Big Five Factors and Employees’ Voice Behavior among Employees in Small-Medium Enterprise in Penang
Bibliographic record
Abstract
This paper examined the relationship between the Big Five factors (openness to experience, conscientiousness, extraversion, agreeableness and neuroticism) and employees’ voice behavior among employees of SME in Penang. The independent variables are the Big Five factors while the dependent variable is employees’ voice behavior. The hypothesized relationship between the Big Five factors and employees’ voice behavior is based on a logical argument that those who demonstrate Big Five factors would be positively and negatively related to employees’ voice behavior to their superior. The theories that support the theoretical framework are the theory of individual difference in task and contextual performance. A total of 292 questionnaires were distributed to employees of a small-medium enterprise in Penang. A total of 108 usable questionnaires were returned yielding a usable response rate of 74%. The collected data were analyzed statistically using multivariate statistics. Factor analysis, reliability analysis, descriptive analysis, correlational analysis, and regression analysis were used as the bases of analyses. The results only indicated that agreeableness and neuroticism among the five independent variables were significantly related to employees’ voice behavior, but positively significant, which did not support the hypotheses of the study. Therefore, all the hypotheses were not supported by the study results.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".