Exploring the Impact of Innovation Resistance on Public Adoption of Urban Air Mobility: Environmental Concern and Innovativeness as Moderators
Bibliographic record
Abstract
Urban air mobility (UAM) helps to revolutionize intra‐ and intercity transportation systems and fosters a more sustainable future. Prior research has primarily concentrated on consumers’ adoption of UAM from the perspective of technology acceptance and diffusion, overlooking the crucial dimension of innovation resistance. This study addresses this oversight by integrating the stimulus–organism–response (SOR) framework with the innovation resistance theory (IRT). Specifically, it employs personal innovativeness and environmental awareness as moderating variables and negative attitude as a mediation factor. An online survey in 2024 in China, and 695 valid responses were used to test the proposed hypotheses. The results indicate that usage barriers, value concerns, risk perceptions, and traditional norms are significantly and positively correlated with negative attitudes, ultimately leading to a diminished intention to adopt UAM. Notably, personal innovativeness and environmental awareness mitigate the impact of risk perceptions and traditional norms on these negative effects. The findings of this study contribute to the understanding of consumer resistance toward UAM and provide valuable insights for scholars and marketers in devising strategies to overcome these barriers and facilitate the adoption of UAM systems.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".