The potential of generative AI for personalized persuasion at scale
Open accessAbstract
Matching the language or content of a message to the psychological profile of its recipient (known as “personalized persuasion”) is widely considered to be one of the most effective messaging strategies. We demonstrate that the rapid advances in large language models (LLMs), like ChatGPT, could accelerate this influence by making personalized persuasion scalable. Across four studies (consisting of seven sub-studies; total N = 1788), we show that personalized messages crafted by ChatGPT exhibit significantly more influence than non-personalized messages. This was true across different domains of persuasion (e.g., marketing of consumer products, political appeals for climate action), psychological profiles (e.g., personality traits, political ideology, moral foundations), and when only providing the LLM with a single, short prompt naming or describing the targeted psychological dimension. Thus, our findings are among the first to demonstrate the potential for LLMs to automate, and thereby scale, the use of personalized persuasion in ways that enhance its effectiveness and efficiency. We discuss the implications for researchers, practitioners, and the general public.
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AI chatbots can craft hyper-tailored messages using psychographic and behavioral data.
“AI chatbots can craft hyper-tailored messages using psychographic and behavioral data (Costello, Pennycook, and Rand 2024; Matz et al. 2024)”
From Emotional Manipulation by AI Companions (2025) · cites this paper as Matz et al. 2024
LLMs can influence and persuade humans, with research demonstrating that LLM-based writing assistants are capable of swaying people's views.
“On the other hand, LLMs can influence and persuade humans26, with research demonstrating that LLM-based writing assistants are capable of swaying people’s views27.”
From Generative language models exhibit social identity biases (2024) · cites this paper as [26]
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“…generative AI can automate the creation of personalized marketing content, for instance, different sales slogans for introverts vs. extroverts (Matz et al. 2017) or other personality traits as personalized marketing content is more effective than a one-content-fits-all approach (Matz et al. 2023).”
Generative artificial intelligence enhances creativity but reduces the diversity of novel content
Doshi, A., & Hauser, O. (2023) · doi:10.2139/ssrn.4535536 · Cited by 887 · see citing works
“That a tightly controlled prompt requesting a generative AI idea shows sizable effects on creativity in our study provides a promising starting point for future researchers to delve deeper into customization and personalization of generative AI for different writers ( 8 ).”
Bengesi, S., El-Sayed, H., Sarker, M. K., Houkpati, Y., Irungu, J., & Oladunni, T. (2023) · IEEE Access · doi:10.1109/access.2024.3397775 · Cited by 286 · see citing works
Durably reducing conspiracy beliefs through dialogues with AI
Costello, T. H., Pennycook, G., & Rand, D. G. (2024) · Science · doi:10.1126/science.adq1814 · Cited by 354 · see citing works
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On the conversational persuasiveness of GPT-4
Salvi, F., Ribeiro, M. H., Gallotti, R., & West, R. (2025) · Nature Human Behaviour · doi:10.1038/s41562-025-02194-6 · Cited by 326 · see citing works
“Our study suggests that concerns around personalization and AI persuasion are warranted, reinforcing previous results 42,44,45,48 by show-casing how LLMs can outpersuade humans in online conversations through microtargeting.”
Artificial intelligence and consumer behavior: From predictive to generative AI
Hermann, E., & Puntoni, S. (2024) · Journal of Business Research · doi:10.1016/j.jbusres.2024.114720 · Cited by 284 · see citing works
The power of generative marketing: Can generative AI create superhuman visual marketing content?
Hartmann, J., Exner, Y., & Domdey, S. (2025) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2024.09.002 · Cited by 229 · see citing works
Generative AI and its Transformative Value for Digital Platforms
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“The integration of GenAI expands these capabilities beyond traditional personalization, enabling emotionally adaptive interactions and deeply contextualized experiences that respond to users’ psychological and emotional states [69].”
