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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.029 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".