How and for whom using generative AI affects creativity: A field experiment.

创造力 心理学 生成语法 领域(数学) 认知心理学 社会心理学 人工智能 计算机科学 数学 纯数学
作者
Shuhua Sun,Zhuyi Angelina Li,Maw‐Der Foo,Jing Zhou,Jie Lu
出处
期刊:Journal of Applied Psychology [American Psychological Association]
卷期号:110 (12): 1561-1573 被引量:41
标识
DOI:10.1037/apl0001296
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. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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