The development of Artificial Intelligence (AI) has significantly transformed how companies create and distribute digital marketing content (Davenport et al., 2020). One of the fastest-growing phenomena in the digital marketing ecosystem is AI-generated content (AIGC), which refers to content produced by AI algorithms to increase user engagement and influence consumer behavior on social media platforms such as TikTok (Xu, 2024). AI- generated content has substantial potential to improve brand awareness, user engagement, and purchase intention through attractive, personalized, and audience-oriented content (Meguellati, 2025). TikTok, as a short-video platform, has become a strategic medium for digital marketing content to interact directly with potential consumers. The platform has changed how consumers search for product information and make purchasing decisions, particularly among Generation Z users who dominate TikTok globally (Djafarova & Bowes, 2021). Previous studies have shown that various forms of digital content, including user-generated content and social media marketing content, significantly influence consumer engagement and purchase intention (Lou & Yuan, 2019; Schweidel, 2022). However, empirical studies specifically examining the influence of AI-generated content on purchase intention remain limited.
In consumer behavior theory, self-congruence theory holds that consumers tend to prefer products or brands whose images align with their self-concept (Sirgy, 1985). High self-congruence can strengthen the emotional relationship between consumers and promotional content, thereby increasing the likelihood of purchase intention (Japutra et al., 2019). Therefore, this study considers self-congruence as a moderating variable that may strengthen or weaken the relationship between AI-generated content and purchase intention. This study integrates perspectives from digital marketing technology and consumer behavior theory to examine how AI-generated content interacts with consumers’ psychological characteristics, particularly self-congruence, to influence purchase intention. The research is expected to contribute to the digital marketing literature by providing empirical evidence regarding the effectiveness of AI-generated content on TikTok.
AI-generated content refers to promotional content created or curated by artificial intelligence systems to deliver persuasive, relevant, and personalized messages to consumers (Davenport et al., 2020). The advancement of large language models and AI-driven marketing technologies enables companies to create content more efficiently and effectively (Kaplan & Haenlein, 2019). AI- generated content can enhance personalization, message consistency, creativity, and relevance in marketing communication (Xu, 2024). According to the Stimulus–Organism–Response (S–O–R) theory, marketing content serves as a stimulus that influences consumers’ cognitive and emotional states, which, in turn, affect behavioral responses such as purchase intention (Mehrabian & Russell, 1974). AI-generated content possesses advantages in personalization and adaptability, making it highly effective in digital marketing environments (Schweidel et al., 2022). Empirical studies have found that AI-generated advertising positively influences purchase intention through perceived usefulness, relevance, and engagement (Arachchi & Samarasinghe, 2025). AI-based marketing communication can produce persuasive messages that are comparable to, or even more effective than, human-generated content when properly personalized (Meguellati, 2025).
TikTok has become one of the most influential social media platforms in modern digital marketing (Djafarova & Bowes, 2021). The platform’s algorithm- driven short-video format enables marketers to distribute highly engaging and personalized promotional content (Schweidel, 2022). TikTok users are generally characterized by high interaction rates, active engagement with visual content, and responsiveness toward marketing communication (Vorderer et al., 2016). Previous research demonstrates that TikTok content marketing significantly affects purchase intention and consumer engagement (Lou & Yuan, 2019). Effective content strategies involving creativity, entertainment, and relevance can increase customer interaction and influence purchasing decisions (Xu et al., 2024; Djafarova & Bowes, 2021)
Self-congruence theory, introduced by M. Joseph Sirgy (1985), states that consumers tend to respond positively to products, brands, or marketing messages that match their self-image (Sirgy, 1985). Consumers often use products and brands as symbolic representations of their identity, values, and lifestyle (Hosany, 2012). In digital marketing contexts, self-congruence extends beyond brand image and includes promotional content itself. Marketing content that reflects consumers’ personality, values, and lifestyle is more likely to generate positive emotional responses and stronger purchase intention (Japutra et al., 2019). Several empirical studies support the role of self-congruence in strengthening consumer attitudes and purchase intention in digital marketing environments. Consumers who perceive alignment between promotional content and their identity tend to experience higher emotional attachment and engagement (Chan, 2023; Simatupang et al., 2026).
This study employed a quantitative explanatory research design to examine the causal relationships among AI-generated content, self-congruence, and purchase intention (Creswell & Creswell, 2017). A cross-sectional survey approach was used to capture respondents’ perceptions regarding AI-generated promotional content on TikTok (Sekaran & Bougie, 2016)
The population consisted of active TikTok users in Indonesia who had previously interacted with promotional content on the platform. Purposive sampling was employed because not all TikTok users had relevant experiences with AI-generated promotional content (Sekaran & Bougie, 2016). The sampling criteria included: 1 Respondents aged at least 17 years old. 2 Active TikTok users. 3 Individuals who had viewed or interacted with promotional content on TikTok within the last three months. The sample size determination followed the recommendation of Hair et al. (2019), which suggests using 5–10 times the number of indicators employed in Structural Equation Modeling (SEM). Therefore, the minimum required sample ranged from 150 to 200 respondents
Data were collected using a structured online questionnaire distributed through social media and instant messaging platforms. All questionnaire items were measured using a five-point Likert scale ranging from strongly disagree (1) to strongly agree (5)
The respondents consisted primarily of young TikTok users aged between 17 and 30 years old, representing Generation Z and young millennials. Most respondents were female users, reflecting the high engagement of women with digital promotional content and social media marketing activities (Djafarova & Bowes, 2021). The majority of respondents used TikTok daily for more than one hour, indicating a high level of exposure to promotional content, including AI-generated content (Vorderer et al., 2016). High usage intensity suggests that respondents were familiar with social media marketing content and actively engaged with digital communication on TikTok (Schweidel et al., 2022).
