Does promoting student satisfaction factors, and experience via social media enhance their loyalty? The mediating role of positive eWOM and university’s image
Bibliographic record
Abstract
Given that social media (SM) has become an integral part of the daily lives of university students and their main window to communicate with the world around them, higher education institutions can no longer help but adopt it as a major part of their marketing strategy and as a major communication channel to enhance their ability to retain their current students and attract new students. Therefore, this study aims to reveal the role of Social Media Marketing (SMM) in promoting student satisfaction factors at Al-Ahliyya Amman University (AAU) and improving their experience, which must have a significant successive impact, starting from enhancing the university's image, to expanding the spread of positive eWOM, and ultimately, increase students' loyalty to the university, To create a practical framework for building a marketing strategy that invests in SMM to enhance student loyalty, relevant literature was evaluated. Business school students were surveyed using a paper questionnaire distributed to students to increase interest and accuracy in answering, to extract conclusions from the data and model variables, a structural equation modelling using Smart-PLS was used. The survey results indicate that (AAU) has not invested sufficiently in promoting some of the satisfaction factors of its students and improving their experience. But in general, it affected somewhat positively the loyalty of its students. The research has important theoretical and practical implications.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".