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.

The adoption of social networks introduced a new set of components to the e-commerce environment, which are called social commerce components (SCCs) (e.g., forums and communities, rating and reviews and social recommendations). Although various SCCs have transformed customer behaviors and decision patterns, few studies have investigated their roles together in enhancing customers' decision quality. In this study, based on the social learning theory, the authors develop a research model to explore how customers learn from the SCCs to influence their uncertainty in shopping experience, and thus improve their decision quality. The results from 243 actual customers of social commerce site in China suggest that demand uncertainty is one of the most important factors that reduces decision quality, whereas product quality uncertainty has a significant positive influence on decision quality, and seller quality uncertainty has no influence on decision quality. Also, learning from forums and communities, learning from rating and reviews and learning from social recommendations play different roles on forming customers' uncertainty. In addition, product type can moderate most of the above associations. In summary, these findings increase one's understanding of the customers' decision pattern in social commerce context and extend the scope of social learning theory and uncertainty theory. The findings also provide insights for social commerce practitioners in developing strategies for improved implementation of social commerce as well as the design of social commerce sites.