{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T19:44:16Z","timestamp":1789155856259,"version":"build-2803163510"},"reference-count":84,"publisher":"Emerald","issue":"11","license":[{"start":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T00:00:00Z","timestamp":1691107200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["K"],"published-print":{"date-parts":[[2024,11,12]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>Integrating the Chat Generative Pre-Trained Transformer-type (ChatGPT-type) model with government services has great development prospects. Applying this model improves service efficiency but has certain risks, thus having a dual impact on the public. For a responsible and democratic government, it is necessary to fully understand the factors influencing public acceptance and their causal relationships to truly encourage the public to accept and use government ChatGPT-type services.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>This study used the Latent Dirichlet\u00a0allocation (LDA) model to analyze comment texts and summarize 15 factors that affect public acceptance. Multiple-related matrices were established using the grey decision-making trial and evaluation laboratory (grey-DEMATEL) method to reveal causal relationships among factors. From the two opposite extraction rules of result priority and cause priority, the authors obtained an antagonistic topological model with comprehensive influence values using the total adversarial interpretive structure model (TAISM).<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>Fifteen factors were categorized in terms of cause and effect, and the antagonistic topological model with comprehensive influence values was also analyzed. The analysis showed that perceived risk, trust and meeting demand were the three most critical factors of public acceptance. Meanwhile, perceived risk and trust directly affected public acceptance and were affected by other factors. Supervision and accountability had the highest driving power and acted as the causal factor to influence other factors.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>This study identified the factors affecting public acceptance of integrating the ChatGPT-type model with government services. It analyzed the relationship between the factors to provide a reference for decision-makers. This study introduced TAISM to form the LDA-grey-DEMATEL-TAISM method to provide an analytical paradigm for studying similar influencing factors.<\/jats:p><\/jats:sec>","DOI":"10.1108\/k-06-2023-1011","type":"journal-article","created":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T09:14:34Z","timestamp":1691140474000},"page":"4948-4975","source":"Crossref","is-referenced-by-count":29,"title":["Factors influencing initial public acceptance of integrating the ChatGPT-type model with government services"],"prefix":"10.1108","volume":"53","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1968-7776","authenticated-orcid":false,"given":"Liu","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"140","published-online":{"date-parts":[[2023,8,4]]},"reference":[{"key":"key2024110805472025700_ref001","doi-asserted-by":"publisher","first-page":"461","DOI":"10.1038\/s42256-021-00359-2","article-title":"Large language models associate Muslims with violence","volume":"3","year":"2021","journal-title":"Nature Machine Intelligence"},{"key":"key2024110805472025700_ref002","doi-asserted-by":"publisher","DOI":"10.1016\/j.giq.2021.101601","article-title":"Discerning the effect of privacy information transparency on privacy fatigue in e-government","volume":"38","year":"2021","journal-title":"Government Information Quarterly"},{"key":"key2024110805472025700_ref003","doi-asserted-by":"publisher","DOI":"10.1016\/j.giq.2021.101664","article-title":"Digital transformation toward AI-augmented public administration: the perception of government employees and the willingness to use AI in government","volume":"39","year":"2022","journal-title":"Government Information Quarterly"},{"key":"key2024110805472025700_ref004","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/00036846.2023.2186367","article-title":"The impact of Smart city construction on labour spatial allocation: evidence from China","year":"2023","journal-title":"Applied Economics"},{"key":"key2024110805472025700_ref005","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1016\/0749-5978(91)90020-T","article-title":"The theory of planned behavior","volume":"50","year":"1991","journal-title":"Organizational Behavior and Human Decision Processes"},{"key":"key2024110805472025700_ref006","doi-asserted-by":"publisher","DOI":"10.2196\/27618","article-title":"Public awareness and utilization of 937-telephone health services in the kingdom of Saudi Arabia before and during the COVID-19 pandemic: longitudinal study","volume":"23","year":"2021","journal-title":"Journal of Medical Internet Research"},{"key":"key2024110805472025700_ref007","doi-asserted-by":"publisher","DOI":"10.3390\/su13063028","article-title":"Sustainable applications of smart-government services: a model to understand smart-government adoption","volume":"13","year":"2021","journal-title":"Sustainability"},{"key":"key2024110805472025700_ref008","doi-asserted-by":"publisher","DOI":"10.1016\/j.clsr.2022.105733","article-title":"Shifting the digital paradigm: towards a sui generis competition policy","volume":"46","year":"2022","journal-title":"Computer Law and Security Review"},{"key":"key2024110805472025700_ref009","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1016\/j.foodpol.2012.02.013","article-title":"Impact of modern agricultural technologies on smallholder welfare: evidence from Tanzania and Ethiopia","volume":"37","year":"2012","journal-title":"Food Policy"},{"key":"key2024110805472025700_ref010","doi-asserted-by":"publisher","DOI":"10.3390\/admsci12040129","article-title":"The influence of trustworthiness and technology acceptance factors on the usage of e-government services during COVID-19: a case study of post COVID-19 Greece","volume":"12","year":"2022","journal-title":"Administrative Sciences"},{"key":"key2024110805472025700_ref011","doi-asserted-by":"publisher","DOI":"10.1002\/ctm2.1206","article-title":"The potential impact of ChatGPT in clinical and translational medicine","volume":"13","year":"2023","journal-title":"Clinical and Translational Medicine"},{"key":"key2024110805472025700_ref012","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1108\/JEIM-06-2020-0233","article-title":"Can we trust AI? 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