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Factors Influencing User Retention of Kios-K Machines in Food and Beverage Services

2024· article· en· W4403212895 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Topics in Contemporary Research
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsComputer scienceFood scienceBusinessChemistry

Abstract

fetched live from OpenAlex

Self-service kiosks are interactive devices that allow customers to access information and services without direct human interaction, enabling businesses to scale operations efficiently and reduce costs. This study examines the effectiveness of self-service kiosks in fast food services and customer satisfaction with this technology. The research addresses varying adaptability among different age groups, such as boomers and millennials. Data was collected using Google Forms via purposive sampling from February to March 2024, involving 443 respondents, 369 of whom had used Kios-K machines. The respondents were predominantly from Indonesia, with a few from Malaysia and the United Kingdom. The study employs Partial Least Squares Structural Equation Modeling (PLS-SEM) using SMART-PLS 4 software. Six variables are analyzed: Self-efficacy, Trust, Ease of Use, Accessibility, Perceived Value, and Use Retention. Results indicate that all hypotheses are significant.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.082
GPT teacher head0.356
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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