InfluentialGenerative AI in Marketing and Principles for Ethical Design and Deployment
Hermann, E., & Puntoni, S. (2024) · Journal of Public Policy & Marketing · doi:10.1177/07439156241309874 · Cited by 93 · see citing works
“First, insights generated through marketing research can inform segmentation, targeting, and positioning (Huang and Rust 2021), which can be based, for example, on psychological pro fi les derived by GenAI (Matz et al. 2024).”
Generative language models exhibit social identity biases
Hu, T., Kyrychenko, Y., Rathje, S., Collier, N., van der Linden, S., & Roozenbeek, J. (2024) · Nature Computational Science · doi:10.1038/s43588-024-00741-1 · Cited by 100 · see citing works
“GPT-2-Large Positive .5106 .9375 2 Results”
Large language models can infer psychological dispositions of social media users
Peters, H., & Matz, S. (2024) · PNAS Nexus · doi:10.1093/pnasnexus/pgae231 · Cited by 102 · see citing works
“On the one hand, our findings contribute to a growing body of research comparing the abilities of LLMs to those observed in humans [5, 7, 8].”
ResultSelf-citationLLM-generated messages can persuade humans on policy issues
Bai, H., Voelkel, J. G., Muldowney, S., Eichstaedt, J. C., & Willer, R. (2025) · Nature Communications · doi:10.1038/s41467-025-61345-5 · Cited by 110 · see citing works
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Generative AIPreprint
Feuerriegel, S., Hartmann, J., Janiesch, C., & Zschech, P. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4443189 · Cited by 65 · see citing works
Ideation with Generative AI—in Consumer Research and Beyond
Freitas, J. D., Nave, G., & Puntoni, S. (2025) · Journal of Consumer Research · doi:10.1093/jcr/ucaf012 · Cited by 61 · see citing works
“LLMs are also capable of personalizing persuasive messages to the characteristics of the recipient (Matz et al. 2024).”
Nepal, S. K., Pillai, A., Campbell, W., Massachi, T., Choi, E. S., Xu, X., Kuc, J., Huckins, J. F., Holden, J. L., Depp, C. A., et al. (2024) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3613905.3650767 · Cited by 65 · see citing works
Intelligent financial system: How AI is transforming finance
Aldasoro, I., Gambacorta, L., Korinek, A., Shreeti, V., & Stein, M. (2025) · Journal of Financial Stability · doi:10.1016/j.jfs.2025.101472 · Cited by 76 · see citing works
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The Persuasive Power of Large Language Models
Breum, S. M., Egdal, D. V., Gram Mortensen, V., Møller, A. G., & Aiello, L. M. (2024) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v18i1.31304 · Cited by 57 · see citing works
Developing Personalized Marketing Service Using Generative AI
Lee, G. H., Lee, K. J., Jeong, B., & Kim, T. (2024) · IEEE Access · doi:10.1109/access.2024.3361946 · Cited by 44 · see citing works
“The format and content of these questions have been adapted based on insights from previous studies [8].”
BackgroundMethodInfluentialRisks and protective measures for synthetic relationships
Starke, C., Ventura, A., Bersch, C., Cha, M., de Vreese, C. H., Doebler, P., Dong, M., Krämer, N., Leib, M., Peter, J., et al. (2024) · Nature Human Behaviour · doi:10.1038/s41562-024-02005-4 · Cited by 42 · see citing works
Persuading voters using human–artificial intelligence dialogues
Lin, H., Czarnek, G., Lewis, B., White, J. P., Berinsky, A. J., Costello, T., Pennycook, G., & Rand, D. G. (2025) · Nature · doi:10.1038/s41586-025-09771-9 · Cited by 76 · see citing works
“Recent studies indicate that when creating static persuasive messages, generative AI can sometimes match—but not typically exceed—the persuasive effect of human-generated content 13 , and due to scaling laws this result is unlikely to change even with much more sophisticated AI models 14 .”
