Evaluating the acceptance of CBDCs: experimental research with artificial intelligence (AI) generated synthetic response
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, s...
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| Format: | Article |
| Language: | English |
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AIMS Press
2025-03-01
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| Series: | Quantitative Finance and Economics |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/QFE.2025008 |
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| author | Sergio Luis Náñez Alonso Peterson K. Ozili Beatriz María Sastre Hernández Luís Miguel Pacheco |
| author_facet | Sergio Luis Náñez Alonso Peterson K. Ozili Beatriz María Sastre Hernández Luís Miguel Pacheco |
| author_sort | Sergio Luis Náñez Alonso |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9564449cfe644ee2ba3637b45f7928b6 |
| institution | OA Journals |
| issn | 2573-0134 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Quantitative Finance and Economics |
| spelling | doaj-art-9564449cfe644ee2ba3637b45f7928b62025-08-20T01:57:08ZengAIMS PressQuantitative Finance and Economics2573-01342025-03-019124227310.3934/QFE.2025008Evaluating the acceptance of CBDCs: experimental research with artificial intelligence (AI) generated synthetic responseSergio Luis Náñez Alonso0Peterson K. Ozili1Beatriz María Sastre Hernández2Luís Miguel Pacheco3DEKIS Research Group, Department of Economics, Catholic University of Ávila, 05005, Ávila, SpainCentral Bank of Nigeria, Abuja, NigeriaDEKIS Research Group, Department of Economics, Catholic University of Ávila, 05005, Ávila, SpainREMIT-Investigação em Economia, Gestão e Tecnologias da Informação, Universidade Portucalense-Infante D, Henrique, Porto, PortugalThis 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.https://www.aimspress.com/article/doi/10.3934/QFE.2025008cbdc adoptionlarge language modelsai generativesurvey experimentsynthetic responsesbehavioral financedigital finance |
| spellingShingle | Sergio Luis Náñez Alonso Peterson K. Ozili Beatriz María Sastre Hernández Luís Miguel Pacheco Evaluating the acceptance of CBDCs: experimental research with artificial intelligence (AI) generated synthetic response Quantitative Finance and Economics cbdc adoption large language models ai generative survey experiment synthetic responses behavioral finance digital finance |
| title | Evaluating the acceptance of CBDCs: experimental research with artificial intelligence (AI) generated synthetic response |
| title_full | Evaluating the acceptance of CBDCs: experimental research with artificial intelligence (AI) generated synthetic response |
| title_fullStr | Evaluating the acceptance of CBDCs: experimental research with artificial intelligence (AI) generated synthetic response |
| title_full_unstemmed | Evaluating the acceptance of CBDCs: experimental research with artificial intelligence (AI) generated synthetic response |
| title_short | Evaluating the acceptance of CBDCs: experimental research with artificial intelligence (AI) generated synthetic response |
| title_sort | evaluating the acceptance of cbdcs experimental research with artificial intelligence ai generated synthetic response |
| topic | cbdc adoption large language models ai generative survey experiment synthetic responses behavioral finance digital finance |
| url | https://www.aimspress.com/article/doi/10.3934/QFE.2025008 |
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