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Drawing upon the unified theory of acceptance and use of technology (UTAUT) and the technology threat avoidance theory (TTAT), this research integrates perceived AI literacy into the AI acceptance\u2013avoidance framework as a central variable. This study gathered 583 valid survey responses from China and validated its model using a dual-phase, combined method that integrates structural equation modeling and artificial neural networks. Research findings indicate that the model explains 51.6% of the variance in generative AI usage intention. Except for social influence, all variables within the extended framework significantly impact the usage intention, with perceived AI literacy being the strongest predictor (\u03b2 = 0.33, p &lt; 0.001). Additionally, perceived AI literacy mitigates the adverse effect of perceived threats on the intention to use AI. Practical implications suggest that enterprises adopt a tiered strategy, as follows: maximize perceived benefits by integrating AI skills into reward systems and providing task-automation training; minimize perceived costs through dedicated technical support and transparent risk mitigation plans; and cultivate AI literacy via progressive learning paths, advancing from data analysis to innovation.<\/jats:p>","DOI":"10.3390\/systems13080639","type":"journal-article","created":{"date-parts":[[2025,8,5]],"date-time":"2025-08-05T08:46:55Z","timestamp":1754383615000},"page":"639","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Factors Influencing Generative AI Usage Intention in China: Extending the Acceptance\u2013Avoidance Framework with Perceived AI Literacy"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2419-9862","authenticated-orcid":false,"given":"Chenhui","family":"Liu","sequence":"first","affiliation":[{"name":"School of Public Policy and Administration, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}]},{"given":"Libo","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Public Policy and Administration, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}]},{"given":"Xinyu","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Public Policy and Administration, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}]},{"given":"Xiaocui","family":"Li","sequence":"additional","affiliation":[{"name":"School of Management, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"688","DOI":"10.1016\/j.procs.2019.09.104","article-title":"Industry 5.0 and Human-Robot Co-Working","volume":"158","author":"Demir","year":"2019","journal-title":"Procedia Comput. 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