Approach or avoidance? Relationship between perceived AI explainability and employee job crafting

Abstract

Amid growing concerns about the lack of transparency in algorithms, heightened focus has been placed on artificial intelligence (AI) explainability in workplace decision-making processes. This study leverages work design theory to explore when and how perceived AI explainability impacts two types of employee job crafting: approach job crafting and avoidance job crafting. We analysed multi-wave survey data of 278 medical staff to examine the effects of perceived AI explainability on approach and avoidance job crafting through a dual-pathway model. Results indicated that perceived AI explainability enhanced AI-oriented benefit perception and reduced AI-oriented threat perception, resulting in an increase in approach and avoidance job crafting. Furthermore, our findings suggested that ethical climate strengthened the impacts of perceived AI explainability on AI-oriented benefit perception and AI-oriented threat perception. We discuss key theoretical insights of our findings for advancing AI and job crafting research as well as implications for organisational practice.

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