Vindicating Service Quality of Education through Structural Equation Modeling (SEM): International Students’ Perspective
Bibliographic record
Abstract
The prime objective of this paper is to identify the factors that influence service quality of education mediating by institutional image in higher education perspective. To this aim, the Nordic model was used as a theoretical base of the study. Program quality as a technical quality as well as industrial link and student satisfaction as functional quality were postulated to positively influence image toward the service quality education. Data (n=294) were collected from foreign students studying at University Utara Malaysia located in northern Malaysia through convenient sampling procedure. The structural equation modeling (SEM) was utilized for analyzing the data. Analysis of the data indicates that image has a full mediating role in the relationship between industrial link and service quality education. To uphold service quality in education, the academic authorities need to nurture earnestly industrial link and image of the institution because image occupied full mediation role between industrial link and service quality of education. Moreover, program quality and student satisfaction have shown direct significant impact on image and service quality of education and can be regarded as critical factors for certifying image and service quality in higher education. The conceptual model of this study would give more recognition if any tertiary level institution addresses this in order to increase their image and overall ranking. This study incorporated institutional image as a mediating variable, which is an exceptional endeavor in tertiary level education for enriching existing body of literature in perspective of international students.
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 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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".