Consumers’ Ethical Perceptions of Autonomous Service Robots in Hotels
Bibliographic record
Abstract
This study empirically and comprehensively explores consumers’ ethical perceptions of autonomous service robots (ASRs) in hotels. Under the triangulation approach, this study has identified eight themes of consumer perceived ethical issues (privacy, security, safety, transparency, fairness, socialization, autonomy, and responsibility). Each theme can be explained from two dimensions: ethical issues arise during the interaction (i.e., ubiquitous surveillance, excessive data, unidentified risks, service disclosure, inaccessibility, dehumanization, selection of services, and service recovery), and ethical issues can be raised by the characteristics of ASRs (i.e., privacy infringement, malicious use, malfunctions, untrustworthiness, biased features, job replacement, inflexibility, and self-identified solutions). This study is the first to propose ethical issues of ASRs from two dimensions with different intelligence levels, and to highlight ethical issues during hotel service interactions. The findings contribute to ethics studies of service robots from consumers’ perspectives and offer managerial insights to reduce ethical concerns and enhance ASRs usage in hotels.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".