Factors Influence Use Retention of Kios-K Machines Service in Food and Beverages Services
Bibliographic record
Abstract
Kiosk machines are interactive tablets or touchscreen computers that allow customers to access information and services without direct interaction. By deploying self-service kiosks, businesses can scale their operations faster and more efficiently while reducing costs. The problem in this research is whether the self-service machine Kiosk in fast food service is effective or not and whether it makes all the customers that use it satisfied with the existence of this technology. Not all people, like the boomers or millennials, can adapt and easily use this new technology. Some of the users can do it fast, some are not. The data was collected using Google Forms using Purposive Sampling. It was conducted from February until March 2024. The data were collected from 443 respondents, only 369 of whom had used the Kios-K Machine. The countries that filled it were Indonesia, about 439 people around Jakarta, Depok, Tangerang, Bogor, and Bekasi, a few foreigners, three respondents from Malaysians, and one from the United Kingdom. The research methodology employs Partial Least Squares Structural Equation Modeling (PLS-SEM) through SMART-PLS 4 Software. There are Six variables: Self-efficacy, Trust, Ease of Use, Accessibility, Perceived Value, and Use Retention. The results found that all hypotheses have a significant impact.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".