This study investigated how scientific consensus messaging influences public support for socially good artificial intelligence (AI), drawing on the gateway belief model. Two preregistered experiments found that consensus messages increased support for AI research and willingness to raise awareness indirectly through increasing perceived consensus, which enhanced favorable beliefs and hope. These effects varied by collective scientific efficacy—perceived ability of scientists to work together effectively. Belief-based pathways were stronger under low efficacy, while hope-based pathways were stronger under high efficacy. Findings underscore the value of combining consensus and efficacy cues in science communication to promote engagement with emerging technologies like AI.
AjzenI.FishbeinM. (1972). Attitudes and normative beliefs as factors influencing behavioral intentions. Journal of Personality and Social Psychology, 21(1), 1–9.
2.
BanduraA. (2000). Exercise of human agency through collective efficacy. Current Directions in Psychological Science, 9(3), 75–78.
3.
BanduraA. (2001). Social cognitive theory: An agentic perspective. Annual Review of Psychology, 52(1), 1–26.
4.
BaumS. D. (2017). On the promotion of safe and socially beneficial artificial intelligence. AI & Society, 32(4), 543–551.
5.
BayesR.BolsenT.DruckmanJ. N. (2023). A research agenda for climate change communication and public opinion: The role of scientific consensus messaging and beyond. Environmental Communication, 17(1), 16–34.
6.
Bieniek-TobascoA.RimalR. N.McCormickS.HarringtonC. B. (2020). The power of being transported: Efficacy beliefs, risk perceptions, and political affiliation in the context of climate change. Science Communication, 42(6), 776–802.
7.
BilaliR. (2024). Narrative media interventions influence efficacy beliefs, social norms, and choice of behavioral options: A field experiment in Burkina Faso. Group Processes & Intergroup Relations: GPIR, 27(4), 845–858.
8.
BilandzicH.KalchA.SoentgenJ. (2017). Effects of goal framing and emotions on perceived threat and willingness to sacrifice for climate change. Science Communication, 39(4), 466–491.
9.
BodeL.VragaE. K.TullyM. (2021). Correcting misperceptions about genetically modified food on social media: Examining the impact of experts, social media heuristics, and the gateway belief model. Science Communication, 43(2), 225–251.
10.
BorkovecT. D.RobinsonE.PruzinskyT.DePreeJ. A. (1983). Preliminary exploration of worry: Some characteristics and processes. Behaviour Research and Therapy, 21(1), 9–16.
11.
ChadwickA. E. (2015). Toward a theory of persuasive hope: Effects of cognitive appraisals, hope appeals, and hope in the context of climate change. Health Communication, 30(6), 598–611.
12.
ChaikenS. (1980). Heuristic versus systematic information processing and the use of source versus message cues in persuasion. Journal of Personality and Social Psychology, 39(5), 752–766.
13.
ChinnS.LaneD. S.HartP. S. (2018). In consensus we trust? Persuasive effects of scientific consensus communication. Public Understanding of Science (Bristol, England), 27(7), 807–823.
14.
ChoH.SongC.LiW.AdamsD. (2023). Social processes of participatory engagement effects: A longitudinal examination with a sample of young women in the United States. Journal of Applied Communication Research: JACR, 51(3), 320–339.
15.
CialdiniR. B.KallgrenC. A.RenoR. R. (1991). A focus theory of normative conduct: A theoretical refinement and reevaluation of the role of norms in human behavior. In Advances in Experimental Social Psychology (pp. 201–234). Elsevier.
16.
CowlsJ.TsamadosA.TaddeoM.FloridiL. (2021). A definition, benchmark and database of AI for social good initiatives. Nature Machine Intelligence, 3(2), 111–115.
17.
CuadradoE.TaberneroC. (2015). Affective balance, team prosocial efficacy and team trust: A multilevel analysis of prosocial behavior in small groups. PLOS ONE, 10(8), Article e0136874.
18.
DangJ.SedikidesC.WildschutT.LiuL. (2024). More than a barrier: Nostalgia inhibits, but also promotes, favorable responses to innovative technology. Journal of Personality and Social Psychology, 126(6), 998–1018.
19.
DangJ.SedikidesC.WildschutT.LiuL. (2025). Nostalgia encourages exploration and fosters uncertainty in response to AI technology. The British Journal of Social Psychology, 64(1), Article e12843.
20.
da Silva FrostA.LedgerwoodA. (2020). Calibrate your confidence in research findings: A tutorial on improving research methods and practices. Journal of Pacific Rim Psychology, 14, Article e14.
21.
DixonG. (2016). Applying the gateway belief model to genetically modified food perceptions: New insights and additional questions: Applying the gateway belief model to GMO beliefs. The Journal of Communication, 66(6), 888–908.
22.
