Factors Influencing Students’ Satisfaction with Online Food Delivery Services: An Empirical Study
Bibliographic record
Abstract
This study examines the determinants of consumer satisfaction in Dubai’s rapidly expanding app-based food delivery sector, drawing on the theory of consumer behavior and analyzing data from 180 undergraduate students at Canadian University Dubai using Structural Equation Modeling (SEM) from January to February 2024. We evaluated the effects of delivery service price, meal pricing, food quality, restaurant variety, e-service quality, delivery timing, product advertisements, and sales promotions. The sample represents mobile-first generations. The findings reveal that food price, restaurant food quality, restaurant variety, e-service quality, delivery time, product advertisements, and sales promotions all positively influence consumer satisfaction. However, delivery charges, delivery promotions, and online payment security had no significant impact on the satisfaction of this mobile-first generation. Thus, businesses should streamline delivery times and emphasize value through targeted marketing to mobile-first consumers, ensuring that delivery charges and payment security do not hinder consumer satisfaction. These findings may help inform the development of meal delivery applications designed to attract and retain students in this specific market segment.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".