The Role of Leadership Competencies in Supporting the Al Nahda University for Becoming a Learning Organization: A New Qualitative Framework of the DLOQ
Bibliographic record
Abstract
Purpose: The purpose of this study is to contribute to research on learning organizations in higher education institutions (HEIs), by researching the role of individual, group, and organization competencies and skills that support the (NUB) Al Nahda University in Egypt toward becoming a learning organization.Design/methodology/approach: Semi-structured interviews were conducted with eight executive academics and researchers in (NUB) Al Nahda University in Egypt. Questions emphasised leadership competencies, including those at individual, group, and organizational level, for utilising their skills in creating, sharing and transferring knowledge for modifying and changing their behaviour to achieve a learning organization.Findings: Leadership competencies emerged as a complementary component to the DLOQ framework and it was found that the Seven Characteristics (7Cs) proposed by Watkins and Marsick (2003) did not lead to being a learning organization, nor did being a learning organization lead to knowledge performance and financial performance by itself unless fully supported by leadership competencies, as was confirmed in the case of the Al-Nahda University operating in Egypt.Originality/value: There is still a lack of investigation and global response to the question of how leadership competencies can support learning inside higher education institutions. The outcomes of this research allow a better understanding of how leadership competencies can support the process of becoming a learning organization in HEIs, via a qualitative investigation of the DLOQ framework.
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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 | 0.019 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".