The levers of political persuasion with conversational artificial intelligence
Hackenburg, K., Tappin, B. M., Hewitt, L., Saunders, E., Black, S., Lin, H., Fist, C., Margetts, H. Z., Rand, D. G., & Summerfield, C. (2025) · Science · doi:10.1126/science.aea3884 · Cited by 59 · see citing works
Ethics of generative AI and manipulation: a design-oriented research agenda
Klenk, M. (2024) · Ethics and Information Technology · doi:10.1007/s10676-024-09745-x · Cited by 55 · see citing works
“Matz et al. (2023) already demonstrate that GPT-3 can be prompted to produce personalised and more persuasive outputs when the ‘human prompter’ is able to match prompt and target.”
Zehnle, M., Hildebrand, C., & Valenzuela, A. (2025) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2025.02.005 · Cited by 45 · see citing works
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Pantano, E., Serravalle, F., & Priporas, C. (2024) · Journal of Marketing Management · doi:10.1080/0267257x.2024.2436096 · Cited by 38 · see citing works
“These align with findings from previous studies on the usage of GAI and LLMs to generate new solutions (Fang et al., 2024; Matz et al., 2024; Sarstedt et al., 2024), which can be biased or not completely reflective of human needs or desires.”
Farah, M. F., Ramadan, Z. B., & Nassereddine, Y. (2024) · Psychology and Marketing · doi:10.1002/mar.22097 · Cited by 41 · see citing works
Normative Challenges of Risk Regulation of Artificial Intelligence
Orwat, C., Bareis, J., Folberth, A., Jahnel, J., & Wadephul, C. (2024) · NanoEthics · doi:10.1007/s11569-024-00454-9 · Cited by 52 · see citing works
Attitude Towards AI: Potential Influence of Conspiracy Belief, XAI Experience and Locus of Control
Babiker, A., Alshakhsi, S. A. A., Al‐Thani, D., Montag, C., & Ali, R. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2401249 · Cited by 36 · see citing works
Deception and manipulation in generative AI
Tarsney, C. J. (2025) · Philosophical Studies · doi:10.1007/s11098-024-02259-8 · Cited by 30 · see citing works
“First, they can personalize misin-formation on a large scale, crafting individualized messages for and even engaging in conversations with millions of targets at once (Matz et al., 2024).”
Madathil, J. C. (2025) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2025.104381 · Cited by 36 · see citing works
From killer bunnies to talking cupcakes: theorizing the diverse universe of virtual influencers
Gambetti, R. C., & Kozinets, R. V. (2024) · European Journal of Marketing · doi:10.1108/ejm-12-2022-0948 · Cited by 30 · see citing works
Durably reducing conspiracy beliefs through dialogues with AI
Costello, T. H., Pennycook, G., & Rand, D. G. (2024) · doi:10.31234/osf.io/xcwdn · Cited by 28 · see citing works
Biased AI writing assistants shift users’ attitudes on societal issues
Williams‐Ceci, S., Jakesch, M., Bhat, A., Kadoma, K., Zalmanson, L., & Naaman, M. (2026) · Science Advances · doi:10.1126/sciadv.adw5578 · Cited by 27 · see citing works
The effects of generative AI’s human-like competencies on clinical decision-making
Spatscheck, N., Schaschek, M., & Winkelmann, A. (2024) · Journal of Decision System · doi:10.1080/12460125.2024.2430731 · Cited by 27 · see citing works
Testing theories of political persuasion using AI
Argyle, L. P., Busby, E. C., Gubler, J. R., Lyman, A., Olcott, J., Pond, J., & Wingate, D. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2412815122 · Cited by 25 · see citing works
Blömker, J., & Albrecht, C. (2024) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2024.103806 · Cited by 29 · see citing works
Market research and knowledge using Generative AI: the power of Large Language Models
Estévez, M., Ballestar, M. T., & Sáinz, J. (2025) · Journal of Innovation & Knowledge · doi:10.1016/j.jik.2025.100796 · Cited by 34 · see citing works
How do people react to political bias in generative artificial intelligence (AI)?
