The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Decision support aids with anthropomorphic characteristics influence trust and performance in younger and older adults
Abstract
This study examined the use of deliberately anthropomorphic automation on younger and older adults' trust, dependence and performance on a diabetes decision-making task. Research with anthropomorphic interface agents has shown mixed effects in judgments of preferences but has rarely examined effects on performance. Meanwhile, research in automation has shown some forms of anthropomorphism (e.g. etiquette) have effects on trust and dependence on automation. Participants answered diabetes questions with no-aid, a non-anthropomorphic aid or an anthropomorphised aid. Trust and dependence in the aid was measured. A minimally anthropomorphic aide primarily affected younger adults' trust in the aid. Dependence, however, for both age groups was influenced by the anthropomorphic aid. Automation that deliberately embodies person-like characteristics can influence trust and dependence on reasonably reliable automation. However, further research is necessary to better understand the specific aspects of the aid that affect different age groups. Automation that embodies human-like characteristics may be useful in situations where there is under-utilisation of reasonably reliable aids by enhancing trust and dependence in that aid. Practitioner Summary: The design of decision-support aids on consumer devices (e.g. smartphones) may influence the level of trust that users place in that system and their amount of use. This study is the first step in articulating how the design of aids may influence user's trust and use of such systems.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research
(1988) · Advances in Psychology · doi:10.1016/s0166-4115(08)62386-9 · Cited by 15,003
A threat in the air: How stereotypes shape intellectual identity and performance
Steele, C. M. (1997) · American Psychologist · doi:10.1037/0003-066x.52.6.613 · Cited by 6,770
Trust in Automation: Designing for Appropriate Reliance
Lee, J. D., & See, K. A. (2004) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1518/hfes.46.1.50_30392 · Cited by 6,605 · cited 1×
“Lee (2007) and Lee and See (2004) discussed the importance of understanding the boundary conditions under which social emotions influence users’ response to technology, particularly automation.”
BackgroundHumans and automation: Use, misuse, disuse, abuse
Parasuraman, R., & Riley, V. (1997) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1518/001872097778543886 · Cited by 4,554
A model for types and levels of human interaction with automation
Parasuraman, R., Sheridan, T. B., & Wickens, C. D. (2000) · IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans · doi:10.1109/3468.844354 · Cited by 4,195 · cited 1×
“Using the levels of automation concept proposed by Parasuraman et al. (2000) the aid used in the current study would be classified as a stage 3 aid – one that proposes a course of action but allows the patient to veto.”
MethodMerritt, S. M., & Ilgen, D. R. (2008) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1518/001872008x288574 · Cited by 527 · cited 1×
“It is implied that the previously mentioned studies are primarily history-based measures of trust; that is trust that is developed after some period of interaction (Merritt and Ilgen 2008).”
ResultNass, C., & Lee, K. M. (2001) · Journal of Experimental Psychology: Applied · doi:10.1037/1076-898x.7.3.171 · Cited by 522
Sproull, L., Subramani, M., Kiesler, S., Walker, J., & Waters, K. (1996) · Human-Computer Interaction · doi:10.1207/s15327051hci1102_1 · Cited by 399
The Perceived Utility of Human and Automated Aids in a Visual Detection Task
Dzindolet, M. T., Pierce, L. G., Beck, H. P., & Dawe, L. A. (2002) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1518/0018720024494856 · Cited by 386 · cited 1×
“Studies with both younger (e.g. Dzindolet et al. 2002) and older adults (Ezer et al. 2008) show that users tend to under-use automated aids although they are as accurate as they are in the task.”
'It's just like you talk to a friend': relational agents for older adults
Bickmore, T. W., Caruso, L., Clough-Gorr, K., & Heeren, T. (2005) · Interacting with Computers · doi:10.1016/j.intcom.2005.09.002 · Cited by 341 · cited 1×
“Bickmore et al. (2005) compared whether older adults would increase their physical activity (walking) when they interacted with (1) an anthropomorphic agent that engaged in dialogue, (2) an agent that did not engage in dialogue or (3) no agent, instead received pamphlets about the benefits of…”
Background
Cited by
Human trust in artificial intelligence: Review of empirical research
Glikson, E., & Woolley, A. W. (2020) · Academy of Management Annals · doi:10.5465/annals.2018.0057 · Cited by 2,921
Trust in Automation: Integrating Empirical Evidence on Factors That Influence Trust
Hoff, K. A., & Bashir, M. (2015) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720814547570 · Cited by 2,817
The mind in the machine: Anthropomorphism increases trust in an autonomous vehicle
Waytz, A., Heafner, J., & Epley, N. (2014) · Journal of Experimental Social Psychology · doi:10.1016/j.jesp.2014.01.005 · Cited by 1,439
Schaefer, K. E., Chen, J. Y. C., Szalma, J. L., & Hancock, P. A. (2016) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720816634228 · Cited by 768
Almost human: Anthropomorphism increases trust resilience in cognitive agents.
