In group decision-making, the behavior of each member is sensitive to the social influence of other majority members. Research on majority influence has shown that multiple non-human agents with anthropomorphic cues can exert normative pressure on a lone human decision-maker. However, how individuals perceive and respond to minority influence exerted by a lone machine is rarely discussed. Hence, a between-subjects experiment was conducted to examine how different minority identity (human vs artificial intelligence [AI]) and specialization (specialist vs generalist) cues influence individuals’ perceptions and behavior in response to moral dilemmas in a joint human–AI group. The results confirmed the significant role of specialization cues in predicting in-group identification, source credibility, and conversion behavior. In addition, the participants perceived the human minority as more credible than the AI minority, which prompted conversion behavior when the minority was labeled as a specialist rather than as a generalist.
AschSE (1956) Studies of independence and conformity: I. A minority of one against a unanimous majority. Psychological Monographs: General and Applied70(9): 1–70.
2.
AwadEDsouzaSKimR, et al. (2018) The moral machine experiment. Nature563(7729): 59–64.
3.
BurtonJWSteinM-KJensenTB (2020) A systematic review of algorithm aversion in augmented decision making. Journal of Behavioral Decision Making33(2): 220–239.
4.
ClarkRDMaassA (1988) The role of social categorization and perceived source credibility in minority influence. European Journal of Social Psychology18(5): 381–394.
5.
ClarksonJJDuganRGCrolicC, et al. (2020) Influencing those who influence us: the role of expertise in the emergence of minority influence. Journal of Experimental Social Psychology89: 103976.
6.
DahlbäckNJönssonAAhrenbergL (1993) Wizard of Oz studies — why and how. Knowledge-Based Systems6(4): 4258–266.
7.
De VriesNKDe DreuCKWGordijnE, et al. (1996) Majority and minority influence: a dual role interpretation. European Review of Social Psychology7(1): 145–172.
8.
DietvorstBJSimmonsJMasseyC (2015) Algorithm aversion: people erroneously avoid algorithms after seeing them err. Journal of Experimental Psychology: General144(1): 114–126.
9.
EtienneH (2021) The dark side of the “moral machine” and the fallacy of computational ethical decision-making for autonomous vehicles. Law, Innovation and Technology13(1): 85–107.
10.
EysselFKuchenbrandtD (2012) Social categorization of social robots: anthropomorphism as a function of robot group membership. British Journal of Social Psychology51(4): 724–731.
11.
FaulFErdfelderEBuchnerA, et al. (2009) Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses. Behavior Research Methods41(4): 1149–1160.
12.
FilippinAGualaF (2017) Group identity as a social heuristic: an experiment with reaction times. Journal of Neuroscience, Psychology, and Economics10(4): 153–166.
13.
FiskeSTNeubergSL (1990) A continuum of impression formation, from category-based to individuating processes: influences of information and motivation on attention and interpretation. In: ZannaMP (ed.) Advances in Experimental Social Psychology. Cambridge, MA: Academic Press, pp. 1–74.
14.
ForsythDR (2019) Group Dynamics. 7th ed.Boston, MA: Cengage Learning.
15.
GardikiotisA (2011) Minority influence. Social and Personality Psychology Compass5(9): 679–693.
GassRH (2015) Social influence, sociology of. In: WrightJD (ed.) International Encyclopedia of the Social & Behavioral Sciences. 2nd ed.Oxford: Elsevier, pp. 348–354.
HaslamSA (2004) Psychology in Organizations. 2nd ed.London: SAGE.
20.
HayesAF (2018) Introduction to Mediation, Moderation, and Conditional Process Analysis, Second Edition: A Regression-Based Approach. 2nd ed.New York: Guilford Publications.
21.
HidalgoCAOrghianDCanalsJA, et al. (2021) How Humans Judge Machines. Cambridge, MA: MIT Press.
22.
KimKJ (2014) Can smartphones be specialists? Effects of specialization in mobile advertising. Telematics and Informatics31(4): 640–647.
23.
KimKJ (2016) Interacting socially with the Internet of Things (IoT): effects of source attribution and specialization in human–IoT interaction. Journal of Computer-Mediated Communication21(6): 420–435.
24.
KnippenbergDVan LossieNWilkeH (1994) In-group prototypicality and persuasion: determinants of heuristic and systematic message processing. British Journal of Social Psychology33(3): 289–300.
25.
KohYJSundarSS (2010a) Effects of specialization in computers, web sites, and web agents on e-commerce trust. International Journal of Human-Computer Studies68(12): 899–912.
26.
KohYJSundarSS (2010b) Heuristic versus systematic processing of specialist versus generalist sources in online media. Human Communication Research36(2): 103–124.
27.
KuchenbrandtDEysselFBobingerS, et al. (2013) When a robot’s group membership matters. International Journal of Social Robotics5(3): 409–417.
28.
LeeMK (2018) Understanding perception of algorithmic decisions: fairness, trust, and emotion in response to algorithmic management. Big Data & Society5(1): 1–16.
29.
LeippeMRElkinRA (1987) When motives clash: issue involvement and response involvement as determinants of persuasion. Journal of Personality and Social Psychology52(2): 269–278.