Messer, U. (2024) · Computers in Human Behavior Artificial Humans · doi:10.1016/j.chbah.2024.100108 · Cited by 24 · see citing works
Sun, H., Xie, P., & Sun, Y. (2025) · Journal of Advertising Research · doi:10.1080/00218499.2025.2462405 · Cited by 29 · see citing works
Balaskas, S., Konstantakopoulou, M., Yfantidou, I., & Komis, K. (2025) · Societies · doi:10.3390/soc15080232 · Cited by 21 · see citing works
Large Language Models Can Infer Personality from Free-Form User InteractionsPreprint
Peters, H., Cerf, M., & Matz, S. (2024) · doi:10.31219/osf.io/apc5g · Cited by 20 · see citing works
Self-citationUnderstanding model power in social AI
Brandtzæg, P. B., Skjuve, M. B., & Følstad, A. (2024) · AI & Society · doi:10.1007/s00146-024-02053-4 · Cited by 20 · see citing works
The Double-Edged Sword of Anthropomorphism in LLMs
Reinecke, M. G., Ting, F., Savulescu, J., & Singh, I. (2025) · Proceedings · doi:10.3390/proceedings2025114004 · Cited by 20 · see citing works
“We expect this challenge to become even greater as LLMs become further anthropomorphic [42], persuasive [61], and potentially even deceptive [62,63].”
Do Expressions Change Decisions? Exploring the Impact of AI's Explanation Tone on Decision-Making
Okoso, A., Yang, M., & Baba, Y. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713744 · Cited by 19 · see citing works
“Moreover, several studies demonstrated that tailoring advertisements using LLMs to match the personality traits of users increases the likelihood of persuasion [45, 47].”
Yan, H. Y., Morrow, G., Yang, K.-C., & Wihbey, J. (2025) · Harvard Kennedy School Misinformation Review · doi:10.37016/mr-2020-171 · Cited by 18 · see citing works
“Unlike traditional forms of political manipulation, which typically involve human-crafted disinformation or propaganda, AI tools can be used to not only generate vast amounts of content swiftly and inexpensively but also tailor this content to individuals (Matz et al., 2024).”
Yang, Y., Li, C., & Qu, Z. (2025) · Journal of Hospitality and Tourism Management · doi:10.1016/j.jhtm.2025.05.012 · Cited by 17 · see citing works
When cutting edge meets silver tongue: Understanding the word-of-machine effect on travel decisions
Sun, D., Wong, I. A., Xiong, X., & Li, S. (2025) · Tourism Management · doi:10.1016/j.tourman.2025.105271 · Cited by 20 · see citing works
The impact of advanced AI systems on democracy
Summerfield, C., Argyle, L. P., Bakker, M. A., Collins, T., Durmus, E., Eloundou, T., Gabriel, I., Ganguli, D., Hackenburg, K., Hadfield, G. K., et al. (2025) · Nature Human Behaviour · doi:10.1038/s41562-025-02309-z · Cited by 41 · see citing works
“However, there is concern that LLMs could distort campaigning by inferring voter preferences from open-source data (perhaps a single tweet 42 ), and rewording messages to match a citizen’s perspectives, demographics or personality traits 39,43 .”
Navigating the AI era: university communication strategies and perspectives on generative AI tools
Henke, J. (2024) · Journal of Science Communication · doi:10.22323/2.23030205 · Cited by 16 · see citing works
Merl, N. B., Schramm, F., Wies, C., Winterstein, J. T., & Brinker, T. J. (2025) · European Journal of Cancer · doi:10.1016/j.ejca.2025.116114 · Cited by 22 · see citing works
A meta-analysis of the persuasive power of large language models
Hölbling, L., Maier, S., & Feuerriegel, S. (2025) · Scientific Reports · doi:10.1038/s41598-025-30783-y · Cited by 14 · see citing works
“Large language models (LLMs) such as GPT-4 are increasingly embedded in communication settings that aim to shape attitudes, preferences, or behaviors [1–3].”
