The rapid advancement of digital technologies has fundamentally reshaped how firms communicate and interact with consumers. Among recent technological developments, artificial intelligence (AI) has emerged as one of the most transformative forces in digital marketing. By enabling large-scale consumer data analysis, automated content generation, and personalized communication, AI has become deeply embedded in contemporary marketing practices ([7]; [8]; [4]; [5]). Applications such as AI-generated advertising visuals, automated copywriting, intelligent recommendation systems, and virtual brand representatives are now widely implemented across digital platforms ([2]).

As AI technologies expand within the marketing domain, consumer decision-making processes have become increasingly complex. Consumers are continuously exposed to algorithm-driven content across social media, e-commerce platforms, and short-video applications ([17]). AI-generated advertising leverages behavioral data, browsing histories, and preference signals to deliver highly targeted and interactive messages. This shift has altered how consumers evaluate advertising credibility, emotional appeal, and ultimately their purchase intentions ([10]; [11]).

However, responses to technological innovation are not uniform across age groups. Generational cohort theory suggests that individuals shaped by different socio-technological environments develop distinct attitudes toward innovation and digital systems. Generation Z, often described as digital natives, grew up in highly interactive digital environments and tends to exhibit stronger acceptance of algorithmic personalization and virtual communication. Millennials experienced the transition from traditional media to digital platforms and often display a balanced perspective that integrates openness to innovation with rational evaluation ([12]; [18]). In contrast, Generation X formed consumption habits prior to the widespread adoption of intelligent technologies and typically emphasizes reliability, transparency, and informational authenticity.

AI-generated advertising influences consumers through both cognitive and affective mechanisms. From a cognitive perspective, perceived credibility plays a central role in shaping trust and reducing uncertainty. Consumers evaluate whether AI-generated content is reliable, accurate, and aligned with brand consistency before forming purchase intentions ([1]). From an affective perspective, emotional resonance reflects the extent to which AI-generated advertising evokes psychological connection and engagement. Features such as anthropomorphic avatars, immersive storytelling, and interactive formats may enhance emotional responses, particularly among younger cohorts.

Existing research on AI in marketing has largely focused on technology acceptance and general consumer attitudes. While prior studies acknowledge the importance of trust, personalization, and emotional engagement, limited research has systematically examined the mediating roles of perceived credibility and emotional resonance within a unified conceptual framework ([13]). Moreover, comparative investigations across multiple generational cohorts remain scarce. This gap restricts a comprehensive understanding of how persuasion pathways differ across age groups in the context of AI-generated advertising.

Within China’s rapidly evolving digital economy, AI-generated advertising has become especially prominent. Urban consumers are frequently exposed to AI-driven promotional content through digital ecosystems that integrate social media, e-commerce, and mobile applications. This context provides an appropriate empirical setting for examining generational differences in psychological responses to AI-enabled advertising strategies.

Building on generational cohort theory and advertising persuasion theory, this study develops a conceptual framework in which AI-generated advertising features influence purchase intention through perceived credibility and emotional resonance. Furthermore, generational cohort is proposed as a boundary condition affecting the strength of these relationships.

Accordingly, the study proposes the following hypotheses:

H1: AI-generated advertising features positively influence perceived credibility.

H2: AI-generated advertising features positively influence emotional resonance.

H3: Perceived credibility positively influences purchase intention.

H4: Emotional resonance positively influences purchase intention.

H5: Perceived credibility mediates the relationship between AI-generated advertising features and purchase intention.

H6: Emotional resonance mediates the relationship between AI-generated advertising features and purchase intention.

H7: The relationships among AI-generated advertising features, perceived credibility, emotional resonance, and purchase intention differ significantly across generational cohorts.

In this study, AI-generated advertising refers to advertising content created or substantially assisted by algorithmic systems, including generative visuals, automated copywriting, and virtual brand representations.

This study adopted a quantitative research approach using a survey method to examine the effects of AI-generated advertising on consumers’ purchase intention across different generational cohorts. A quantitative design was selected because it allows systematic examination of relationships among variables through numerical data and statistical techniques, enabling objective hypothesis testing ([6]). The analytical framework of the study is grounded in generational cohort theory and the Technology Acceptance Model (TAM), which together provide a theoretical basis for explaining variations in consumer responses to AI-generated advertising content ([3]; [15]; [9]).

The research was designed as explanatory research, focusing on identifying and testing relationships among AI-generated advertising features, perceived credibility, emotional resonance, and purchase intention. The study empirically tested a conceptual model derived from prior research in AI-driven marketing and consumer behavior ([14]; [16]).

