| Abstract [eng] |
In recent years, increasing attention has been devoted to marketing content generated by artificial intelligence (AI) and its impact on consumer behaviour in the digital environment. However, academic literature still lacks consensus on how the labelling of AI authorship influences consumer responses. Some studies suggest that AI-generated content may be evaluated as favourably as that created by humans (Kirkby et al., 2023). However, other authors point out that labelling content as AI-generated often triggers scepticism, reduces perceived authenticity, and negatively affects content evaluation (Zhang & Gosline, 2023; Lim & Schmalzle, 2024; Shantatula et al., 2024). The focus of this study is on consumer response to the labelling of AI-generated marketing content and its authorship in social media. The aim of the study is to provide a theoretical and empirical justification of the impact of labelling AI-generated marketing content and authorship on consumer response on social media. The results of the conducted study confirm that the impact of AI-generated content on consumers depends on contextual congruence – content labelled as AI-generated elicits a positive response only when its topic relates to artificial intelligence and when the source and the content appear conceptually consistent to the consumer (Lee et al., 2025). This effect is explained by schema congruity theory, which posits that individuals respond more positively to information that aligns with their prior knowledge or expectations (Mandler, 1982; Meyers-Levy & Tybout, 1989). Consequently, the alignment between artificial intelligence and the content it generates predicts higher credibility ratings – across dimensions such as source empathy, goodwill, competence, and the perceived credibility of the content itself. Nevertheless, the study revealed that such source–content congruence does not have a significant practical effect on consumers’ engagement intentions; only perceived content credibility and the emotional characteristics of the source – particularly empathy and goodwill – drive behavioural responses. Furthermore, semantic analysis indicated that consumers place higher qualitative and ethical demands on AI-generated content, especially when it is published under the name of academic institutions – they expect linguistic accuracy, stylistic naturalness, and transparent labelling. These insights may contribute to the more responsible use of AI solutions in marketing and inform organisations seeking to ensure ethical and trust-enhancing communication on social media. |
