Employee Participation in Decision-making (PDM) and Firm Performance
Bibliographic record
Abstract
The objective of this study is to examine the influence of employee participation in decision-making on firm performance in Saudi Arabia’s manufacturing sector. Data were collected through pre-validated, piloted questionnaires, which were e-mailed to 341 manufacturing firms. The questionnaires asked about employee involvement in decision-making and performance variables. The response rate was 63.4 percent. Dimensions of PDM were rendered into 20 statements in the form of a five-point Likert scale. The scale, ranging from no involvement to substantial involvement, measured the degree of PDM. Additionally we used a five-point Likert scale to determine the extent of the firms’ performance in terms of the 10 criteria. The scores of the 10 items were summed and averaged to establish the mean index of the firms’ performance. An index of less than 4.0 was regarded as low firm performance; an index of 4.0 and above was considered to represent high firm performance. Statistical tools were used in analysis. Through product–moment correlation, we examined whether a relationship existed between employee participation in decision-making and firm performance. Regression analysis provided the extent of variation in the dependent variable and Z-test (approximated by the independent samples t-test). Findings showed a significant positive relationship exists between PDM and firm performance, suggesting that PDM is an essential component influencing firm performance. The higher the level of employee participation in decision-making, the higher the level of firm performance.Future studies involving the service industry would shed light on PDM in industries besides manufacturing.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 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".