Bandwagon cues are system-aggregated information about crowd behavior or peer endorsement displayed on a web interface (e.g., the number of likes on a Facebook post). Despite the recent proliferation of research on the effect of bandwagon cues on credibility perceptions, a comprehensive meta-analytic review of this effect has not yet been performed and published. Based on 161 effect sizes from 41 studies, the current meta-analysis revealed that bandwagon cues had a positive, albeit small, effect on credibility perceptions. Moderator analyses indicated that this effect was stronger (a) when the message was related to the marketing topic, (b) when the source was a non-expert (vs. an expert), and (c) when participants were from collectivistic (vs. individualistic) cultures. However, the bandwagon effect did not vary by cue feature (e.g., deliberateness). These findings are discussed in light of theoretical implications, practical guidelines, and directions for future research.
AllenM. (2017). The SAGE encyclopedia of communication research methods. SAGE Publications.
2.
AppelmanA.SundarS. S. (2016). Measuring message credibility: Construction and validation of an exclusive scale. Journalism & Mass Communication Quarterly, 93(1), 59–79. https://doi.org/10.1177/1077699015606057
3.
AssinkM.WibbelinkC. J. M. (2016). Fitting three-level meta-analytic models in R: A step-by-step tutorial. The Quantitative Methods for Psychology, 12(3), 154–174. https://doi.org/10.20982/tqmp.12.3.p154
4.
BellurS.SundarS. S. (2014). How can we tell when a heuristic has been used? Design and analysis strategies for capturing the operation of heuristics. Communication Methods and Measures, 8(2), 116–137. https://doi.org/10.1080/19312458.2014.903390
5.
BieB. (2015). Effects of stigma, message valence and virality, and audience characteristics on the persuasiveness of anti-stigma messages via social media [Unpublished doctoral dissertation]. The University of Alabama.
6.
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. https://doi.org/10.1177/1075547020981375
7.
BondR.SmithP. B. (1996). Culture and conformity: A meta-analysis of studies using Asch’s (1952b, 1956) line judgment task. Psychological Bulletin, 119(1), 111–137. https://doi.org/10.1037/0033-2909.119.1.111
8.
BondR. M.FarissC. J.JonesJ. J.KramerA. D.MarlowC.SettleJ. E.FowlerJ. H. (2012). A 61-million-person experiment in social influence and political mobilization. Nature, 489(7415), 295–298. https://doi.org/10.1038/nature11421
9.
ChaikenS. (1987). The heuristic model of persuasion. In ZannaM. P.OlsonJ. M.HermanC. P. (Eds.), Social influence: The Ontario symposium (Vol. 5, pp. 3–39). Lawrence Erlbaum Associates.
10.
ChaikenS.EaglyA. H. (1976). Communication modality as a determinant of message persuasiveness and message comprehensibility. Journal of Personality and Social Psychology, 34(4), 605–614. https://doi.org/10.1037/0022-3514.34.4.605
11.
ChaikenS.LibermanA.EaglyA. H. (1989). Heuristic and systematic information processing within and beyond the persuasion context. In UlemanJ. S.BarghJ. A. (Eds.), Unintended thought (pp. 212–252). Guilford Press.
12.
CheungM. W. (2014). Modeling dependent effect sizes with three-level meta-analyses: A structural equation modeling approach. Psychological Methods, 19(2), 211–229. https://doi.org/10.1037/a0032968
13.
ChungM. (2017). Not just numbers: The role of social media metrics in online news evaluations. Computers in Human Behavior, 75, 949–957. https://doi.org/10.1016/j.chb.2017.06.022
14.
CialdiniR. (2001). Influence: Science and practice (4th ed.). Allyn & Bacon.
CialdiniR. B.WosinskaW.BarrettD. W.ButnerJ.Gornik-DuroseM. (1999). Compliance with a request in two cultures: The differential influence of social proof and commitment/consistency on collectivists and individualists. Personality and Social Psychology Bulletin, 25(10), 1242–1253. https://doi.org/10.1177/0146167299258006
17.
DeAndreaD. C.Van Der HeideB.EasleyN. (2015). How modifying third-party information affects interpersonal impressions and the evaluation of collaborative online media. Journal of Communication, 65(1), 62–78. https://doi.org/10.1111/jcom.12139
18.
DeVossC. (2018). When friends share about brands: A study of eWOM on social networking sites [Unpublished doctoral dissertation]. University of Connecticut.
19.
EarleyP. C. (1993). East meets West meets Mideast: Further explorations of collectivistic and individualistic work groups. Academy of Management Journal, 36(2), 319–348. https://doi.org/10.5465/256525
20.
