Impact of Promotion on Students’ Enrollment: A Case of Private Schools in Pakistan
Bibliographic record
Abstract
Purpose – This study is designed to explore the variables that have a strong impact on students’ enrollment inprivate schools and by the optimal use of these promotional variables private schools attract students forenrollment.Design/methodology/approach – The study is qualitative and exploratory in nature, since the aim was toexplore the principals/administrators’ views and experiences about promotional variables within the educationsector in Pakistan. Data was collected by carrying out in-depth qualitative interviews with schoolprincipals/administrators in 16 schools of two neighboring cities namely Islamabad and Rawalpindi (Pakistan).The qualitative data was analyzed and presented in a tabulated and descriptive form.Findings – The analysis revealed four variables of promotion namely: school appearance, public relations,publicity, and advertisement (print and electronic media). These four variables were found to be the main formsof promotion used by private schools for recruiting students. There was also a strong impact of schoolappearance being used as the standard tool for promotion.Research limitations/implications – The study focused on one sector, the education sector in Pakistan. In orderto enhance one’s understanding and knowledge regarding the promotional variables. This needs to be taken intoconsideration in generalizing the findings to other sectors and geographic markets. The study is also limited toonly two cities in Pakistan. Although these cities are highly representative of other cities in Pakistan but there arecertain regional idiosyncrasies that can have varying impacts.Practical implications – This exploratory study establishes the forms of promotion that are used by mostschools in Pakistan. School administrations can look at this study and further explore the link betweenrecruitment and types of promotion. This study can also help promotional companies to develop servicepackages for the education sector in Pakistan.Originality/value – How private schools attract and influence students for enrollment and what are the sourcesfor that influences and attraction, is not an actively researched topic in Pakistan. The study would thereforecontribute towards better understanding of promotional variables and designing of effective promotionalstrategies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".