Beware the Intention Economy: Collection and Commodification of Intent via Large Language Models
Chaudhary, Y., & Penn, J. (2024) · Harvard Data Science Review · doi:10.1162/99608f92.21e6bbaa · Cited by 13 · see citing works
Generative AI, Work and Risks in Cultural and Creative IndustriesPreprint
Walkowiak, E., & Potts, J. (2024) · Social Science Research Network · doi:10.2139/ssrn.4830265 · Cited by 13 · see citing works
Capturing a moving target: Developing research on and with AI for Human Relations
Stollberger, J., Anand, S., & Dick, P. (2025) · Human Relations · doi:10.1177/00187267251332075 · Cited by 13 · see citing works
“…a rather modest impact on coaching effectiveness (Graßmann and Schermuly, 2021; Weber et al., 2021); however, latest versions of ChatGPT can remember prior interaction content and analyze text for user preferences to make interactions more bespoke and pleasant (Fan et al., 2023; Matz et al., 2024).”
Emotional Manipulation by AI Companions
De Freitas, J., Oguz-Uguralp, Z., & Kaan-Uguralp, A. (2025) · arXiv · doi:10.48550/arxiv.2508.19258 · Cited by 12 · see citing works
Allen, J. W., Earp, B. D., & Wilkinson, D. J. C. (2025) · Journal of Medical Ethics · doi:10.1136/jme-2024-110624 · Cited by 14 · see citing works
“39 Unlike human conversation which adapts to social cues, LLMs risk exerting undue influence through persistent messaging that could constitute manipulation, particularly through their ability to engage in reciprocal exchanges and adjust manipulation strategies based on user input in real time.”
Beyond the Turing Test: Exploring the implications of generative AI for category construction
Phillips, N., Kalvapalle, S. G., & Kennedy, M. (2024) · Organization Theory · doi:10.1177/26317877241275113 · Cited by 12 · see citing works
“For example, Matz et al. (2024) carried out several studies that showed that personalized messages (i.e. messages that were customized to the psychological profile of the recipient) crafted by ChatGPT were significantly more influential than non-personalized messages.”
Artificial Intelligence in Marketing: From Computer Science to Social Science
Puntoni, S. (2024) · Journal of Macromarketing · doi:10.1177/02761467241282666 · Cited by 13 · see citing works
We need to understand “when” not “if” generative AI can enhance personalized persuasion
Teeny, J. D., & Matz, S. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2418005121 · Cited by 12 · see citing works
“In contrast, Hackenburg and Margetts employed simple sociodemograph - ics (e.g., age, education), which offer a shallower range for meaningfully personalizing the appeal to the target ( 6 ).”
BackgroundMethodResultInfluentialSelf-citationAutomated Parasociality: From Personalization to Personification
Andrejevic, M., & Volčić, Z. (2024) · Television & New Media · doi:10.1177/15274764241300436 · Cited by 17 · see citing works
“…claim to have demonstrated that, “personalized messages crafted by ChatGPT exhibit significantly more influence than non-personalized messages” (Matz et al. 2024, 4692) and that, “modern language models can generate content perceived as at least on par and often more persuasive than…”
Enabling people-centric climate action using human-in-the-loop artificial intelligence: a review
Debnath, R., Tkachenko, N., & Bhattacharyya, M. (2025) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2025.101482 · Cited by 12 · see citing works
AI can outperform humans in predicting correlations between personality items
Schoenegger, P., Greenberg, S., Grishin, A. V., Lewis, J., & Caviola, L. (2025) · Communications Psychology · doi:10.1038/s44271-025-00205-w · Cited by 12 · see citing works
