Impact of Academic and Non-Academic Service Quality on International Students Satisfaction in Chinese Universities
Bibliographic record
Abstract
Satisfied students are easy to retain and perform better in academics. That consequently affects higher educational institutions’ success and reputation. To become a top higher educational institution, every institution tries to provide the best academic facilities and services to its students. However, non-academic services are not given equal importance in academic settings, which could significantly determine students’ satisfaction. Therefore, this paper aims to examine the impact of academic and non-academic service quality on international student satisfaction in China. This study followed a deductive approach and is carried out in a non-contrived setting. The convenience sampling technique was used in the study for data collection. A total of 143 responses from the international students is received through an online survey. Descriptive and Structured Equation Modeling (SEM) analysis techniques were performed for analyzing the variables. The results indicate that both academic and non-academic service quality equally contributes to student satisfaction. The findings of this study reveal that non-academic services aspects are as important as academic services when it comes to affecting student satisfaction. This study highlights the significance of different service quality dimensions for student satisfaction which is sometimes not given equal importance in the educational environment. The trend of the internationalization of education in China is increasing rapidly. The higher educational institutions in China need to acknowledge different aspects of students’ satisfaction to become a top higher-level educational destination for international students.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".