Information Technology, Food Service Quality and Restaurant Revisit Intention
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it