How and for Whom Using Generative AI Affects Creativity: A Field Experiment

• 成果类型: Article; Early Access
• 署名作者: Sun, Shuhua; Li, Zhuyi Angelina; Foo, Maw-Der; Zhou, Jing; Lu, Jackson G.
• 署名单位: Tulane University; Renmin University of China; Nanyang Technological University; Rice University; Massachusetts Institute of Technology (MIT)
• 刊物名称: JOURNAL OF APPLIED PSYCHOLOGY
• ISSN/ISSBN: 0021-9010
• DOI: 10.1037/apl0001296
• 发表日期: 2025
• 关键词: generative artificial intelligence; Large language models; creativity; metacognitive strategies; cognitive job resources

摘要

We develop a theoretical perspective on how and for whom large language model (LLM) assistance influences creativity in the workplace. We propose that LLM assistance increases employees' creativity by providing cognitive job resources. Furthermore, we hypothesize that employees with high levels of metacognitive strategies-who actively monitor and regulate their thinking to achieve goals and solve problems-are more likely to leverage LLM assistance effectively to acquire cognitive job resources, thereby increasing creativity. Our hypotheses were supported by a field experiment, in which we randomly assigned employees in a technology consulting firm to either receive LLM assistance or not. The results are robust across both supervisor and external evaluator ratings of employee creativity. Our findings indicate that LLM assistance enhances employees' creativity by providing cognitive job resources, especially for employees with high (vs. low) levels of metacognitive strategies. Overall, our field experiment offers novel insights into the mediating and moderating mechanisms linking LLM assistance and employee creativity in the workplace.