Investigating the Impact of Communication Satisfaction on Organizational Commitment: A Practical Approach to Increase Employees’ Loyalty
Bibliographic record
Abstract
Recently, employees are seen as important assets of organizations in which the majority of them cannot deal with employees properly or even underestimate their importance. One of the essential issues is increasing employees’ organizational commitment, which in turn minimizes customers’ switching behaviour and the way organizations usually communicate appropriately their internal market strategy. In order to have a better vision about such issue, this study is planned to investigate the impact of communicational satisfaction on organizational commitment. A variety of communicational satisfaction dimensions are taken into analysis; such dimensions include: communication climate, relationship to superiors, organizational integration, media quality, horizontal and informal communication, organizational perspective, relationship with subordinates and the personal feedback. In addition, three factors of organizational commitment were taken into considerations that are affective commitment, continuance commitment and normative commitment. The study followes the qualitative approach in collecting data from employees of Yahoo- Maktoob office in Amman/Jordan. Eight sub-hypotheses are developed and tested accordingly to conclude with the fact that communicational satisfaction has a significant and direct impact on organizational commitment.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".