A Study on the Application of STP Marketing Strategy in the MBA Education Program of Universities in China
Bibliographic record
Abstract
The MBA education has begun in America and now it has become mature after nearly one hundred years’ development. Although China’s MBA merely has a history of 17 years, it has already gained great achievements. In 1991, 9 universities in China had MBA program and 86 students enrolled in. In 2007, the two numbers were 127 and 59,776 respectively. The law of things’ development is to progress in a wave style and to rise in a spiral way. China’s MBA education is not an exception. In 2002, the enrolling number was 67,506, being the highest ever. In 2003 and 2004, the number decreased heavily, respectively reducing 16 percent and 13 percent. From 2005 to 2007, the number increased with a rate of 9 percent, 3 percent, and 6 percent respectively. The development of MBA in China’s universities has experienced a process of taking reference, exploring, developing, and regulating. In the aspects of teaching materials and methods, it chiefly takes references from experiences of universities in developed countries, such as America. In the aspects of enrollment and popularization, it makes a series of effective explorations based on China’s practical situation and others’ experiences. The application of STP marketing strategy exerts an extremely important driving effect on the development of China’s MBA program.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".