The Relationship Between Ethics and Credibility with Media Exposure Behavior Created with Generative Artificial Intelligence - Nookhong, Jittinapa; Sasithanakornkaew, Saranthorn; Apisuphachok, Warapan (Journal of Multidisciplinary in Humanities and Social Sciences, 2026)
This Article aimed to study 1) To examine differences between demographic characteristics and receptivity to media generated by generative artificial intelligence. 2) To investigate the relationship between perceived ethics of using generative artificial intelligence and receptivity to generative artificial intelligence–produced media. 3) To investigate the relationship between perceived credibility of generative artificial intelligence and receptivity to generative artificial intelligence–produced media. And 4) To assess the influence of perceived ethics and media credibility on receptivity to generative artificial intelligence–produced media. The research employed quantitative design. The sample comprised 400 individuals with experience using social media and viewing creative-AI–generated works in the forms of images, animations, and videos, selected by non-probability sampling. Data was collected via an online questionnaire and analyzed using t-tests, one-way ANOVA, simple correlation analysis, and multiple regression analysis. The findings indicate that differing educational levels and occupations exhibit significantly different behaviors in the exposure to creatively generative artificial intelligence–produced media (statistically significant at the .05 level). Perceived ethics in the use of generative artificial intelligence did not exert a significant influence on exposure behavior to generative artificial intelligence produced media, whereas perceived credibility of generative artificial intelligence–produced media significantly influenced exposure behavior to such media (statistically significant at the .05 level), with a standardized regression coefficient of 0.48. This study can inform improvements in practices for producing creative outputs via generative artificial intelligence, guide the development of curricula and training on ethics related to the use of generative artificial intelligence, and contribute to the scholarly knowledge on generative artificial intelligence for academic advancement.