Sage Journals: Discover world-class research

Abstract

As Generative AI (GenAI) becomes increasingly integrated into everyday information environments, many individuals remain uncertain or reluctant to engage with these technologies despite their growing potentials. By extending the Cognitive Mediation Model (CMM), the study emphasizes the cognitive and emotional processes that underpin public engagement with emerging digital information technologies. A survey of 527 Chinese participants was conducted using an online questionnaire. Structural equation modeling was employed to test the extended CMM, examining the relationships between exposure to traditional and social media, elaboration, interpersonal communication, knowledge acquisition, anxiety, and GenAI usage intention. Results indicate that exposure to both traditional and online social media were positively associated with elaboration and interpersonal communication, which were in turn negatively associated with anxiety and positively associated with perceived knowledge. However, elaboration does not significantly relate to factual knowledge, while interpersonal communication was negatively associated with factual knowledge. Anxiety was negatively associated with GenAI usage intention, whereas factual and perceived knowledge were positively associated with adoption. Theoretical and practical contributions are discussed as well.

Keywords

cognitive mediation model online information social media AI anxiety knowledge generative AI

Get full access to this article

View all access options for this article.

References

Al-Okaily

Mater

Matar

Shiyyab

F. S.

(2025). Understanding of ChatGPT adoption among business school students. Computers and Education: Artificial Intelligence, 9(2), Article 100441. https://doi.org/10.1016/j.caeai.2025.100441

Amores

C. M. L.

Bael

M. G. L.

Escabarte

R. J. D.

Batara

E. B.

Jovita-Olvez

H. D.

Tomaro

Q. P. V.

(2023). Are citizens engaging with government social media covid 19 pandemic information? Why media richness and dialogic loop features matter. The Electronic Journal of Information Systems in Developing Countries, 89(3), Article e12251. https://doi.org/10.1002/isd2.12251

Aziz

Khan

A. U.

(2025). Transforming behavioral intention and academic performance: Chatgpt-4.0 insights through SEM, ANN, and cIPMA analysis. Information Development, 41(3), 933–956. https://doi.org/10.1177/02666669251336560

Bateman

J. A.

(2021). What are digital media? Discourse, Context & Media, 41(2), Article 100502. https://doi.org/10.1016/j.dcm.2021.100502

Beaudoin

C. E.

Thorson

(2004). Testing the cognitive mediation model: The roles of news reliance and three gratifications sought. Communication Research, 31(4), 446–471. https://doi.org/10.1177/0093650204266098

Bhattacherjee

(2001). An empirical analysis of the antecedents of electronic commerce service continuance. Decision Support Systems, 32(2), 201–214. https://doi.org/10.1016/S0167-9236(01)00111-7

Campion

Gasco-Hernandez

Jankin Mikhaylov

Esteve

(2022). Overcoming the challenges of collaboratively adopting artificial intelligence in the public sector. Social Science Computer Review, 40(2), 462–477. https://doi.org/10.1177/0894439320979953

Chen

(2022). Let’s fight the infodemic: The third-person effect process of misinformation during public health emergencies. Internet Research, 32(4), 1357–1377. https://doi.org/10.1108/INTR-03-2021-0194

Chen

Zheng

(2023). Examining factors influencing public knowledge, risk perception, and policy support for waste classification: A multigroup comparison of the cognitive mediation model based on gender differences. Environmental Communication, 17(7), 759–774. https://doi.org/10.1080/17524032.2023.2253499

10.

Chidiac

Bowden

(2023). When media matters: The role of media richness and naturalness on purchase intentions within influencer marketing. Journal of Strategic Marketing, 31(6), 1178–1198. https://doi.org/10.1080/0965254X.2022.2062037

11.

Denizci Guillet

Kucukusta

Liu

(2016). An examination of social media marketing in China: How do the top 133 hotel brands perform on the top four Chinese social media sites? Journal of Travel & Tourism Marketing, 33(6), 783–805. https://doi.org/10.1080/10548408.2015.1064337

12.

Depoux

(2025). Five key trends in China’s generative AI market in 2025. Roland Berger. https://www.rolandberger.com/en/Insights/Publications/Five-key-trends-in-China-s-generative-AI-market-in-2025.html

13.

De Vreese

C. H.

Neijens

(2016). Measuring media exposure in a changing communications environment. Communication Methods and Measures, 10(2–3), 69–80. https://doi.org/10.1080/19312458.2016.1150441

14.

Eveland

W. P.

(2001). The cognitive mediation model of learning from the news: Evidence from nonelection, off-year election, and presidential election contexts. Communication Research, 28(5), 571–601. https://doi.org/10.1177/009365001028005001

15.

