arbiter
JournalsAccount

User trust in AI and major tech companies in twelve countries

Anica Cvetkovic et al.

Behaviour and Information Technology2026https://doi.org/10.1080/0144929x.2026.2619648article
AJG 2ABDC A
Weight
0.50

Abstract

Artificial intelligence (AI) technologies are increasingly present in virtually all life domains, but the trustworthiness of these new technologies and the companies developing them has been a topic of heated public debate. We examined how basic psychological needs in technology use, AI self-efficacy, and positive attitudes toward AI are associated with trust in AI and in major tech companies. Data were collected in 2024 and include 11,259 participants from Africa, Asia, Europe, North and South America, and Oceania. We used linear regression for data analysis. We found that having positive attitudes toward AI and experiencing relatedness in technology use consistently predicted trust in AI and in major tech companies. However, technological autonomy, competence, and self-efficacy in AI use predicted trust only in specific countries. Our findings provide novel insights into the human factors that affect trust in AI and its developers, and as such, they are of relevance for successful AI development, integration, and use. Our study includes culturally diverse perspectives and thus contributes to the debate on establishing fair global AI practices and overcoming the ‘AI divide’.

Open via your library →

Cite this paper

https://doi.org/https://doi.org/10.1080/0144929x.2026.2619648

Or copy a formatted citation

@article{anica2026,
  title        = {{User trust in AI and major tech companies in twelve countries}},
  author       = {Anica Cvetkovic et al.},
  journal      = {Behaviour and Information Technology},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1080/0144929x.2026.2619648},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

User trust in AI and major tech companies in twelve countries

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.