Prioritizing Factors of Entrepreneurial University to Inculcate Enterprise Formation Pursuit Among University Graduates Using Analytical Hierarchical Process
Bibliographic record
Abstract
The objective of this paper is to prioritize entrepreneurial activities in higher education institutions to inculcate enterprise development pursuit among university graduates. Various phases of enterprise development process are determined along with prominent activities of entrepreneurial universities through literature review. Problem formulation is done by structuring goal, objectives and alternatives into a hierarchical model in order to prioritize factors with the help of Analytical Hierarchical Process (AHP). Experts of academic entrepreneurship are approached for pairwise comparison of factors on the preference scale of nine levels with the help of software tool Expert Choice 11. After recording judgments, preferences of all experts are combined in order to get overall priorities of objectives and alternatives. Results show that Internal Motivation has highest and Business Growth and Sustainability have lowest priority along with priorities of all other objectives of enterprise formation process falling between both. Moreover, University Incubation Center is prioritized among alternatives in order to achieve objectives. Sensitivity analysis of results is carried out with the help of Expert Choice in which weight of a single objective is varied to observe effect on hierarchical model. Research presents a complete picture to academicians and policy makers to determine role of universities for entrepreneurship development in country. It will also help government officials to allocate resources in a prioritized way in order to achieve specific objectives. .
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".