Analysis of the Correlation Between Digital Advertising Creativity and User Behaviour

Authors

  • Meng Xia

DOI:

https://doi.org/10.54097/7gd47495

Keywords:

Digital advertising creativity, User behaviour analysis, Creative decision-making, Behavioural conversion, Multi-modal analysis, Intelligent creative system

Abstract

The transformation of digital technology and consumer behaviour is reshaping the development path of the advertising creativity field. This study delves into the bidirectional correlation mechanism between digital advertising creativity and user behaviour, revealing how behavioural data drives the optimisation of creative decision-making and how creative design precisely guides user conversion behaviour. The study constructs a multi-modal behaviour analysis framework and an intelligent creative generation system, proposing practical strategies to balance core contradictions such as precise personalisation and privacy protection, algorithm automation and creative uniqueness. Based on this, the paper proposes an integrated approach combining data infrastructure construction, layered creative strategies, and a multi-dimensional evaluation system, providing theoretical support and methodological guidance for digital marketing practices.

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References

[1] Boienko O, Yanchuk T, Fedchenko O. CREATIVITY IN DIGITAL MARKETING [J]. Three Seas Economic Journal, 2025, 6(1): 19-26. DOI: https://doi.org/10.30525/2661-5150/2025-1-4

[2] Acar S, Neumayer M, Burnett C. Social media use and creativity: Exploring the influences on ideational behavior and creative activity [J]. The Journal of Creative Behavior, 2021, 55(1): 39-52. DOI: https://doi.org/10.1002/jocb.432

[3] Alzoubi H M. Does BLE technology contribute towards improving marketing strategies, customers’ satisfaction and loyalty? The role of open innovation [J]. International journal of data and network science, 2022, 6(2): 449-460. DOI: https://doi.org/10.5267/j.ijdns.2021.12.009

[4] Klug D, Qin Y, Evans M, et al. Trick and please. A mixed-method study on user assumptions about the TikTok algorithm [C]//Proceedings of the 13th ACM web science conference 2021. 2021: 84-92. DOI: https://doi.org/10.1145/3447535.3462512

[5] Lorenz-Spreen P, Oswald L, Lewandowsky S, et al. A systematic review of worldwide causal and correlational evidence on digital media and democracy [J]. Nature human behaviour, 2023, 7(1): 74-101. DOI: https://doi.org/10.1038/s41562-022-01460-1

[6] Cao D, Meadows M, Wong D, et al. Understanding consumers’ social media engagement behaviour: An examination of the moderation effect of social media context [J]. Journal of Business Research, 2021, 122: 835-846. DOI: https://doi.org/10.1016/j.jbusres.2020.06.025

[7] Al-Azzam A F, Al-Mizeed K. The effect of digital marketing on purchasing decisions: A case study in Jordan [J]. The Journal of Asian Finance, Economics and Business, 2021, 8(5): 455-463.

[8] Tafesse W, Wood B P. Social media influencers’ community and content strategy and follower engagement behavior in the presence of competition: an Instagram-based investigation [J]. Journal of Product & Brand Management, 2023, 32(3): 406-419. DOI: https://doi.org/10.1108/JPBM-02-2022-3851

[9] Ameen N, Sharma G D, Tarba S, et al. Toward advancing theory on creativity in marketing and artificial intelligence [J]. Psychology & marketing, 2022, 39(9): 1802-1825. DOI: https://doi.org/10.1002/mar.21699

[10] Chintalapati S, Pandey S K. Artificial intelligence in marketing: A systematic literature review [J]. International Journal of Market Research, 2022, 64(1): 38-68. DOI: https://doi.org/10.1177/14707853211018428

[11] Krishen A S, Dwivedi Y K, Bindu N, et al. A broad overview of interactive digital marketing: A bibliometric network analysis [J]. Journal of Business Research, 2021, 131: 183-195. DOI: https://doi.org/10.1016/j.jbusres.2021.03.061

[12] Bresciani S, Huarng K H, Malhotra A, et al. Digital transformation as a springboard for product, process and business model innovation [J]. Journal of Business Research, 2021, 128: 204-210. DOI: https://doi.org/10.1016/j.jbusres.2021.02.003

[13] Zhou Y, Wang Z, Wang T, et al. Anyprefer: An agentic framework for preference data synthesis [J]. arXiv preprint arXiv:2504.19276, 2025.

[14] Wang J, Ding W, Zhu X. Financial Analysis: Intelligent Financial Data Analysis System Based on LLM-RAG [J]. 2025. DOI: https://doi.org/10.20944/preprints202503.1532.v1

[15] Liu X, Wang F, Zeng H, et al. PRNet: A Priori Embedded Network for Real-World Blind Micro-Expression Recognition [J]. Mathematics, 2025, 13(5): 749. DOI: https://doi.org/10.3390/math13050749

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Published

09-09-2025

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Articles

How to Cite

Xia, M. (2025). Analysis of the Correlation Between Digital Advertising Creativity and User Behaviour. Frontiers in Business, Economics and Management, 20(3), 93-97. https://doi.org/10.54097/7gd47495