Improving student satisfaction through social media marketing activities: The mediating role of perceived quality
Bibliographic record
Abstract
The connection between education and technology offers an exciting opportunity for universities to utilize social media marketing activities not only to recruit new students or to create brand image and reputation, but also to enhance student satisfaction. Despite having a large number of studies indicating the importance of social media as a tool for student recruitment, the studies on the importance of social media marketing activities (SMMA) for students who are already enrolled are rare. This study aims at examining the relationship between SMMA and student satisfaction, considering perceived quality as a mediator in the higher education sector in North Cyprus. Utilizing a cross-sectional online survey, the data for this study was collected during October- November 2020 from 424 international students enrolled in North Cyprus universities. The model was assessed and the data was analyzed using Structural Equation Model (SEM) in AMOS statistical software. The findings suggest that SMMA has a positive relationship with the perceived quality of education, and perceived quality mediates the relationship between SMMA and student satisfaction positively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".