EomK.ChoyB. K. C. (2024). Exposure to different motives of scientists moderates responses to scientific consensus: The case of cultured meat. Science Communication, 47(3), 319–347. https://doi.org/10.1177/10755470241277196
FeldmanL.HartP. S. (2016). Using political efficacy messages to increase climate activism. Science Communication, 38(1), 99–127.
25.
FilhoE.TenenbaumG.YangY. (2015). Cohesion, team mental models, and collective efficacy: Towards an integrated framework of team dynamics in sport. Journal of Sports Sciences, 33(6), 641–653.
26.
GkinkoL.ElbannaA. (2022). Hope, tolerance and empathy: Employees’ emotions when using an AI-enabled chatbot in a digitalised workplace. Information Technology & People, 35(6), 1714–1743.
27.
GoldbergM. H.van der LindenS.LeiserowitzA.MaibachE. (2020). Perceived social consensus can reduce ideological biases on climate change. Environment and Behavior, 52(5), 495–517.
28.
HaenleinM.KaplanA. (2019). A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California Management Review, 61(4), 5–14.
29.
HarrisA. J. L.SildmäeO.SpeekenbrinkM.HahnU. (2019). The potential power of experience in communications of expert consensus levels. Journal of Risk Research, 22(5), 593–609.
30.
HässlerT.UllrichJ.BernardinoM.ShnabelN.Van LaarC.ValdenegroD.SebbenS.TroppL. R.VisintinE. P.GonzálezR.DitlmannR. K.AbramsD.SelvanathanH. P.BrankovićM.WrightS.von ZimmermannJ.PasekM.AydinA. L.ŽeželjI.. . .UgarteL. M. (2020). A large-scale test of the link between intergroup contact and support for social change. Nature Human Behaviour, 4(4), 380–386.
31.
HochschildJ.SenM. (2015). Technology optimism or pessimism about genomic science. The Annals of the American Academy of Political and Social Science, 658(1), 236–252.
32.
HuangC.-L.HariedP. (2020). An evaluation of uncertainty and anticipatory anxiety impacts on technology use. International Journal of Human-Computer Interaction, 36(7), 641–649.
33.
KerrJ. R.WilsonM. S. (2018). Changes in perceived scientific consensus shift beliefs about climate change and GM food safety. PLOS ONE, 13(7), Article e0200295.
34.
KobayashiK. (2018). The impact of perceived scientific and social consensus on scientific beliefs. Science Communication, 40(1), 63–88.
35.
LarsenD. J.WheltonW. J.RogersT.McElheranJ.HerthK.TremblayJ.GreenJ.DushinskiK.SchalkK.ChamodrakaM.DomeneJ. (2020). Multidimensional hope in counseling and psychotherapy scale. Journal of Psychotherapy Integration, 30(3), 407–422.
36.
LazarusR. S. (1991). Emotion and adaptation. Oxford University Press.
37.
LazarusR. S.FolkmanS. (1984). Stress, appraisal, and coping. Springer.
38.
LeeC.-J.ScheufeleD. A.LewensteinB. V. (2005). Public attitudes toward emerging technologies. Science Communication, 27(2), 240–267.
39.
LeshnerA. I. (2003). Public engagement with science. Science (New York, N.Y.), 299(5609), 977.
40.
LiW.LuS.XuS.ZhengX. (2025). Welcome to the brave new world: Lay definitions of AI at work and in daily life. Social Science Computer Review. Advance online publication. https://doi.org/10.1177/08944393251382233
41.
LiW.XuS.ZhengX.SunR. (2024). Bridging the knowledge gap in artificial intelligence: The roles of social media exposure and information elaboration. Science Communication, 46(4), 399–430.
42.
LiW.ZhengX.XuS. (2025). Ingredients of “a confusing cocktail”: Testing explanations for public opinions of AI. AI & Society. Advance online publication. https://doi.org/10.1007/s00146-025-02648-5
43.
LiuS.YangJ. Z.ChuH. (2021). When we increase fear, do we dampen hope? Using narrative persuasion to promote human papillomavirus vaccination in China. Journal of Health Psychology, 26(11), 1999–2009.
44.
LuS. (2020). Taming the news feed on Facebook: Understanding consumptive news feed curation through a social cognitive perspective. Digital Journalism, 8(9), 1163–1180.
45.
LupiaA.AllisonD. B.JamiesonK. H.HeimbergJ.SkipperM.WolfS. M. (2024). Trends in U.S. public confidence in science and opportunities for progress. Proceedings of the National Academy of Sciences of the United States of America, 121(11), Article e2319488121.
46.
MathewsA. (1990). Why worry? The cognitive function of anxiety. Behaviour Research and Therapy, 28(6), 455–468.
MonatA.AverillJ. R.LazarusR. S. (1972). Anticipatory stress and coping reactions under various conditions of uncertainty. Journal of Personality and Social Psychology, 24(2), 237–253.
49.
NabiR. L. (2002a). Anger, fear, uncertainty, and attitudes: A test of the cognitive-functional model. Communication Monographs, 69(3), 204–216.
50.
