“What Might Happen With Generative AI?” Examining the Role of Prefactual Thinking in the Cognitive Mediation Model in the Context of Emerging Technologies

Abstract

Prefactual thinking, a form of prospective mental simulation, significantly impacts individuals’ decision-making. To understand the influence of media consumption on public opinion regarding emerging technologies, this study, in the context of generative artificial intelligence (AI), examined prefactual thinking as a cognitive process of media consumption, extending the cognitive mediation model. Using quota sampling, we conducted an online survey of 1,129 Hong Kong adult residents. The findings revealed that elaborative processing was positively associated with prefactual thinking, which, in turn, influenced emotional responses. Prefactual thinking and emotional responses shaped benefit-risk perceptions, which influenced individuals’ generative AI opinion. Theoretical and practical implications were discussed.

Keywords

prefactual thinking artificial intelligence cognitive mediation model media effects cognitive process mental simulation public opinion benefit and risk perceptions emerging technology

Get full access to this article

View all access options for this article.

References

Adhikari

Hamal

Baidoo Jnr

(2024). Impact and regulations of AI on labor markets and employment in USA. International Journal of Science and Research Archive, 13, 470–476. https://doi.org/10.30574/ijsra.2024.13.1.1670

Anderson

A. A.

Scheufele

D. A.

Brossard

Corley

E. A.

(2012). The role of media and deference to scientific authority in cultivating trust in sources of information about emerging technologies. International Journal of Public Opinion Research, 24(2), 225–237. https://doi.org/10.1093/ijpor/edr032

Babatunde

S. O.

Odejide

O. A.

Edunjobi

T. E.

Ogundipe

D. O.

(2024). The role of AI in marketing personalization: A theoretical exploration of consumer engagement strategies. International Journal of Management & Entrepreneurship Research, 6(3), 936–949. https://doi.org/10.51594/ijmer.v6i3.964

Bacon

A. M.

Walsh

C. R. A.

Briazu

(2020). Looking behind and looking ahead: Personality differences in counterfactual and prefactual thinking. Imagination, Cognition and Personality, 40(2), 111–125. https://doi.org/10.1177/0276236620905810

Baumgartner

Pieters

Bagozzi

R. P.

(2008). Future-oriented emotions: Conceptualization and behavioral effects. European Journal Social Psychology, 38, 685–696. https://doi.org/10.1002/ejsp.467

Besley

J. C.

McComas

K. A.

(2015). Something old and something new: Comparing views about nanotechnology and nuclear energy. Journal of Risk Research, 18(2), 215–231. https://doi.org/10.1080/13669877.2014.896397

Binder

A. R.

Cacciatore

M. A.

Scheufele

D. A.

Shaw

B. R.

Corley

E. A.

(2012). Measuring risk/benefit perceptions of emerging technologies and their potential impact on communication of public opinion toward science. Public Understanding of Science, 21(7), 830–847. https://doi.org/10.1177/0963662510390159

Boon

Moors

(2008). Exploring emerging technologies using metaphors: A study of orphan drugs and pharmacogenomics. Social Science & Medicine, 66(9), 1915–1927. https://doi.org/10.1016/j.socscimed.2008.01.012

Brossard

Scheufele

D. A.

Kim

Lewenstein

B. V.

(2009). Religiosity as a perceptual filter: Examining processes of opinion formation about nanotechnology. Public Understanding of Science, 18(5), 546–558. https://doi.org/10.1177/0963662507087304

10.

Byrne

R. M. J.

Egan

S. M.

(2004). Counterfactual and prefactual conditionals. Canadian Journal of Experimental Psychology, 58(2), 113–120. https://doi.org/10.1037/h0085791

11.

Cacciatore

M. A.

Scheufele

D. A.

Corley

E. A.

(2011). From enabling technology to applications: The evolution of risk perceptions about nanotechnology. Public Understanding of Science, 20(3), 385–404. https://doi.org/10.1177/0963662509347815

12.

