Approach or avoidance? A dual-pathway model of job crafting in response to generative AI and its impact on career sustainability

Introduction: As generative artificial intelligence (AI) is increasingly integrated into employees' daily workflows, it is profoundly reshaping the nature of work, which raises critical theoretical questions about how employees can build sustainable careers. Drawing on approach-avoidance motivation theory, this study distinguishes between two types of proactive employee adaptation to AI (i.e., AI job crafting): an approach-oriented type aimed at leveraging AI to expand job boundaries and enhance personal capabilities, and an avoidance-oriented type involving contractive or defensive strategies to mitigate the negative perceptions of AI. Based on this distinction, this study develops and tests a dual-pathway mediation model. Methods: Data were collected through a multi-source, multi-wave survey of 287 employee-leader dyads in China, utilizing the newly developed and validated AI Job Crafting Scale. Results: The findings indicate that AI approach job crafting positively predicts professional proximal indicators of career sustainability (i.e., career satisfaction and performance) by enhancing work meaningfulness, whereas AI avoidance job crafting negatively predicts them via work alienation. Notably, both pathways failed to significantly affect life satisfaction, providing compelling evidence for the domain specificity of AI-related psychological mechanisms. Furthermore, work autonomy not only strengthens the positive impact of AI approach job crafting on work meaningfulness but also weakens the positive effect of AI avoidance job crafting on work alienation. Discussion: This study contributes a dual-pathway model and measurement tool for AI job crafting, highlighting employee autonomy as a key practical strategy.