Evidence map›Paper›PMID 41776686›Full record

ArticleBMC psychology2026

The dark side of AI transparency: investigating AI-induced anxiety, technostress, and consumer satisfaction through a hybrid SEM-ANN approach.

Weichen Jia, Ahmed Muneeb Mehta, Chao Han, Muhammad Asif, Muhammad Farrukh Shahzad

Abstract read
In one paragraph

Article in BMC psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

5 authors.

Weichen JiaSchool of Media and Law, NingboTech University, Ningbo, China.
Ahmed Muneeb MehtaHailey College of Banking and Finance, University of the Punjab, Lahore, Pakistan.
Chao HanSchool of Media and Law, NingboTech University, Ningbo, China. hanchao9128@nbt.edu.cn.
Muhammad AsifUE Business School, University of Education, Lahore, Pakistan.ORCID http://orcid.org/0000-0003-0408-7628
Muhammad Farrukh ShahzadCollege of Economics and Management, Beijing University of Technology, Beijing, China.ORCID http://orcid.org/0000-0002-6578-4139

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As AI-driven technologies transform customer interactions, understanding their impact on consumer satisfaction is crucial. This study investigates the relationship between artificial intelligence (AI) transparency and consumer satisfaction, incorporating AI-induced anxiety and technostress as mediators and consumer digital literacy as a moderator. A hybrid methodology combining structural equation modelling and artificial neural network (SEM-ANN) was employed to analyse data from 341 e-commerce consumers from China. The SEM results reveal that AI transparency positively influences consumer satisfaction, while AI-induced anxiety and technostress mediate this relationship negatively. However, the moderating role of consumer digital literacy is not significant. The ANN findings identify consumer digital literacy as the most influential factor, highlighting its critical role in shaping consumer responses to AI-powered interactions. These results provide novel insights into the psychological impact of AI transparency on consumers and offer theoretical and practical implications for enhancing AI-driven customer service strategies.

Indexed as

AnxietyArtificial IntelligenceComputer LiteracyConsumer BehaviorNeural Networks, ComputerAdultChinaFemaleHumansLatent Class AnalysisMaleAI-induced anxietyAI transparencyArtificial neural networkConsumer satisfactionTechnostress

Identifiers

PMID41776686
PMCPMC13063937

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.