欧梦雪,李佰珏

• 1:北京大学新闻与传播学院
• 2:武汉大学信息管理学院

摘要(Abstract)：

生成式人工智能(GAI)已成为公众获取健康信息的重要渠道。基于推定媒介影响理论(IPMI),本研究通过横断面调查(N=1040)探讨个人社交媒体关注与人际讨论如何通过“推定他人社交媒体关注”影响个体对GAI健康信息搜索的态度与持续使用意愿。结果表明，社交媒体关注和人际讨论均通过推定他人媒体关注与态度形成间接关联。同时，GAI准备度发挥调节作用：低准备度个体相较于高准备度个体更易受人际讨论的影响。本研究拓展了IPMI在AI健康传播情境中的应用，并为差异化沟通策略提供参考。

关键词(KeyWords)： 健康信息搜索;推定媒介影响;生成式人工智能准备度

Abstract:

Keywords:

基金项目(Foundation): 北京大学中央高校基本科研业务费资助项目(项目批准号:7100605194)的阶段性研究成果

作者(Author): 欧梦雪,李佰珏

参考文献(References)：

• 国家卫生健康委办公厅、国家中医药局综合司、国家疾控局综合司(2024年11月6日):国家卫生健康委员会办公厅关于印发卫生健康行业人工智能应用场景参考指引的通知，获取自https://www.nhc.gov.cn/guihuaxxs/c100133/202411/3dee425b8dc34f739d63483c4e5c334c.shtml
• 聂静虹、宋甲子(2020):泛化与偏见：算法推荐与健康知识环境的构建研究——以今日头条为例，《新闻与传播研究》,第27卷第9期，23-42,126页。
• 宋小康、赵宇翔、宋士杰、朱庆华(2023):社会技术系统范式下AI赋能的替代信息搜索：特征、理论框架与研究展望，《图书情报知识》,第40卷第4期，111-121页。
• 张爱卿(2003):归因理论研究的新进展，《教育研究与实验》,第1期，38-41页。
• 张连珊、牟怡(2024):隐匿的藩篱：老年群体社交机器人抵触的心理机制探析，《全球传媒学刊》,第11卷第6期，72-95页。
• Akiba,D.& Fraboni,M.C.(2023).AI-supported academic advising:Exploring ChatGPT's current state and future potential toward student empowerment.Education Sciences,13(9),885.doi:10.3390/educsci13090885.
• Bhattacherjee,A.(2001).Understanding information systems continuance:An expectation-confirmation model.MIS Quarterly,25(3),351-370.doi:10.2307/3250921.
• Browne,M.W.& Cudeck,R.(1992).Alternative ways of assessing model fit.Sociological Methods & Research,21(2),230-258.doi:10.1177/0049124192021002005.
• Cho,H.,Shen,L.J.& Peng,L.L.(2021).Examining and extending the influence of presumed influence hypothesis in social media.Media Psychology,24(3),413-435.doi:10.1080/15213269.2020.1729812.
• Davis,F.D.(1989).Perceived usefulness,perceived ease of use,and user acceptance of information technology.MIS Quarterly,13(3),319-340.doi:10.2307/249008.
• Falebita,O.S.& Kok,P.J.(2025).Artificial intelligence tools usage:A structural equation modeling of undergraduates' technological readiness,self-efficacy and attitudes.Journal for STEM Education Research,8(2),257-282.doi:10.1007/s41979-024-00132-1.
• Gunther,A.C.& Storey,J.D.(2003).The influence of presumed influence.Journal of Communication,53(2),199-215.doi:10.1111/j.1460-2466.2003.tb02586.x.
• Ho,S.S.,Chuah,A.S.F.,Koh,E.L.Q.,Ong,L.& Kwan,V.Q.Y.(2022).Understanding public willingness to pay more for plant-based meat:Environmental and health consciousness as precursors to the influence of presumed media influence model.Environmental Communication,16(4),520-534.doi:10.1080/17524032.2022.2051576.
• Ho,S.S.,Goh,T.J.,Chuah,A.S.F.,Leung,Y.W.,Bekalu,M.A.& Viswanath,K.(2020).Past debates,fresh impact on nano-enabled food:A multigroup comparison of presumed media influence model based on spillover effects of attitude toward genetically modified food.Journal of Communication,70(4),598-621.doi:10.1093/joc/jqaa019.
• Ho,S.S.,Lee,E.W.J.,Ng,K.,Leong,G.S.H.& Tham,T.H.M.(2016).For fit's sake:A norms-based approach to healthy behaviors through influence of presumed media influence.Health Communication,31(9),1072-1080.doi:10.1080/10410236.2015.1038772.
• Ho,S.S.,Wijaya,S.A.& Ou,M.(2024).Examining muslims opinions toward cultured meat in Singapore:The influence of presumed media influence and Halal consciousness.Science Communication,46(2),151-177.doi:10.1177/10755470231225684.
• Hu,L.T.& Bentler,P.M.(1999).Cutoff criteria for fit indexes in covariance structure analysis:Conventional criteria versus new alternatives.Structural Equation Modeling:A Multidisciplinary Journal,6(1),1-55.doi:10.1080/10705519909540118.
• Issa,H.,Jabbouri,R.& Palmer,M.(2022).An artificial intelligence (AI)-readiness and adoption framework for AgriTech firms.Technological Forecasting and Social Change,182,121874.doi:10.1016/j.techfore.2022.121874.
• Kahlor,L.(2010).PRISM:A planned risk information seeking model.Health Communication,25(4),345-356.doi:10.1080/10410231003775172.
• Karaca,O.,??al????kan,S.A.& Demir,K.(2021).Medical artificial intelligence readiness scale for medical students (MAIRS-MS)-development,validity and reliability study.BMC Medical Education,21(1),112.doi:10.1186/s12909-021-02546-6.
• Li,Z.,Shi,J.,Zhao,Y.,Zhang,B.& Zhong,B.(2024).Indirect media effects on the adoption of artificial intelligence:The roles of perceived and actual knowledge in the influence of presumed media influence model.Journal of Broadcasting & Electronic Media,68(4),581-600.doi:10.1080/08838151.2024.2377244.
