Does Marketing Success Factors Influence Private College Admission? Evidence From Malaysia
Bibliographic record
Abstract
Private colleges are predicted to be presented with many opportunities as well as challenges in the coming years. Admission pressures become one of the challenges face by most of Private Colleges in Malaysia. Lacking of marketing mix strategy are claimed to contribute to this admission pressure. This study was conducted firstly, to determine the relationship between marketing success factors (Price, Place, Product, Promotion, People, Process, Physical Evidence, Partnership, Publication and Conference, Presentation and Extracurricular Program) with the marketing mix strategy of private colleges. Secondly, to determine the relationship between Marketing Mix Strategy with Private College Admission. Similarly, in this study, these 11Ps are the success factors of private college marketing mix strategy in influencing student to study in private colleges. Structural Equation Model (SEM) is conducted to estimate the effects of the main construct on its subcontracts, exogeneous and endogenous variables and its significant relationship. The result found the factors with the highest percentage of variation in contributing to Marketing Mix Strategy are Promotion, Product, Place, Price, Process, Partnership, Presentation, People, Physical Evidence, Publication and Conference and lastly Extracurricular Program. Thus, concluding that 11Ps Marketing Mix Strategy has a significant relationship with Private College Admissions. National private colleges can create a strategy based on the marketing mix strategy in competing for students. The study area is Malaysia, and it was conducted over a sample of 366 executive and marketing officers as the respondents.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.006 | 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".