Eveland

W. P.

(2002). News information processing as mediator of the relationship between motivations and political knowledge. Journalism & Mass Communication Quarterly, 79(1), 26–40. https://doi.org/10.1177/107769900207900103

16.

Eveland

W. P.

(2004). The effect of political discussion in producing informed citizens: The roles of information, motivation, and elaboration. Political Communication, 21(2), 177–193. https://doi.org/10.1080/10584600490443877

17.

Farrell

A. M.

(2010). Insufficient discriminant validity: A comment on bove, pervan, beatty, and shiu (2009). Journal of Business Research, 63(3), 324–327. https://doi.org/10.1016/j.jbusres.2009.05.003

18.

Feng

(2025). From ChatGPT to sora: Analyzing public opinions and attitudes on generative artificial intelligence in social media. IEEE Access, 13(2), 14485–14498. https://doi.org/10.1109/ACCESS.2025.3530683

19.

Fornell

Larcker

D. F.

(1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104

20.

Lei

Chen

(2024). Text, short video, or long video? Effects of exposure to various types of social media on public knowledge of dual carbon: A multigroup comparison based on environmental concern levels. Environmental Communication, 18(5), 610–627. https://doi.org/10.1080/17524032.2023.2298939

21.

Guszcza

Lewis

Evans-Freenwood

(2017). Cognitive collaboration: Why humans and computers think better together. Deloitte Insights. https://www2.deloitte.com/us/en/insights/deloitte-review/issue-20/augmented-intelligence-human-computer-collaboration.html

22.

Hair

J. F.

Black

W. C.

Babin

B. J.

(2010). Multivariate data analysis: A global perspective. Pearson Education.

23.

Hase

Boczek

Scharkow

(2023). Adapting to affordances and audiences? A cross-platform, multi-modal analysis of the platformization of news on Facebook, Instagram, TikTok, and Twitter. Digital Journalism, 11(8), 1499–1520. https://doi.org/10.1080/21670811.2022.2128389

24.

S. S.

Chuah

A. S. F.

(2022). Thinking, not talking, predicts knowledge level: Effects of media attention and reflective integration on public knowledge of nuclear energy. Public Understanding of Science, 31(5), 572–589. https://doi.org/10.1177/09636625211070786

25.

S. S.

Peh

Soh

V. W. L.

(2013). The cognitive mediation model: Factors influencing public knowledge of the H1N1 pandemic and intention to take precautionary behaviors. Journal of Health Communication, 18(7), 773–794. https://doi.org/10.1080/10810730.2012.743624

26.

S. S.

Yang

(2018). Communication, cognitive processing, and public knowledge about climate change. Asian Journal of Communication, 28(5), 449–467. https://doi.org/10.1080/01292986.2018.1453847

27.

Hollander

B. A.

(1995). The new news and the 1992 presidential campaign: Perceived vs. actual political knowledge. Journalism & Mass Communication Quarterly, 72(4), 786–798. https://doi.org/10.1177/107769909507200403

28.

(2025). Decoding the ChatGPT mystery: A comprehensive exploration of factors driving AI language model adoption. Information Development, 41(3), 875–895. https://doi.org/10.1177/02666669231202764

29.

Johnson

D. G.

Verdicchio

(2017). AI anxiety. Journal of the Association for Information Science and Technology, 68(9), 2267–2270. https://doi.org/10.1002/asi.23867

30.

Jonassen

D. H.

Beissner

Yacci

(1993). Structural knowledge: Techniques for representing, conveying, and acquiring structural knowledge. Erlbaum.

31.

Hamilton

McLarnon

(2021). The medium is the message: weChat, YouTube, and Facebook usage and acculturation outcomes. International Journal of Communication, 15(1), 4011–4033.

32.

Katz

(1957). The two-step flow of communication: An up-to-date report on an hypothesis. Public Opinion Quarterly, 21(1), 61–78. https://doi.org/10.1086/266687

33.

Kharpal

(2024). China wants to dominate in AI — And some of its models are already beating their U.S. rivals. CNBC. https://www.cnbc.com/2024/12/17/chinese-ai-models-are-popular-globally-and-are-beating-us-rivals-in-some-areas.html

34.

Kratzke

Wilson

Vilchis

(2013). Reaching rural women: Breast cancer prevention information seeking behaviors and interest in internet, cell phone, and text use. Journal of Community Health, 38(1), 54–61. https://doi.org/10.1007/s10900-012-9579-3

35.

