Thinking twice and weighing outcomes: comparing the dual influence of intelligent healthcare features on word-of-mouth
Aihui Chen, Yini Zhang, Nan Feng · Behaviour and Information Technology · 2025
The integration of artificial intelligence (AI) technology into medical consultations has revolutionised the approach to seeking medical care. However, public opinions on intelligent healthcare systems vary. The impact mechanisms of these system features on positive or negative word-of-mouth (WOM), as well as the relative importance of the dual effects for each feature, remains unclear. Based on the dual-process theory and two-factor theory, this study constructs a research model examining the relationship between AI healthcare features, user trust, and WOM. Experimental data (n = 338) is used to validate this model and compare the differences in the dual impact of each predictor variable. Through the method of path coefficients comparison, this study reveals that the influence of intelligence on cognitive trust is significantly greater than its impact on emotional trust. Furthermore, explicability positively and significantly affects both cognitive and emotional trust. Additionally, emotional trust has a significantly greater impact on positive WOM compared to negative WOM. This study reveals the nuanced impact of AI healthcare system features on trust and the subsequent effects on WOM, aiding relevant organisations in formulating strategies to optimise the design features of such systems. Simultaneously, it also promotes the wider adoption of intelligent healthcare systems.