Factors Affecting on Gaining a Sustainable Competitive Advantage for Sri Lankan Higher Educational Institutes
Bibliographic record
Abstract
Over the past few years, number of seats has grown significantly in Higher Education Institutes (HEI), thus it becomes prudent to look at the ways of improving decision making of the HEI. Thus, the aim of this study is to investigate factors affecting the sustainable competitive advantage. Since literature does not support strong underpinnings in this area, an exploratory and grounded theory-based study was designed to conduct this study. The main contribution of this research is that we propose factors to consider for an HEI to achieve sustainable competitive advantage. Our findings indicate that proper student-teacher relationship, maintaining good reputation, maintaining a high rank and good indexing’s, maintain good relationships with industries, student participation in competitions, accreditation from reputable institutions are the most significant factors affecting the sustainable competitive advantage (AA) within Sri Lankan HEIs. To remain competitive and obtain competitive advantages, HEI decision makers can try to increase organizational performance by managing each dimension of core competence, i.e. Market profile; Innovation and Core Competencies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".