A comparative review of user acceptance factors for drones and sidewalk robots in autonomous last mile delivery
Bibliographic record
Abstract
Autonomous delivery technologies play a pivotal role in meeting the high expectations of customers while addressing the sustainability challenges posed by last-mile delivery traffic, particularly in urban areas. Over the past five years, research on user acceptance of these groundbreaking technologies has surged. This paper represents the first comprehensive review that consolidates and compares user acceptance factors related to deliveries by drones and sidewalk robots, drawing from global questionnaire-based studies. Our research reveals some common factors that consistently influence user acceptance for both drone and sidewalk robot deliveries and also sheds light on technology-specific acceptance factors. However, it's important to recognize that some of these factors may vary depending on the demographics and location of the studies conducted. Our findings intend to provide managerial insights to technology and policy makers, enabling strategic planning for the adoption of these innovative technologies. • First comprehensive review comparing user acceptance of drone and robot deliveries based on studies from the past five years. • Identifying common factors that consistently influence user acceptance for both drone and sidewalk robot deliveries. • Presenting opportunities to reduce environmental impact and improve cost efficiency in the delivery sector. • Discussing mainstream user acceptance theories, which guide the empirical studies on user acceptance of autonomous last mile delivery. • Managerial insights for technology developers and policymakers to aid in strategic planning for the adoption of autonomous delivery technologies.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".