The Investigation on Brand Image of University Education and Students’ Word-of-Mouth Behavior
Bibliographic record
Abstract
This study aimed to find how the brand image and satisfaction of universities influence university students’ word-of-mouth behavior, including the sharing of satisfying experiencesand recommendations to others. This study conducted a questionnaire survey and distributed 400 questionnaires to students and graduates of universities in Taiwan; 336 valid questionnaires were retrieved. Data were analyzed using Structural Equation Models (SEM). According to the findings, brand image significantly and positively influences loyalty; satisfaction significantly and positively influences loyalty; loyalty significantly and positively influences haring of satisfying experience; and loyalty significantly and positively influences recommendations to others. Brand image and satisfaction can influence the sharing of satisfying experience sand recommendations to others by the moderating effect of loyalty. The loyalty effect model of higher education institutes constructed by this study could explain and predict the effects of brand image and satisfaction of higher education institutes on university students’ word-of-mouth behavior, and it could function as the criteria for marketing strategies of higher education institutes.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".