Motivations for Using Religious Digital Media: A Hierarchical Regression Analysis Among Jordanian University Students

This study investigated the factors influencing motivations for using religious digital media (RDMUM) among 330 Jordanian university students through hierarchical regression analysis. The analysis revealed that demographic factors, particularly age and gender, significantly predicted motivation, with younger individuals and males showing higher engagement. Educational level, explicitly having a BA degree, also influences motivation, whereas university location and specialization do not have significant effects. The type of digital media used (websites, social media, podcasts) does not significantly impact motivation, although a negative association with Twitter suggests that platform-specific characteristics may affect engagement. Prior knowledge and specific interests in Islamic thought, faith, and jurisprudential topics are strong motivators that highlight the importance of content relevance and depth. The final model, which explains 49% of the variance in the RDMUM, underscores the robust explanatory power of the included variables. These findings emphasize the need for tailored content to address user interests and knowledge levels. Content creators and practitioners can leverage these insights to develop more engaging and effective religious digital media, ultimately enhancing users’ engagement and satisfaction. Moreover, this study's potential to inspire and guide future research in this field is significant and contributes to the academic community's understanding of religious digital media.

Authors

Institutions

Publication Details

Journal
Millah Journal of Religious Studies
Published
2024-08-31
DOI
https://doi.org/10.20885/millah.vol23.iss2.art12
Citations
2
Primary Topic
Media, Religion, Digital Communication
Type
article
Field-Weighted Citation Impact
8.43

Funders

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

Motivations for Using Religious Digital Media: A Hierarchical Regression Analysis Among Jordanian University Students

Mohammad Jaber Thalgi
2 citations
Millah Journal of Religious Studies
Media, Religion, Digital Communication
8.43
article

Motivations for Using Religious Digital Media: A Hierarchical Regression Analysis Among Jordanian University Students

Mohammad Jaber Thalgi
article en
2 citations

Abstract

This study investigated the factors influencing motivations for using religious digital media (RDMUM) among 330 Jordanian university students through hierarchical regression analysis. The analysis revealed that demographic factors, particularly age and gender, significantly predicted motivation, with younger individuals and males showing higher engagement. Educational level, explicitly having a BA degree, also influences motivation, whereas university location and specialization do not have significant effects. The type of digital media used (websites, social media, podcasts) does not significantly impact motivation, although a negative association with Twitter suggests that platform-specific characteristics may affect engagement. Prior knowledge and specific interests in Islamic thought, faith, and jurisprudential topics are strong motivators that highlight the importance of content relevance and depth. The final model, which explains 49% of the variance in the RDMUM, underscores the robust explanatory power of the included variables. These findings emphasize the need for tailored content to address user interests and knowledge levels. Content creators and practitioners can leverage these insights to develop more engaging and effective religious digital media, ultimately enhancing users’ engagement and satisfaction. Moreover, this study's potential to inspire and guide future research in this field is significant and contributes to the academic community's understanding of religious digital media.

Millah Journal of Religious Studies
Yarmouk University (JO)
Yarmouk University
Quality Education, Gender equality
Openalex Percentile: Top 3%
Media, Religion, Digital Communication
8.43
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.