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Generative Artificial Intelligence Literacy: Scale Development and Its Effect on Job Performance
Xin Liu1, Longxin Zhang1, Xiaochong Wei1
1School of Public Administration and Policy, Renmin University of China, Beijing 100872, China.
Abstract:
With the rapid development of generative artificial intelligence, its application in the workplace has shown significant innovative potential and practical value. However, the existing literature lacks a systematic and widely applicable definition and measurement framework for Generative AI Literacy. Based on the existing literature and following a rigorous scale development process, this study constructs a Generative AI Literacy measurement framework that covers five core dimensions, basic technical competence, prompt optimization, content evaluation, innovative application, and ethical and compliance awareness, and validates its reliability and validity. Furthermore, based on the Ability-Motivation-Opportunity (AMO) theory, this study explores the mechanism through which Generative AI Literacy influences employee job performance and examines the mediating role of Creative Self-Efficacy. The results show that Generative AI Literacy has a significant positive impact on job performance (β = 0.680, p < 0.001), with Creative Self-Efficacy playing a partial mediating role (indirect effect = 0.537). The developed five-dimensional framework demonstrates strong psychometric properties and provides empirical evidence for AI literacy's role in enhancing workplace performance through Creative Self-Efficacy mechanisms. This study provides an effective measurement tool for research on the application of Generative AI Literacy in workplace settings and offers practical insights for organizations to optimize performance and promote the responsible use of AI.
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