The Ripple Effects of Performance Management on Employees’Perceptions and Affective Commitment among Small and Medium Scale Enterprises (SMEs)
Bibliographic record
Abstract
Management literature acknowledges the important role played by performance management systems (PMS) in business organization, however, a little empirical studies exist in the Ghanaian context.Using a sample data of 180 from managements and staffs of thirty-eight (38) Small and Medium Scale Enterprises (SMEs), the current study presents the effects of performance management systems on employees perception and organizational commitment (affective) among Small and Medium Scale Enterprises (SMEs) located in the Greater Accra region of Ghana.Statistical Package for Social Sciences 20.0 version (SPSS) and Microsoft Word 2010 were employed for the data analysis. From the result, performance management had a positive significant relationship with employees’ perceptions and affective organizational commitment.The study further examined the mediating role of both employees’ perceptions and affective commitment on performance management towards operational performance, it was revealed that both variables positively mediate the relationship between performance management and organizational performance of the SMEs in Ghana. This indicates that SMEs in Ghana have seen the need for implementing proper performance management systems based on their own capabilities to ensure effectiveness in meeting the organizational 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".