Building user trust is critical for the widespread adoption of automated vehicles (AVs) as they become more integrated into our daily lives. This study explores how the voice used by AVs can influence two types of trust: cognitive trust (belief in the AV’s competence and reliability) and affective trust (emotional connection with the AV). Drawing from similarity-attraction theory, the research investigates whether users are more likely to trust AVs whose voices match their age and gender. In an online study involving over 300 U.S. drivers, participants experienced AV explanations delivered in voices that aligned with or differed from their demographic characteristics. The results revealed that users reported significantly higher cognitive and affective trust when the AV voice matched their own. Gender similarity strongly impacted both types of trust, while age similarity mainly affected affective trust. These findings highlight the power of personalized voice design in making AVs feel more relatable and trustworthy. This research offers valuable insights for designers and developers aiming to enhance human-AV interaction through more socially attuned and emotionally resonant communication strategies.
Azevedo-SaH.ZhaoH.EsterwoodC.YangX. J.TilburyD. M.RobertL. P.Jr. (2021). How internal and external risks affect the relationships between trust and driver behavior in automated driving systems. Transportation Research Part C: Emerging Technologies, 123, Article 102973.
3.
BarbutoJ.GiffordG. T. (2012). Motivation and leader-member exchange: Evidence counter to similarity attraction theory. International Journal of Leadership Studies, 7(1), 18–28.
4.
BarkanaB. D.ZhouJ. (2015). A new pitch-range based feature set for a speaker’s age and gender classification. Applied Acoustics, 98, 52–61.
5.
BarringtonS.BaruaR.KoormaG.FaridH. (2023). Single and multi-speaker cloned voice detection: From perceptual to learned features [Conference session]. IEEE International Workshop on Information Forensics and Security (WIFS), Nürnberg, Germany (pp. 1–6). IEEE.
6.
CalefatoF.LanubileF.NovielliN. (2015). The role of social media in affective trust building in customer–supplier relationships. Electronic Commerce Research, 15(4), 453–482. https://doi.org/10.1007/s10660-015-9194-3
7.
DongJ.LawsonE.OlsenJ.JeonM. (2020). Female voice agents in fully autonomous vehicles are not only more likeable and comfortable, but also more competent. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 64, 1033–1037.
8.
DuN.HaspielJ.ZhangQ.TilburyD.PradhanA. K.YangX. J.RobertL. P. (2019). Look who’s talking now: Implications of AV’s explanations on driver’s trust, AV preference, anxiety and mental workload. Transportation Research Part C: Emerging Technologies, 104, 428–442. https://doi.org/10.1016/j.trc.2019.05.025
9.
EdwardsC.EdwardsA.StollB.LinX.MasseyN. (2019). Evaluations of an artificial intelligence instructor’s voice: Social Identity Theory in human-robot interactions. Computers in Human Behavior, 90, 357–362.
10.
ForsterY.NaujoksF.NeukumA. (2017). Increasing anthropomorphism and trust in automated driving functions by adding speech output [Conference session]. IEEE Intelligent Vehicles Symposium (IV), Los Angeles, CA (pp. 365–372). IEEE. https://doi.org/10.1109/IVS.2017.7995746
11.
IrwinM. R.ColeJ. C.NicassioP. M. (2006). Comparative meta-analysis of behavioral interventions for insomnia and their efficacy in middle-aged adults and in older adults 55+ years of age. Health Psychology, 25(1), 3–14. https://doi.org/10.1037/0278-6133.25.1.3
12.
KooJ.KwacJ.JuW.SteinertM.LeiferL.NassC. (2015). Why did my car just do that? Explaining semi-autonomous driving actions to improve driver understanding, trust, and performance. International Journal on Interactive Design and Manufacturing (IJIDeM), 9(4), 269–275. https://doi.org/10.1007/s12008-014-0227-2
13.
