CONFIRMATORY FACTOR ANALYSIS FOR TESTING THE VALIDITY AND RELIABILITY OF AN INTERNAL CAPABILITY AND LOGISTICS OUTSOURCING MEASUREMENT SCALE
Bibliographic record
Abstract
This study aims to develop and validate a measurement scale for internal capability and logistics outsourcing. Regarding the goal of this study, data were obtained from 180 respondents who are currently performing logistics outsourcing in different fields in Sudan (oil industry, telecommunications, logistics services and manufacturing). Depending on the questioner technique, a total of 36 items and 5 subscales were generated based on the literature. The study uses a five-point Likert-style response scale (ranging from strongly agree to strongly disagree). The scale was subjected to confirmatory factor analysis (CFA) for determining the validity and reliability of the study dataset. Cronbach’s alpha reliability coefficient (α) of the scale was reported to be 0.88. Findings of the study indicate that the resulting internal capability and logistics outsourcing measurement scale can serve as a valuable tool for measuring the logistics outsourcing drivers, namely time-related drivers, cost-related drivers, flexibility-related drivers, and quality-related drivers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".