Individual Recognition of Generative AI: An Empirical Analysis Based on Digital Attention and Platform Engagement
Main article
Abstract
This study examines how individual recognition of generative artificial intelligence is shaped by digital attention signals and platform engagement. Rather than treating recognition as a purely cognitive outcome, the article conceptualizes recognition as a socially amplified judgment that emerges from repeated exposure, interaction, and interpretation across digital platforms. Using a structured dataset of 864 respondents and item-level indicators for digital attention, platform engagement, trust, perceived usefulness, and usage intensity, the study estimates ordinary least squares and logistic models to explain both continuous recognition scores and the probability of high recognition. The findings show that digital attention and platform engagement are the strongest predictors of recognition, while trust and perceived usefulness play complementary mediating roles. Individuals who actively follow, discuss, and experiment with generative AI report significantly higher recognition than passive observers. The results suggest that recognition is not only an informational process but also a platform-conditioned social process. The article contributes to technology innovation research by linking attention economics, platform behavior, and technology evaluation, and it offers practical implications for AI communication, product design, education, and governance.