“Other research has focused on LLMs ’ capability to simulate personality-congruent responses and adaptively interact with humans 24,25 , as well as assessed the psychological pro fi les of LLMs themselves through psychometric inventories 26 , and outlined concerns about the generalizability of…”
On the emergent capabilities of ChatGPT 4 to estimate personality traits
Piastra, M., & Catellani, P. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2025.1484260 · Cited by 12 · see citing works
Lu, H. (2025) · Vaccine · doi:10.1016/j.vaccine.2025.127018 · Cited by 12 · see citing works
Blömker, J., & Albrecht, C. (2025) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2025.104349 · Cited by 12 · see citing works
Unwrapping generative AI paradigms for product and service innovation differentiation
Shonubi, O. (2026) · Journal of Strategy & Innovation · doi:10.1016/j.jsinno.2025.200562 · Cited by 11 · see citing works
LLM-Aided Social Media Influence Operations
Meier, R. (2024) · Springer eBooks · doi:10.1007/978-3-031-54827-7_11 · Cited by 11 · see citing works
Clicks and tricks: The dark art of online persuasion
Fagan, P. (2024) · Current Opinion in Psychology · doi:10.1016/j.copsyc.2024.101844 · Cited by 12 · see citing works
Something AI Should Tell You – The Case for Labelling Synthetic Content
Fisher, S. A. (2024) · Journal of Applied Philosophy · doi:10.1111/japp.12758 · Cited by 11 · see citing works
Gonzalez, K., Kanitz, R., & Briker, R. (2024) · The Journal of Applied Behavioral Science · doi:10.1177/00218863241279916 · Cited by 15 · see citing works
“Although AI tools have shown promise to excel in interpersonal domains as well (e.g., listening, interpreting, persuasion; Dillion et al., 2024; Matz et al., 2024; Strachan et al., 2024), once people are aware that they are interacting with AI, it neutralizes the ability for AI to outperform humans…”
Allen, J. W., Schaefer, G. O., Mann, S. P., Earp, B. D., & Wilkinson, D. J. C. (2024) · Research Ethics · doi:10.1177/17470161241298726 · Cited by 11 · see citing works
“While this function allows for more personalised and engaging consent interactions, it may (inadvert-ently) influence participants’ decision-making processes, often without individuals’ specific awareness (Matz et al., 2024; Ye et al., 2024a).”
Warning people that they are being microtargeted fails to eliminate persuasive advantage
Carrella, F., Simchon, A., Edwards, M., & Lewandowsky, S. (2025) · Communications Psychology · doi:10.1038/s44271-025-00188-8 · Cited by 12 · see citing works
“Similarly, targeted messagescrafted by ChatGPT consistently outperformed non-targeted messages across several studies,irrespectiveofthepersuasiondomainandevenwhenprovidedwith onlyasingleshortprompt 17,24 .”
Biswas, S., Erlei, A., & Gadiraju, U. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713201 · Cited by 12 · see citing works
The psychological mechanism of value co-creation with human-centred generative AI robot assistants
Li, Z., Yuen, K. F., & Teo, C. (2025) · Technology in Society · doi:10.1016/j.techsoc.2025.103015 · Cited by 11 · see citing works
Srba, I., Razuvayevskaya, O., Leite, J. A., Moro, R., Schlicht, I. B., Tonelli, S., García, F. M., Lottmann, S. B., Teyssou, D., Porcellini, V., et al. (2026) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3770077 · Cited by 19 · see citing works
Blömker, J., & Albrecht, C. (2025) · Computers in Human Behavior Artificial Humans · doi:10.1016/j.chbah.2025.100126 · Cited by 9 · see citing works
Lim, R. E., Kim, S., Abernathy, A. M., Shin, S., & Avance, R. (2025) · Journal of Marketing Communications · doi:10.1080/13527266.2025.2497259 · Cited by 11 · see citing works
How AI sources can increase openness to opposing views
Lu, L., Tormala, Z. L., & Duhachek, A. (2025) · Scientific Reports · doi:10.1038/s41598-025-00791-z · Cited by 9 · see citing works
“This research also advances an exploding literature on the role of AI in persuasion 52,53 .”
