What is good? Exploring the applicability of a one item measure as a proxy for measuring acceptance in driver-vehicle interaction studies
DOI: 10.1007/s12193-024-00432-1
archive: archived pipeline: cataloged verified
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Summary
This study addresses the challenge of measuring user acceptance for driver assistance systems (ADAS) in practical, applied research settings. While acceptance is a critical variable for the adoption of Level 2 automation, standard multi-item scales like the Technology Acceptance Model (TAM) and the van der Laan (VDL) scale are often too lengthy and intrusive for repeated measurement during driving. The authors investigate the validity and applicability of a Single-Item Acceptance Measure (SIAM), specifically the statement “I perceive the system as good,” as an efficient proxy for measuring acceptance attitude. The goal was to determine if this single item could reliably correlate with established multi-item scales and predict behavioral intention to use, thereby offering a viable tool for real-time data collection without compromising safety or cognitive load. To test this, the researchers conducted a driving simulator study with 63 participants using a high-fidelity BMW simulator. The experimental design was between-subjects, manipulating the level of system introduction: one group received a full explanation of the ADAS functionalities, while the other received an incomplete introduction. Participants navigated four complex driving scenarios, including highway driving, urban traffic, and edge cases requiring system supervision. Acceptance was measured repeatedly throughout the drive using the SIAM, the VDL, and the TAM. The SIAM was administered via auditory presentation with verbal responses to minimize distraction, allowing for measurements during the drive, whereas the more extensive TAM and VDL scales were administered post-drive or during stops. The results supported the construct validity of the SIAM. The single-item measure showed significant correlations with the VDL scale across different measurement points, demonstrating convergent validity. Furthermore, the SIAM successfully differentiated between the two experimental groups; participants who received a full system introduction reported higher acceptance scores than those with incomplete instructions, confirming the measure’s sensitivity to known variables. Crucially, the SIAM also demonstrated predictive validity by significantly predicting the behavioral intention to use the system, aligning with the theoretical framework of the TAM where attitude mediates usage intention. The study concludes that the SIAM is a promising, efficient tool for assessing acceptance in applied driver-vehicle interaction studies. While acknowledging the psychometric limitations of single-item scales compared to multi-item measures, the authors argue that the SIAM offers a necessary compromise for settings requiring frequent, unintrusive data collection. By validating this proxy against established models, the research provides evidence that simple, one-item measures can yield comparable insights to complex scales, facilitating more dynamic and user-centered evaluation of driver assistance technologies in both simulated and real-world contexts.
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-06 |
| archive | success | canonical_url | — | — | 1 | 2026-06-09 |
| extract | success | cached | — | — | 2 | 2026-06-09 |
| clean | success | clean | — | — | 1 | 2026-06-07 |
| chunk | success | chunk | — | — | 1 | 2026-06-07 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-07 |
| promote | success | — | — | — | 1 | 2026-06-06 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-09 |
| tag | success | vector_similarity | — | — | 15 | 2026-06-11 |
| verify | success | — | — | — | 1 | 2026-06-09 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-09; verification: verified.
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- Empirical Findings: self report data
- Methodological Resource: validation psychometrics, tool software