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
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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
Trust in AI and Its Role in the Acceptance of AI Technologies
Choung, H., David, P., & Ross, A. (2023) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2022.2050543 · Cited by 963
“Past studies have applied interpersonal trust to human technology relationships (Calhoun et al., 2019), especially when the technology has human-like characteristics (Gillath et al., 2021).”
BackgroundInfluentialResponsible Artificial Intelligence Governance: A Review and Research Framework
Papagiannidis, E., Mikalef, P., & Conboy, K. (2025) · The Journal of Strategic Information Systems · doi:10.1016/j.jsis.2024.101885 · Cited by 565
Worker and workplace Artificial Intelligence (AI) coexistence: Emerging themes and research agenda
Zirar, A., Ali, S. I., & Islam, N. (2023) · Technovation · doi:10.1016/j.technovation.2023.102747 · Cited by 484
Arslan, A., Cooper, C. L., Khan, Z., Gölgeci̇, İ., & Ali, I. (2021) · International Journal of Manpower · doi:10.1108/ijm-01-2021-0052 · Cited by 477
Dey, P. K., Chowdhury, S., Abadie, A., Vann Yaroson, E., & Sarkar, S. (2024) · International Journal of Production Research · doi:10.1080/00207543.2023.2179859 · Cited by 330
“Gillath et al. (2021) showed the positive influence of individual trust on AI adoption, consistent with the argument of Seeber et al. (2020), also showing that AI adoption disseminates through peer stimulation.”
Result
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.
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