EggerM.Davey SmithG.SchneiderM.MinderC. (1997). Bias in meta-analysis detected by a simple, graphical test. BMJ, 315(7109), 629–634. https://doi.org/10.1136/bmj.315.7109.629
21.
FiskeS. T.TaylorS. E. (1991). Social cognition (2nd ed.). McGraw-Hill.
GuntherA. C. (1992). Biased press or biased public? Attitudes toward media coverage of social groups. Public Opinion Quarterly, 56(2), 147–167. https://doi.org/10.1086/269308
24.
HaimM.KümpelA. S.BrosiusH. B. (2018). Popularity cues in online media: A review of conceptualizations, operationalizations, and general effects. SCM Studies in Communication and Media, 7(2), 186–207. https://doi.org/10.5771/2192-4007-2018-2-58
25.
HayesJ. L.ShanY.KingK. W. (2018). The interconnected role of strength of brand and interpersonal relationships and user comment valence on brand video sharing behaviour. International Journal of Advertising, 37(1), 142–164. https://doi.org/10.1080/02650487.2017.1360576
26.
HofstedeG.HofstedeG. J.MinkovM. (2010). Cultures and organizations: Software of the mind (3rd ed.). McGraw-Hill.
27.
HomerP. M.KahleL. R. (1990). Source expertise, time of source identification, and involvement in persuasion: An elaborative processing perspective. Journal of Advertising, 19(1), 30–39. https://doi.org/10.1080/00913367.1990.10673178
28.
HongS.PittmanM. (2020). eWOM anatomy of online product reviews: Interaction effects of review number, valence, and star ratings on perceived credibility. International Journal of Advertising, 39(7), 892–920. https://doi.org/10.1080/02650487.2019.1703386
29.
HowardJ. (2019). Bandwagon effect and authority bias. In HowardJ. (Ed.), Cognitive errors and diagnostic mistakes: A case-based guide to critical thinking in medicine (pp. 21–56). Springer.
30.
HuangS.AralS.HuY. J.BrynjolfssonE. (2020). Social advertising effectiveness across products: A large-scale field experiment. Marketing Science, 39(6), 1142–1165. https://doi.org/10.1287/mksc.2020.1240
31.
JinS. A. A.PhuaJ. (2014). Following celebrities’ tweets about brands: The impact of twitter-based electronic word-of-mouth on consumers’ source credibility perception, buying intention, and social identification with celebrities. Journal of Advertising, 43(2), 181–195. https://doi.org/10.1080/00913367.2013.827606
32.
KaoK. C.Rao HillS.TroshaniI. (2021). A cross-country comparison of online deal popularity effect. Journal of Retailing and Consumer Services, 60, 102402. https://doi.org/10.1016/j.jretconser.2020.102402
33.
KimH.MarkusH. R. (1999). Deviance or uniqueness, harmony or conformity? A cultural analysis. Journal of Personality and Social Psychology, 77(4), 785–800. https://doi.org/10.1037/0022-3514.77.4.785
34.
KimH. S.BrubakerP.SeoK. (2015). Examining psychological effects of source cues and social plugins on a product review website. Computers in Human Behavior, 49, 74–85. https://doi.org/10.1016/j.chb.2015.02.058
35.
KimJ. W. (2018). Rumor has it: The effects of virality metrics on rumor believability and transmission on Twitter. New Media & Society, 20(12), 4807–4825. https://doi.org/10.1177/1461444818784945
36.
KimK. J. (2016). Interacting socially with the Internet of Things (IoT): Effects of source attribution and specialization in Human-IoT Interaction. Journal of Computer-Mediated Communication, 21(6), 420–435. https://doi.org/10.1111/jcc4.12177
37.
KulkarniS. P.HudsonT.RamamoorthyN.MarchevA.Georgieva-KondakovaP.GorskovV. (2010). Dimensions of individualism-collectivism: A comparative study of five cultures. Verslo ir Teisės Aktualijos, 5(1), 93–109. https://doi.org/10.5200/1822-9530.2010.03
38.
KümpelA. S.HaimM. (2016, October5–8). Popularity indicators in online media. A review of research on the effects of metric user information [Conference session]. The 17th Annual Conference of the Association of Internet Researchers, Berlin, Germany. https://journals.uic.edu/ojs/index.php/spir/article/view/8794
LazardA.AtkinsonL. (2015). Putting environmental infographics center stage: The role of visuals at the elaboration likelihood model’s critical point of persuasion. Science Communication, 37(1), 6–33. https://doi.org/10.1177/1075547014555997
41.
LeeJ. Y.SundarS. S. (2013). To tweet or to retweet? That is the question for health professionals on Twitter. Health Communication, 28(5), 509–524. https://doi.org/10.1080/10410236.2012.700391
42.