Kwan

L. Y.

Hung

Y. S.

(2025). Does AI usage diminish human creativity? How goal orientation theory moderates the negative effects between AI usage and creative output. Social Science Computer Review, 08944393251389125. Online Published: https://doi.org/10.1177/08944393251389125

36.

Lai

C.-H.

(2022). Examining the paths of influence between individual motivators, information behaviors, and outcomes in disaster risk reduction. International Journal of Disaster Risk Reduction, 71(2), Article 102826. https://doi.org/10.1016/j.ijdrr.2022.102826

37.

Lawrence

R. G.

Napoli

P. M.

(Eds.), (2023). News quality in the digital age. Taylor & Francis.

38.

Lee

E. J.

(2024). Minding the source: Toward an integrative theory of human–machine communication. Human Communication Research, 50(2), 184–193. https://doi.org/10.1093/hcr/hqad034

39.

Lee

E. W. J.

Shin

Kawaja

S. S.

(2016). The augmented cognitive mediation model: Examining antecedents of factual and structural breast cancer knowledge among Singaporean women. Journal of Health Communication, 21(5), 583–592. https://doi.org/10.1080/10810730.2015.1114053

40.

Bautista

J. R.

(2021). Elaboration, cancer worry, and risk perception mediate the association between news attention on the internet and intention to uptake HPV vaccination: Extending the cognitive mediation model. International Journal of Communication, 15(2), 4862–4883.

41.

Liu

Dong

Wang

Gao

(2025). The framing in media policy narratives of artificial intelligence generated content: A comparative between traditional media and social media in China. Review of Policy Research, 43(2), Article e70030. https://doi.org/10.1111/ropr.70030

42.

Liu

(2025). Exploring the discourse representation of generative artificial intelligence in Chinese news media. In Global dialogue on media dynamics, trends and perspectives on public relations and communication (pp. 427–434). CRC Press.

43.

Markus

Zajonc

R. B.

(1985). The cognitive perspective in social psychology. In Lindzey

Aronson

(Eds.), The handbook of social psychology (3rd ed., pp. 137–230). Random House.

44.

McCombs

M. E.

Shaw

D. L.

(1972). The agenda-setting function of mass media. Public Opinion Quarterly, 36(2), 176–187.

45.

Méndez-Suárez

Monfort

Hervas-Oliver

J.-L.

(2023). Are you adopting artificial intelligence products? social-demographic factors to explain customer acceptance. European Research on Management and Business Economics, 29(3), Article 100223. https://doi.org/10.1016/j.iedeen.2023.100223

46.

Morgan

S. E.

(2009). The intersection of conversation, cognitions, and campaigns: The social representation of organ donation. Communication Theory, 19(1), 29–48. https://doi.org/10.1111/j.1468-2885.2008.01331.x

47.

Park

Y. J.

Chung

J. E.

Kim

J. N.

(2022). Social media, misinformation, and cultivation of informational mistrust: Cultivating Covid-19 mistrust. Journalism, 23(12), 2571–2590. https://doi.org/10.1177/14648849221085050

48.

Park

Y. J.

Jones-Jang

S. M.

(2023). Surveillance, security, and AI as technological acceptance. AI & SOCIETY, 38(6), 2667–2678. https://doi.org/10.1007/s00146-021-01331-9

49.

Park

Y. J.

Lee

Jones-Jang

S. M.

Y. W.

(2022). Digital assistants: Inequalities and social context of access, use, and perceptual understanding. Poetics, 93(1), Article 101689. https://doi.org/10.1016/j.poetic.2022.101689

50.

Raju

P. S.

Lonial

S. C.

Mangold

W. G.

(1995). Differential effects of subjective knowledge, objective knowledge, and usage experience on decision making: An exploratory investigation. Journal of Consumer Psychology, 4(2), 153–180. https://doi.org/10.1207/s15327663jcp0402_04

51.

Saif

Khan

S. U.

Shaheen

ALotaibi

F. A.

Alnfiai

M. M.

Arif

(2024). Chat-GPT; validating technology acceptance model (TAM) in education sector via ubiquitous learning mechanism. Computers in Human Behavior, 154(2), Article 108097. https://doi.org/10.1016/j.chb.2023.108097

52.

Shah

D. V.

Cho

Nah

Gotlieb

M. R.

Hwang

Lee

N.-J.

Scholl

R. M.

McLeod

D. M.

(2007). Campaign ads, online messaging, and participation: Extending the communication mediation model. Journal of Communication, 57(4), 676–703. https://doi.org/10.1111/j.1460-2466.2007.00363.x

53.

Sharma

(2025). China vs USA: Who is losing the AI race? Analytics Vidhya. https://www.analyticsvidhya.com/blog/2025/01/china-vs-usa-who-is-losing-the-ai-race/

54.

