The Impact of Generative AI-Based Information Overload and AI Literacy on Individual Innovation Capability
Seung Hee Oh, Sang Hyeok Park · IEEE Access · 2026
Generative artificial intelligence (AI) is driving innovation in the information search process by rapidly providing users with vast amounts of information. However, as AI easily generates excessive content, users experience the cognitive burden of having to process even greater amounts of information, intensifying digital information overload. This study analyzed the impact of information overload induced by generative AI on individual innovation capability through the psychological and behavioral factors of information avoidance. In addition, it examined whether individual capabilities and affordances, such as AI literacy and metacognition, mitigate the negative effects of information overload. To test the research hypotheses, this study conducted quantitative empirical research using a survey method. Survey data were collected from a professional panel of an online survey firm, specifically targeting users of generative AI services. To validate the hypotheses in the research model, regression and mediation analyses were performed. The results indicate that higher information overload arising from the use of generative AI services leads to more pronounced information avoidance behaviors. Further, a higher perception of affordance enhances strategic thinking ability, creative problem-solving ability, and adaptability. Moreover, individuals with higher AI literacy exhibit stronger metacognitive abilities. These enhance their affordance perception and innovation capability, demonstrating a virtuous cycle. Overall, this study presents strategic approaches for individuals in generative AI environments to effectively manage information overload and overcome its negative effects on innovation capability.