An Appealing Connection–The Role of Relationship Marketing in the Attraction and Retention of Students in an Australian Tertiary Context
Bibliographic record
Abstract
The higher education sector is increasingly facing competition from tertiary providers at both a domestic and international level. This has led to a range of ever more complex challenges with regard to the attraction, maintenance and retention of the student base. There is an important need therefore to understand the factors which contribute to positive perceptions of tertiary services and the way in which these affect the student experience and drive student retention. The improvement of retention rates through the formation of meaningful and long-term relationships with students is subsequently of high importance. This research explores students’ perceptions of the relationship that they enter into with their chosen tertiary institution and the effect that this has on the development of student loyalty. In particular this research examines the salience of relationship appeal, satisfaction, affective commitment and trust on student loyalty. A structural equation modelling approach was adopted using a sample of 426 first year undergraduate students of a large Australian metropolitan university. Importantly, next to satisfaction, relationship appeal was found to be the second strongest determinant of student loyalty. This was then followed by affective commitment. Interestingly trust did not influence relationship development. Conclusions, implications and opportunities for future research are presented. From a managerial perspective, it is expected that uncovering first year students’ perceptions of the student-institution relationship will enable higher education institutions to develop more targeted relationship marketing programs and increase student retention.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".