The DOI matches its Crossref record; 80 of the 83 references it cites were found in a registry or an index.
Attachment and trust in artificial intelligence
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Attachment also shapes trust in AI agents: experimentally increasing security cues elevates trust in AI, whereas activating anxiety cues reduces it (Gillath et al. 2021).”
From Not a Silver Bullet for Loneliness: How Attachment and Age Shape Intimacy with AI Companions (2026) · cites this paper as Gillath et al. 2021
In the citing paper
“Trust acts as a validation mechanism [58].”
From Modeling teachers’ acceptance of generative artificial intelligence use in higher education: the role of AI literacy, intelligent TPACK, and perceived trust (2024) · cites this paper as [58]
In the citing paper
“Many studies have demonstrated that when educators trust AI-based tools, they are more likely to recognize and utilize these tools' capabilities in enhancing teaching and learning outcomes [5,58].”
From Modeling teachers’ acceptance of generative artificial intelligence use in higher education: the role of AI literacy, intelligent TPACK, and perceived trust (2024) · cites this paper as [58]
Citations
Cites
Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007) · Behavior Research Methods · doi:10.3758/bf03193146 · Cited by 65,971
The need to belong: Desire for interpersonal attachments as a fundamental human motivation.
Baumeister, R. F., & Leary, M. R. (1995) · Psychological Bulletin · doi:10.1037//0033-2909.117.3.497 · Cited by 23,245
Intrinsic and Extrinsic Motivations: Classic Definitions and New Directions
Ryan, R. M., & Deci, E. L. (2000) · Contemporary Educational Psychology · doi:10.1006/ceps.1999.1020 · Cited by 18,666
(1978) · Medical Entomology and Zoology · Cited by 12,570
Artificial intelligence: A modern approach
Nilsson, N. J. (1996) · Artificial Intelligence · doi:10.1016/0004-3702(96)00007-0 · Cited by 10,730
A very brief measure of the Big-Five personality domains
Gosling, S. D., Rentfrow, P. J., & Swann, W. B. (2003) · Journal of Research in Personality · doi:10.1016/s0092-6566(03)00046-1 · Cited by 8,307
Handbook of attachment: theory, research, and clinical applications
(1999) · Choice Reviews Online · doi:10.5860/choice.37-1841 · Cited by 5,386
Humans 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
Lewis, J. D., & Weigert, A. (1985) · Social Forces · doi:10.2307/2578601 · Cited by 4,163
Handbook of attachment: theory, research, and clinical applications
(2009) · Choice Reviews Online · doi:10.5860/choice.46-4135 · Cited by 3,819
Research works printed but not linked: 6
- Andriole, 2018, AI: The good, the disruptive, and the scary, Cutter Business Technology Journal, 31, 6
- Bowlby, 1982, Vol. 1
- Brachman, 2006, AI more than the sum of its parts, AI Magazine, 27
- Morsünbül, 2018, Attachment and sex with robots: An assessment from mental health perspective, Current Approaches in Psychiatry, 10, 417
- Rosenberg, 1965, Rosenberg self-esteem scale (RSE). Acceptance and commitment therapy, Measures package, 61, 18
- Yokoi, 2018, The effects of shared policy of medical treatment on trust in artificial intelligence, Japanese Journal of Social Psychology, 34, 16
Cited by
Sorted by citations
Items 401–448 of 448
Song, S., Shi, Y., Ge, X., Yu, H., Wang, W., & Tian, A. W. (2026) · The International Journal of Human Resource Management · doi:10.1080/09585192.2026.2670478
“Moreover, some employees who trust intelligent machines may easily develop an irrational dependence on them, which over the long term can lead to social withdrawal and decline in autonomous decision-making (Gillath et al., 2021).”
Lin, X., Wu, C., Wang, T., & Sheng, F. (2026) · Scientific Reports · doi:10.1038/s41598-026-46103-x
“They should strengthen the “technology-driven” mechanism to leverage AI as an innovation partner 13,33 , while containing the “wicked curse” through dynamic calibration of trust in AI.”
Turhan, Z., Sinan, A. C. A. R., Canbakış, R., Kızılçay, N., & Doğan, S. Ö. (2026) · Journal of Child and Family Studies · doi:10.1007/s10826-026-03309-x · Cited by 3
“Technological advancements are gaining momentum, and individuals’ access to technological devices is increasing (Gillath et al., 2021 ; Jeffery, 2021 ).”
