Research suggests that consumers are averse to relying on algorithms to perform tasks that are typically done by humans, despite the fact that algorithms often perform better. The authors explore when and why this is true in a wide variety of domains. They find that algorithms are trusted and relied on less for tasks that seem subjective (vs. objective) in nature. However, they show that perceived task objectivity is malleable and that increasing a task’s perceived objectivity increases trust in and use of algorithms for that task. Consumers mistakenly believe that algorithms lack the abilities required to perform subjective tasks. Increasing algorithms’ perceived affective human-likeness is therefore effective at increasing the use of algorithms for subjective tasks. These findings are supported by the results of four online lab studies with over 1,400 participants and two online field studies with over 56,000 participants. The results provide insights into when and why consumers are likely to use algorithms and how marketers can increase their use when they outperform humans.
ArmstrongJ. Scott (1980), “The Seer-Sucker Theory: The Value of Experts in Forecasting,” Technology Review, 82 (7), 16–24.
2.
BassFrank M. (1969), “A New Product Growth for Model Consumer Durables,” Management Science, 15 (5), 215–27.
3.
BearAdamRandDavid G. (2016), “Intuition, Deliberation, and the Evolution of Cooperation,” Proceedings of the National Academy of Sciences, 113 (4), 936–41.
4.
BeisswangerAmy H.StoneEric R.HuppJulie M.AllgaierLiz (2003), “Risk Taking in Relationships: Differences in Deciding for Oneself Versus for a Friend,” Basic and Applied Social Psychology, 25 (2), 121–35.
5.
BlattbergRobert C.HochStephen J. (1990), “Database Models and Managerial Intuition: 50% Model + 50% Manager,” Management Science, 36 (8), 887–99.
6.
BucklinRandolph E.LehmannDonald R.LittleJohn D.C. (1998), “From Decision Support to Decision Automation: A 2020 Vision,” Marketing Letters, 9 (3), 235–46.
7.
ChopraKariWallaceWilliam A. (2003), “Trust in Electronic Environments,” in Proceedings of the 36th Annual Hawaii International Conference on System Sciences, HICSS 2003. New York: Association for Computing Machinery, 1–10.
8.
DavisFredBagozziRichardWarshawPeter (1989), “User Acceptance of Computer Technology: A Comparison of Two Theoretical Models,” Management Science, 35 (8), 982–1003.
9.
DawesRobyn M.FaustDavidMeehlPaul E. (1989), “Clinical Versus Actuarial Judgment,” Science, 243 (4899), 1668–74.
DiabDalia L.PuiShuang-YuehYankelevichMayaHighhouseScott (2011), “Lay Perceptions of Selection Decision Aids in US and Non-US Samples,” International Journal of Selection and Assessment, 19 (2), 209–16.
12.
DietvorstBerkeley J.SimmonsJoseph P.MasseyCade (2016), “Overcoming Algorithm Aversion: People Will Use Imperfect Algorithms If They Can (Even Slightly) Modify Them,” Management Science, 64 (3), 1155–70.
13.
DietvorstBerkeley J.SimmonsJoseph PMasseyCade (2014), “Algorithm Aversion: People Erroneously Avoid Algorithms After Seeing Them Err,” Journal of Experimental Psychology: General, 143 (6), 1–13.
14.
DijksterhuisA.P.BosMaarten W.NordgrenLoran F.BaarenRick B. Van (2006), “On Making the Right Choice: The Deliberation-Without-Attention Effect,” Science, 311 (5763), 1005–07.
15.
DinnerIsaacJohnsonEric J.GoldsteinDaniel G.LiuKaiya (2011), “Partitioning Default Effects: Why People Choose Not to Choose,” Journal of Experimental Psychology: Applied, 17 (4), 332–41.
16.
EastwoodJosephSnookBrentLutherKirk (2012), “What People Want from Their Professionals: Attitudes Toward Decision-Making Strategies,” Behavioral Decision Making, 25 (5), 458–68.
17.
EisenhardtKathleen M. (1989), “Agency Theory: An Assessment and Review,” Academy of Management Review, 14 (1), 57–74.
18.
FerrariFrancescoPaladinoMaria PaolaJettenJolanda (2016), “Blurring Human–Machine Distinctions: Anthropomorphic Appearance in Social Robots as a Threat to Human Distinctiveness,” International Journal of Social Robotics, 8 (2), 287–302.
GrayHeather M.GrayKurtWegnerDaniel M. (2007), “Dimensions of Mind Perception,” Science, 619 (5812), 10–11.
23.
GrayKurtWegnerDaniel M. (2012), “Feeling Robots and Human Zombies: Mind Perception and the Uncanny Valley,” Cognition, 125 (1), 125–30.
24.
GreifenederRainerBlessHerbertPhamMichel Tuan (2011), “When Do People Rely on Affective and Cognitive Feelings in Judgment? A Review,” Personality and Social Psychology Review, 15 (2), 107–41.
25.
GroveWilliam M.ZaldDavid H.LebowBoyd S.SnitzBeth E.NelsonChad (2000), “Clinical Versus Mechanical Prediction: A Meta-Analysis,” Psychological Assessment, 12 (1), 19–30.
26.
HaslamNick (2006), “Dehumanization: An Integrative Review,” Personality and Social Psychology Review, 10 (3), 252–64.
27.
HaslamNickBainePaulDougeLaurenLeeMaxBastianBrock (2005), “More Human Than You: Attributing Humannness to Self and Others,” Journal of Personality and Social Psychology, 89 (6), 937–50.
