Given the rise of automation adoption in a variety of industries, it is essential we understand how individuals perceive these systems. Previous studies have found that a failure of one component in a system leads to decreased trust across a whole system, but few, if any, studies have considered how users define the confines of a system. To address this gap, this study replicated and extended Mehta et al. (2019) by incorporating measures of trust, similarity, and functional relatedness between six human crew members and six automated components. We noted that our functional relatedness and similarity measures were predictors of the magnitude of trust difference between conditions, with a high degree of shared variance. Our lack of a within subjects design means that future studies should further test whether measures approximating perceptions of ‘system’ (e.g., perceived functional relatedness and similarity) predict the size of contagion effects.
Get full access to this article
View all access options for this article.
References
1.
AtchleyA.BarrH. M.O’HearE. H.WegerK.MesmerB.GholstonS.TenhundfeldN.(in preparation). (2022) Trust in Systems: Identification of 17 Unresolved Research Questions and the Highlighting of Inconsistencies.
2.
BeanN. H.RiceS. C.KellerM. D. (2011). The effect of gestalt psychology on the system-wide trust strategy in automation. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 55(1). https://doi.org/10.1177/1071181311551295
3.
BenamatiJ. S.FullerM. A.ServaM. A.BaroudiJ. (2010). Clarifying the integration of trust and TAM in E-commerce environments: Implications for systems design and management. IEEE Transactions on Engineering Management, 57(3), 380–393. https://doi.org/10.1109/TEM.2009.2023111
4.
ChoJ. (2018). Mental models and home virtual assistants (HVAs). Conference on Human Factors in Computing Systems, 1–6. https://doi.org/10.1145/3170427.3180286
5.
de VisserE. J.PeetersM. M.JungM. F.KohnS.ShawT. H.PakR.NeerincxM. A. (2020). Towards a theory of longitudinal trust calibration in human robot teams. International Journal of Social Robotics, 12(2), 459-478.
6.
DoyleJ. K.FordD. N. (1998). Mental models concepts for system dynamics research. System Dynamics Review, 14(1),3–29. https://doi.org/10.1002/(SICI)1099-1727(199821)14:1<3::AIDSDR140>3.0.CO;2-K
7.
Geels-BlairK.RiceS.SchwarkJ. (2013). Using System-Wide Trust Theory to Reveal the Contagion Effects of Automation False Alarms and Misses on Compliance and Reliance in a Simulated Aviation Task. The International Journal of Aviation Psychology, 23(3), 245–266. https://doi.org/10.1080/10508414.2013.79955
8.
HalaszF. G.MoranT. P. (1983). Mental models and problem solving in using a calculator. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 212-216.
9.
HogarthR. M.EinhornH. J. (1992). Order effects in belief updating: The belief-adjustment model. Cognitive Psychology, 24(1), 1-55.
LiM.HolthausenB. E.StuckR. E.WalkerB. N. (2019). No risk no trust: Investigating perceived risk in highly automated Driving. Proceedings of the Annual Automotive UI Conference, 177–185. https://doi.org/10.1145/3342197.3344525
13.
LopezJ.PakR. (2020, December). Does One Bad Machine Spoil the Bunch?: A Review of Trust in Multiple-Component Systems. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 64(1), 1546-1550.
14.
MehtaR.DunbarV.ChauhanB.RiceS.EdwardsM.RosserT.DunbarV.RiceS. (2019). Does a SWT reverse contagion effect exist from humans to automation?Collegiate Aviation Review, 37(1), 90-106. https://doi.org/10.22488/okstate.19.100206
NormanD. A. (1983). Some Observations on Mental Models. In Mental Models. 7–14.
17.
RiceS.Geels-BlairK. (2010). Using System Wide Trust Theory to Make Predictions About Dependence on Four Diagnostic Aids. The Journal of General Psychology, 137(4). https://doi.org/10.1080/00221309.200.499397
18.
RobinetteP.LiW.AllenR.HowardA. M.WagnerA. R. (2016). Overtrust of robots in emergency evacuation scenarios. ACM/IEEE International Conference on Human-Robot Interaction, March, 101–108. https://doi.org/10.1109/HRI.2016.7451740
SchmidD.StantonN. (2019). Exploring Bayesian analyses of a small-sample-size factorial design in human systems integration: the effects of pilot incapacitation. Human-Intelligent Systems Integration. 1. 1-18. https://doi.org/10.1007/s42454-020-00012-0.
21.
TanakaJ. W.TaylorM. (1991). Object categories and expertise: Is the basic level in the eye of the beholder?Cognitive Psychology, 23(3). https://doi.org/10.1016/00100285(91)9001
22.
TenhundfeldN.L.BarrH.M.O’HearE.H.WegerK. (2021) Is My Siri the Same as Your Siri? An Exploration of Users’ Mental Model of Virtual Personal Assistants, Implications for Trust. IEEE Transactions on Human-Machine Systems. https://doi.org/10.1109/THMS.2021.3107493
23.
VakilianM.ChenN.NegaraS.RajkumarB. A.BaileyB. P.JohnsonR. E. (2012). Use, disuse, and misuse of automated refactorings. In 34th International Conference on Software Engineering, 233-243. https://doi.org/10.1109/ICSE.2012.6227190
24.
WickensC. D.CleggB. A.WittJ. K.SmithC. A. P.HerdenerN.SpahrK. S. (2020). Model of variability estimation: factors influencing human prediction and estimation of variability in continuous information. Theoretical Issues in Ergonomics Science, 21(2), 220–238. https://doi.org/10.1080/1463922X.2019.1679907
