Organizational Commitment in a Health NGO in Pakistan
Bibliographic record
Abstract
Abstract The non-NGO literature provides evidence that employees’ perceptions about their organization’s support to them influences their commitment to the organization. NGOs, which have an increasing presence in developing countries, have not been a target for this type of organizational research. This cross-sectional study, based in a health NGO in Pakistan, examined relationship between employees’ perceived organizational support (POS) and their organizational commitment, and relationship between perceptions about organizational fairness, supervisor support, and job conditions with POS. The current organizational commitment literature guided the design of the survey tool. Focus group discussions were carried out at another health NGO to identify NGO and developing country specific items for inclusion in the survey tool. A total of 249 employees participated in the study, yielding a response rate of 96%. Factor analysis of the survey items indicated that the current scales used for measuring the study variables in the non-NGO sector were valid for the NGO sector as well. In addition, three new variables, namely female supportiveness, personal supportiveness, and favorableness of work conditions were assessed. Findings revealed that POS was significantly related with organizational commitment and actions, such as organizational fairness, supervisor support, and extrinsically satisfying job conditions. Female and personal supportiveness, which are generally ignored in developing countries on the pretext of limited resources and lack of organizational capacity to address them, were also found to be important in influencing POS.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".