Evidence map›Paper›PMID 42792507›Full record

ArticleBehavioral sciences (Basel, Switzerland)2026

Navigating the Human Side of AI: A Socio-Technical Model of Employee Attitudes Toward Algorithmic Recruitment.

Hasan Beyari

Abstract read
In one paragraph

Article in Behavioral sciences (Basel, Switzerland), 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

1 author.

Hasan BeyariDepartment of Administrative and Financial Sciences, Applied College, Umm Al-Qura University, Makkah 24382, Saudi Arabia.ORCID 0000-0003-0943-0317

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study investigates employee perceptions of artificial intelligence (AI) in the field of recruitment and selection in Saudi Arabian companies. While the use of recruitment systems based on AI is growing, their acceptance by organisations is influenced by whether employees consider these systems to be useful, transparent, and fair, and how this affects their professional judgement. This study is based on the Unified Theory of Acceptance and Use of Technology (UTAUT) and Socio-Technical Theory (STT) and explores a two-path explanatory model. Perceived usefulness is linked to perceived AI effectiveness in the AI-related pathway and is associated with transparency of AI, trust in AI systems, and organisational support for AI use. On the HR pathway, there is a negative association between technostress and HR support and readiness, while clarity of integration of AI is positively associated with HR support and readiness. The sample comprised 450 purposively selected respondents from Jeddah, Riyadh and Dammam, and data were analysed using Structural Equation Modelling (SEM) in AMOS. The perceived effectiveness of AI and HR's support/readiness for AI was positively and significantly correlated with employee perceptions of AI in recruitment and selection. The relationships of these data should be interpreted as structural relationships; since the data are cross-sectional, they cannot be interpreted as causation or mediation. Further methodological issues include common method bias since all constructs were assessed via self-report and in the same questionnaire.

Indexed as

AI integration clarityartificial intelligence (AI)employee perceptionorganisational supportrecruitment and selectiontechnostress

Identifiers

PMID42792507
PMCPMC13603644

What OpenQuestion holds

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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.