The Implementation of High Performance Work System in Public Organizations: Implication for Organizational Performance.
Bibliographic record
Abstract
Abstract A principal concern express among organizational researchers is to understand why some organizations irrespective of size, location and sector outperform others. High performance work system (HPWS) offers an explanation for this phenomenon. The implementation of unique practices leads some organizations to outperform others and give organizations the competitive advantage over others. While it has been well established that HPWS practices affect organizational performance within a large and complex organizations, less have been empirically established if they also create benefit for public organizations and this has generated concerns among researchers in the field of HPWS. Following this argument, this study examines this theoretical gap with a survey data collected from employees in the public sector. Overall, three dimensions of HPWS were identified by the researchers and the level of awareness was assessed on a seven point Likert scale. We found that two out of the three dimensions of HPWS identified in this have a positive relationship with organizational performance. Keywords: HPWS, organizational performance, selective training and development, PMS, individual role.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".