Author response of: A Simple Model for Complex Technology: Introducing and Testing a Framework to Understand Acceptance of Shared Automated Vehicles. Round#1
DOI: 10.24072/pci.rr.101115.ar1
archive: archived pipeline: cataloged verified
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Summary
This study addresses the fragmented and complex nature of existing theoretical frameworks regarding public acceptance of Shared Automated Vehicles (SAVs). While SAVs offer potential benefits for urban mobility, widespread adoption is hindered by public resistance and a lack of parsimonious models that account for the specific social dynamics of shared transport. The authors propose the Shared Automated Vehicle Acceptance (SAVA) model, a simplified framework identifying trust, utility, and social comfort as the three core predictors of SAV acceptance. The research aims to determine whether these factors constitute a single General Acceptance Factor (GAF) or function as distinct but correlated predictors, and to test whether targeted informational interventions can effectively enhance these factors. The study employed a Stage 2 Registered Report design with a final sample of 1,250 respondents after data cleaning, exceeding the minimum power requirements determined by Monte Carlo simulations. The researchers utilized Structural Equation Modeling (SEM) to compare two models: one positing a second-order GAF and another treating trust, utility, and social comfort as distinct direct predictors of behavioral intention. Additionally, a 2x2x2 between-subjects experimental design was used to test the efficacy of informational vignettes targeting trust, utility, and social comfort. The analysis focused on model fit indices (CFI, TLI, RMSEA, SRMR) and effect sizes to evaluate the structural validity of the SAVA model and the impact of the interventions. Results indicate that SAV acceptance is best modeled as distinct but correlated factors rather than a single overarching General Acceptance Factor. The simplified SAVA model explained a large degree of variation in behavioral intention, with utility demonstrating the largest effect size among the three predictors. The model was well-constituted by its indicators, supporting the conceptual distinction between trust, utility, and social comfort. However, the experimental vignettes failed to produce significant effects on any of the three factors. This suggests that the informational interventions used were insufficient to shift public acceptance rates, implying that more impactful strategies may be required to alter public perception. The significance of this research lies in providing a parsimonious, experimentally testable framework that simplifies the complex landscape of technology acceptance models like UTAUT. By validating trust, utility, and social comfort as distinct predictors, the SAVA model offers a streamlined tool for policymakers and service providers to assess public response to SAVs. The findings highlight that while utility is a primary driver of acceptance, current informational campaigns may be inadequate for overcoming barriers related to trust and social comfort. The study advocates for the adoption of such simplified frameworks to facilitate theoretical convergence and practical application in future SAV deployment and research.
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-25 |
| archive | success | canonical_url | — | — | 1 | 2026-06-26 |
| extract | success | cached | — | — | 5 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-26 |
| chunk | success | chunk | — | — | 1 | 2026-06-26 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-26 |
| enrich | success | openalex | — | — | 1 | 2026-06-26 |
| promote | success | — | — | — | 1 | 2026-06-25 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 4 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-26 |
| verify | partial | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified_with_issues.
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- Empirical Findings: self report data
- Methodological Resource: validation psychometrics