Understanding the Drivers of Adoption for Blockchain-enabled Intelligent Transportation Systems
Bibliographic record
Abstract
This study tended to present a model for examining the effective factors on adoption of blockchain-based intelligent transportation systems (ITS). The methodology employed was correlational descriptive approach using structural equation modeling. The research involved collecting data through a survey, which was completed by 368 individuals residing in prominent cities across Iran. The findings of the study suggest that perceived quality of digital services significantly impacts perceived ease of use, perceived usefulness, and trust in a positive manner. Additionally, the positive influence of information technology infrastructure and social influence on perceived ease of use and perceived usefulness is evident. Moreover, perceived ease of use and perceived usefulness play pivotal roles in building trust and fostering adoption of blockchain-based ITS. The study also highlights the positive and significant impact of transparent governance on trust and adoption. Furthermore, policies and regulations are identified as key drivers of perceived usefulness and adoption of such systems. Lastly, perceived trust and security are found to positively influence adoption. These findings underscore the potential of the examined variables to serve as robust predictors for the adoption of blockchain-based ITS by citizens.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 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".