What's Driving Me? Exploration and Validation of a Hierarchical Personality Model for Trust in Automated Driving
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Summary
This paper investigates the psychological mechanisms underlying trust in automated driving, specifically focusing on how personality traits influence the formation of dynamic learned trust. The research addresses the critical need for calibrated trust in automation to prevent misuse (excessive reliance) and disuse (underutilization), which are significant safety concerns in human-automation interaction. Motivated by the lack of empirical evidence regarding the specific personality factors that drive trust in automated vehicles, the authors propose and validate a hierarchical personality model based on Mowen’s 3M model. This framework integrates traits across different levels of specificity—elemental, situational, and surface—to provide a comprehensive prediction of trust outcomes. The study employed a two-phase experimental design using structural equation modeling (SEM). In Study 1, the authors explored a full hierarchical model incorporating dispositional variables from three levels: elemental traits (Big Five personality traits, locus of control, and self-esteem), situational traits (affinity for technology and dispositional interpersonal trust), and surface traits (propensity to trust in automation and a priori acceptability of automated driving). The model was refined to identify significant mediation paths and reduce complexity. Study 2 replicated this final model in an independent sample to confirm the robustness of the findings. The research tested specific hypotheses regarding the direct and mediated relationships between these personality constructs and dynamic learned trust in automated driving systems. The results demonstrated that the hierarchical personality model provided a good fit and explained a large proportion of variance in trust in automation across both studies. The findings confirmed that extraversion, neuroticism, and self-esteem at the elemental level significantly influence trust. At the situational level, affinity for technology and dispositional interpersonal trust were key predictors. At the surface level, propensity to trust in automation and a priori acceptability of automated driving directly predicted trust outcomes. The combined evidence supports the hypothesis that broader personality traits affect trust through mediating pathways involving more specific situational and surface-level dispositions. Notably, the study identified the significant roles of self-esteem, dispositional interpersonal trust, and affinity for technology, which had not been previously investigated in the context of trust in automation. The significance of this research lies in its confirmation that personality plays a substantial role in trust formation, offering a nuanced understanding of the psychological antecedents of trust in automated systems. By validating a hierarchical model, the authors provide a theoretical solution to the bandwidth-fidelity dilemma in personality research, demonstrating how broad and specific traits jointly predict behavior. These findings have practical implications for the personalization of information campaigns, driver training programs, and user interface designs aimed at calibrating trust in automated driving. Understanding these individual differences can help mitigate maladaptive usage patterns and enhance the safe integration of automated vehicles into daily life.
Key finding
A hierarchical personality model incorporating elemental, situational, and surface traits significantly predicts dynamic learned trust in automated driving, with extraversion, neuroticism, self-esteem, affinity for technology, and dispositional interpersonal trust emerging as key determinants.
Methodology
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Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via verifier_reassess on 2026-05-08.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | — | — | — | 1 | 2026-05-07 |
| archive | success | openalex | — | — | 5 | 2026-06-06 |
| extract | success | cached | — | — | 3 | 2026-06-10 |
| clean | success | clean | — | — | 1 | 2026-06-04 |
| chunk | success | chunk | — | — | 1 | 2026-06-04 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-04 |
| enrich | success | — | — | — | 1 | 2026-05-07 |
| promote | success | — | — | — | 1 | 2026-05-07 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 15 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
- trust in automation foundations
- trust calibration
- personality driving
- automation
- acceptance adoption
- sensation seeking
Information type
What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).
- Empirical Findings: self report data
- Theoretical Contribution: computational model