Data were collected through a structured questionnaire, which served as the primary research instrument. Measurement items were adapted from established scales in existing literature and refined to fit the context of AI-generated advertising. All variables were measured using a seven-point Likert scale ranging from 1 (“strongly disagree”) to 7 (“strongly agree”). The questionnaire comprised four primary constructs: AI-generated advertising features (including innovation, anthropomorphism, transparency, and information quality), perceived credibility, emotional resonance, and purchase intention. A pilot test was conducted prior to formal data collection to ensure clarity and content validity.

The study measured four primary constructs. AI-generated advertising features refer to consumers’ perceptions of technological and creative characteristics of AI-driven advertisements, including innovation, anthropomorphic elements, transparency, and information quality. Perceived credibility reflects consumers’ evaluation of the trustworthiness and reliability of AI-generated advertising content. Emotional resonance captures the extent to which AI-generated advertising evokes affective engagement and psychological connection with the brand. Purchase intention represents consumers’ likelihood of purchasing the advertised product after exposure to AI-generated advertising. Measurement items were adapted from established scales in prior literature and modified to fit the AI advertising context.

Participants were recruited through an online survey distribution approach targeting urban consumers in China. Screening questions were included to ensure that respondents had prior exposure to AI-generated advertising content. Participation was voluntary and anonymous. Responses with excessive missing data or patterned answering were excluded during the data-cleaning process to ensure data quality.

The unit of analysis was individual consumers who had prior exposure to AI-generated advertising through digital platforms, including social media, e-commerce websites, and short-video applications. The target population consisted of urban consumers in China aged between 18 and 65 years. To examine generational differences, respondents were categorized into three cohorts based on commonly adopted generational definitions: Generation X (1965-1979), Millennials (1980-1994), and Generation Z (1995-2009). After removing incomplete and invalid responses, a total of 200 valid questionnaires were retained for subsequent analysis (Generation X: n = 62; Millennials: n = 74; Generation Z: n = 64).

A cross-sectional research design was employed, as data were collected at a single point in time to capture respondents’ current perceptions and behavioral intentions toward AI-generated advertising.

This study examined the mechanisms through which AI-generated advertising influences consumers’ purchase intention, with a particular focus on the mediating roles of perceived credibility and emotional resonance and differences across generational cohorts. The findings provide empirical support for the proposed hypotheses and demonstrate that the persuasive impact of AI-generated advertising is not uniform across consumers; instead, it operates through distinct psychological pathways shaped by generational characteristics.

First, the results confirm that AI-generated advertising features positively influence perceived credibility and emotional resonance, supporting H1 and H2. Furthermore, perceived credibility and emotional resonance both positively influence purchase intention, supporting H3 and H4. The influence of AI-generated advertising on purchase intention is primarily indirect, operating through these two mediating mechanisms rather than through a direct effect. These findings provide support for the mediation hypotheses (H5 and H6). Consistent with persuasion theory, consumer responses to AI-generated advertising involve both cognitive and affective processing. Perceived credibility represents consumers’ rational evaluation of information reliability and authenticity, while emotional resonance captures affective engagement and psychological connection with advertising content. Together, these two pathways form a dual persuasion mechanism through which AI-generated advertising shapes behavioral intention.

Importantly, the findings demonstrate that perceived credibility and emotional resonance function as distinct and complementary mechanisms. High levels of creativity or emotional stimulation alone are insufficient to drive purchase intention if credibility is lacking, while credible but emotionally neutral advertising may fail to sustain attention or engagement. AI-generated advertising therefore achieves stronger persuasive outcomes when both credibility and emotional resonance are simultaneously addressed, highlighting the need for balanced message design.

Second, the study reveals clear generational differences in the dominance of these mediation pathways, supporting H7. For Generation Z, emotional resonance emerges as the primary determinant of purchase intention. This cohort responds more strongly to creative expression, anthropomorphic elements, and emotionally engaging content, reflecting their immersion in interactive digital environments such as short-video platforms and social media. Although credibility remains relevant, emotional engagement constitutes the central route through which AI-generated advertising influences behavior among younger consumers.

For Millennials, the findings indicate a balanced dual-route persuasion pattern. Both perceived credibility and emotional resonance exert comparable influence on purchase intention, suggesting that this cohort integrates affective engagement with rational evaluation. Millennials are receptive to creative and emotionally appealing advertising, yet they also require credible and informative content to support decision-making.