Tang

Zheng

(2025). Between promise and peril: Users’ risk–benefit trade-offs in their generative AI usage. Big Data & Society, 12(4), Article 20539517251410046. https://doi.org/10.1177/20539517251410046

55.

Treem

J. W.

Leonardi

P. M.

(2013). Social media use in organizations: Exploring the affordances of visibility, editability, persistence, and association. Annals of the International Communication Association, 36(1), 143–189. https://doi.org/10.1080/23808985.2013.11679130

56.

Tseng

F.-C.

Huang

T.-L.

Pham

T. T. L.

Cheng

T. C. E.

Teng

C.-I.

(2022). How does media richness foster online gamer loyalty? International Journal of Information Management, 62(1), Article 102439. https://doi.org/10.1016/j.ijinfomgt.2021.102439

57.

Violot

Elmas

Bilogrevic

Humbert

(2024). Shorts vs. regular videos on YouTube: A comparative analysis of user engagement and content creation trends. In Proceedings of the 16th ACM web science conference (pp. 213–223). ACM.

58.

Wang

Downey

(2025). Mapping the sociotechnical imaginaries of generative AI in UK, US, Chinese and Indian newspapers. Public Understanding of Science, 34(7), 930–948. https://doi.org/10.1177/09636625251328518

59.

Wang

Zhang

Zhao

(2022). Understanding the determinants in the different government AI adoption stages: Evidence of local government chatbots in China. Social Science Computer Review, 40(2), 534–554. https://doi.org/10.1177/0894439320980132

60.

Wang

Y.-Y.

Wang

Y.-S.

(2022). Development and validation of an artificial intelligence anxiety scale: An initial application in predicting motivated learning behavior. Interactive Learning Environments, 30(4), 619–634. https://doi.org/10.1080/10494820.2019.1674887

61.

Weeks

B. E.

Holbert

R. L.

(2013). Predicting dissemination of news content in social media: A focus on reception, friending, and partisanship. Journalism & Mass Communication Quarterly, 90(2), 212–232. https://doi.org/10.1177/1077699013482906

62.

Xinhua . (2025). China’s generative AI users double to 515 mln: Report. https://english.www.gov.cn/archive/statistics/202510/18/content_WS68f30556c6d00ca5f9a06e05.html

63.

Zhou

Ren

(2025). Generative artificial intelligence and the media digital divide: Comparison of four tiers of Chinese media. Journalism Practice, 1–19. https://doi.org/10.1080/17512786.2025.2577708

64.

Yang

Chen

S. S.

(2020). Does media exposure relate to the illusion of knowing in the public understanding of climate change? Public Understanding of Science, 29(1), 94–111. https://doi.org/10.1177/0963662519877743

65.

Yang

Chuah

A. S. F.

Lee

E. W. J.

S. S.

(2017). Extending the cognitive mediation model: Examining factors associated with perceived familiarity and factual knowledge of nanotechnology. Mass Communication and Society, 20(3), 403–426. https://doi.org/10.1080/15205436.2016.1271436

66.

Yang

Z. J.

Kahlor

(2014). A united states–china comparison of risk information–seeking intentions. Communication Research, 41(7), 935–960. https://doi.org/10.1177/0093650213479795

67.

Yan

Jiang

(2024). Privacy and personal data risk governance for generative artificial intelligence: A Chinese perspective. Telecommunications Policy, 48(10), Article 102851. https://doi.org/10.1016/j.telpol.2024.102851

68.

Zhan

(2024). Structural knowledge and subjective knowledge, not factual knowledge, promotes corrective and restrictive actions towards healthy eating misinformation in China: A multigroup comparison of extended cognitive mediation model based on altruism. Current Psychology, 43(34), 27886–27900. https://doi.org/10.1007/s12144-024-06422-6

69.

Zhang

(2026). Exploring the role of personal innovativeness on purchase intention of artificial intelligence products: An investigation using social influence theory and value-based adoption model. International Journal of Human–Computer Interaction, 42(2), 752–766. https://doi.org/10.1080/10447318.2025.2513037

70.

Zhang

Yan

Yuan

(2024). How generative AI was mentioned in social media and academic field? A text mining based on internet text data. IEEE Access, 12(2), 43940–43947. https://doi.org/10.1109/ACCESS.2024.3379010

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.10 MB

Online Information Exposure,Knowledge Development,and AI Anxiety: Extending the Cognitive Mediation Model to Explain GenAI Adoption in China

Abstract

Keywords

Get full access to this article

References

Supplementary Material