LeibensteinH. (1950). Bandwagon, snob, and Veblen effects in the theory of consumers’ demand. The Quarterly Journal of Economics, 64(2), 183–207. https://doi.org/10.2307/1882692
43.
LinX.SpenceP. R. (2018). Identity on social networks as a cue: Identity, retweets, and credibility. Communication Studies, 69(5), 461–482. https://doi.org/10.1080/10510974.2018.1489295
44.
LiR.SundarS. S. (2022). Can interactive media attenuate psychological reactance to health messages? A study of the role played by user commenting and audience metrics in persuasion. Health Communication, 37, 1355–1367. https://doi.org/10.1080/10410236.2021.1888450
45.
LiuJ.ZhaoS.ChenX.FalkE.AlbarracínD. (2017). The influence of peer behavior as a function of social and cultural closeness: A meta-analysis of normative influence on adolescent smoking initiation and continuation. Psychological Bulletin, 143(10), 1082–1115. https://doi.org/10.1037/bul0000113
46.
LuoM.HancockJ. T.MarkowitzD. M. (2022). Credibility perceptions and detection accuracy of fake news headlines on social media: Effects of truth-bias and endorsement cues. Communication Research, 49, 171–195. https://doi.org/10.1177/0093650220921321
47.
McCroskeyJ. C.TevenJ. J. (1999). Goodwill: A reexamination of the construct and its measurement. Communication Monographs, 66(1), 90–103. https://doi.org/10.1080/03637759909376464
48.
MessingS.WestwoodS. J. (2014). Selective exposure in the age of social media: Endorsements trump partisan source affiliation when selecting news online. Communication Research, 41(8), 1042–1063. https://doi.org/10.1177/0093650212466406
49.
MetzgerM. J. (2007). Making sense of credibility on the web: Models for evaluating online information and recommendations for future research. Journal of the American Society for Information Science and Technology, 58(13), 2078–2091. https://doi.org/10.1002/asi.20672
50.
MetzgerM. J.FlanaginA. J.EyalK.LemusD. R.MccannR. M. (2003). Credibility for the 21st century: Integrating perspectives on source, message, and media credibility in the contemporary media environment. Annals of the International Communication Association, 27(1), 293–335. https://doi.org/10.1080/23808985.2003.11679029
51.
MetzgerM. J.FlanaginA. J.MeddersR. B. (2010). Social and heuristic approaches to credibility evaluation online. Journal of Communication, 60(3), 413–439. https://doi.org/10.1111/j.1460-2466.2010.01488.x
52.
MutzD. C. (1998). Impersonal influence: How perceptions of mass collectives affect political attitudes. Cambridge University Press.
53.
NaK. (2015). Persuasion and news sharing: Sharer, sharing frequency, and framing [Unpublished doctoral dissertation]. The Ohio State University.
54.
PettyR. E.CacioppoJ. T. (1990). Involvement and persuasion: Tradition versus integration. Psychological Bulletin, 107(3), 367–374. https://doi.org/10.1037/0033-2909.107.3.367
55.
RatcliffC. L.SunY. (2020). Overcoming resistance through narratives: Findings from a meta-analytic review. Human Communication Research, 46(4), 412–443. https://doi.org/10.1093/hcr/hqz017
SagyS.OrrE.Bar-OnD. (1999). Individualism and collectivism in Israeli society: Comparing religious and secular high-school students. Human Relations, 52(3), 327–348. https://doi.org/10.1177/001872679905200303
58.
Schmitt-BeckR. (2015). Bandwagon effect. In MazzoleniG. (Ed.). The international encyclopedia of political communication (pp. 1–5). John Wiley & Sons. https://doi.org/10.1002/9781118541555.wbiepc015
59.
ShenC.KasraM.PanW.BassettG. A.MallochY.O’BrienJ. F. (2019). Fake images: The effects of source, intermediary, and digital media literacy on contextual assessment of image credibility online. New Media & Society, 21(2), 438–463. https://doi.org/10.1177/1461444818799526
60.
SimonH. A. (1956). Rational choice and the structure of the environment. Psychological Review, 63(2), 129–138. https://doi.org/10.1037/h0042769
61.
SterneJ. A. C.BeckerB. J.EggerM. (2006). The funnel plot. In RothsteinH. R.SuttonA. J.BorensteinM. (Eds.), Publication bias in meta-analysis: Prevention, assessment and adjustments (pp. 75–98). John Wiley & Sons.
62.
SterneJ. A. C.EggerM. (2006). Regression methods to detect publication and other bias in meta-analysis. In RothsteinH. R.SuttonA. J.BorensteinM. (Eds.), Publication bias in meta-analysis: Prevention, assessment and adjustments (pp. 99–110). John Wiley & Sons.
