Consumers increasingly encounter recommender systems when making consumption decisions of all kinds. While numerous efforts have aimed to improve the quality of algorithm-generated recommendations, evidence has indicated that people often remain averse to superior algorithmic sources of information in favor of their own personal intuitions (a type II problem). The current work highlights an additional (type I) problem associated with the use of recommender systems: algorithm overdependence. Five experiments illustrate that, stemming from a belief that algorithms hold greater domain expertise, consumers surrender to algorithm-generated recommendations even when the recommendations are inferior. Counter to prior findings, this research indicates that consumers frequently depend too much on algorithm-generated recommendations, posing potential harms to their own well-being and leading them to play a role in propagating systemic biases that can influence other users. Given the rapidly expanding application of recommender systems across consumer domains, the authors believe that an appreciation and understanding of these risks is crucial to the effective guidance and development of recommendation systems that support consumer interests.
AdomaviciusGediminasTuzhilinAlexander (2005), “Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions,” IEEE Transactions on Knowledge and Data Engineering, 17 (6), 734–49.
2.
AlbaJoseph W.Wesley HutchinsonJ. (1987), “Dimensions of Consumer Expertise,” Journal of Consumer Research, 13 (4), 411–54.
BankerSachinAinsworthSarah E.BaumeisterRoy F.ArielyDanVohsKathleen D. (2017), “The Sticky Anchor Hypothesis: Ego Depletion Increases Susceptibility to Situational Cues,” Journal of Behavioral Decision Making, 30 (5), 1027–40.
6.
BaumeisterRoy F.TiceDianne M.VohsKathleen D. (2018), “The Strength Model of Self-Regulation: Conclusions From the Second Decade of Willpower Research,” Perspectives on Psychological Science, 13 (2), 141–45.
7.
BettmanJames R.LuceMary FrancesPayneJohn W. (1998), “Constructive Consumer Choice Processes,” Journal of Consumer Research, 25 (3), 187–217.
8.
BodapatiAnand V. (2008), “Recommendation Systems with Purchase Data,” Journal of Marketing Research, 45 (1), 77–93.
9.
BurghardtKeithAlsinaEmanuel F.GirvanMichelleRandWilliamLermanKristina (2017), “The Myopia of Crowds: Cognitive Load and Collective Evaluation of Answers on Stack Exchange,” PloS One, 12 (3), e0173610.
10.
ChernevAlexander (2003), “When More Is Less and Less Is More: The Role of Ideal Point Availability and Assortment in Consumer Choice,” Journal of Consumer Research, 30 (2), 170–83.
11.
ChernevAlexanderBöckenholtUlfGoodmanJoseph (2015), “Choice Overload: A Conceptual Review and Meta-Analysis,” Journal of Consumer Psychology, 25 (2), 333–58.
12.
ChungJaihakRaoVithala R. (2012), “A General Consumer Preference Model for Experience Products: Application to Internet Recommendation Services,” Journal of Marketing Research, 49 (3), 289–305.
13.
CoupeyEloiseIrwinJulie R.PayneJohn W. (1998), “Product Category Familiarity and Preference Construction,” Journal of Consumer Research, 24 (4), 459–68.
14.
DattaAmitTschantzMichael CarlDattaAnupam (2015), “Automated Experiments On Ad Privacy Settings,” Proceedings on Privacy Enhancing Technologies, 2015 (1), 92–112.
15.
DattaAnupamSenShayakZickYair (2016), “Algorithmic Transparency via Quantitative Input Influence: Theory and Experiments with Learning Systems,” in 2016 IEEE Symposium on Security and Privacy (SP). New York, IEEE, 598–617.
16.
DellaertBenedict G.C.StremerschStefan (2005), “Marketing Mass-Customized Products: Striking a Balance Between Utility and Complexity,” Journal of Marketing Research, 42 (2), 219–27.
17.
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.
18.
DiehlKristin (2005), “When Two Rights Make a Wrong: Searching Too Much in Ordered Environments,” Journal of Marketing Research, 42 (3), 313–22.
19.
DiehlKristinPoynorCait (2010), “Great Expectations?! Assortment Size, Expectations, and Satisfaction,” Journal of Marketing Research, 47 (2), 312–22.
20.
DietvorstBerkeley J.SimmonsJoseph P.MasseyCade (2015), “Algorithm Aversion: People Erroneously Avoid Algorithms After Seeing Them Err,” Journal of Experimental Psychology: General, 144 (1), 114–26.
21.
EastwoodJosephSnookBrentLutherKirk (2012), “What People Want from Their Professionals: Attitudes Toward Decision-Making Strategies,” Journal of Behavioral Decision Making, 25 (5), 458–68.
FildesRobertGoodwinPaul (2007), “Against Your Better Judgment? How Organizations Can Improve Their Use of Management Judgment in Forecasting,” Interfaces, 37 (6), 570–76.
27.
FitzsimonsGavan J.LehmannDonald R. (2004), “Reactance to Recommendations: When Unsolicited Advice Yields Contrary Responses,” Marketing Science, 23 (1), 82–94.
28.
FlederDanielHosanagarKartik (2009), “Blockbuster Culture’s Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity,” Management Science, 55 (5), 697–712.
29.
GhoseAnindyaIpeirotisPanagiotis G.LiBeibei (2012), “Designing Ranking Systems for Hotels on Travel Search Engines by Mining User-Generated and Crowdsourced Content,” Marketing Science, 31 (3), 493–520.
30.