Chang

(2024). Does the media’s partisanship influence news coverage on artificial intelligence issues? media coverage analysis on artificial intelligence issues. Social Science Computer Review, 43, 564–577. https://doi.org/10.1177/08944393241268526

13.

Chen

Zhang

Jin

(2025). Public opinion outweighs knowledge: A dual-process framework for understanding acceptance of genetic modification among scientists and laypeople. Risk Analysis, 1–11. https://doi.org/10.1111/risa.17704

14.

Chen

Weijie

(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

15.

Cho

Shah

D. V.

McLeod

J. M.

McLeod

D. M.

Scholl

R. M.

Gotlieb

M. R.

(2009). Campaigns, reflection, and deliberation: Advancing an O-S-R-O-R model of communication effects. Communication Theory, 19(1), 66–88. https://doi.org/10.1111/j.1468-2885.2008.01333.x

16.

Chuan

Tsai

Cho

(2019). Framing artificial intelligence in American newspapers. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society. https://doi.org/10.1145/3306618.3314285

17.

Cui

(2021). The influence of media use on public perceptions of artificial intelligence in China: Evidence from an online survey. Information Development, 37(1), 45–57. https://doi.org/10.1177/0266666919893411

18.

De Angelis

Baglivo

Arzilli

Privitera

G. P.

Ferragina

Tozzi

A. E.

Rizzo

. (2023). ChatGPT and the rise of large language models: The new AI-driven infodemic threat in public health. Frontiers in Public Health, 11. https://doi.org/10.3389/fpubh.2023.1166120

19.

Dixon

Hart

P. S.

Clarke

O’Donnell

N. H.

Hmielowski

(2020). What drives support for self-driving car technology in the United States? Journal of Risk Research, 23(3), 275–287. https://doi.org/10.1080/13669877.2018.1517384

20.

Ellsworth

P. C.

(1991). Some implications of cognitive appraisal theories of emotion. In Strongman

K. T.

(Ed.), International review of studies on emotion (Vol. 1, pp. 143–161). Wiley.

21.

Epstude

Roese

N. J.

(2008). The functional theory of counterfactual thinking. Personality and Social Psychology Review, 12(2), 168–192. https://doi.org/10.1177/1088868308316091

22.

Epstude

Scholl

Roese

N. J.

(2016). Prefactual thoughts: Mental simulations about what might happen. Review of General Psychology, 20(1), 48–56. https://doi.org/10.1037/gpr0000064

23.

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

24.

Eveland

W. P.

Shah

D. V.

Kwak

(2003). Assessing causality in the cognitive mediation model: A panel study of motivations, information processing, and learning during campaign 2000. Communication Research, 30(4), 359–386. https://doi.org/10.1177/0093650203253369

25.

Fast

Horvitz

(2017). Long-term trends in the public perception of artificial intelligence. Proceedings of the AAAI Conference on Artificial Intelligence, 31(1). https://doi.org/10.1609/aaai.v31i1.10635

26.

Fernandez

D. K.

Gan

H. H. M.

Chan

A. Y. C.

(2021). Comparing the functional benefits of counterfactual and prefactual thinking: The content-specific and content-neutral pathways. Thinking & Reasoning, 28(2), 261–289. https://doi.org/10.1080/13546783.2021.1989034

27.

Fung

T. K. F.

(2019). The role of counterfactual thinking in narrative persuasion: Its impact on patients’ adherence to treatment regimen. Health Communication, 34(12), 1482–1493. https://doi.org/10.1080/10410236.2018.1500432

28.

Fung

T. K. F.

Brossard

(2011). There is water everywhere: How news framing amplifies the effect of ecological worldviews on preference for flooding protection policy. Mass Communication & Society, 14(5), 553–577. https://doi.org/10.1080/15205436.2010.521291

29.

