Determinants of sustainable performance: The mediating role of strategic agility and the moderating role of leadership
Bibliographic record
Abstract
In facing increasingly stiff competition in higher education services, it is imperative to institute precise management of higher education organizations to maintain their ideal aspects. Beyond merely implementing marketing concepts, every individual within the organization must be capable of envisioning the institution's vision and mission, bolstered by formulating appropriate tactical strategies to foster competitive advantage for the University. This research aims to ascertain the influence of the relationship between competitive advantage, digital transformation, and resource advantages on Sustainable Performance College through strategy agility as a mediating variable and leadership as a moderating variable. The research methodology employs path analysis using Partial Least Square (Smart-PLS) software version 3.0 with a population of 66 private universities in the LLDikti III region, namely private universities. A sample of 198 respondents is selected using the saturated sample method. The research findings demonstrate that Competitive Advantage positively and significantly affects Strategic Agility, Digital Transformation positively and substantially impacts Strategic Agility, Resource Advantage positively influences Strategic Agility, Competitive Advantage positively influences Sustainable Performance College, Digital Transformation positively and significantly affects Sustainable Performance College, Resource Advantage positively impacts Sustainable Performance College, and Strategic Agility positively and significantly influences Sustainable Performance College.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".