NabiR. L. (2002b). The theoretical versus the lay meaning of disgust: Implications for emotion research. Cognition & Emotion, 16(5), 695–703.
51.
NabiR. L.GustafsonA.JensenR. (2018). Framing climate change: Exploring the role of emotion in generating advocacy behavior. Science Communication, 40(4), 442–468.
52.
NabiR. L.MyrickJ. G. (2019). Uplifting fear appeals: Considering the role of hope in fear-based persuasive messages. Health Communication, 34(4), 463–474.
53.
NysveenH. (2005). Intentions to use mobile services: Antecedents and cross-service comparisons. Journal of the Academy of Marketing Science, 33(3), 330–346.
54.
QuY.LuS. (2025). A news ecology perspective to information verification: Examining the effects of news repertoire and news capital. Journalism & Mass Communication Quarterly. Advance online publication. https://doi.org/10.1177/10776990251318636
55.
SaidN.FrauhammerL. T.HuffM. (2023). Consensus messaging in climate change communication: Metacognition as moderator variable in the gateway belief model. Journal of Environmental Psychology, 91, 102128.
56.
SchönbergerD. (2019). Artificial intelligence in healthcare: A critical analysis of the legal and ethical implications. International Journal of Law and Information Technology, 27(2), 171–203.
57.
SelwynN.CordobaB. G.AndrejevicM.CampbellL. (2020). AI for social good—Australian attitudes toward AI and society report.Pdf. Monash University. https://doi.org/10.26180/13159781.V1
58.
SlaterM. D.PeterJ.ValkenbergP. (2015). Message variability and heterogeneity: A core challenge for communication research. Communication Yearbook, 39, 3–31.
59.
SlovicP. (2000). The perception of risk. Routledge.
60.
SnyderC. R. (2000). Hypothesis: There is hope. In SnyderC. R. (Ed.), Handbook of hope: Theory, measures, and applications (pp. 3–21). Academic Press.
61.
SnyderC. R.SympsonS. C.YbascoF. C.BordersT. F.BabyakM. A.HigginsR. L. (1996). Development and validation of the State Hope Scale. Journal of Personality and Social Psychology, 70(2), 321–335.
62.
TaberneroC.HernándezB.CuadradoE.LuqueB.PereiraC. R. (2015). A multilevel perspective to explain recycling behaviour in communities. Journal of Environmental Management, 159, 192–201.
63.
van der LindenS.LeiserowitzA.MaibachE. (2019). The gateway belief model: A large-scale replication. Journal of Environmental Psychology, 62, 49–58.
64.
van der LindenS.MaibachE.LeiserowitzA. (2015). Improving public engagement with climate change: Five “best practice” insights from psychological science. Perspectives on Psychological Science: A Journal of the Association for Psychological Science, 10(6), 758–763.
65.
van der LindenS. L.ClarkeC. E.MaibachE. W. (2015). Highlighting consensus among medical scientists increases public support for vaccines: Evidence from a randomized experiment. BMC Public Health, 15, Article 1207.
66.
van der LindenS. L.LeiserowitzA. A.FeinbergG. D.MaibachE. W. (2015). The scientific consensus on climate change as a gateway belief: Experimental evidence. PLOS ONE, 10(2), Article e0118489.
67.
van StekelenburgA.SchaapG.VelingH.van ’t RietJ.BuijzenM. (2022). Scientific-consensus communication about contested science: A preregistered meta-analysis. Psychological Science, 33(12), 1989–2008.
68.
van ZomerenM.SpearsR.LeachC. W. (2010). Experimental evidence for a dual pathway model analysis of coping with the climate crisis. Journal of Environmental Psychology, 30(4), 339–346.
69.
VelasquezA.LaRoseR. (2015). Youth collective activism through social media: The role of collective efficacy. New Media & Society, 17(6), 899–918.
70.
VolkmanJ. E.ParrottR. L. (2012). Expressing emotions as evidence in osteoporosis narratives: Effects on message processing and intentions. Human Communication Research, 38(4), 429–458.
71.
WickesR.HippJ. R.SargeantE.HomelR. (2013). Collective efficacy as a task specific process: Examining the relationship between social ties, neighborhood cohesion and the capacity to respond to violence, delinquency and civic problems. American Journal of Community Psychology, 52(1–2), 115–127.
72.
WilliamsonB.EynonR. (2020). Historical threads, missing links, and future directions in AI in education. Learning, Media and Technology, 45(3), 223–235.
73.
XuS.KeeK. F.LiW.YamamotoM.RiggsR. E. (2023). Examining the diffusion of innovations from a dynamic, differential-effects perspective: A longitudinal study on AI adoption among employees. Communication Research, 51(7), 843–866.
74.
YinY.JiaN.WakslakC. J. (2024). AI can help people feel heard, but an AI label diminishes this impact. Proceedings of the National Academy of Sciences of the United States of America, 121(14), Article e2319112121.
