Evaluating the acceptance of CBDCs: experimental research with artificial intelligence (AI) generated synthetic response
Year: 2025
Type: article
Abstract: This research examines the factors that influence the public's expectation for more information, acceptance or rejection of central bank digital currencies (CBDC). Using generative AI (ChatGPT 4.0), responses were simulated to mimic CBDC adoption scenarios, considering demographic attributes, such as gender, income, education, age, level of financial literacy, network effect, media influence, and merchant acceptance. A total of 663 synthetic responses were generated and analyzed using statistical methods and multinomial logistic regression to assess the probability of acceptance, rejection, or waiting for more information to decide. The chi-squared automatic interaction detection (CHAID) model showed a high performance in correctly classifying cases of acceptance, indecision, and rejection, presenting an accuracy of 92.6%. Multinomial logistic regression revealed that factors, such as educational level, financial experience, and income level, significantly influence the decision to accept a CBDC. This method also shows a high performance, as it obtained an accuracy of 96.4%. These results are in line with previous research and underline the effectiveness of generative AI as a reproducible and low-cost tool for analyzing hypothetical scenarios. Generative AI, with its algorithmic fidelity, has great potential for predicting human behavior in economic contexts. However, synthetic data may not capture the complexities and nuances of actual human decision making. As a result, certain contextual factors, emotional influences, and unique personal experiences that may significantly influence an individual's decision to accept or reject CBDC may be overlooked.
DOI: 10.3934/qfe.2025008
Authors: Sergio Luis Náñez Alonso, Peterson K. Ozili, Beatriz María Sastre Hernández, Luís Miguel Pacheco, Central Bank of Nigeria, Abuja, Nigeria
Institutions: Catholic University of Ávila, Universidade Portucalense
Language: English
Subfield: Information Systems and Management
Field: Decision Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4409026187