PERSUASIVE STRATEGIES IN ARTIFICIAL INTELLIGENCE-GENERATED MARKETING CONTENT: A LINGUISTIC AND BUSINESS COMMUNICATION ANALYSIS

Abstract

The rapid advancement of Artificial Intelligence (AI) has transformed the practice of digital marketing by enabling the automated generation of promotional content. While AI-generated marketing texts are increasingly utilized by businesses to enhance communication efficiency and audience reach, limited attention has been paid to the linguistic mechanisms through which these texts construct persuasion. Drawing on Appraisal Theory, this study investigates the appraisal resources employed in AI-generated marketing content and examines their contribution to persuasive business communication. This research adopted a qualitative content analysis approach. The data consisted of AI-generated marketing texts collected from leading generative AI platforms. The texts were analyzed using the Appraisal framework proposed by Martin and White (2005), focusing on the subsystems of attitude, engagement, and graduation. The findings reveal that all three appraisal resources were systematically employed in the corpus, with attitude resources, particularly appreciation, occurring most frequently. The analysis further demonstrates that attitude resources were used to create positive product evaluations and emotional appeal, engagement resources enhanced credibility and trust through authoritative positioning and external endorsements, while graduation resources intensified promotional claims and emphasized product distinctiveness. Moreover, the interaction among these appraisal subsystems enabled AI-generated content to construct persuasive messages aligned with key business communication objectives, including brand image development, consumer engagement, trust-building, and purchase stimulation.