Customer Preferences for Restaurant Technology Innovations
Bibliographic record
Abstract
When restaurateurs evaluate whether to adopt technology-based service innovations, they must consider not only the costs and benefits of that technology, but also customers’ reactions to the procedural changes accompanying the innovation. Technology that damages customer satisfaction may not be worthwhile, no matter how much it reduces labor costs. In this report we present the results of a national survey on customers’ perceptions of eleven restaurant technologies, as well as whether respondents use those technologies and the value they see in them. The technologies are pagers for table management, handheld order taking while waiting in line, internet-based ordering, kiosk-based payment, kiosk-based food ordering, online reservations, payment via SMS or text message, payment via (RFID) smart card, payment via cell phone using NFC technology, virtual menus available tableside with nutritional information, and virtual menus online with nutritional information. These technologies are categorized in the following five categories: kiosk, menu, online usage, payment-based service innovations, and queuing. Using a research technique called best-worst choice analysis, the study found that the technologies used most commonly were pagers and online reservations, while cell-phone payment was used hardly at all. The results show that the perceived value of a specific technology increases after the customers have had the opportunity to use it, and different demographic segments valued the technologies differently. Frequent technology users visited restaurants more often than infrequent technology users did.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".