FACTORS INFLUENCING EMPLOYEE PERFORMANCE FOR IMPROVING ORGANIZATIONAL EFFECTIVENESS AT PHNOM PENH WATER SUPPLY AUTHORITY
Bibliographic record
Abstract
The purpose of this study was to determine the factors influencing employee performance for improving organizational effectiveness at the Phnom Penh Water Supply Authority (PPWSA). This study used a descriptive method to gather, analyze, interpret, and present the information, applying a quantitative approach. The target population was permanent employees from the PPWSA. The sample size of the study is 500 respondents. The Google Forms tool created the survey for data collection and distributed the link to respondents through Telegram. The study collected data using a closed-ended questionnaire and analyzed it using both descriptive and inferential statistics. Confirmatory factor analysis (CFA) is used to make sure the dimensions are reliable and valid, and structural equation modeling (SEM) is used to test the model and hypotheses. After running a SEM, the research result indicates that training and development and organizational development significantly influence employee performance, while leadership and performance appraisal do not. Moreover, the study found that organizational development significantly influences organizational effectiveness, while training and development do not. Furthermore, employee performance has a positive and significant effect on organizational effectiveness. The study concluded that this finding will be useful to the management of PPWSA and other water supply firms in terms of understanding and learning more about how those variables interact and affect employee performance in order to improve organizational effectiveness in the workplace.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".