Innovating with Publicly Available LLMs at Work: A Lifespan Perspective

Abstract

This study examines how various facets of user’s age, i.e. chronological age, professional age and organizational tenure, shape the use of publicly available large language models chatbots (LLMs) at work. Adopting a lifespan perspective, we analyze how combinations of the different facets of age, alongside AI literacy and job autonomy explain extent and innovative LLM use. Using fsQCA on data from the U.S.-based professionals, we identify multiple equifinal pathways to LLMs use. The findings reveal that while chronological age plays a role, it does not operate in isolation. The extent of LLM use is more age-sensitive, with different pathways for younger and older employees, whereas innovative use is driven more by contextual and experiential conditions. Overall, younger workers require high AI literacy, autonomy and longer tenure, while older employees are driven by high AI literacy and shorter tenure. Our results contribute to the research on LMM use in the workplace by providing a nuanced understanding of the role of age.