HannakAnikoSoellerGaryLazerDavidMisloveAlanWilsonChristo (2014), “Measuring Price Discrimination and Steering on E-Commerce Web Sites,” in Proceedings of the 2014 Conference on Internet Measurement Conference, IMC ’14. New York: Association for Computing Machinery, 305–18.
31.
HäublGeraldTriftsValerie (2000), “Consumer Decision Making in Online Shopping Environments: The Effects of Interactive Decision Aids,” Marketing Science, 19 (1), 4–21.
32.
Hernández-OralloJoséFlachPeterFerriCèsar (2012), “A Unified View of Performance Metrics: Translating Threshold Choice into Expected Classification Loss,” Journal of Machine Learning Research, 13 (1), 2813–69.
33.
HochStephen J.BradlowEric T.WansinkBrian (1999), “The Variety of an Assortment,” Marketing Science, 18 (4), 527–46.
34.
HuberJoelPayneJohn W.PutoChristopher (1982), “Adding Asymmetrically Dominated Alternatives: Violations of Regularity and the Similarity Hypothesis,” Journal of Consumer Research, 9 (1), 90–98.
35.
KonstanJoseph A.RiedlJohn (2012), “Recommender Systems: From Algorithms to User Experience,” User Modeling and User-Adapted Interaction, 22 (1/2), 101–23.
36.
KurzbanRobertDuckworthAngelaKableJoseph W.MyersJustus (2013), “An Opportunity Cost Model of Subjective Effort and Task Performance,” Behavioral and Brain Sciences, 36 (6), 661–79.
37.
LouroMaria J.PietersRikZeelenbergMarcel (2007), “Dynamics of Multiple-Goal Pursuit,” Journal of Personality and Social Psychology, 93 (2), 174–93.
38.
LuShashaXiaoLiDingMin (2016), “A Video-Based Automated Recommender (VAR) System for Garments,” Marketing Science, 35 (3), 484–510.
39.
MikiansJakubGyarmatiLászlóErramilliVijayLaoutarisNikolaos (2012), “Detecting Price and Search Discrimination on the Internet,” in Proceedings of the 11th ACM Workshop on Hot Topics in Networks. New York: Association for Computing Machinery, 79–84.
40.
MobasherBamshadBurkeRobinBhaumikRunaWilliamsChad (2007), “Toward Trustworthy Recommender Systems: An Analysis of Attack Models and Algorithm Robustness,” ACM Transactions on Internet Technology, 7 (4), 23.
41.
MullainathanSendhilObermeyerZiad (2017), “Does Machine Learning Automate Moral Hazard and Error?” American Economic Review, 107 (5), 476–80.
42.
NordgrenLoran F.DijksterhuisA.P. (2008), “The Devil Is in the Deliberation: Thinking Too Much Reduces Preference Consistency,” Journal of Consumer Research, 36 (1), 39–46.
43.
PaulSharoda A.HongLichanChiEd H. (2012), “Who Is Authoritative? Understanding Reputation Mechanisms in Quora,” presented at the Collective Intelligence Conference, arXiv:1204.3724.
44.
ResnickPaulVarianHal R. (1997), “Recommender Systems,” Communications of the ACM, 40 (3), 56–58.
45.
SandersNada R.ManrodtKarl B. (2003), “Forecasting Software in Practice: Use, Satisfaction, and Performance,” Interfaces, 33 (5), 90–93.
46.
ShafferVictoria A.ProbstC. AdamMerkleEdgar 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.
47.
SimonsDaniel J.ChabrisChristopher F. (1999), “Gorillas in Our Midst: Sustained Inattentional Blindness for Dynamic Events,” Perception, 28 (9), 1059–74.
48.
SwaminathanVanitha (2003), “The Impact of Recommendation Agents on Consumer Evaluation and Choice: The Moderating Role of Category Risk, Product Complexity, and Consumer Knowledge,” Journal of Consumer Psychology, 13 (1), 93–101.
49.
SweeneyLatanya (2013), “Discrimination in Online Ad Delivery,” Queue, 11 (3), 10.
50.
TsaiClaire I.McGillAnn L. (2011), “No Pain, No Gain? How Fluency and Construal Level Affect Consumer Confidence,” Journal of Consumer Research, 37 (5), 807–21.
51.
VriezeScott I.GroveWilliam M. (2009), “Survey on the Use of Clinical and Mechanical Prediction Methods in Clinical Psychology,” Professional Psychology: Research and Practice, 40 (5), 525–31.
52.
WachterSandraMittelstadtBrentFloridiLuciano (2017), “Why a Right to Explanation of Automated Decision-Making Does not Exist in the General Data Protection Regulation,” International Data Privacy Law, 7 (2), 76–99.
53.
WalkerKristen L. (2016), “Surrendering Information Through the Looking Glass: Transparency, Trust, and Protection,” Journal of Public Policy & Marketing, 35 (1), 144–58.
54.
WestPatricia M.ArielyDanBellmanSteveBradlowEricHuberJoelJohnsonEric, et al. (1999), “Agents to the Rescue?” Marketing Letters, 10 (3), 285–300.
55.
WilsonTimothy D.SchoolerJonathan W. (1991), “Thinking Too Much: Introspection Can Reduce the Quality of Preferences and Decisions,” Journal of Personality and Social Psychology, 60 (2), 181.
56.
XiaoBoBenbasatIzak (2007), “E-Commerce Product Recommendation Agents: Use, Characteristics, and Impact,” MIS Quarterly, 31 (1), 137–209.
57.
YingYuanpingFeinbergFredWedelMichel (2006), “Leveraging Missing Ratings to Improve Online Recommendation Systems,” Journal of Marketing Research, 43 (3), 355–65.
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.