28.
HaslamNickKashimaYoshihisaLoughnanStephenShiJunqiSuitnerCaterina (2008), “Subhuman, Inhuman, and Superhuman: Contrasting Humans with Nonhumans in Three Cultures,” Social Cognition, 26 (2), 248–58.
29.
HerremansDorienMartensDavidSörensenKenneth (2014), “Dance Hit Song Prediction,” Journal of New Music Research, 43 (3), 291–302.
30.
InbarYoelConeJeremyGilovichThomas (2010), “People’s Intuitions About Intuitive Insight and Intuitive Choice,” Journal of Personality and Social Psychology, 99 (2), 232–47.
31.
JohnsonDevonGraysonKent (2005), “Cognitive and Affective Trust in Service Relationships,” Journal of Business Research, 58 (4), 500–07.
32.
KodraEvanSenechalThibaudMcDuffDanielKalioubyRana El (2013), “From Dials to Facial Coding: Automated Detection of Spontaneous Facial Expressions for Media Research,” in Proceedings of the 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition. New York: IEEE, 15–21.
33.
KomiakSherrieBenbasatIzak (2006), “The Effects of Personalization and Familiarity on Trust and Adoption of Recommendation Agents,” Management Information Systems Quarterly, 30 (4), 941–60.
LiXinHessTraci J.ValacichJoseph S. (2008), “Why Do We Trust New Technology? A Study of Initial Trust Formation with Organizational Information Systems,” Journal of Strategic Information Systems, 17 (1), 39–71.
36.
LoggJennifer M. (2017), “Theory of Machine: When Do People Rely on Algorithms?” Working Paper No. 17-086, Harvard Business School, Harvard University.
37.
LoggJennifer M.MinsonJulia A.MooreDon A. (2019), “Algorithm Appreciation: People Prefer Algorithmic to Human Judgment,” Organizational Behavior and Human Decision Processes, 151 (March), 90–103.
38.
LongoniChairaBonezziAndreaMorewedgeCarey (2019), “Resistance to Medical Artificial Intelligence,” Journal of Consumer Research (published online May 3), DOI:10.1093/jcr/ucz013.
39.
LoughnanStephenHaslamNick (2007), “Animals and Androids: Implicit Associations Between Social Categories and Nonhumans,” Psychological Science, 18 (2), 116–22.
40.
MatzSandraKosinskiMichaelNaveGideonStillwellDavid (2017), “Psychological Targeting as an Effective Approach to Digital Mass Persuasion,” Proceedings of the National Academy of Sciences, 114 (48), 12714–19.
41.
McDuffDanielKalioubyRana ElSenechalThibaudAmrMayCohnJeffrey F.PicardRosalind (2013), “Affectiva-MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected ‘In-the-Wild,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. New York: IEEE, 881–88.
42.
MeehlPaul E. (1954), Clinical vs. Statistical Prediction: A Theoretical Analysis and a Review of the Evidence. Minneapolis: University of Minnesota Press.
43.
O’NeilCathy (2016), Weapons of Math Destruction. New York: Crown.
44.
ÖnkalDilekGoodwinPaulThomsonMaryGönülSinanPollockAndrew (2009), “The Relative Influence of Advice from Human Experts and Statistical Methods on Forecast Adjustments,” Behavioral Decision Making, 22 (4), 390–409.
45.
PavlouPaul A.GefenDavid (2004), “Building Effective Online Marketplaces with Institution-Based Trust,” Information Systems Research, 15 (1), 1–108.
46.
PeerEyalBrandimarteLauraSamatSonamAcquistiAlessandro (2017), “Beyond the Turk: Alternative Platforms for Crowdsourcing Behavioral Research,” Journal of Experimental Social Psychology, 70, 153–63.
47.
PolmanEvan (2012), “Effects of Self-Other Decision Making on Regulatory Focus and Choice Overload,” Journal of Personality and Social Psychology, 102 (5), 980–93.
RogersEverett M. (1976), “New Product Adoption and Diffusion,” Journal of Consumer Research, 2 (4), 290–301.
51.
ShafferVictoria A.Adam ProbstC.MerkleEdgar C.ArkesHal R.MedowMitchell A. (2013), “Why Do Patients Derogate Physicians Who Use a Computer-Based Diagnostic Support System?” Medical Decision Making, 33 (1), 108–18.
SlovicPaulFinucaneMelissa L.PetersEllenMacGregorDonald G. (2004), “Risk as Analysis and Risk as Feelings: Some Thoughts about Affect, Reason, Risk, and Rationality,” Risk Analysis, 24 (2), 311–22.
54.
SniezekJanet A.BuckleyTimothy (1995), “Cueing and Cognitive Conflict in Judge-Advisor Decision Making,” Organizational Behavior and Human Decision Processes, 62 (2), 159–74.
55.
SollJack B.LarrickRichard P. (2009), “Strategies for Revising Judgment: How (and How Well) People Use Others’ Opinions,” Journal of Experimental Psychology: Learning Memory and Cognition, 35 (3), 780–805.
56.
TropeYaacovLibermanNiraWakslakCheryl (2007), “Construal Levels and Psychological Distance: Effects on Representation, Prediction, Evaluation, and Behavior,” Journal of Consumer Psychology, 17 (2), 83–95.
57.
YeomansMichaelShahAnujMullainathanSendhilKleinbergJon (2019), “Making Sense of Recommendations,” Journal of Behavioral Decision Making, (published online February 14), DOI:10.1002/bdm.2118.
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.
