Evidence map›Paper›PMID 42729439›Full record

ArticleFrontiers in psychology2026

Assessing parent privacy awareness and coping appraisals in the adoption of child-monitoring technologies.

Ali Alkhalifah, Umar Ali Bukar

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.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

2 authors.

Ali AlkhalifahDepartment of Information Technology, College of Computer, Qassim University, Buraidah, Saudi Arabia.
Umar Ali BukarDepartment of Computer Science, Faculty of Computing and Artificial Intelligence, Taraba State University, Jalingo, Nigeria.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Due to the serious privacy risks inherent in child-monitoring technologies, privacy implications should be a fundamental component of digital parenting guidelines. Several adoption studies indicate that privacy concerns are among the most influential criteria determining users' intentions toward parental control applications (PCAs). However, the role of PCAs in enhancing children's digital literacy and cybersecurity awareness remains largely unexamined. Therefore, this study extends protection motivation theory (PMT) to investigate how privacy awareness and coping/threat appraisals influence parents' behavioral intentions toward PCA adoption. Methods: To achieve this, we collected empirical survey data from a sample of 541 parents in Saudi Arabia who were recruited via random intercept sampling. A sophisticated, multistage data analysis framework was employed, combining partial least squares structural equation modeling (PLS-SEM), the PLSpredict algorithm, and artificial neural networks (ANNs) to capture both linear and non-linear psychological complexities. Results: The findings show that privacy concerns significantly influence the behavioral intention to adopt PCAs. The results also indicate that privacy awareness, parental self-efficacy, perceived threat susceptibility, and perceived threat severity have significant effects on parents' privacy concerns. However, the data do not support the hypothesized effect of assurance mechanisms efficacy (AME). These findings enhance our understanding of user mentality regarding the privacy dimensions of protective parenting software.

Indexed as

artificial neural networkschild monitoringparental control appsprivacyprotection motivation theory

Identifiers

PMID42729439
PMCPMC13562062

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.