Rethinking trust in AI: empirical insights from labor and healthcare domains
Jung, Y., Oh, P., & Zhai, J. (2026) · Communication and Change · doi:10.1007/s44382-026-00029-0 · Cited by 1
Yang, Y., Pan, Z., & Hu, Q. (2026) · Information Technology and Management · doi:10.1007/s10799-026-00500-w
Luo, J. (2026) · Journalism Studies · doi:10.1080/1461670x.2026.2680639
“Second, participants were asked to rate their awareness, usage, and knowledge of six different AI applications on a 5-point scale (Gillath et al. 2021, 1 = Not at all, 5 = Extremely), including (1) self-driving vehicles, (2) medical diagnosis, (3) dating apps (4) product recommendations, (5)…”
Fang, Y. D., Zha, R., Lv, X., & Chengkun, T. (2026) · International Conference on Intelligent Systems, Communications and Computer Networks (ISCCN) · doi:10.1109/isccn69074.2026.11564326
“…through demographic variables (as control measures) and standardized scales measuring AI usage habits (4 items, α=.85; Gillath et al., 2021) [1][2][3][4][5] , AI attitudes (16 items, α=.92; Nass et al., 1995; Zhou Baohua, 2024) [6][7][ 8 ] , AI trust (11 items, α=.89; Davis, 1993; Sundar et al.,…”
Regulations Addressing Key Challenges of Artificial Intelligence: Managing Usage Knowledge
Lawson‐Body, A., & Jensen, J. J. (2026) · Journal of Computer Information Systems · doi:10.1080/08874417.2026.2688779
Wang, T. R., Yang, Y., & Zhou, J. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2686773 · Cited by 1
Understanding What Builds and Breaks Travelers' Trust in ChatGPT's Travel Recommendations
Khairallah, M. (2026) · International Symposium of Systems, Advanced Technologies and Knowledge (ISSATK) · doi:10.1109/issatk69667.2026.11566711
“Recent work on trust in AI has thus focused on identifying factors that underpin trustworthy AI [45,48].”
Zhang, Z., Zhou, Y., & Chen, X. H. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2695435
Trust in AI as a Mediator in the Future Adoption of AI-Powered Financial Consultancy Services
Sayem, S. M., Saki, S. I., Anjum, N., Ruhi, S. A., Alam, Z., Alam, A., Gupta, A. S., & Rahman, M. I. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-25187-9_31
Zhao, H., Ke, X., Min, Q., & Yu, L. (2026) · Internet Research · doi:10.1108/intr-05-2025-0782
Shortland, N. D., Smith, K., & Alison, L. (2026) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2026.101213
Long, Y., Zhou, Y., Luo, D., & Chang, M. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1851183
Zhang, F., Pan, Z., Hu, Q., & Lu, Y. (2026) · Information & Management · doi:10.1016/j.im.2026.104403 · Cited by 1
Obuobi, B., Liu, C., Awuah, F., Nketiah, E., & Adu‐Gyamfi, G. (2026) · Employee Relations · doi:10.1108/er-02-2025-0100
Generative AI Adoption in Local Government: A PLS-SEM and fsQCA Study of Vietnamese Civil Servants
Duy, P. N., Ruangthamsing, C., Kamnuansilpa, P., Lowatcharin, G., & Setthasuravich, P. (2026) · Administrative Sciences · doi:10.3390/admsci16080365
“Prior studies show that trust facilitates the acceptance of conversational and autonomous AI systems (Gillath et al., 2021; Calhoun et al., 2019).”
Advancing sustainable construction: A trust-enhanced model of AI adoption and realised use
Regona, M., Yiğitcanlar, T., Hon, C. K. H., & Teo, M. (2026) · Sustainable Cities and Society · doi:10.1016/j.scs.2026.107755
Attachment towards generative AI influences self‐ AI agreement in personality reports
Yang, F., & Oshio, A. (2026) · Asian Journal Of Social Psychology · doi:10.1111/ajsp.70102
Institutional archetypes of resistance to artificial intelligence
Kubovics, M. (2026) · Frontiers in Human Dynamics · doi:10.3389/fhumd.2026.1871733
Xie, L., Shan, K., Tang, H., Li, H., & Wang, S. (2026) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2026.117392
Generative AI and the Reconfiguration of Symbolic Authority: Implications of a Psychoanalytic View
Hamamra, B. T., & Uebel, M. (2026) · Human Arenas · doi:10.1007/s42087-026-00687-y
Respess, S., & Counted, V. (2026) · Springer eBooks · doi:10.1007/978-3-032-29101-1_6
Ji, X. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7294159
Duncan, E. A., Inkumsah, W. A., & Opare-Boateng, R. (2026) · Frontiers in Communication · doi:10.3389/fcomm.2026.1931016
Wang, F., Huang, Y., Jiang, Q., & Chen, S. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2705666
Liu, D., & Niu, X. (2026) · Journal of theoretical and applied electronic commerce research · doi:10.3390/jtaer21090291
“Work on automation and algorithmic advice shows that users update competence judgments from cues such as accuracy and performance [78,79].”