Palm, S., & Tims, M. (2025) · Personality and Individual Differences · doi:10.1016/j.paid.2025.113412 · Cited by 9 · see citing works
Assessing public support for degrowth: survey-based experimental and predictive studies.
Krpan, D., Basso, F., Hickel, J., & Kallis, G. (2025) · Lancet Planetary Health · doi:10.1016/j.lanplh.2025.101326 · Cited by 9 · see citing works
“The regression models (figure 4) explained 45 ⋅ 31% of the variance in the UK and 51 ⋅ 88% in the USA; comprehensive analysis output is available in the appendix (pp 52–55).”
Neuroscientific Analysis of Logo Design: Implications for Luxury Brand Marketing
Šola, H. M., Khawaja, S., & Qureshi, F. H. (2025) · Behavioral Sciences · doi:10.3390/bs15040502 · Cited by 8 · see citing works
“This approach not only enhances customer satisfaction but also drives business model innovation in the luxury sector (Alipour et al., 2024; Hermann & Puntoni, 2024; Madanchian, 2024; Matz et al., 2024).”
Exploring the Productivity of Generative AI-Powered Ad Campaigns: A Consumer Response Perspective
Yang, J. (2025) · Journal of Interactive Advertising · doi:10.1080/15252019.2025.2551598 · Cited by 9 · see citing works
“Matz et al. (2024) demonstrates the potential for large language to automate and scale the use of personalized persuasion in ways that enhance its effectiveness and efficiency.”
Applications of generative artificial intelligence to influence climate change decisions
Richards, D. R., & Worden, D. (2024) · npj Climate Action · doi:10.1038/s44168-024-00202-5 · Cited by 7 · see citing works
Ethical Foundations of AI-Driven Avatars in the Metaverse for Innovation and User Privacy
Almomani, A., Al–Qerem, A., Alauthman, M., Aldweesh, A. Y., Aoudi, S., & Salloum, S. A. (2025) · IEEE Access · doi:10.1109/access.2025.3589714 · Cited by 7 · see citing works
“According to recent market analyses, the Metaverse economy could surpass hundreds of billions of dollars in value within the coming decade [7].”
Rodríguez-Sánchez, C., Esper, F. S., Casaló, L. V., & López, M. (2025) · Technology in Society · doi:10.1016/j.techsoc.2025.103036 · Cited by 7 · see citing works
Using artificial intelligence in digital branding: a two-phase qualitative approach
Jalilvand, M. R., & Ataei, J. (2026) · EuroMed Journal of Business · doi:10.1108/emjb-12-2024-0353 · Cited by 7 · see citing works
Zhang, Y., Huang, B., Yuan, W., Jiang, Z., Peng, L., Chen, S., & Tan‐Soo, J. (2025) · Information Processing & Management · doi:10.1016/j.ipm.2025.104090 · Cited by 7 · see citing works
How the Role of Generative AI Shapes Perceptions of Value in Human-AI Collaborative Work
Schecter, A., & Richardson, B. (2025) · International Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713946 · Cited by 21 · see citing works
“Subsequently, AI can refine this content, focusing on optimizing accuracy, coherence, and stylistic consistency by leveraging its unique strengths in stability and consistency [25], memory [22], personalization [27], [28], [29], iterability [30], [31], and speed and efficiency [3].”
Trzebiński, W., Konuk, F. A., & Marciniak, B. (2025) · Journal of Business Research · doi:10.1016/j.jbusres.2025.115487 · Cited by 7 · see citing works
Twenty Years of Personality Computing: Threats, Challenges and Future Directions
Celli, F., Kartelj, A., Dordevič, M., Suhartono, D., Filipović, V., Milutinović, V. M., Spathoulas, G. P., Vinciarelli, A., Kosiński, M., & Lepri, B. (2026) · ACM Computing Surveys · doi:10.1145/3806009 · Cited by 12 · see citing works
“Additional Key Words and Phrases: Personality Computing, Personality Recognition, Personality Perception, Personality Synthesis”
Is Undisclosed LLM Use Morally Wrong?