Glynn

C. J.

Herbst

Lindeman

O’Keefe

G. J.

(2015). Perception and opinion formation. In Glynn

(Ed.), Public opinion (pp. 176–211). Taylor & Francis Group.

30.

Hajjar

S. T.

(2018). Statistical analysis: Internal-consistency reliability and construct validity. International Journal of Quantitative and Qualitative Research Methods, 6(1), 27–38. https://doi.org/10.37745/ijqqrm.13

31.

Hammell

Chan

A. Y. C.

(2016). Improving physical task performance with counterfactual and prefactual thinking. PLOS ONE, 11(12), e0168181. https://doi.org/10.1371/journal.pone.0168181

32.

Helgason

B. A.

Effron

D. A.

(2022). It might become true: How prefactual thinking licenses dishonesty. Journal of Personality and Social Psychology, 123(5), 909–940. https://doi.org/10.1037/pspa0000308

33.

Hmielowski

J. D.

Kirkpatrick

A. W.

Boyd

A. D.

(2021). Understanding public support for smart meters: Media attention, misperceptions, and knowledge. Journal of Risk Research, 24(11), 1388–1404. https://doi.org/10.1080/13669877.2020.1863844

34.

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

35.

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

36.

S. S.

Scheufele

D. A.

Corley

E. A.

(2011). Value predispositions, mass media, and attitudes toward nanotechnology: The interplay of public and experts. Science Communication, 33(2), 167–200. https://doi.org/10.1177/1075547010380386

37.

S. S.

Yang

Thanwarani

Chan

J. M.

(2017). Examining public acquisition of science knowledge from social media in Singapore: An extension of the cognitive mediation model. Asian Journal of Communication, 27(2), 193–212. https://doi.org/10.1080/01292986.2016.1240819

38.

Ittefaq

Zain

Arif

Ala-Uddin

Ahmad

Iqbal

(2025). Global news media coverage of artificial intelligence (AI): A comparative analysis of frames, sentiments, and trends across 12 countries. Telematics Informatics, 96, 102223. https://doi.org/10.1016/j.tele.2024.102223

39.

Kasneci

Sessler

Küchemann

Bannert

Dementieva

Fischer

Gasser

Groh

Günnemann

Hüllermeier

Krusche

Kutyniok

Michaeli

Nerdel

Pfeffer

Poquet

Sailer

Schmidt

Seidel

. . .Kasneci

(2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274

40.

Kim

K. E.

(2014). Framing as a strategic persuasive message tactic. In Holtzhausen

Zerfass

(Eds.). The Routledge handbook of strategic communication (pp. 285–302). Routledge.

41.

Klopp

(2020). A tutorial on testing the equality of standardized regression coefficients in structural equation models using Wald tests with lavaan. The Quantitative Methods for Psychology, 16(4), 315–333. https://doi.org/10.20982/tqmp.16.4

42.

Lao

You

(2024). Unraveling generative AI in BBC News: Application, impact, literacy and governance. Transforming Government People Process and Policy. Advance online publication. https://doi.org/10.1108/tg-01-2024-0022

43.

Larue

Poirier

Nkambou

(2013). Hypothetical-thinking based on cognitive decoupling and thinking dispositions in a dual cognitive agent. Biologically Inspired Cognitive Architectures, 6, 67–75. https://doi.org/10.1016/j.bica.2013.06.004

44.

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

45.

Lee

Chung

Kim

Jones-Jang

S. M.

(2024). Public perceptions of ChatGPT: Exploring how nonexperts evaluate its risks and benefits. Technology, Mind, and Behavior, 5(4). https://doi.org/10.1037/tmb0000140

46.

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 cancer worry, elaboration, HPV vaccination, news attention, risk perception. International Journal of Communication, 15(22). https://ijoc.org/index.php/ijoc/article/view/16314/3598

47.

