The DOI matches its Crossref record; 96 of the 97 references it cites were found in a registry or an index.
Generative AI for Consumer Behavior Prediction: Techniques and Applications
Open accessAbstract
Generative AI techniques, such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and transformers, have revolutionized consumer behavior prediction by enabling the synthesis of realistic data and extracting meaningful insights from large, unstructured datasets. However, despite their potential, the effectiveness of these models in practical applications remains inadequately addressed in the existing literature. This study aims to investigate how generative AI models can effectively enhance consumer behavior prediction and their implications for real-world applications in marketing and customer engagement. By systematically reviewing 31 studies focused on these models in e-commerce, energy data modeling, and public health, we identify their contributions to improving personalized marketing, inventory management, and customer retention. Specifically, transformer models excel at processing complicated sequential data for real-time consumer insights, while GANs and VAEs are effective in generating realistic data and predicting customer behaviors such as churn and purchasing intent. Additionally, this review highlights significant challenges, including data privacy concerns, the integration of computing resources, and the limited applicability of these models in real-world scenarios.
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“However, high-involvement purchases trigger central processing and scrutiny of source benevolence and authenticity (Han, 2024;Madanchian, 2024).”
From AI-generated versus human-created advertising: Effects on consumer trust and purchase intent (2025) · cites this paper as Madanchian, 2024
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“This study's finding that high-involvement product categories increase consumer scepticism towards AI supports theoretical frameworks that emphasise deeper cognitive engagement for high-stakes purchases (Park et al., 2023;Madanchian, 2024).”
From AI-generated versus human-created advertising: Effects on consumer trust and purchase intent (2025) · cites this paper as Madanchian, 2024
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The PRISMA 2020 statement: An updated guideline for reporting systematic reviews
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The theory of planned behavior
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Machine learning: Algorithms, real-world applications and research directions
Sarker, I. H. (2021) · SN Computer Science · doi:10.1007/s42979-021-00592-x · Cited by 5,583 · cited 1×
“Acquiring such data can be difficult in many real-world applications, particularly in developing industries like public health and e-commerce [98].”
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How artificial intelligence will change the future of marketing
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Toward a theory of customer engagement marketing
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Yenduri, G., Ramalingam, M., Selvi, G. C., Supriya, Y., Srivastava, G., Maddikunta, P. K. R., Raj, G. D., Jhaveri, R. H., Prabadevi, B., Wang, W., et al. (2024) · IEEE Access · doi:10.1109/access.2024.3389497 · Cited by 916
Generative artificial intelligence
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Flagged first, then by citations
1 of the 97 references couldn’t be matched to a known paper.
Research works printed but not linked: 7
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Cited by
Sorted by citations
AI-generated versus human-created advertising: Effects on consumer trust and purchase intent
Israfilzade, K. (2025) · Equilibrium. Quarterly Journal of Economics and Economic Policy · doi:10.24136/eq.4038 · Cited by 21
Unveiling the intellectual landscape of artificial intelligence and consumer behavior
Zulaikha, S., Dewi, I. R., & Kurniawati, M. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-025-00740-9 · Cited by 18
Alsulami, B., Alwated, B. S., Barashid, K., Abdullah, M., AlOsaimi, M., & ALhusayni, S. M. S. (2025) · Archives for Technical Sciences · doi:10.70102/afts.2025.1834.035 · Cited by 17
Rahman, A., Khan, M. A., Mahmud, F. U., Bishnu, K. K., Rahman, M. A., Mridha, M. F., & Hossen, M. J. (2026) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2026.071029 · Cited by 13
Artificial Intelligence and the Future of Marketing Education
Schlegelmilch, B. B., & Mills, A. J. (2025) · Journal of Marketing Education · doi:10.1177/02734753251336956 · Cited by 13
Metadata
- Authors
- Madanchian, M.
- Affiliations
- University Canada West (CA)
- Corresponding author
- Madanchian, Mitra
- ORCID
- Madanchian, Mitra
- Venue
- Sustainability
- Publisher
- MDPI AG
- Published
- 2024-11-15
- Volume
- 16
- Issue
- 22
- Pages
- 9963
- ISSN
- 2071-1050 (ISSN-L)
- OpenAlex
- W4404418369
- Cited by
- 60
- References
- 97
- Citations by year
- 2026: 28 · 2025: 31 · 2024: 1
- FWCI
- 27.71
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sustainability (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Marketing › Customer churn and segmentation
- Social Sciences › Business, Management and Accounting › Marketing › AI in Service Interactions
- Social Sciences › Business, Management and Accounting › Marketing › Consumer Market Behavior and Pricing
- Keywords
- generative AI, customer behavior prediction, generative adversarial networks, variational autoencoder, transformer models, personalized marketing, customer retention, inventory management, customer churn prediction, e-commerce, data privacy, customer engagement
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