Zhang, T. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1877802
Jang, J., & Myeong, S. (2026) · Systems · doi:10.3390/systems14091122
How does sense of security promote internet altruistic behavior? a diary study
Zheng, X., Yuan, Y., Liu, G., & Li, T. (2026) · Current Psychology · doi:10.1007/s12144-026-10042-7
Aydın, O. (2026) · Güncel Turizm Araştırmaları Dergisi · doi:10.32572/guntad.1958595
Soni, D., Sankathala, S., & Das, S. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-0383-1.ch002
Liu, J., Liang, X., Sun, Y., Hu, Y., & Liu, L. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1889095
“This is because, in AI-generated content contexts, users not only evaluate whether the information is useful but also further judge whether it is cred-ible and dependable (Afroogh et al., 2024; Gillath et al., 2021).”
Emikönel, S., & TÜRKMEN, İ. (2026) · Acibadem Universitesi Saglik Bilimleri Dergisi · doi:10.31067/acusaglik.1990872
Do Undergraduates Trust Information More from Humans or GenAI? An Experimental Study
Mattison, T. S., & Conrad, J. (2026) · Open Praxis · doi:10.55982/openpraxis.18.4.1271
Tran, K. D., Nguyen, T. A. D., & Tran, K. P. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7571959
Mental Health: Ethics and the Role and Limits of Artificial Intelligence
Castellví, P. (2026) · Springer eBooks · doi:10.1007/978-3-032-22853-6_2
Trustworthy AI Is Not Valuable
Lim, J. (2026) · Journal of Applied Philosophy · doi:10.1002/japp.70137
When Trust and Privacy Pull in Opposite Directions: Consumer Decision Making with Conversational AI
Zhuo, F., Zhu, J., & Liu, C. (2026) · Behavioral Sciences · doi:10.3390/bs16101854
“Because consumers cannot fully observe how AI recommendations are generated, such trust is often formed from observable system characteristics and users’ interpretations of AI performance (Gillath et al., 2021).”
Yamamoto, K., Tanikawa, T., & Kuzuoka, H. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-02469-5
“The same route may be understood differently depending on a user’s age, AI familiarity, literacy, or domain knowledge [5, 50, 157].”
Eren, C. D., & Geçer, A. K. (2026) · Acta Infologica · doi:10.26650/acin.1893001
Employee acceptance of AI-driven performance management systems in hospitality
Yu, L.-J., Konar, R., Liang, X.-X., & Wirtz, J. (2026) · Technology and Society · doi:10.1016/j.techsoc.2026.103585
Khassawneh, O., Mohammad, T., Al-khafaji, Q., & Qureshi, T. M. (2026) · Journal of Workplace Learning · doi:10.1108/jwl-10-2025-0346
패션 제품 구매에 대한 디자인 주체(인간 vs. AI)의 영향
尹前进, & Yoon, N. (2026) · Journal of the Korean Society of Clothing and Textiles · doi:10.5850/jksct.2026.50.4.707
Develı, A., & Özyer, Y. S. (2026) · International journal of organizational analysis · doi:10.1108/ijoa-03-2026-6619
Wang, D., & Song, Y. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1867910
Yang, W., Hu, B., & Yan, S. (2026) · Business Process Management Journal · doi:10.1108/bpmj-01-2026-0025
Metadata
- Authors
- Gillath, O., Ai, T., Branicky, M. S., Keshmiri, S., Davison, R. B., & Spaulding, R.
- Affiliations
- University of Kansas (US)
- Colorado State University (US)
- Corresponding author
- Gillath, Omri
- Venue
- Computers in Human Behavior
- Publisher
- Elsevier BV
- Published
- 2021-02
- Volume
- 115
- Pages
- 106607
- ISSN
- 0747-5632 (ISSN-L)
- OpenAlex
- W3093987005
- Cited by
- 448
- References
- 83
- Citations by year
- 2026: 113 · 2025: 123 · 2024: 92 · 2023: 41 · 2022: 49 · 2021: 27
- FWCI
- 32.25
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Computers in Human Behavior (publishedVersion)
- Topics
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Social Sciences › Psychology › Social Psychology › Attachment and Relationship Dynamics
- Social Sciences › Human Factors and Ergonomics › Human-Automation Interaction and Safety
- Keywords
- trust in AI, attachment styles, attachment security, attachment anxiety, affective trust, priming, positive affect
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
