influence
- healthcare AI