Generative Artificial Intelligence is now used to generate all parts of advertising, such as concept ideation, copy creation, image generation, and revisions to these outputs. However, its effect on user clicks, conversions, emotional response and trust in a linear manner is not evident. This paper introduces an analytical system for investigating the impact of "AIGC advertising creative generation" on "user response patterns", and integrates creative quality evaluation scores, user response probability estimates, gain calculations, heterogeneity identification, and combination optimization within this framework. Three types of reproducible simulated delivery experiments are conducted to compare the three strategies: human creatives, human + AIGC collaborative creatives, and AIGC closed-loop creatives. According to the above results, AIGC closed-loop creatives have achieved higher click-through rates, interaction scores and creative costs compared with typical human creatives. However, the AI disclosure, privacy sensitivity and authenticity verification will likely have different conversion rates. Therefore, AIGC should not be treated as a cheap material production line; instead, a governance-oriented creative system that caters to the various needs of users and brands, allows for open disclosure, and responds promptly to real-time changes needs to be established.

Jinxin Li. Analysis of Advertising Creativity Generation and User Response Mode Based on AIGC. International Journal of Big Data Intelligent Technology (2026), Vol. 7, Issue 2: 1-11. https://doi.org/10.38007/IJBDIT.2026.070201.

[1] Kshetri, N., Dwivedi, YK, Davenport, TH, & Panteli, N. (2024). Generative artificial intelligence in marketing: Applications, opportunities, challenges, and research agenda. International Journal of Information Management, 75, 102716.

[2] Grewal, D., Satornino, CB, Davenport, T., & Guha, A. (2025). How generative AI is shaping the future of marketing. Journal of the Academy of Marketing Science, 53, 702–722. https://doi.org/10.1007/s11747-024-01064-3

[3] Cillo, P., & Rubera, G. (2025). Generative AI in innovation and marketing processes: A roadmap of research opportunities. Journal of the Academy of Marketing Science, 53, 684–701.

[4] Hermann, E., & Puntoni, S. (2024). Artificial intelligence and consumer behavior: From predictive to generative AI. Journal of Business Research, 180, 114720.

[5] Arango, L., Singaraju, SP, & Niininen, O. (2023). Consumer responses to AI-generated charitable giving ads. Journal of Advertising, 52(4), 486–503.

[6] Baek, TH, Kim, J., & Kim, JH (2024). Effect of disclosing AI-generated content on prosocial advertising evaluation. International Journal of Advertising.

[7] Chang, C. W. (2026). Privacy Infrastructure Vulnerability Mining and Automated Framework Based on Multimodal AI. Procedia Computer Science, 279, 702-711.

[8] Wang, Z. (2026). Mineral Commodity Time-Series Cycle Modeling and Feature Learning Algorithm Based On Improved Transformer. Procedia Computer Science, 281, 1347-1356.

[9] Liang, Q. (2026). Reconstruction of Commercial Building Space Reuse Mode Driven by Composite Business Types. International Journal of Engineering Advances, 3(1), 107-113.

[10] Liu, Z. (2026). Research on Measuring User Behavior Response Differences Supported by Propensity Scoring Method. European Journal of AI, Computing & Informatics, 2(2), 47-53.

[11] Gao, Y. (2026). Governance Structures and Checks and Balances in Private Equity Funds: Practice, Problems, and Improvement Paths. Financial Economics Insights, 3(2), 82-91.

[12] Liang, Q. (2026). Research on Low-Energy Space Organization Methods in the Context of Building System Integration. Global Journal of Science & Innovation, 3(1), 9-15.