Evidence map›Paper›PMID 42819447›Full record

ArticleFrontiers in artificial intelligence2026

The context of AI transformation: perceptions of AI ethicality and their impact on trust and use intentions.

Rachel Detherage, Shane Connelly

Abstract read
In one paragraph

Article in Frontiers in artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Rachel DetherageDepartment of Psychology, University of Oklahoma, Norman, OK, United States.
Shane ConnellyDepartment of Psychology, University of Oklahoma, Norman, OK, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: As the implementation of AI accelerates within organizations, concerns emerge about the ethicality of AI-driven systems and tools and the ways people use them. Although previous literature proposes numerous theories regarding how to develop and use AI ethically, questions remain about how users perceive the ethicality of AI and the effects this has on their engagement with AI tools. The study aims to explore whether and how certain perceptions of AI tool attributes, including beliefs about the explainability of the AI tool (xAI) and moral intensity dimensions (i.e., magnitude of consequences and proximity), influence perceptions of AI ethicality. Additionally, the study examines the effects of AI ethicality perceptions on the likelihood to recommend AI tools and functionality trust in AI. Methods: Researchers employ a randomized, controlled study design to investigate the relationships between the antecedents, perceptions of AI ethicality, and outcomes of functionality trust and behavioral intention. Results: Analyses revealed that AI ethicality perceptions significantly mediated the relationship between xAI and the likelihood to recommend the AI tool, whereas functionality trust in the AI tool significantly mediated the relationship between proximity and the likelihood to recommend the AI tool. Discussion: The results indicate the strength of users' beliefs about xAI on their perceptions of AI ethicality and how users perceive AI as less capable of making decisions for human decision recipients. The perceptual nature of the study provides a theoretical contribution to the growing body of AI-related research relevant to organizations. The findings also offer a perspective to organizations implementing AI tools, signaling the need for management to consider context and ethical perceptions when implementing AI tools in the workplace.

Indexed as

AI usebehavioral intentionsethical AIexplainable AI (xAI)functionalityproximitysituational factorstrust

Identifiers

PMID42819447
PMCPMC13624155

What OpenQuestion holds

Textmetadata
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.