de Visser, E. J., Monfort, S. S., McKendrick, R., Smith, M. A. B., McKnight, P. E., Krueger, F., & Parasuraman, R. (2016) · Journal of Experimental Psychology: Applied · doi:10.1037/xap0000092 · Cited by 614
What influences algorithmic decision-making? A systematic literature review on algorithm aversion
Mahmud, H., Islam, A. K. M. N., Ahmed, S. I., & Smolander, K. (2022) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2021.121390 · Cited by 601
Towards a Theory of Longitudinal Trust Calibration in Human–Robot Teams
de Visser, E. J., Peeters, M. M. M., Jung, M. F., Kohn, S., Shaw, T. H., Pak, R., & Neerincx, M. A. (2020) · International Journal of Social Robotics · doi:10.1007/s12369-019-00596-x · Cited by 447
Self-citationMeasurement of trust in automation: A narrative review and reference guide
Kohn, S. C., de Visser, E. J., Wiese, E., Lee, Y.-C., & Shaw, T. H. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.604977 · Cited by 315
SupportingTrust in Autonomous Driving
Häuslschmid, R., von Bülow, M., Pfleging, B., & Butz, A. (2017) · Proceedings of the 22nd International Conference on Intelligent User Interfaces · doi:10.1145/3025171.3025198 · Cited by 170
Kunze, A., Summerskill, S. J., Marshall, R., & Filtness, A. J. (2019) · Ergonomics · doi:10.1080/00140139.2018.1547842 · Cited by 165
Christoforakos, L., Gallucci, A., Surmava-Große, T., Ullrich, D., & Diefenbach, S. (2021) · Frontiers in Robotics and AI · doi:10.3389/frobt.2021.640444 · Cited by 154
Kim, T., & Song, H. (2021) · Telematics and Informatics · doi:10.1016/j.tele.2021.101595 · Cited by 101
A Conceptual Model of Trust, Perceived Risk, and Reliance on AI Decision Aids
Solberg, E., Kaarstad, M., Eitrheim, M. H. R., Bisio, R., Reegård, K., & Bloch, M. (2022) · Group & Organization Management · doi:10.1177/10596011221081238 · Cited by 102
Kulms, P., & Kopp, S. (2019) · Proceedings of Mensch und Computer 2019 · doi:10.1145/3340764.3340793 · Cited by 68
Daronnat, S., Azzopardi, L., Halvey, M., & Dubiel, M. (2021) · Frontiers in Robotics and AI · doi:10.3389/frobt.2021.642201 · Cited by 45
The relationship of trust and dependence
Patton, C. E., & Wickens, C. D. (2024) · Ergonomics · doi:10.1080/00140139.2024.2342436 · Cited by 44
Determinants of Laypersons’ Trust in Medical Decision Aids: Randomized Controlled Trial
Kopka, M., Schmieding, M. L., Rieger, T., Roesler, E., Balzer, F., & Feufel, M. A. (2022) · JMIR Human Factors · doi:10.2196/35219 · Cited by 37
When Should a Robot Apologize? Understanding How Timing Affects Human-Robot Trust Repair
Nayyar, M., & Wagner, A. R. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-05204-1_26 · Cited by 28
Advice from artificial intelligence: a review and practical implications
Baines, J. I., Dalal, R. S., Ponce, L. P., & Tsai, H.-C. (2024) · Frontiers in Psychology · doi:10.3389/fpsyg.2024.1390182 · Cited by 28
Jensen, T., Khan, M. M. H., Fahim, M. A. A., & Albayram, Y. (2021) · Designing Interactive Systems Conference 2021 · doi:10.1145/3461778.3462102 · Cited by 26
The Role of Behavioral Anthropomorphism in Human-Automation Trust Calibration
Jensen, T., Khan, M. M. H., & Albayram, Y. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-50334-5_3 · Cited by 12
Xu, Z., Wang, G., Zhai, S., & Liu, P. (2025) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2023.2176986 · Cited by 12
Can Semi-Anthropomorphism Influence Trust and Compliance?: Exploring Image Use in App Interfaces
Gruber, D., Aune, A., & Koutstaal, W. (2018) · Proceedings of the Technology, Mind, and Society · doi:10.1145/3183654.3183700 · Cited by 8
Metadata
- Authors
- Pak, R., Fink, N., Price, M., Bass, B., & Sturre, L.
- Venue
- Ergonomics
- Publisher
- Informa UK Limited
- Published
- 2012-07-17
- Volume
- 55
- Issue
- 9
- Pages
- 1059-1072
- ISSN
- 0014-0139 (ISSN-L)
- 1366-5847
- OpenAlex
- W2029591514
- Cited by
- 186
- References
- 14
- FWCI
- 7.97
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.