For Generation X, perceived credibility becomes the dominant pathway influencing purchase intention, while emotional resonance plays a secondary role. This pattern suggests a credibility-first persuasion mechanism in which trust, transparency, and information accuracy are critical prerequisites for persuasion.

Taken together, these findings demonstrate that generational cohort acts as an important boundary condition in the persuasion process of AI-generated advertising. Advertising effectiveness is shaped not only by technological features but also by age-related differences in cognitive orientation, media consumption habits, and trust formation processes.

From a theoretical perspective, this study contributes to the literature on AI-driven marketing by integrating generational cohort theory with advertising persuasion mechanisms. It provides empirical support for a dual-path persuasion mechanism of AI advertising effectiveness and highlights generational variation in persuasion processes.

From a practical perspective, the findings suggest that firms should adopt differentiated AI advertising strategies tailored to specific generational segments. For Generation Z, advertisers should emphasize creativity, anthropomorphic design, and emotionally engaging narratives. For Millennials, effective campaigns should integrate credible information with emotional storytelling. For Generation X, AI-generated advertising should prioritize transparency, information reliability, and brand consistency while avoiding excessive novelty.

Overall, this study demonstrates that the effectiveness of AI-generated advertising depends not only on technological sophistication but also on its alignment with consumers’ psychological expectations across generations. AI advertising becomes most persuasive when innovation is balanced with trust and emotional engagement tailored to the characteristics of each generational cohort.

This study explored the influence of AI-generated advertising on consumers’ purchase intention by examining the mediating roles of perceived credibility and emotional resonance, as well as differences across generational cohorts. The findings indicate that AI-generated advertising does not produce a uniform persuasive effect across consumers; rather, its effectiveness depends on the specific psychological mechanisms activated and the generational characteristics of the target audience.

The results confirm that perceived credibility and emotional resonance constitute two central mediating pathways through which AI-generated advertising affects purchase intention. Perceived credibility reflects consumers’ cognitive assessment of information reliability and trustworthiness, whereas emotional resonance captures affective engagement and psychological connection with advertising content. The persuasive impact of AI-generated advertising is strongest when these two dimensions are jointly addressed, indicating that neither credibility nor emotional appeal alone is sufficient to fully drive consumer intention.

A key contribution of this study lies in identifying substantial generational differences in the dominance of these mediation mechanisms. For Generation Z, emotional resonance plays a primary role in shaping purchase intention, suggesting that creative design, anthropomorphic elements, and emotionally engaging narratives are particularly effective for younger consumers. For Millennials, perceived credibility and emotional resonance exert relatively balanced effects, reflecting a dual-route persuasion pattern that combines rational evaluation with affective engagement. In contrast, for Generation X, perceived credibility emerges as the dominant determinant of purchase intention, highlighting stronger sensitivity to transparency, risk reduction, and brand familiarity.

From a theoretical perspective, this research extends existing literature on AI-driven marketing by integrating persuasion theory with generational cohort theory in the context of AI-generated advertising. The findings demonstrate that advertising effectiveness is shaped not only by technological features but also by age-related differences in cognitive orientation, trust formation, and media consumption habits. By highlighting generational variation in persuasion mechanisms, this study provides a more nuanced understanding of how intelligent advertising systems influence consumer decision-making.

From a managerial standpoint, the results underscore the importance of adopting differentiated AI advertising strategies across generational segments. Advertisers targeting Generation Z should prioritize emotional storytelling, creativity, and human-like interaction. Campaigns aimed at Millennials should balance credible information with emotional appeal. For Generation X, AI-generated advertising should focus on transparency, informational reliability, and consistency with established brand identities, while avoiding excessive novelty or exaggerated emotional cues.

Despite its contributions, this study has several limitations. First, the use of a cross-sectional survey design limits the ability to infer causal relationships over time. Future research could employ longitudinal or experimental approaches to examine dynamic changes in consumer responses to AI-generated advertising. Second, the sample was restricted to Chinese consumers, which may limit the generalizability of the findings. Cross-cultural studies could further investigate whether the observed generational patterns are consistent across different markets. Finally, while this study focused on perceived credibility and emotional resonance, future research may incorporate additional mediators such as privacy concerns, perceived control, or algorithm transparency to enhance explanatory power.

In conclusion, the effectiveness of AI-generated advertising depends not merely on technological sophistication but on its alignment with consumers’ psychological expectations across generations. By balancing innovation with trust and combining emotional engagement with credibility, AI-generated advertising can serve as an effective and responsible communication tool in the evolving digital marketing landscape.