Information Technology, Food Service Quality and Restaurant Revisit Intention
Bibliographic record
Abstract
In this article, we determine whether there is a link between information technology (IT) use in ensuring food service quality and revisit intention. We examined how the use of IT applications in food service affects revisit intention to a hotel's food outlet. To conduct the study, we used a 29-item DINESERV: A Tool for Measuring Service Quality in Restaurants. The DINESERV questionnaire helps restaurateurs gauge customer satisfaction, identify problems, and find solutions. The 29-item questionnaire includes five service-quality categories: assurance, Empathy, reliability, responsiveness, and tangibles. It's meant to help operators gauge what consumers expect from a restaurant. We collected 280 responses from guests visiting Bangladesh's five-star hotels' food service outlets and executed the proposed correlations using PLS-SEM. This study showed that IT application use in determining food service quality does not correlate with revisit intention and that it influences guest confidence, which greatly influences revisit intention.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".