The potential of generative AI for personalized persuasion at scale

被引:40
作者
Matz, S. C. [1 ,2 ]
Teeny, J. D. [3 ]
Vaid, S. S. [4 ]
Peters, H. [1 ]
Harari, G. M. [5 ]
Cerf, M. [1 ]
机构
[1] Columbia Business Sch, New York, NY 10027 USA
[2] Columbia Business Sch, Ctr Adv Technol & Human Performance, New York, NY 10027 USA
[3] Kellogg Sch Management, Evanston, IL USA
[4] Stanford Univ, Harvard Business Sch, Dept Commun, Negotiat Org & Mkt Unit, Stanford, CA USA
[5] Stanford Univ, Dept Commun, Stanford, CA USA
关键词
WILLINGNESS-TO-ACCEPT; REGULATORY FIT; PREVENTION; MOTIVATION; ATTITUDES; APPEALS;
D O I
10.1038/s41598-024-53755-0
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
收藏
页数:16
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