KooJ.ShinD.SteinertM.LeiferL. (2016). Understanding driver responses to voice alerts of autonomous car operations. International Journal of Vehicle Design, 70(4), 377–392. https://doi.org/10.1504/IJVD.2016.076740
14.
KörberM.PraschL.BenglerK. (2018). Why do I have to drive now? Post Hoc explanations of takeover requests. Human Factors, 60(3), 305–323. https://doi.org/10.1177/0018720817747730
15.
KoswaraA. (2024). The halal perspective on AI voice over usage in marketing and advertising: Ethical and sustainable insights. ASEAN Journal of Halal Study, 1(2), 1–8.
16.
KuwabaraK.CaoJ.ChoS. S.IngramP. (2023). Lay theories of instrumental relations: Explaining individual differences in dispositional similarity-attraction. Academy of Management Journal, 66(2), 667–687. https://doi.org/10.5465/amj.2020.0770
17.
LeeJ.LeeK. M. (2022). Polite speech strategies and their impact on drivers’ trust in autonomous vehicles. Computers in Human Behavior, 127, Article 107015. https://doi.org/10.1016/j.chb.2021.107015
McAllisterD. J. (1995). Affect- and cognition-based trust as foundations for interpersonal cooperation in organizations. Academy of Management Journal, 38(1), 24–59. https://doi.org/10.5465/256727
20.
MontoyaR. M.HortonR. S. (2013). A meta-analytic investigation of the processes underlying the similarity-attraction effect. Journal of Social and Personal Relationships, 30(1), 64–94.
21.
MorenoR.FlowerdayT. (2006). Students’ choice of animated pedagogical agents in science learning: A test of the similarity-attraction hypothesis on gender and ethnicity. Contemporary Educational Psychology, 31(2), 186–207.
22.
NassC.MoonY.FoggB. J.ReevesB.DryerC. (1995). Can computer personalities be human personalities? [Conference session]. CHI ’95: Conference Companion on Human Factors in Computing Systems, Denver, CO (pp. 228–229). Association for Computing Machinery.
23.
NassC.MoonY.GreenN. (1997). Are machines gender neutral? Gender-stereotypic responses to computers with voices. Journal of Applied Social Psychology, 27(10), 864–876. https://doi.org/10.1111/j.1559-1816.1997.tb00275.x
24.
NassC.SteuerJ.TauberE. R. (1994). Computers are social actors. In Proceedings of the SIGCHI conference on Human factors in computing systems (pp. 72–78). Association for Computing Machinery.
25.
PtacekP. H.SanderE. K. (1966). Age recognition from voice. Journal of Speech and Hearing Research, 9(2), 273–277.
26.
RempelJ. K.HolmesJ. G.ZannaM. P. (1985). Trust in close relationships. Journal of Personality and Social Psychology, 49(1), 95.
27.
RobertL. P. (2019). Are automated vehicles safer than manually driven cars?AI & SOCIETY, 34(3), 687–688.
28.
RuijtenP. A. M.TerkenJ. M. B.ChandramouliS. N. (2018). Enhancing trust in autonomous vehicles through intelligent user interfaces that mimic human behavior. Multimodal Technologies and Interaction, 2(4), Article 4. https://doi.org/10.3390/mti2040062
29.
TaeihaghA.LimH. S. M. (2019). Governing autonomous vehicles: Emerging responses for safety, liability, privacy, cybersecurity, and industry risks. Transport Reviews, 39(1), 103–128.
30.
WiegandG.SchmidmaierM.WeberT.LiuY.HussmannH. (2019). I drive - you trust: Explaining driving behavior of autonomous cars [Conference session]. Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems, Glasgow (pp. 1–6). Association for Computing Machinery. https://doi.org/10.1145/3290607.3312817
31.
ZhangQ.YangX. J.RobertL. P. (2021). What and when to explain? A survey of the impact of explanation on attitudes toward adopting automated vehicles. IEEE Access, 9, 159533–159540.
