Modeling Jordanian audience engagement and news credibility in the digital media era: a PLS-SEM approach

The rise of digital news has reshaped the landscape of news consumption, especially in emerging media environments such as Jordan, where audiences increasingly depend on online and social platforms for information. This study examines how Trust in Digital News (TND) and Social Media Use (SMU) influence Audience Engagement (AE) and Perceived News Credibility (PNC) among Jordanian users. A two-step methodological approach was adopted: first, a bibliometric mapping was conducted to identify research trends and gaps in digital media studies; second, Partial Least Squares Structural Equation Modeling (PLS-SEM) was applied to survey data collected from 1,200 respondents. The results demonstrate that TND has a significant positive effect on AE (β = 0.45, p < 0.001) and PNC (β = 0.28, p < 0.01). SMU also shows a positive and significant impact on AE (β = 0.32, p < 0.01), but only a weakly significant effect on PNC (β = 0.15, p = 0.051). AE emerges as the strongest predictor of PNC (β = 0.56, p < 0.001), underscoring its mediating role in linking engagement and credibility. These findings highlight the crucial role of trust and interaction in strengthening perceptions of credible digital journalism, offering practical implications for media professionals and policymakers aiming to foster informed, democratic, and trust-based public discourse in the algorithmic information age.

Institutions

Publication Details

Journal
Frontiers in Communication
Published
2025-10-27
DOI
https://doi.org/10.3389/fcomm.2025.1675693
Citations
1
Primary Topic
Misinformation and Its Impacts
Type
article
Field-Weighted Citation Impact
4.20

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Modeling Jordanian audience engagement and news credibility in the digital media era: a PLS-SEM approach

1 citations
Frontiers in Communication
Misinformation and Its Impacts
4.20
article

Modeling Jordanian audience engagement and news credibility in the digital media era: a PLS-SEM approach

article en
1 citations

Abstract

The rise of digital news has reshaped the landscape of news consumption, especially in emerging media environments such as Jordan, where audiences increasingly depend on online and social platforms for information. This study examines how Trust in Digital News (TND) and Social Media Use (SMU) influence Audience Engagement (AE) and Perceived News Credibility (PNC) among Jordanian users. A two-step methodological approach was adopted: first, a bibliometric mapping was conducted to identify research trends and gaps in digital media studies; second, Partial Least Squares Structural Equation Modeling (PLS-SEM) was applied to survey data collected from 1,200 respondents. The results demonstrate that TND has a significant positive effect on AE (β = 0.45, p < 0.001) and PNC (β = 0.28, p < 0.01). SMU also shows a positive and significant impact on AE (β = 0.32, p < 0.01), but only a weakly significant effect on PNC (β = 0.15, p = 0.051). AE emerges as the strongest predictor of PNC (β = 0.56, p < 0.001), underscoring its mediating role in linking engagement and credibility. These findings highlight the crucial role of trust and interaction in strengthening perceptions of credible digital journalism, offering practical implications for media professionals and policymakers aiming to foster informed, democratic, and trust-based public discourse in the algorithmic information age.

Frontiers in CommunicationVol. 10
Zarqa University (JO)
Zarqa University
Openalex Percentile: Top 5%
Misinformation and Its Impacts
4.20
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.