<b>Human-AI Collaboration</b>

The widespread application of medical artificial intelligence (AI) raises a critical question: Who should be held accountable when AI-assisted diagnostic decisions result in errors? Based on the Stimulus-Organism-Response (S-O-R) framework, this study employs a scenario-based survey experiment to examine how three responsibility attribution models (AI-primary, shared, and physician-primary responsibility) influence physicians' AI adoption intention and verification behavior through risk perception, internal moral attribution, and trust, while exploring the moderating role of AI literacy. Analysis of data from 487 clinical physicians reveals three key findings. First, physician-primary responsibility significantly reduced internal moral attribution (β=-0.804) and risk perception (β=-0.436), while enhancing benevolence trust (β=0.257). Second, internal moral attribution emerged as the strongest mediator affecting both adoption intention (β=-0.258) and verification behavior (β=0.298), surpassing risk perception. Third, MGA results also show group differences. AI literacy moderated the effects of ability trust and risk perception on adoption intention, with stronger effects observed in the high literacy group. The study contributes by introducing responsibility attribution as an institutional stimulus into medical AI acceptance research, revealing the "responsibility attribution→tool-ification cognition→behavioral change" mechanism. Findings suggest that physician-primary responsibility models coupled with AI literacy enhancement programs can optimize human-AI collaborative decision-making in healthcare.

中央银行数字货币（Central Bank Digital Currency, CBDC）已成为打破现有支付垄断、促进数字经济与实体经济深度融合的关键载体。然而，CBDC的推广与广泛采纳仍面临多重障碍。本研究以中国为例，探究影响商户采纳数字人民币（E-CNY）意愿的因素。从商户视角来看，采纳数字货币不仅能为消费者提供新的支付选择，还有助于构建更具竞争力与多样性的支付生态系统。基于网络效应与技术-组织-环境（Technology-Organization-Environment, TOE）框架，本研究构建了涵盖技术适配性、组织支持与环境推动三个维度的数字人民币采纳因素综合模型。本研究运用结构方程模型（Structural Equation Modeling, SEM）与模糊集定性比较分析（fuzzy-set Qualitative Comparative Analysis, fsQCA），探究多重前置条件与商户使用行为之间的复杂交互关系。