Liu

(2025, February 25). Hong Kong government to use local ChatGPT-style AI tool powered by DeepSeek. https://www.scmp.com/news/hong-kong/hong-kong-economy/article/3300066/hong-kong-government-adopts-chatgpt-style-tool-powered-deepseek

48.

Wang

Pang

X. S.

Wang

(2024). Exploring User Adoption of ChatGPT: A Technology Acceptance Model Perspective. International Journal of Human–Computer Interaction, 41(2), 1431–1445. https://doi.org/10.1080/10447318.2024.2314358

49.

Macnaghten

Davies

S. R.

Kearnes

(2015). Understanding public responses to emerging technologies: A narrative approach. Journal of Environmental Policy & Planning, 21(5), 504–518. https://doi.org/10.1080/1523908X.2015.1053110

50.

Mahmutović

(2024). AI generative Chatbot in the media: Journalistic coverage of ChatGPT in Bosnia and Herzegovina. Media Studies and Applied Ethics, 5(1), 7–25. https://msae.rs/index.php/home/article/view/106

51.

Mallikarjuna

Chittemsetty

(2024). Generative artificial intelligence: Fundamentals and evolution. In Raza

Ahmad

Singh

(Eds.), Generative AI: Current trends and applications (pp. 3–17). Springer Nature. https://doi.org/10.1007/978-981-97-8460-8_1

52.

McConnell

A. R.

Niedermeier

K. E.

Leibold

J. M.

El-Alayli

A. G.

Chin

P. P.

Kuiper

N. M.

(2000). What if I find it cheaper someplace else? Role of prefactual thinking and anticipated regret in consumer behavior. Psychology & Marketing, 17(4), 281–298. https://doi.org/10.1002/(SICI)1520-6793(200004)17:4<281::AID-MAR2>3.0.CO;2-5

53.

Miyazaki

Murayama

Uchiba

Kwak

(2024). Public perception of generative AI on Twitter: An empirical study based on occupation and usage. EPJ Data Science, 13(1), 2. https://doi.org/10.1140/epjds/s13688-023-00445-y

54.

Nabi

(2009). Emotion and media effects. In Nabi

Oliver

M. B.

(Eds.), Media processes and effects (pp. 205–221). Sage.

55.

Nah

F. F.

Zheng

Cai

Siau

Chen

(2023). Generative AI and ChatGPT: Applications, challenges, and AI-human collaboration. Journal of Information Technology Case and Application Research, 25(3), 277–304. https://doi.org/10.1080/15228053.2023.2233814

56.

Namkoong

Fung

T. K. F.

Scheufele

D. A.

(2012). The politics of emotion: News media attention, emotional responses, and participation during the 2004 U.S. presidential election. Mass Communication and Society, 15(1), 25–45. https://doi.org/10.1080/15205436.2011.563894

57.

Nanay

(2024). Perception: The basics. Routledge.

58.

Noy

Zhang

(2023). Experimental evidence on the productivity effects of generative artificial intelligence. Science, 381(6654), 187–192. https://doi.org/10.1126/science.adh2586

59.

Nyaboke

(2024). Intellectual property rights in the era of artificial intelligence. Journal of Modern Law and Policy, 4(2), 57–72. https://doi.org/10.47941/jmlp.2162

60.

Pavlik

(2025, February 11). What is generative AI(GenAI)? How does it work? Oracle Hong Kong. https://www.oracle.com/hk/artificial-intelligence/generative-ai/what-is-generative-ai/#gai-benefits

61.

Ritchey

Dolcos

Eddington

K. M.

Strauman

T. J.

Cabeza

(2010). Neural correlates of emotional processing in depression: Changes with cognitive behavioral therapy and predictors of treatment response. Journal of Psychiatric Research, 45(5), 577–587. https://doi.org/10.1016/j.jpsychires.2010.09.007

62.

Rotolo

Hicks

Martin

B. R.

(2015). What is an emerging technology? Research Policy, 44(10), 1827–1843. https://doi.org/10.1016/j.respol.2015.06.006

63.

