Factors Influencing Users’ Willingness to Adopt Connected and Autonomous Vehicles: Net and Configurational Effects Analysis Using PLS-SEM and FsQCA
Bibliographic record
Abstract
To accelerate the widespread adoption of connected and autonomous vehicles (CAVs) and take full advantage of CAVs’ transportation safety, efficiency, and pro-environment, a deep understanding of CAVs acceptance is needed. However, little is known about the combined effects of factors influencing CAVs acceptance using traditional statistical methods. We developed an integrated model to explore how various antecedent factors work together on CAVs’ acceptance. The symmetric (Structure Equation Modeling) and asymmetric (Qualitative Comparative Analysis) techniques were utilized for analyzing data from 362 Chinese. PLS-SEM assesses the net effect of each antecedent on CAVs’ adoption, while fsQCA provides supplementary analysis by revealing the configurations of causal conditions associated with CAVs’ adoption. PLS-SEM results show that perceived usefulness, perceived ease of use, and initial trust directly influence users’ willingness to adopt CAVs, while perceived risk, social influence, and facilitating conditions do not. Meanwhile, automation, ubiquitous connectivity, structural assurance, and corporation reputation indirectly influence CAVs adoption, while environmental performance and technological uncertainty have no statistically significant indirect effect. Interestingly, fsQCA reveals five configurations resulting in a high level of CAVs’ acceptance, and seven configurations leading to the negation of CAVs’ acceptance. The complementary analysis results provide insights into both theory and practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".