Date Presented 04/13/21
Automated vehicles (AVs) may provide many societal benefits, including reducing road fatalities, improving participation, and increasing equity of transportation services. However, societal benefits will not occur if individuals do not accept and adopt this technology. The aim of this study was to design a survey that would assess users' perceptions of AVs after exposure to the technology. Although further validation is required, this survey provides insight into users' perceptions of AVs.
Primary Author and Speaker: Justin Mason
Additional Authors and Speakers: Linda Olson
PURPOSE: Automated vehicles (AVs) hold promise towards providing numerous societal benefits for productive aging, including drastically reducing road fatalities, improving participation, and enhancing equity of services to the medically at-risk and economically disadvantaged populations. However, we know little about the acceptance and adoption practices of individuals related to automated vehicles.
RESEARCH AIM: To design a survey to assess transportation users’ perceptions of AVs. Perceptions entail media, trust, control and driving efficacy, social influence, intention to use, cost, authority, perceived usefulness, perceived ease of use, and safety.
DESIGN: Descriptive study that assesses the face and content validity of the user perception survey.
METHOD: Seven technology acceptance models (self-driving car acceptance scale, technology acceptance model [TAM], TAM-extended framework, 4P acceptance model, unified theory of acceptance and use of technology, car technology acceptance model, safety critical technology acceptance model) guided the development of the conceptual framework used to construct a 40-item survey. A focus group of 11 members from the Institute of Mobility, Activity, and Participation assessed face validity to ensure items appeared credible and understandable to the layperson. Seven subject-matter experts in psychology, measurement, transportation, and/or human factors rated items for their relevance to provide a content validity index (CVI) for each item and the overall survey.
RESULTS: Face validity was established by removing one item (2.5%) and rephrasing nine items (22.5%) to enhance the concision and clarity of the items. While establishing content validity, six items were removed from the survey and five items were amended and sent back to the experts for further evaluation. The final scale had a scale CVI rating of 1.00, with 32 of 32 items rated > 0.86 and a scale CVI of 0.96 (mean CVI of all items), indicating acceptable content validity. The final survey consisted of 32 items that assessed users’ perceptions of AVs, 28 items were placed on a visual analog scale ranging from 1-10 and four items were open-ended questions.
CONCLUSION: The approach adopted in this study ensured the face and content validity of the survey and enhanced the items’ relevance, concision, and clarity. The survey is a valuable tool to assess user’s perceptions towards AVs to address the perceptions of consumers across the lifespan. Future work on this survey will focus on further validation (i.e., test-retest reliability and factor analysis) to ensure consistency in findings and to inform clinicians and other transportation stakeholders. The survey is currently being used in three other research projects examining older adults (> 65), young to middle adults (18-64), and those with spinal cord injuries and/or related diseases. An occupational therapist can use the tool to assess the clients’ propensity to use AVs, develop a plan to address areas of concern (e.g., safety, trust, usefulness) through graded exposure, and advocate for the clients’ needs to the private and public transportation stakeholders.
References
Mason, J., Classen, S., Wersal, J., & Sisiopiku, V. (in press). Establishing face and content validity of a survey to assess user perceptions of automated vehicles. Transportation Research Records.
Classen, S., Jeghers, M., Morgan-Daniel, J., Winter, S., King, L., & Struckmeyer, L. (2019). Smart in-vehicle technologies and older drivers: A scoping review. OTJR: Occupation, Participation and Health, 153944921983037. https://doi.org/10.1177/1539449219830376
Madigan, R., Louw, T., Dziennus, M., Graindorge, T., Ortega, E., Graindorge, M., & Merat, N. (2016). Acceptance of automated road transport systems (ARTS): An adaptation of the UTAUT model. Transportation Research Procedia, 14(0), 2217–2226. https://doi.org/10.1016/j.trpro.2016.05.237
Madigan, R., Louw, T., Wilbrink, M., Schieben, A., & Merat, N. (2017). What influences the decision to use automated public transport? Using UTAUT to understand public acceptance of automated road transport systems. Transportation Research Part F: Traffic Psychology and Behaviour, 50, 55–64. https://doi.org/10.1016/j.trf.2017.07.007
