How trust and attachment styles jointly shape job candidates’ AI receptivity
通过三项实验发现，求职者认为AI评估者比人类更不可信，从而降低工作接受意愿；回避型依恋风格低的人受影响更大，且AI识别独特技能的能力感知是信任缺失的前因。
Despite the growing application of artificial intelligence in hiring practices, job applicants still express general reservations about the use of this technology. What factors help to explain this low acceptance rate and are all job applicants equally affected? Across three controlled experiments, we demonstrate that job applicants perceive an AI evaluation agent (vs. a human) as less trustworthy, which reduced their job acceptance intentions (Study 1). This effect was stronger for job applicants who scored low on avoidant attachment style (Study 2). Using a serial mediation model, Study 3 tested one explanatory mechanism for how avoidant attachment style shapes trust, finding that perceptions of the AI agent’s ability to parse unique skills are an antecedent of job applicants' low trust toward the AI. These findings deepen our understanding of the factors that shape applicants’ job acceptance intentions regarding AI and provide actionable insights for integrating AI into hiring practices.