30.
LiewTWTanS-MTeeJ, et al. (2021) The effects of designing conversational commerce chatbots with expertise cues. In: 2021 14th international conference on human system interaction (HSI), Gdańsk, 08–10 July, pp. 1–6. Piscataway, NJ: IEEE.
31.
McCroskeyJCTevenJJ (1999) Goodwill: a reexamination of the construct and its measurement. Communication Monographs66(1): 90–103.
32.
MahmudHIslamAKMNAhmedSI, et al (2022) What influences algorithmic decision-making? A systematic literature review on algorithm aversion. Technological Forecasting and Social Change175: 121390.
33.
MartinRHewstoneM (2008) Majority versus minority influence, message processing and attitude change: the source-context-elaboration model. Advances in Experimental Social Psychology 40: 237–326.
34.
MolinaMDSundarSS (2022) Does distrust in humans predict greater trust in AI? Role of individual differences in user responses to content moderation. New Media & Society. Epub ahead of print 23 June. DOI: 10.1177/14614448221103534.
35.
MoscoviciS (1980) Toward a theory of conversion behavior. In: BerkowitzL (ed.) Advances in Experimental Social Psychology. Cambridge, MA: Academic Press, pp. 209–239.
36.
MoscoviciSLageENaffrechouxM (1969) Influence of a consistent minority on the responses of a majority in a color perception task. Sociometry32(4): 365–380.
37.
NassCMoonY (2000) Machines and mindlessness: social responses to computers. Journal of Social Issues56(1): 81–103.
38.
NassCFoggBJMoonY (1996) Can computers be teammates?International Journal of Human-Computer Studies45(6): 669–678.
39.
PapacharissiZ (2002) The virtual sphere: the internet as a public sphere. New Media & Society4(1): 9–27.
40.
ReevesBNassC (1996) The Media Equation: How People Treat Computers, Television, and New Media Like Real People and Places. New York: Cambridge University Press.
41.
ReevesBJeff HancockJLiuX (2020) Social robots are like real people: first impressions, attributes, and stereotyping of social robots. Technology, Mind, and Behavior1(1): 1–37.
42.
SalomonsNSeboSSQinM, et al. (2021) A minority of one against a majority of robots: robots cause normative and informational conformity. ACM Transactions on Human-Robot Interaction10(2): 1–22.
43.
ScheepersDEllemersN (2019) Social identity theory. In: SassenbergKVliekMLW (eds) Social Psychology in Action: Evidence-Based Interventions from Theory to Practice. Cham: Springer, pp. 129–143.
44.
ShiomiMHagitaN (2019) Do the number of robots and the participant’s gender influence conformity effect from multiple robots?Advanced Robotics33(15–16): 756–763.
45.
SundarSS (2008) The MAIN model: a heuristic approach to understanding technology effects on credibility. In: MetzgerMFlanaginA (eds) Digital Media, Youth, and Credibility. (The John D. and Catherine T. Macarthur foundation series on digital media and learning). Cambridge, MA: The MIT Press, pp. 73–100.
46.
SundarSS (2020) Rise of machine agency: a framework for studying the psychology of human–AI interaction (HAII). Journal of Computer-Mediated Communication25(1): 74–88.
47.
SundarSSKimJ (2019) Machine heuristic: when we trust computers more than humans with our personal information. In: Proceedings of the 2019 CHI conference on human factors in computing systems, Scotland, 4–9 May, pp. 1–9. New York: Association for Computing Machinery.
48.
SundarSSJiaHWaddellTF, et al. (2015) Toward a theory of interactive media effects (TIME): four models for explaining how interface features affect user psychology. In: SundarSS (ed.) The Handbook of the Psychology of Communication Technology. Hoboken, NJ: Wiley Blackwell, pp. 47–86.
49.
SundarSSKnobloch-WesterwickSHastallMR (2007) News cues: information scent and cognitive heuristics. Journal of the American Society for Information Science and Technology58(3): 366–378.
50.
TurnerJC (1975) Social comparison and social identity: some prospects for intergroup behaviour. European Journal of Social Psychology5(1): 1–34.
51.
TurnerJC (1991) Social Influence. Belmont, CA: Thomson Brooks/Cole Publishing Co.
52.
VollmerA-LReadRTrippasD, et al. (2018) Children conform, adults resist: a robot group induced peer pressure on normative social conformity. Science Robotics3(21): 1–7.
53.
WaddellTF (2018) A robot wrote this?Digital Journalism6(2): 236–255.
54.
WuYMouYLiZ, et al. (2020) Investigating American and Chinese subjects’ explicit and implicit perceptions of AI-generated artistic work. Computers in Human Behavior104: 106186.
55.
XuKLombardM (2017) Persuasive computing: feeling peer pressure from multiple computer agents. Computers in Human Behavior74: 152–162.
56.
XuKLiuFMouY, et al. (2020) Using machine learning to learn machines: a cross-cultural study of users’ responses to machine-generated artworks. Journal of Broadcasting & Electronic Media64(4): 566–591.
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.