• Liao,Y.Q.,Ho,S.S.& Yang,X.D.(2016).Motivators of pro-environmental behavior:Examining the underlying processes in the influence of presumed media influence model.Science Communication,38(1),51-73.doi:10.1177/1075547015616256.
• Lim,J.S.,Shin,D.,Lee,C.,Kim,J.& Zhang,J.(2025).The role of user empowerment,AI hallucination,and privacy concerns in continued use and premium subscription intentions:An extended technology acceptance model for generative AI.Journal of Broadcasting & Electronic Media,69(3),183-199.doi:10.1080/08838151.2025.2487679.
• Liu,Y.,Su,Y.Y.,Alhur,A.A.& Naeem,S.B.(2025).Factors influencing artificial intelligence (AI) literacy in the age of generative AI chatbots for health information seeking.Information Development,02666669251343030.doi:10.1177/02666669251343030.
• Maduneme,E.(2024).Some slice of climate anxiety is good:A cross-sectional survey exploring the relationship between college students media exposure and perceptions about climate change.Journal of Health Communication,29(sup1),45-56.doi:10.1080/10810730.2024.2354370.
• Miller,D.T.& McFarland,C.(1987).Pluralistic ignorance:When similarity is interpreted as dissimilarity.Journal of Personality and Social Psychology,53(2),298-305.doi:10.1037//0022-3514.53.2.298.
• Perloff,R.M.(1999).The third person effect:A critical review and synthesis.Media Psychology,1(4),353-378.doi:10.1207/s1532785xmep0104_4.
• Petty,R.E.& Cacioppo,J.T.(1986).The elaboration likelihood model of persuasion.In Petty,R.E.& Cacioppo,J.T.(Eds.),Communication and Persuasion:Central and Peripheral Routes to Attitude Change (pp.1-24).New York:Springer.doi:10.1007/978-1-4612-4964-1_1.
• Rimal,R.N.& Lapinski,M.K.(2015).A re-explication of social norms,ten years later.Communication Theory,25(4),393-409.doi:10.1111/comt.12080.
• Shahsavar,Y.& Choudhury,A.(2023).User intentions to use ChatGPT for self-diagnosis and health-related purposes:Cross-sectional survey study.JMIR Human Factors,10,e47564.doi:10.2196/47564.
• Shi,J.Y.,Chen,L.& Tsang,S.J.(2022).Integrating interpersonal communication into the influence of presumed media influence model:Understanding intentions to censor and correct COVID-19 misinformation on social media.Journal of Broadcasting & Electronic Media,66(3),464-483.doi:10.1080/08838151.2022.2109638.
• Svestkova,A.,Huang,Y.& Smahel,D.(2025).Factors that influence trust and willingness to use generative AI for health information:A cross-sectional study.Digital Health,11,20552076251360973.doi:10.1177/20552076251360973.
• Tversky,A.& Kahneman,D.(1973).Availability:A heuristic for judging frequency and probability.Cognitive Psychology,5(2),207-232.doi:10.1016/0010-0285(73)90033-9.
• Uren,V.& Edwards,J.S.(2023).Technology readiness and the organizational journey towards AI adoption:An empirical study.International Journal of Information Management,68,102588.doi:10.1016/j.ijinfomgt.2022.102588.
• Valkenburg,P.M.,Peter,J.& Walther,J.B.(2016).Media effects:Theory and research.Annual Review of Psychology,67,315-338.doi:10.1146/annurev-psych-122414-033608.
• van der Meer,T.W.G.,Hakhverdian,A.& Aaldering,L.(2016).Off the fence,onto the bandwagon?A large-scale survey experiment on effect of real-life poll outcomes on subsequent vote intentions.International Journal of Public Opinion Research,28(1),46-72.doi:10.1093/ijpor/edu041.
• Wang,X.H.,Li,L.L.,Tan,S.C.,Yang,L.& Lei,J.(2023).Preparing for AI-enhanced education:Conceptualizing and empirically examining teachers' AI readiness.Computers in Human Behavior,146,107798.doi:10.1016/j.chb.2023.107798.
• Weiner,B.(1986).An attributional theory of motivation and emotion (pp.3-15).New York:Springer.doi:10.1007/978-1-4612-4948-1.
• Yang,X.D.,Song,B.,Chen,L.,Ho,S.S.& Sun,J.(2025).Technological optimism surpasses fear of missing out:A multigroup analysis of presumed media influence on generative AI technology adoption across varying levels of technological optimism.Computers in Human Behavior,162,108466.doi:10.1016/j.chb.2024.108466.
• Yzer,M.C.(2012).The integrated model of behavioral prediction as a tool for designing health messages.In Cho,H.(Ed.),Health Communication Message Design:Theory and Practice (pp.21-40).Thousand Oaks:SAGE Publications.