The Development of the Higher Education Relationship Marketing Model
Bibliographic record
Abstract
Over the past several decades, higher education in Canada has expanded dramatically. Postsecondary institutions (PSIs) have struggled to both respond to this competitive environment, while simultaneously generating value for its core stakeholders – students – which align with its mission and vision. Comprehending the underlying relational dynamics between a student and their PSI will aid in improving retention rates, satisfaction levels, shared values, advocacy, loyalty, and efficiency overall. The current project will investigate what variables contribute to the creation of relational value between students and a PSI. Relationship marketing (RM) is the theoretical foundation of this study. RM is the principle of establishing, maintaining, and enhancing mutually successful relationships, where value is created for all parties. Previous research was adapted for application in a higher education context to explore the relationship between PSIs and students. Past research conducted has focused on a diverse range of relationships and industries using RM. Spectator affiliations to sports teams, students’ affiliation with varsity sports, and student affiliation with education are some examples of RM studies. Herein, we will explore the systematic relationship between a student and a PSI and how this relationship generates mutual value. To do so, we conducted a phenomenological study. This encompassed interviewing a minimum of 12 experts in higher education. The outcome of this study will be a refined higher education relationship marketing model (HERMM) and a suggested quantitative instrument that can be utilized by future researchers.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".