<b>When Does “Assistant Heuristic” Work? Examining the Effect of AI Job Titles in Tasks with Varying Criticalities on the Use of Conversational AI-Based Services</b>

Recent marketing trends involve companies using low-status job titles, such as "assistant" (e.g., Google Home Assistant), to label conversational AI agents. This strategy aims to activate an altruistic "assistant" heuristic and enhance users' willingness to use these AI agents. However, this paper—comprising one pretest (N=313), three experiments (N=307, N=300, N=308), and one partial least squares structural equation modeling (PLS-SEM) analysis (N=309)—demonstrates that the effect of this strategy on willingness to use is positive only when the task criticality is high. When the task criticality is not high, higher-hierarchy AI titles (e.g., "manager," "teacher," "analyst") generate greater willingness to use. The research examines three alternative serial mediation pathways—perceived warmth, perceived control, and perceived risks—to test for competing explanations alongside the focal serial mediation through perceived humanlikeness and competence. Across the four studies, the serial mediation via perceived humanlikeness and competence remained robust, even when controlling for alternative pathways and scenario realism (Study 3). The final model indicates that when task criticality is not high, increased perceptions of hierarchical status in conversational AI settings enhance perceived humanlikeness. This, in turn, boosts perceived competence, ultimately increasing users' willingness to use the AI. However, when task criticality is high, the effect reverses—higher-status AI is perceived as less humanlike and less competent, reducing users' willingness to engage with it.

最近的营销趋势中，企业倾向于采用“助手”这类低阶职位头衔（例如Google Home Assistant）来命名对话式AI智能体（AI Agent）。该策略旨在激活利他性的“助手”启发式（heuristic），并提升用户使用这些AI智能体的意愿。然而，本研究通过一项预测试（N=313）、三项实验（N=307、N=300、N=308）以及一项偏最小二乘结构方程模型（partial least squares structural equation modeling, PLS-SEM）分析（N=309）表明，只有当任务关键性较高时，该策略对使用意愿的影响才为正向。而当任务关键性不高时，采用“经理”“教师”“分析师”等高阶AI头衔能带来更高的使用意愿。本研究考察了三条替代序列中介路径（感知温暖、感知控制与感知风险），以检验竞争解释，并同时验证了通过感知类人性与感知能力实现的核心序列中介效应。在四项研究中，即便控制了替代路径与场景真实性（研究3），通过感知类人性与感知能力的序列中介效应仍保持稳健。最终模型显示，当任务关键性不高时，对话式AI场景中阶层地位感知的提升会增强感知类人性，进而提高感知能力，最终增加用户的AI使用意愿。然而，当任务关键性较高时，效应会反转：高阶地位的AI被感知为更缺乏类人性与能力，从而降低用户的使用意愿。