Gauging support for autonomous delivery drones powered by artificial intelligence: A public survey in Singapore
Clinical Snapshot
PICO Framework
| P — Population | General public in Singapore (N = 1,002) |
| I — Intervention | Not applicable - this is a cross-sectional survey study |
| C — Comparator | Knowledge deficit model vs cognitive miser model for predicting AI acceptance |
| O — Outcomes | Public support for autonomous delivery drones; associations between knowledge, heuristic cues, and AI acceptance |
Bottom Line
This cross-sectional survey of 1,002 Singapore residents examined factors influencing public acceptance of AI-powered delivery drones. The study found that objective knowledge about AI was not directly associated with support, while heuristic factors like trust, risk/benefit perceptions, and source credibility were significant predictors. Knowledge appeared to influence support indirectly through these heuristic pathways. While methodologically limited by insufficient detail in the abstract and cross-sectional design, the findings support cognitive miser theory over knowledge deficit models in technology acceptance. However, this study has minimal direct relevance to Australian clinical practice, focusing on delivery technology rather than healthcare applications. The results may have indirect implications for understanding patient acceptance of healthcare technologies, but the cultural and contextual differences limit generalisability to Australian healthcare settings.
Key Findings
P Value: Significant associations reported but specific values not provided
Effect Size: Not reported in abstract
Primary Outcome: Public support for autonomous delivery drones
Nnt Or Sensitivity: Not applicable - descriptive/associational study
Confidence Interval: Not reported
Clinical Application
Limited clinical relevance - focuses on delivery technology rather than healthcare interventions Minimal relevance to Australian healthcare practice. May have indirect implications for acceptance of healthcare technologies, but study context (Singapore delivery drones) differs significantly from Australian clinical settings Not directly applicable to clinical populations
Abstract
There has been emerging empirical evidence supporting the role of artificial intelligence (AI) knowledge in public support for AI. These findings stand in sharp contrast to the long-standing cognitive miser view in determining technological acceptance. In this study, by contrasting models of knowledge deficit against cognitive miser using data from a large-scale public opinion survey about autonomous delivery drones in Singapore (N = 1,002), we investigated whether knowledge and various heuristic cues were associated with AI support. Our results revealed that objective knowledge was not a direct determinant of support, while various heuristic cues such as propensity to trust in AI, news media attention, perceived source credibility in scientists and authorities, as well as risk and benefit perceptions presented significant associations. Mediation analyses further revealed knowledge was indirectly associated with support through heuristic cues such as source credibility perceptions as well as risk and benefit perceptions. Our findings were largely consistent with the cognitive miser model in that AI acceptance is immediately associated with cognitive heuristics rather than knowledge. Theoretical and practical implications are discussed.
References
- 1.Cheung, J. C., & Ho, S. S. (2026). Gauging support for autonomous delivery drones powered by artificial intelligence: A public survey in Singapore. PLoS One. https://doi.org/10.1371/journal.pone.0350394
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