The convergent validity test showed that all indicators had loading factor values above 0.70, indicating strong relationships between indicators and their respective constructs (Hair et al., 2019). In addition, the Average Variance Extracted (AVE) values for all constructs exceeded the recommended threshold of 0.50, confirming that the constructs explained more than 50% of the variance of their indicators (Fornell & Larcker, 1981). Reliability Testing The results demonstrated that Cronbach’s Alpha and Composite Reliability values for all constructs exceeded 0.70, indicating good internal consistency and reliability (Nunnally & Bernstein, 1994). Composite Reliability is considered more appropriate in PLS-SEM analysis because it accounts for the outer loadings of indicators, thereby providing a more accurate reliability estimation (Sarstedt et al., 2021).
The structural model evaluation indicated that the research model had satisfactory predictive relevance and explanatory power (Henseler et al., 2015). The R² value for purchase intention was categorized as moderate to strong, indicating that AI-generated content and self-congruence substantially explained purchase intention among TikTok users. According to (Chin, 1998), R² values of 0.25, 0.50, and 0.75 can be interpreted as weak, moderate, and substantial, respectively. Therefore, the obtained R² value suggests that the proposed model had adequate explanatory capability in predicting consumers’ purchase intention behavior. The predictive relevance value (Q²) was positive, indicating that the model possessed adequate predictive capability. A positive Q² value confirms that the model has predictive relevance and is capable of accurately predicting endogenous constructs within the research framework (Stone, 1974; Geisser, 1975).
| Hypothesis | Relationship | Path Coefficient | t-Statistic | p-Value | Result |
|---|---|---|---|---|---|
| H1 | AI-Generated Content → Purchase Intention | 0.412 | 5.873 | 0.000 | Supported |
| H2 | AI-Generated Content × Self-Congruence → Purchase Intention | 0.198 | 3.246 | 0.001 | Supported |
The results show that AI-generated content has a positive and significant effect on purchase intention. Therefore, H1 is supported. The moderation analysis also reveals that self-congruence significantly strengthens the relationship between AI-generated content and purchase intention. Thus, H2 is supported.
The findings demonstrate that AI-generated content significantly influences purchase intention among TikTok users. This result supports the technology-enabled marketing perspective, which argues that AI improves marketing effectiveness through personalization, relevance, and communication efficiency (Davenport et al., 2020). AI-generated content can create promotional messages tailored to users’ preferences, including visual presentation, storytelling, and communication style (Xu et al., 2024). In TikTok’s short-video environment, such content can increase engagement and positively affect consumer responses (Schweidel et al., 2022).
The study also confirms the moderating role of self-congruence. Consumers who perceive AI-generated content as reflecting their identity, values, and lifestyle exhibit stronger purchase intention. This finding aligns with self-congruence theory, which suggests that consumers prefer marketing stimuli compatible with their self-concept (Sirgy, 1985; Japutra et al., 2019). The results imply that AI-generated content does not affect all consumers equally. Its effectiveness depends heavily on consumers’ psychological alignment with the promotional message. Therefore, marketers should not only focus on technological sophistication but also ensure that AI-generated content authentically reflects the target audience’s identity and values (Chan et al., 2023). This study contributes to digital marketing literature by integrating AI- based marketing communication with consumer psychological factors, particularly self-congruence. The findings enrich the understanding of how AI- driven promotional strategies interact with consumers’ psychological characteristics in shaping purchase intention within social media environments (Khuong et al., 2024)
Figure 1:
This study examined the effectiveness of AI-generated content in increasing purchase intention among TikTok users, with self-congruence acting as a moderating variable. The findings indicate that: 1 AI-generated content positively and significantly affects purchase intention. 2 Self-congruence significantly strengthens the relationship between AI- generated content and purchase intention. 3 The effectiveness of AI-generated content depends not only on technological quality but also on the psychological compatibility between promotional content and consumers’ self-image. Overall, the study confirms the strategic importance of integrating AI technologies with consumer psychological understanding in digital marketing practices.
The findings provide several practical implications for marketers and businesses: 1 Companies should use AI not only for content automation but also for creating personalized and psychologically relevant promotional content. 2 Marketers should integrate psychographic segmentation and self- congruence considerations into AI-driven marketing strategies. 3 Businesses should maintain authenticity and ethical standards when using AI-generated promotional content to preserve consumer trust. Limitations and Future Research This study has several limitations. First, the research focused only on TikTok users in Indonesia, limiting the generalizability of the findings. Second, the study employed a cross-sectional design, which may not capture long-term changes in consumer behavior. Future research is encouraged to: 1 Include additional variables such as trust, emotional engagement, and perceived authenticity. 2 Extend the study to other social media platforms such as Instagram and YouTube. 3 Use longitudinal research designs to observe changes in consumer behavior over time.