Organizational Silence: Its Destroying Role of Organizational Citizenship Behavior
Bibliographic record
Abstract
Purpose: The purpose of this research is to identify the types of Organizational Silence (OS) and its effects on Organizational Citizenship Behavior (OCB) at Teaching Hospitals in Egypt. Design/methodology/approach: To assess OS, refer to (OS questionnaire, Schechtman, 2008; Brinsfield, 2009), and OCB (OCB questionnaire Podsakoff, 1990; Konovsky & Pugh, 1994; and Konovsky & Organ, 1996). Out of the 357 questionnaires that were distributed to employees, 315 usable questionnaires were returned, a response rate of 88%. Multiple Regression Analysis (MRA) was used to confirm the research hypotheses. Findings: The research has found that there is significant relationship between OS and OCB. Also, the research has found that OS directly affects OCB. In other words, OS is one of the biggest barriers to OCB at Teaching Hospitals in Egypt. Practical implications: This research pointed to the need for organizations to adopt a culture which encourages and urges employees to speak in the labor issues and the non-silence in order for the administration to be able to realize these issues and try to solve them first hand in order to prevent their aggravation. Originality/value: Silence climate has an impact on the ability of organizations to detect errors and learn. Therefore, organizational effectiveness is negatively affected. This research aims to measure the effect of OS on OCB. Based on the findings of this research, some important implications are discussed.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".