Factors Driving Customer Satisfaction at Shopping Mall Food Courts
Bibliographic record
Abstract
In the service industry, when providers generate a high level of customer satisfaction, they can gain and maintain a major competitive advantage in the marketplace. This competitive advantage can, in turn, lead directly to high profitability and growth. In the present competitive consumer landscape, world, shopping malls must deliver high-quality service to customers given that as a service ecosystem the mall must optimize its own resources and the resources of others to improve both its own circumstances and those of others. Against this general background, in this study, we assess the quality attributes of a food court located in a shopping mall by identifying factors related to the shopping mall—ambience, food variety, convenience, the tenants in the food court, food quality, food price, and restaurant staff. A descriptive analysis and a multivariate analysis, including structural equation modeling, are performed using IBM SPSS and AMOS statistical software. The results of the factor analysis indicate that food quality, followed by convenience and food variety, is the most important factor driving customer satisfaction. The results highlight the importance of networks between different stakeholders in such an ecosystem and provide developers and service providers with information in regard to the attributes most implicated in predicting customer satisfaction in a food court. On this basis, customers are viewed not only as evaluators but also as partners in producing service.
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 | 0.001 | 0.004 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".