Sahebi, S., & Montefiore, T. (2026) · Philosophy & Technology · doi:10.1007/s13347-026-01094-5 · Cited by 6 · see citing works
“…tailor personalised messages based on psychological profiles or personality types leads to significantly greater persua - sion compared to non-personalised messages in a variety of contexts, including political messaging or marketing of consumer products (Matz et al., 2024; Simchon et al., 2024).”
La inteligencia artificial en la publicidad: una revisión sistemática de la década 2020-2024
Sanguineti, R. G. B., Córdova, C. D. C., & Baltazar, A. T. (2024) · Journal of the Academy · doi:10.47058/joa11.4 · Cited by 6 · see citing works
Epstein, R., Newland, A., & Tang, L. (2025) · PLoS ONE · doi:10.1371/journal.pone.0322900 · Cited by 6 · see citing works
“AI-generated political content has already been shown to be highly persuasive, especially when that content is also personalized [116,117].”
Can generative artificial intelligence help or hinder sustainable marketing?
McCarthy, B. (2024) · Journal of Resilient Economies (ISSN 2653-1917) · doi:10.25120/jre.4.2.2024.4153 · Cited by 2 · see citing works
Artificial intelligence, digital social networks, and climate emotions
Galaz, V., Metzler, H., Schill, C., Lindahl, T., Daume, S., Marklund, A., Castro, A. A., Bard, J. S., McPhearson, T., Galafassi, D., et al. (2025) · npj Climate Action · doi:10.1038/s44168-025-00225-6 · Cited by 5 · see citing works
“Intheformercase,studiessuggestthatGenAIcanbeused tonotonlyproducehighlyrealisticsyntheticcontent(i.e.,text,images, videos),butalsopromptsuchproductiontospeci fi callytargettheemotional state, age, and/or ideological predisposition of a target audience 68 – 70 .”
Self-citationMotivation profiles: understanding interplay of persuasive strategies and self-determination theory
Orji, F. A., Gutiérrez, F. J., & Vassileva, J. (2025) · Behaviour and Information Technology · doi:10.1080/0144929x.2025.2522200 · Cited by 6 · see citing works
“Research demonstrates that aligning persuasive messages with users’ personality profiles – such as tailoring messages to appeal to extro-verts versus introverts – significantly increased the effectiveness of persuasion (Hirsh, Kang, and Bodenhausen 2012; Matz et al. 2024).”
Zettler, I., & Strandsbjerg, C. F. (2025) · Current Opinion in Psychology · doi:10.1016/j.copsyc.2025.102147 · Cited by 5 · see citing works
- Authors
- Matz, S. C., Teeny, J. D., Vaid, S. S., Peters, H., Harari, G. M., & Cerf, M.
- Affiliations
- Columbia University (US)
- Northwestern University (US)
- Harvard University (US)
- Stanford University (US)
- Corresponding author
- Matz, S. C.
- Venue
- Scientific Reports
- Published
- 2024-02-26
- Volume
- 14
- Issue
- 1
- Pages
- 4692-4692
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC10897294
- OpenAlex
- W4392153984
- Cited by
- 315
- References
- 74
- Citations by year
- 2026: 133 · 2025: 120 · 2024: 59 · 2023: 3
- FWCI
- 222.98
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- OSF Preprints (OSF Preprints) (submittedVersion)
- PsyArXiv (OSF Preprints) (submittedVersion)
- PsyArXiv (OSF Preprints) (submittedVersion, cc-by)
- Topics
- Social Sciences › Sociology and Political Science › Psychology of Social Influence
- Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- personalized persuasion, generative AI, large language models, ChatGPT, message tailoring, psychological profiling, persuasiveness, political appeal, climate action, political ideology, moral foundations, personality traits
- MeSH terms
- Climate, Language, Morals, Persuasive Communication, Marketing
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