Information transparency, privacy concerns, and customers' behavioral intentions regarding AI-powered hospitality robots: A situational awareness perspective
Bibliographic record
Abstract
Using the theoretical lens of situational awareness, this research tests two competing models and an intervention to examine the effects of information transparency on customers' privacy concerns and behavioral intentions toward interacting with hospitality robots. An exploratory study and two experiments with artificial intelligence-powered anthropomorphic robots were performed. The exploratory study confirmed that information transparency is lacking in practice. Study 1 revealed that such transparency negatively affects customers' behavioral intentions by increasing their situational awareness, which in turn heightens their privacy concerns and reduces their behavioral intentions. Study 2 further identified privacy assurance as a means of mitigating the adverse impact of information transparency on customers' behavioral intentions; the effect of situational awareness on privacy concerns became non-significant when privacy assurance was provided. This research contributes to the hospitality and tourism literature on technological innovation and offers insights into ethically managing customers' privacy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".