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AI and consumer behavior: Trends, technologies, and future directions from a scopus-based systematic review
Open accessAbstract
This systematic literature review, conducted following the PRISMA 2020 guidelines, synthesizes 117 Scopus-indexed articles (2009–April 4, 2025) to address six research questions on artificial intelligence’s (AI) role in consumer behavior. The study examines research trends (RQ1), prevalent AI technologies (RQ2), key variables (RQ3), methodological approaches (RQ4), future research directions (RQ5), and AI’s transformative impact on consumer behavior (RQ6). Findings report exponential publication surges after 2020, driven by machine learning and natural language processing applications. Consumer attitudes (trust, privacy) and behavioral intentions emerge as primary mediators of AI adoption, while ethical concerns underline the need for open algorithmic architectures. Quantitative survey methods dominate, though longitudinal and cross-cultural investigations remain infrequent. Generative AI is a disruptive technology enabling hyper-personalization but also authenticity threats. Practical implications call for ethical imperatives of AI governance and hybrid human-machine processes to weigh innovation against consumer trust. In noting limitations of geographic coverage and linguistic bias, the review proposes future research agendas like cultural mediation of AI acceptance and longitudinal behavioral impact. By examining these dimensions systematically, the research enhances academic understanding of AI’s multifaceted influence and supports actionable insight to ethically grounded marketing practice in evolving technology contexts.
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Marketing scholarship has examined Generative Artificial Intelligence (GenAI) primarily through firm-side strategy.
“Marketing scholarship has examined Generative Artificial Intelligence (GenAI) primarily through firm-side strategy(Prasanna and Kushwaha, 2025;Riandhi et al., 2025;Grewal et al., 2025;Kumar et al., 2026)”
From Shopping by algorithm: How agentic AI deploys human heuristics as a surrogate consumer (2026) · cites this paper as Riandhi et al., 2025
Section 5.7 (page unavailable) discusses firm-side strategies for deploying generative AI, so the paper does examine that perspective. But it does not establish that marketing scholarship examines GenAI primarily through firm-side strategy; Section 4.3 identifies consumer behavior and decision-making as the largest focus in the broader AI literature it reviews. (LLM, conf=0.83)
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Cites
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“Weaknesses: Subjectivity in deriving themes, lack of standardization, and issues of reliability take away from its rigor (naeem et al., 2023).”
Baas, J., Schotten, M., Plume, A., Côté, G., & Karimi, R. (2020) · Quantitative Science Studies · doi:10.1162/qss_a_00019 · Cited by 2,093
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Consumers and artificial intelligence: An experiential perspective
Puntoni, S., Reczek, R. W., Giesler, M., & Botti, S. (2021) · Journal of Marketing · doi:10.1177/0022242920953847 · Cited by 1,547 · cited 2×
“…personalized content that informs unique user experiences, and behavior nudges that are enabled by the strategic organization of information (Puntoni et al., 2021). empirical studies have demonstrated how far-reaching the influence of ai-driven interactions is on customers’ choices, brand…”
Lamberton, C., & Stephen, A. T. (2016) · Journal of Marketing · doi:10.1509/jm.15.0415 · Cited by 1,127 · cited 1×
“…with digital innovation, social media, and mobile marketing. in this way, the literature reviewed will be current and capable of accommodating emerging findings continually, thereby enhancing the validity and exhaustiveness of analysis in systematic literature reviews (lamberton & Stephen, 2016).”
What Is Qualitative Research? An Overview and Guidelines
Lim, W. M. (2025) · Australasian Marketing Journal · doi:10.1177/14413582241264619 · Cited by 985
Data Privacy: Effects on Customer and Firm Performance
Martin, K. D., Borah, A., & Palmatier, R. W. (2017) · Journal of Marketing · doi:10.1509/jm.15.0497 · Cited by 786 · cited 1×
“…of how their data is utilized and how algorithmic systems influence their actions, the demand for ethical responsibility in using ai has increased (Martin et al., 2017). this establishment of consumer confidence, therefore, rests upon a fine line of ethics—a balancing act in bringing technology…”
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The Power of Virtual Influencers: Impact on Consumer Behaviour and Attitudes in the Age of AI
Gerlich, M. (2023) · Administrative Sciences · doi:10.3390/admsci13080178 · Cited by 165
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Shopping by algorithm: How agentic AI deploys human heuristics as a surrogate consumer
Wadi, D., & Ma, Y. (2026) · arXiv · doi:10.48550/arxiv.2609.28372
Metadata
- Authors
- Riandhi, A. N., Arviansyah, M. R., & Sondari, M. C.
- Affiliations
- Padjadjaran University (ID)
- Corresponding author
- Riandhi, Aditya Nanda
- Venue
- Cogent Business & Management
- Publisher
- Informa UK Limited
- Published
- 2025-08-07
- Volume
- 12
- Issue
- 1
- ISSN
- 2331-1975 (ISSN-L)
- OpenAlex
- W4413222052
- Cited by
- 56
- References
- 19
- Citations by year
- 2026: 49 · 2025: 7
- FWCI
- 4.17
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Cogent Business & Management (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Universitas Padjadjaran
- Topics
- Social Sciences › Business, Management and Accounting › Marketing › AI in Service Interactions
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Social Sciences › Business, Management and Accounting › Marketing › Digital Marketing and Social Media
- Keywords
- artificial intelligence, consumer behavior, artificial intelligence adoption, consumer trust, privacy, behavioral intention, generative AI, hyper-personalization, AI ethics, AI governance, machine learning, natural language processing, consumer attitudes, human-machine interaction
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