Revolutionizing education: unleashing the power of social media in Saudi Arabian public universities

The paper addresses how social media adoption influences learning and education in Saudi Arabian public universities amid rapid growth of social media usage globally and nationally. Saudi Arabia’s high internet and social media penetration and its educational modernization agenda (Vision 2030) provide the context and motivation. The study applies the e-learning acceptance model (e-LAM) to assess how four constructs—performance (perceived usefulness/flexibility), effort (ease of use/learning), communication functionality (collaboration/sharing/interaction), and self (social media efficacy/attitude/enjoyment)—shape students’ intentions to use social media for learning. The hypotheses are: (i) Performance positively relates to intention; (ii) Effort positively relates to intention; (iii) Communication functionality positively relates to intention; (iv) Self positively relates to intention. The research question centers on which e-LAM factors significantly predict Saudi students’ intentions to adopt social media for educational purposes.

The review defines social media as interactive, web/mobile-based environments enabling user-generated content creation, sharing, and collaboration (e.g., Facebook, Twitter, YouTube, LinkedIn, ResearchGate). Prior studies report that social media can enhance learner–instructor communication, provide easy access to course information, foster collaboration, and improve engagement and grades (e.g., Facebook for course coordination; Twitter for ongoing discussions and group knowledge; YouTube for personalized learning). ResearchGate facilitates scholarly sharing and networking. Collectively, the literature indicates social media’s educational value in higher education while highlighting the need for context-specific evidence in Saudi Arabia. The e-LAM, extending UTAUT to include flexibility, interaction, and self-efficacy dimensions, comprises four predictors: performance, effort, communication functionality (COM), and self. Prior evidence suggests performance and self-efficacy often drive technology adoption, while effort and communication features may vary by context. This study tests these constructs in Saudi public universities.

Design and sampling: A quantitative, cross-sectional survey targeted students in Saudi Arabian public universities using convenience and snowball sampling via an online questionnaire (Arabic). Ethical procedures ensured informed consent, anonymity, and confidentiality. Measures: The instrument drew on prior literature and expert review, covering five areas: performance–intention (PSI), effort–intention (ESI), communication functionality–intention (CFSI), self–intention (SSI), and use of social media platforms (SMPs). The final scale comprised 37 items. Pretesting assessed clarity, flow, length, and timing. Participants: 369 student respondents from diverse programs and educational levels in public universities across Saudi Arabia. Analysis: Partial Least Squares Structural Equation Modeling (PLS-SEM) using SmartPLS 4.0 evaluated the measurement and structural models. Reliability and validity were assessed via factor loadings (>0.50), Cronbach’s alpha (>0.70), composite reliability, and average variance extracted (AVE >0.50); discriminant validity used Fornell–Larcker and HTMT (≤0.90) criteria. A Breusch–Pagan test assessed heteroscedasticity. Model fit used SRMR (acceptable <0.08). Measurement model results: All constructs showed acceptable reliability and validity. Representative indices included Cronbach’s alpha: Use of social media (0.956), COM (0.935), Effort (0.791), Performance (0.902), Self (0.907); AVE exceeded 0.5 for all constructs. Discriminant validity was supported by Fornell–Larcker and HTMT. Model fit (Table 3): SRMR ≈ 0.053; NFI ≈ 0.882; chi-square ≈ 987.31. Structural model: Bootstrapping tested hypotheses H1–H4. R² for intention to use social media (BI) was 0.404, indicating that the e-LAM predictors jointly explained 40.4% of the variance in intention.

- Sample: 369 students from Saudi public universities; PLS-SEM used. - Model fit and measurement quality were acceptable: SRMR ≈ 0.053; strong internal consistency and convergent validity; discriminant validity met Fornell–Larcker and HTMT ≤0.90. - Explained variance: R²(BI) = 0.404 (40.4% of variance in intention explained). - Hypothesis tests (paths to intention BI): • H1 COM → BI: β = 0.042, t = 0.568, p = 0.570 (not significant; rejected). • H2 Effort → BI: β = −0.009, t = 0.170, p = 0.865 (not significant; rejected). • H3 Performance → BI: β = 0.205, t = 2.740, p = 0.006 (significant; accepted). • H4 Self → BI: β = 0.450, t = 6.228, p < 0.001 (significant; accepted). - Interpretation: Students’ intentions to adopt social media for learning are primarily driven by perceived performance benefits and self-related factors (efficacy, attitude, enjoyment), while communication features and perceived effort do not significantly influence intention in this context.

Findings indicate that in Saudi public universities, intention to use social media for educational purposes is chiefly shaped by perceived performance (usefulness/flexibility) and self-related factors (self-efficacy, positive attitudes, enjoyment). These align with theories positing that clear benefits and users’ confidence and motivation are critical for adoption. The non-significant roles of communication functionality and effort suggest that, in this setting, the mere presence of collaborative features or ease of use is insufficient to influence intention—possibly because students are already familiar with social media, making effort less salient, and because perceived educational value (performance) and personal orientation (self) dominate decision-making. Practically, interventions to enhance social media adoption for learning should highlight and realize tangible performance benefits (e.g., productivity, achievement, flexibility) and strengthen students’ self-efficacy and positive attitudes toward academic uses of social platforms. These results contribute context-specific evidence to the literature on social media in higher education, demonstrating that e-LAM’s performance and self constructs are the most predictive in this Saudi context.

This study applies the e-LAM to the Saudi higher education context and shows that performance expectancy and self-related factors (efficacy, attitude, enjoyment) significantly predict students’ intentions to use social media for learning, whereas communication functionality and effort do not. The work advances understanding of technology acceptance in Arab higher education by quantifying these relationships and validating a robust measurement model. Implications include designing initiatives that foreground academic performance benefits and build students’ confidence and enjoyment in academic uses of social media. Future research could broaden institutional coverage (including private universities), employ longitudinal designs to assess changes over time, examine actual usage and learning outcomes (beyond intention), and test moderators (e.g., discipline, gender, prior experience) to refine interventions.

- Context-specific sample of students from Saudi public universities limits generalizability beyond this sector and country. - Convenience and snowball sampling may introduce selection bias. - Cross-sectional, self-reported survey data constrain causal inference and may be subject to common method bias. - The study focuses on intention rather than observed behavior or objective learning outcomes.