老员工应对生成式AI冲击，通过边界工作重建职场稳定性。
Concerns and Strategic Responses of Older Workers Navigating Generative AI in Bridge Employment
- 老员工通过调整任务边界来应对AI带来的时空干扰。
- AI引发职业决策过程持续中断，需动态适应而非一次性应对。
- 适合关注老龄化职场、人机协同与组织变革的研究者。
生成式AI正快速重塑工作场所，对包括通过过渡就业重返职场的老年工作者（OWs）在内的弱势群体影响尤为显著。通过对21位专业人士的深度半结构化访谈，我们研究了老年工作者在追求过渡性工作期间如何应对生成式AI带来的冲击，重点关注其对AI集成的担忧及相应策略。研究发现，生成式AI在过渡就业决策过程的各个阶段均引发时间与结构性的双重干扰。为此，他们通过不同形式的边界工作重构任务，以恢复稳定与连续性。我们将这些响应机制概念化为‘AI韧性’，使老年工作者的过渡就业决策转变为持续协商与适应的过程。最后，我们提出建议：通过个体层面的AI韧性策略、中观层面的集体韧性网络以及宏观层面对抗性与可争议的AI组织结构，降低老年工作者的职业倦怠风险。
原文摘要 · Abstract (English)
Generative AI (GenAI) is transforming workplaces at a rapid pace. This disproportionately affects vulnerable communities, including older workers (OWs) who re-enter the workforce through bridge employment prior to final retirement. Through in-depth semi-structured interviews with 21 professionals, we examine how OWs navigate GenAI-driven disruptions while pursuing bridge roles, focusing on their concerns about GenAI integration and their responses to these changes. Our findings show that OWs experienced both temporal and structural disruptions across all stages of the bridge employment decision-making process due to GenAI. In response, they reconfigured their tasks through different forms of boundary work aimed at restoring stability and continuity. We conceptualize these responses as AI resilience, which reshaped OWs' bridge employment decision-making into an ongoing process of negotiation and adaptation. We conclude by offering recommendations to reduce burnout among OWs by balancing individual-level AI resilience strategies with meso-level AI resilience collectives and macro-level adversarial and contestable AI-mediated organizational structures.
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