Rye

M. S.

Cahoon

M. B.

Ali

R. S.

Daftary

(2008). Development and validation of the counterfactual thinking for negative events scale. Journal of Personality Assessment, 90(3), 261–269. https://doi.org/10.1080/00223890701884996

64.

Sanna

L. J.

(1996). Defensive pessimism, optimism, and stimulating alternatives: Some ups and downs of prefactual and counterfactual thinking. Journal of Personality and Social Psychology, 71(5), 1020–1036. https://doi.org/10.1037/0022-3514.71.5.1020

65.

Schwarz

(2011). Feelings-as-information theory. In Van Lange

P. A. M.

Kruglanski

A. W.

Higgins

E. T.

(Eds.), Handbook of theories of social psychology (pp. 289–308). Sage.

66.

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

67.

Suk

Zhang

Liu

Yang

(2025). Communicative AI in the scientific public sphere: An analysis of Twitter discourse on generative AI tools. Telematics and Informatics, 98, 102261. https://doi.org/10.1016/j.tele.2025.102261

68.

Sun

(2024). Public perceptions and attitudes towards the application of artificial intelligence in journalism: From a China-based Survey. Journalism Practice, 18(3), 548–570. https://doi.org/10.1080/17512786.2022.2055621

69.

Sun

Zhai

Shen

Chen

(2020). Newspaper coverage of artificial intelligence: A perspective of emerging technologies. Telematics and Informatics, 53, 101433. https://doi.org/10.1016/j.tele.2020.101433

70.

Taber

K. S.

(2018). The use of Cronbach’s alpha when developing and reporting research instruments in science education. Research in Science Education, 48, 1273–1296.

71.

Wach

Duong

C. D.

Ejdys

Kazlauskaitė

Korzynski

Mazurek

Paliszkiewicz

Ziemba

(2023). The dark side of generative artificial intelligence: A critical analysis of controversies and risks of ChatGPT. Entrepreneurial Business and Economics Review, 11(2), 7–30. https://doi.org/10.15678/EBER.2023.110201

72.

Weisskopf

V. F.

(1980). The double-edged sword called technology. Bulletin of the Atomic Scientists, 36(4), 17–21. https://doi.org/10.1080/00963402.1980.11458714

73.

Xian

Zhang

Hemphill

(2024). Landscape of large language models in global English news: Topics, sentiments, and spatiotemporal analysis. Proceedings of the International AAAI conference on Web and Social Media, 18, 1661–1673. https://doi.org/10.1609/icwsm.v18i1.31416

74.

Yadlin-Segal

Oppenheim

(2020). Whose dystopia is it anyway? Deepfakes and social media regulation. Convergence the International Journal of Research Into New Media Technologies, 27(1), 36–51. https://doi.org/10.1177/1354856520923963

75.

Yan

Liu Y-l Li

Song

(2024). Does usage scenario matter? Investigating user perceptions, attitude and support for policies towards ChatGPT. Information Processing & Management, 61(6), 103867. https://doi.org/10.1016/j.ipm.2024.103867

76.

Yang

Chen

Wei

(2023). Extending the cognitive mediation model to examine public support for funding science and technology development in China: Media attention, information processing, scientific literacy, and nationalism. International Journal of Public Opinion Research, 35(1). https://doi.org/10.1093/ijpor/edac037

77.

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

78.

Zhang

T. H.

Tham

J. S.

Lee

E. W. J.

Waheed

(2024). Linking pathways from perceived absolute and comparative risk to colorectal cancer screening intention: Towards an extended cognitive mediation model. Communication Studies, 75(6), 1–24. https://doi.org/10.1080/10510974.2024.2331812

79.

Zhao

Leiserowitz

Maibach

Roser-Renouf

(2011). Attention to science/environment news positively predicts and attention to political news negatively predicts global warming risk perceptions and policy support. Journal of Communication, 61, 713–731.