Evidence map›Paper›PMID 42712516›Full record

ArticleFrontiers in psychology2026

AI-based interviews and applicant perceptions: insights from China and Pakistan.

GuangJun Zhao, Qiaoli Liang, Fahad Alam, Md Billal Hossain

Abstract read
In one paragraph

Article in Frontiers in psychology, 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
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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

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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

4 authors.

GuangJun ZhaoBusiness School, Shandong Management University, Jinan, Shandong, China.
Qiaoli LiangSchool of Business, Beijing Geely University of China, Beijing, China.
Fahad AlamSchool of Economics and Management, Tongji University, Shanghai, China.
Md Billal HossainSustainability Competence Centre, Széchenyi Istvàn University, Győr, Hungary.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Artificial intelligence (AI) technology is increasingly integrated into recruitment processes, yet cross-cultural differences in job applicant responses to AI-based interviews remain understudied. Drawing on Signaling Theory and Fairness Heuristic Theory, this study examines how applicants in China and Pakistan perceive AI-based versus human-based interviews, focusing on three critical outcomes: perceived transparency, interactive communication, and job pursuit intention. It further examines whether trust in AI moderates these relationships and whether this moderating role is comparable across the two cultural contexts. Methods: Data were collected from 225 respondents in China and 213 respondents in Pakistan using a convenience sampling technique. Partial Least Squares Structural Equation Modeling was used to test the proposed relationships, followed by measurement invariance assessment and multi-group analysis. Results: The results show that AI-based interviews were negatively associated with perceived transparency, interactive communication, and job pursuit intention, with stronger negative associations observed among respondents in the Pakistani sample than in the Chinese sample. Moreover, trust in AI systems emerged as a significant positive moderator in both cultural contexts, suggesting that higher trust in AI weakened negative applicant responses to AI-based interviews. Discussion: Overall, the findings enhance understanding of how interview type and related perceptions shape applicant attitudes in cross-cultural recruitment settings. The study provides practical implications for HR managers and organizations, and discusses key findings, managerial recommendations, study limitations, and directions for future research.

Indexed as

AI-based interviewcross-cultureinteractive communicationjob pursuit intentiontrust in AI system

Identifiers

PMID42712516
PMCPMC13549999

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.