Bayes Business School (formerly Cass), City, U. of London, United Kingdom
Creative self-efficacy (CSE) is a well-established construct widely used to understand innovative behavior and creative outcomes. However, the advent of Generative Artificial Intelligence (GenAI) and its growing integration into creative work necessitate a re-evaluation of this construct, particularly as we transition from traditional 'human-only' settings to 'human-GenAI' co-creative environments. This paper introduces creative co-efficacy (CCE) as a novel construct tailored to these shifts, supported by three studies. The first study develops a reliable three-item CCE scale and confirms its distinctiveness from CSE. The second study identifies key antecedents of CCE, including positive creative experiences with AI, prompting skills, and cognitive flexibility. The third study experimentally examines CCE and CSE in a creative task under constraints, demonstrating that while CCE can significantly exceed CSE, this does not necessarily lead to higher creative performance. Such outcomes depend on the intricate dynamics of human-AI collaboration, the risks of over-reliance on AI, and the rapidly evolving capabilities of AI. This research establishes a foundation for further validation of the CCE scale and exploration of its predictive, mediating, and moderating roles in creative output and innovative behavior. Additionally, it opens avenues for extending the co-efficacy concept to other domains, such as writing, learning, technology, and entrepreneurship.
