THE INFLUENCE OF TRANSPORT MANAGEMENT PRACTICES ON CUSTOMER SATISFACTION IN FAST-FOOD SECTOR: EVIDENCE FROM SOUTH-WESTERN NIGERIA
Bibliographic record
Abstract
The fast-food business over the years have been experiencing issues arising from the inefficient transport management practices (TMP), such as delays in delivery, high costs and variable service quality. Such issues negatively affect customer satisfaction (CS) and overall business performance as evidence from the literature. This research examines impact of TMP on customer satisfaction in fast-food sector of Nigeria. The data were sourced from primary source. A structured questionnaire was directed to 400 participants who are customers of fast-food industries from all the six states in south-west Nigeria. Moreover, data were analyzed using descriptive and inferential statistics. The SEM and EFA were used to evaluate relationship amid TMP and CS. The results show high load factors (TMP: 0.884–0.978; CS: 0.846–0.983) and excellent fit criteria for the model (CMIN/DF = 1.20523, RMSEA = 0.029, GFI = 0.951), indicating critical role of TMP in service speed, dependability, and product quality. The research concludes that efficient TMP results in higher customer satisfaction and suggests the use of the GPS-based route planning, computerized delivery systems to curtail delays and inefficiency in operations. It is recommended that the fast-food companies should adopt the real-time order tracking and also regularly train their delivery workforce so as to satisfy their customer as much as possible.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".