Investigating the net benefits of contactless technologies in quick-service restaurants: the moderating roles of social interaction anxiety and language proficiency
Bibliographic record
Abstract
Purpose This study aims to apply the information system success model (ISSM) to examine the relationships among actual use, use continuance intention, user satisfaction and net benefits in the context of quick-service restaurant (QSR) patrons using two contactless technologies (CT): self-service kiosks (SSK) and mobile applications (MA) for food ordering. The study also investigates the moderating roles of social interaction anxiety (SIA) and language proficiency (LP) in the abovementioned relationships. Design/methodology/approach Survey data from 421 QSR patrons with experience using McDonald's SSK and MA were collected and analyzed through a seemingly unrelated regressions (SUR) technique. Findings Research findings reveal positive associations among actual use, use continuance intention and satisfaction with CT (i.e. SSK and MA). The actual use and satisfaction with CT are positively associated with individual benefits, leading to improved patron satisfaction with QSR. Findings also reveal that, in the case of MA, SIA positively moderates relationships between actual use/satisfaction and individual benefits and between satisfaction and organizational benefit, while LP shows negative moderating effects on those relationships. Originality/value This study is one of the first attempts to present empirical evidence of constructs in the ISSM (actual use, use continuance intention, satisfaction and individual/organizational benefits) in the context of QSR patrons using SSK and MA. It also shows that using MA can address some patrons' psychological problems interacting with others in their food-ordering processes.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".