Individual Differences and Expectations of Automated Vehicles

Zhang, Qiaoning · 2021 · International Journal of Human-Computer Interaction

DOI: 10.1080/10447318.2021.1970431

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Summary

This study investigates how individual differences, including demographics and personality traits, influence public expectations of automated vehicles (AVs). While AVs offer potential safety and environmental benefits, widespread adoption is hindered by public reluctance and varying levels of trust. Expectations are a critical determinant of technology adoption; if expectations are too low, users may never try the technology, whereas excessively high expectations can lead to disappointment and rejection if the technology fails to perform as anticipated. Although prior research has linked individual differences to AV adoption attitudes, little is known about how these differences specifically shape initial expectations. This research aims to fill that gap by identifying which demographic and personality subgroups hold higher or lower expectations, thereby informing strategies for expectation calibration and AV design. The researchers conducted an online survey of 443 U.S. drivers recruited via Qualtrics to ensure a representative sample based on national demographic statistics. Participants provided data on age, gender, ethnicity, education, income, marital status, geographic region, driving frequency, driving experience, and Big Five personality traits. AV expectations were measured using a three-item questionnaire assessing overall, effectiveness, and safety expectations on a 7-point Likert scale. Statistical analysis employed one-way analysis of variance (ANOVA) with Bonferroni corrections to detect significant differences in expectations across various subgroups. Results indicated that AV expectations vary significantly across most individual difference categories. Higher expectations were associated with younger age, male gender, non-White non-Hispanic ethnicity (specifically Black/African American and Hispanic groups compared to White non-Hispanic), higher education levels, never-married status, higher driving frequency, and less driving experience. Personality traits also played a significant role; drivers scoring high in extraversion, agreeableness, conscientiousness, and emotional stability held higher expectations. Conversely, openness to experience and geographic region did not significantly predict expectations. Income showed no statistically significant difference, though higher-income drivers tended to have slightly higher mean expectations. These findings provide a foundation for understanding the heterogeneity of public expectations regarding AVs. By identifying which groups are prone to high or low expectations, designers and policymakers can tailor interventions to calibrate expectations appropriately, potentially increasing adoption rates. For instance, strategies might need to address the lower expectations of older drivers or those with extensive driving experience. The study highlights the importance of considering individual differences in human-computer interaction research and suggests that personalized approaches to expectation management could mitigate barriers to AV adoption. Limitations include the cross-sectional design, which prevents causal inference, and the focus on U.S. drivers, suggesting a need for further experimental and international research.

Key finding

Automated vehicle expectations vary significantly by individual differences, with higher expectations associated with younger age, male gender, White non-Hispanic ethnicity, higher education, never-married status, frequent driving, less driving experience, and high levels of extraversion, agreeableness, conscientiousness, and emotional stability.

Methodology

survey

Sample size: 443

Provenance

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Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

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• acceptance adoption
• personality driving