Augmenting Performance Through Strategic Management and Leadership Capabilities
Bibliographic record
Abstract
Small and medium-sized enterprises (SMEs) suffer persistent challenges due to global market competition, time limits to respond strategically, talent retention, productivity, and uncompetitive operational expenses. Enhancing employee performance (EP) then becomes critical in defining SME success. The purpose of this study is to evaluate the influence of strategic management (SM) and leadership capabilities (LC) on EP and recommend solutions to improve EP. The function of employee engagement (EE) as a mediator between interactions involving LC, SM, and EP is also investigated. A quantitative research method was employed by collecting empirical data of employees working with Malaysian SMEs. Analysis including reliability and normality assessments, confirmatory factor analysis, and structural equation modelling with AMOS 22 were carried out. According to the data, SM exerts a positive and substantial impact on EP. In the context of Malaysian SMEs, the novel findings provide a strong reason for the use of SM, emphasising the need to strengthen managers' knowledge in SM capabilities.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".