Investigating Effective Components of Higher Education Marketing and Providing a Marketing Model for Iranian Private Higher Education Institutions
Bibliographic record
Abstract
<p class="apa">Purpose-The purpose of this paper is to study and identify the effective components of higher education marketing and providing a marketing model for Iranian higher education private sector institutions.</p><p class="apa">Design/methodology/ approach- This study is a qualitative research. For identifying the effective components of higher education marketing and providing a marketing model the thematic analysis was used. First all the themes related to higher education marketing from the research references were collected and was analyzed by template analysis and thematic network methods.</p><p class="apa">Findings-The results of thematic analysis revealed that there are 2 dominant themes, 13 global themes, 40 organizing themes and 503 basic themes related to higher education marketing. The relationships between these themes are provided as a marketing model for Iranian private sector higher educational institutions.</p><p class="apa">Originality/value-There are few studies of higher education marketing in the Iranian higher education market. This study provides useful information about effective components of higher education marketing and marketing models.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".