Relationship Marketing in Tertiary Education: Empirical Study of Relationship Commitment and Student Loyalty in Hong Kong
Bibliographic record
Abstract
This study attempts to investigate the applicability of relationship marketing concepts in the private tertiary education industry. With the rapid growth of tertiary education and new academic structure in Hong Kong, it is interesting to investigate the relationship between relationship commitment and student loyalty, and the key determinants of relationship commitment, in a leading private tertiary education institution in Hong Kong. Questionnaires were designed to collect data, and structural equation modeling approach was adopted to evaluate the explanatory power and casual links of the model. The results indicate that relationship commitment is a driver of student loyalty. Relationship benefits and trust are found to have positive influence on relationship commitment. Relationship termination costs and shared values are found to have non-significant roles in determining relationship commitment in private tertiary education environment, while shared value has a significant positive impact on trust. The research provides new insights to the management of private tertiary education providers in building relationship with students and resources allocation. The study discusses the implications of the findings and suggests areas for future research.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".