63.
SundarS. S. (2000). Multimedia effects on processing and perception of online news: A study of picture, audio, and video downloads. Journalism & Mass Communication Quarterly, 77(3), 480–499. https://doi.org/10.1177/107769900007700302
64.
SundarS. S. (2008). The MAIN model: A heuristic approach to understanding technology effects on credibility. In MetzgerM. J.FlanaginA. J. (Eds.), Digital media, youth, and credibility (pp. 73–100). The MIT Press.
65.
SundarS. S.JiaH.WaddellT. F.HuangY. (2015). Toward a theory of interactive media effects (TIME): Four models for explaining how interface features affect user psychology. In SundarS. S. (Ed.), The handbook of the psychology of communication technology (pp. 47–86). Wiley Blackwell.
66.
SundarS. S.KimJ.GambinoA. (2017). Using theory of interactive media effects (TIME) to analyze digital advertising. In RodgersS.ThorsonE. (Eds.), Digital advertising: Theory and research (pp. 86–109). Routledge.
67.
SundarS. S.Knobloch-WesterwickS.HastallM. R. (2007). News cues: Information scent and cognitive heuristics. Journal of the American Society for Information Science and Technology, 58(3), 366–378. https://doi.org/10.1002/asi.20511
68.
SundarS. S.MolinaM. D.ChoE. (2021). Seeing is believing: Is video modality more powerful in spreading fake news via online messaging apps?Journal of Computer-Mediated Communication, 26, 301–319. https://doi.org/10.1093/jcmc/zmab010
69.
SundarS. S.XuQ.Oeldorf-HirschA. (2009). Authority vs. peer: How interface cues influence users. CHI’09 Extended Abstracts on Human Factors in Computing Systems, 4231–4236. https://doi.org/10.1145/1520340.1520645
70.
van de BongardtD.ReitzE.SandfortT.DekovićM. (2015). A meta-analysis of the relations between three types of peer norms and adolescent sexual behavior. Personality and Social Psychology Review, 19(3), 203–234. https://doi.org/10.1177/1088868314544223
71.
Van Den NoortgateW.López-LópezJ. A.Marín-MartínezF.Sánchez-MecaJ. (2013). Three-level meta-analysis of dependent effect sizes. Behavior Research Methods, 45(2), 576–594. https://doi.org/10.3758/s13428-012-0261-6
72.
Van Der HeideB.LimY. S. (2016). On the conditional cueing of credibility heuristics: The case of online influence. Communication Research, 43(5), 672–693. https://doi.org/10.1177/0093650214565915
73.
VeveaJ. L.CoburnK.SuttonA. (2019). Publication bias. In CooperH.HedgesL. V.ValentineJ. C. (Eds.), The handbook of research synthesis and meta-analysis (3rd ed.) (pp. 383–429). Russell SAGE Foundation.
WaltherJ. B.LiangY. J.GansterT.WohnD. Y.EmingtonJ. (2012). Online reviews, helpfulness ratings, and consumer attitudes: An extension of congruity theory to multiple sources in web 2.0. Journal of Computer-Mediated Communication, 18(1), 97–112. https://doi.org/10.1111/j.1083-6101.2012.01595.x
76.
WaltherJ. B.Van Der HeideB.HamelL. M.ShulmanH. C. (2009). Self-generated versus other-generated statements and impressions in computer-mediated communication: A test of warranting theory using Facebook. Communication Research, 36(2), 229–253. https://doi.org/10.1177/0093650208330251
77.
WangY. (2021). Debunking misinformation about genetically modified food safety on social media: Can heuristic cues mitigate biased assimilation?Science Communication, 43, 460–485. https://doi.org/10.1177/10755470211022024
78.
WestermanD.SpenceP. R.Van Der HeideB. (2012). A social network as information: The effect of system generated reports of connectedness on credibility on Twitter. Computers in Human Behavior, 28(1), 199–206. https://doi.org/10.1016/j.chb.2011.09.001
79.
WuT. Y.LinC. A. (2017). Predicting the effects of eWOM and online brand messaging: Source trust, bandwagon effect and innovation adoption factors. Telematics and Informatics, 34(2), 470–480. https://doi.org/10.1016/j.tele.2016.08.001
80.
XuQ. (2013). Social recommendation, source credibility, and recency: Effects of news cues in a social bookmarking website. Journalism & Mass Communication Quarterly, 90(4), 757–775. https://doi.org/10.1177/1077699013503158
81.
XuQ. (2014). Should I trust him? The effects of reviewer profile characteristics on eWOM credibility. Computers in Human Behavior, 33, 136–144. https://doi.org/10.1016/j.chb.2014.01.027
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.
