Dominant factors for the marketing of private higher education
Bibliographic record
Abstract
Higher education institutions, like any business institution, should satisfy their clients (students) for them to survive in the higher education service business market. As a service business, higher education institutions also need to follow marketing principles in their attempt to attract potential students. We investigated the effect of marketing mix dimension on internal and external marketing in universities. The research used primary data from a questionnaire survey of 526 students in West Java Province, Indonesia, and then drew conclusions by a structural equation model (SEM) analysis. The research findings revealed that place, product, price and promotion have a positive effect on external marketing. Whereas physical evidence, people and processes have positive, significant effects on internal marketing. There was also positive, significant correlation between external marketing and internal marketing. The research findings were hopefully beneficial for higher education management, to be made as guidance in implementing their marketing strategy. Higher education leaders may apply the external marketing policy to attract potential student interest and the internal marketing policy to improve the quality of their service and internal marketing. The study delivered a broader picture of the application of marketing mix model on universities. In addition, the discussion presented the implication of the offered theory and practice, the research limitation, and the direction of 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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".