Generative AI’s Impact on Job Crafting and Self-Undermining: Advancing the JD-R Framework

Abstract

The integration of Generative AI (GenAI) into organizational contexts is reshaping foundational workplace dynamics, prompting a critical reexamination of existing theoretical frameworks. This conceptual study extends the Job Demands-Resources (JD-R) framework to explore how GenAI influences both job crafting (positive spirals) and self-undermining (negative spirals). By introducing GenAI-specific constructs, such as training, access, and competence, alongside evolving task demands, the study unpacks the dual impact of GenAI on employee motivation, engagement, strain, and performance. Through a theoretical lens, we propose a refined JD-R model that captures the nuanced interplay between GenAI as both a resource and a demand. This framework distinguishes between exploitative tasks, emphasizing efficiency, and explorative tasks, demanding creativity, to highlight the conditions under which GenAI fosters resilience or exacerbates strain. This poster invites dialogue on the theoretical implications of GenAI’s dual role in shaping workplace outcomes and offers directions for future research to advance the JD-R framework in the age of intelligent technologies.

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