Leveraging social media for sustainable transformation in higher education: the interplay of engagement, green values and loyalty
Bibliographic record
Abstract
Purpose This paper explores how social media marketing activities (SMMAs) in higher education institutions (HEIs) influence brand loyalty, particularly through the mediating role of student engagement. It further investigates the moderating effect of green consumption values (GCV) on these relationships. The purpose of this study is to provide a deeper understanding of how digital engagement strategies can align with sustainability concerns to enhance long-term brand loyalty in HEIs. Design/methodology/approach Using a structured questionnaire distributed to 313 HEIs Asian students, the study applies structural equation modelling structural equation modeling to examine relationships between SMMAs, student engagement, brand loyalty and GCV. Validated scales were used for each construct, and reliability and validity were confirmed through confirmatory factor analysis and internal consistency tests. Findings SMMAs positively influence both engagement and brand loyalty. Engagement also strongly predicts loyalty. However, GCV negatively moderate the relationship between engagement and loyalty, suggesting that sustainability-conscious students may be less loyal unless environmental values are addressed by HEIs. No moderating effect was found between SMMAs and brand loyalty. Research limitations/implications The study is limited to quantitative data from Asian students in the higher education, which may constrain generalisability. Future research could incorporate qualitative approaches or examine other service sectors. Including additional variables like trust or brand anthropomorphism could offer richer insights into student–brand relationships. Originality/value The study uniquely integrates digital marketing, student engagement and GCV within the HEI context, providing empirical evidence of their interplay. It contributes to both marketing and sustainability literature, offering actionable insights for academic marketers seeking to align digital engagement with environmental responsibility.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".