When Source Disclosure Matters: User Perceptions of Generative AI Authorship in Landing Page Content

Abstract

The widespread adoption of generative AI (GenAI) in digital content marketing has outpaced the understanding of how users evaluate it. While marketers deploy GenAI to produce personalised content at scale, user perceptions of authenticity, credibility, and value remain poorly understood. One particular under-researched area is conversion-oriented environments where content must inform, persuade, and establish trust simultaneously. Existing literature on user evaluations of digital content addresses GenAI content perception broadly, but little empirical work examines how source disclosure and functional context together shape user preferences. This study investigates user preferences for GenAI-authored versus human-authored content on website landing pages, a persuasion-critical environment where content credibility directly influences decision-making. Using a 2x2 mixed comparative experimental design, 95 participants recruited via CloudResearch Connect evaluated landing page content under both disclosed and undisclosed authorship conditions to assess how source awareness affects content evaluations. The findings reveal a central paradox. Users evaluate GenAI-authored content favourably when it is informative and well-structured, yet prefer human authorship once the source is disclosed. Disclosure was found to activate relational scrutiny, such as authenticity and human-likeness, without significantly degrading functional evaluations of content quality, persuasiveness and credibility. Rather than confirming AI aversion, results show that human favouritism operates alongside, not instead of, positive GenAI evaluations. The study proposes a dual evaluative framework to account for this. Findings are interpreted through this lens as reflecting functional competence when GenAI content is judged on its informational merit, and relational authenticity when human authorship is valued for its perceived intentionality and authority. Critically, user acceptance of GenAI content is conditional rather than categorical, shaped by task, type, digital environment, and the level of user trust at stake. These findings offer empirical grounding for marketers navigating GenAI adoption and for policymakers designing authorship disclosure standards.