{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T22:39:28Z","timestamp":1777502368433,"version":"3.51.4"},"reference-count":12,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"2024 Scientific Research Ability Improvement Project","award":["2024ZDJS108"],"award-info":[{"award-number":["2024ZDJS108"]}]},{"name":"2022 Guangdong Education Science Planning Project","award":["2022 GXJK377"],"award-info":[{"award-number":["2022 GXJK377"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Big Data"],"published-print":{"date-parts":[[2026,2,1]]},"abstract":"<jats:p>\n                    This study intends to identify the critical factors that shape college students\u2019 adoption of AI-generated news, with a specific focus on integrating Big Data methodologies into the Technology Acceptance Model (TAM) framework. Building on TAM, the research incorporates \u201ctrust\u201d as a core variable to develop a dual-path theoretical model that combines technological cognition (e.g., perceived usefulness, perceived ease of use) and psychological emotions. Unlike traditional TAM-based studies relying solely on questionnaire data, this research enriches its data sources by leveraging Big Data techniques\u2014including the collection and analysis of college students\u2019 real-time behavioral data (e.g., AI news reading duration, sharing frequency, source verification clicks) and unstructured text data (e.g., sentiment orientation in comment sections)\u2014to complement the survey data from 300 college students. Through a questionnaire survey of 300 college students and data analysis using the structural equation model, the study found that trust has the strongest direct positive impact on the willingness to use (\u03b2 = 0.49,\n                    <jats:italic toggle=\"yes\">p<\/jats:italic>\n                    &lt; 0.001), and its influence is significantly greater than perceived usefulness (\u03b2 = 0.35,\n                    <jats:italic toggle=\"yes\">p<\/jats:italic>\n                    &lt; 0.001). Meanwhile, although perceived ease of use does not directly affect the willingness to use, it has significant indirect effects by enhancing trust and perceived usefulness. The results show that in the AI news context with high-risk perception, trust is a more crucial psychological mechanism than traditional technological cognitive factors. These findings have expanded the explanatory boundaries of the TAM model in new technology fields and provided empirical evidence and practical inspiration for AI developers to optimize system credibility and for educators to conduct algorithmic literacy training.\n                  <\/jats:p>","DOI":"10.1177\/2167647x261423109","type":"journal-article","created":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T11:14:03Z","timestamp":1770635643000},"page":"56-61","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Perceived Usefulness, Trust, and Behavioral Intention: A Study on College Student User Adoption Behaviors of Artificial Intelligence Generated News Based on Technology Acceptance Model"],"prefix":"10.1177","volume":"14","author":[{"given":"Xianfeng","family":"Gong","sequence":"first","affiliation":[{"name":"School of Digital Communication, Guangzhou Huashang College, Guangzhou, China."}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7940-6408","authenticated-orcid":false,"given":"Mingyang","family":"Mao","sequence":"additional","affiliation":[{"name":"School of Digital Communication, Guangzhou Huashang College, Guangzhou, China."}]}],"member":"179","published-online":{"date-parts":[[2026,2,9]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.2307\/249008"},{"key":"e_1_3_3_3_2","article-title":"Exploring the impact of AI news writing and anchoring on audience\u2019s perception of news credibility","author":"Gao F","year":"2024","unstructured":"Gao F, , Lin X, , Zhao R. 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J Bus Res, 2024; 178(Part B):114\u2013126.","journal-title":"J Bus Res"},{"issue":"2","key":"e_1_3_3_9_2","first-page":"236","article-title":"A robot wrote this? How perceived machine authorship affects news credibility","volume":"6","author":"Waddell TF","year":"2018","unstructured":"Waddell TF. A robot wrote this? How perceived machine authorship affects news credibility. Digital Journal, 2018; 6(2):236\u2013255.","journal-title":"Digital Journal"},{"issue":"5","key":"e_1_3_3_10_2","first-page":"103","article-title":"The effects of AI-based recommendations on user trust and acceptance: The moderating role of perceived accuracy","volume":"58","author":"Zhou T","year":"2021","unstructured":"Zhou T, , Lu Y. The effects of AI-based recommendations on user trust and acceptance: The moderating role of perceived accuracy. 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