Leveraging socially assistive robots in tourism to promote healthy aging: an exploratory study on aging population
Bibliographic record
Abstract
Purpose This study aims to explore the interactions between older adults and socially assistive robot (SAR) in tourism retirement communities from the perspective of mobility, analyzing their acceptance of SAR and the impact on their well-being, in order to provide theoretical foundations and practical guidance for the technological empowerment of the residential tourism model. Design/methodology/approach This study employs experimental design with two separate studies involving older adults aged 45 to 75 who have experience with travel care. Study 1 includes 97 participants divided into two groups to examine the impact of task types (information vs emotional support) provided by artificial intelligence robots on the Satisfaction with Elderly Life. Study 2 expands to four groups with 155 participants to investigate the moderating effect of care models (family-based vs travel-based) and the mediating role of emotional connection in this relationship. Findings The experimental results indicate that the use of SAR, as an emerging artificial intelligence technology, significantly enhances the Satisfaction with Elderly Life of older adults in residential tourism by providing emotional support and facilitating social interaction (p < 0.01). Further analysis reveals that in the context of residential tourism, emotional support provided by SAR is more effective than informational support in home-based care situations, while no significant differences exist in group travel environments. Emotional connection plays a mediating role in these effects. Originality/value This study approaches the integration of SAR with the residential tourism model from a mobility perspective, exploring the potential and practical characteristics of technological empowerment in new elder care models, thereby addressing some of the existing research gaps. The findings suggest that SAR can effectively enhance the Satisfaction with Elderly Life and acceptance of older adults in residential tourism communities. This study not only enriches the research perspective on SAR in elder care but also provides technological support and theoretical basis for the practice of residential tourism, contributing to the broader goals of active aging and technologically supported care.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".