Validation of the Three-Component Model of Organizational Commitment in Pakistan
Bibliographic record
Abstract
This study examined the construct validity of Meyer and Allen’s (1991) three-component model of organizational commitment (OC) in a Pakistani context. Three separate studies were conducted using data from public sector organizations. Study I was a pilot study conducted to determine the readability, and ease of understanding of the measures being used. In the study II, three component organizational commitment (OC) scales (Meyer and Allen, 1991) were translated into Urdu. In the final study, data were collected from 228 employees of a large public sector organization of Islamabad. Five LISREL models were developed to test the best fitting model from the derived data. Exploratory and Confirmatory Factor Analyses (CFA) were conducted to examine the relationship of various constructed models. The results of CFA indicated that a three factor oblique model fit the data best, consistent with the previous research. Reliabilities of the affective and continuance commitment scales were adequate, however, normative commitment scales exhibited relatively low internal consistency reliability. Finally, the present study found that affective and normative commitment were higher in Pakistani and Chinese employees than in previously published samples from South Korea and Canada. Continuance commitment in Pakistani and Chinese samples was lower, however, non significant differences were found between South Korean and Canadian samples on continuance 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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".