Simplifying acceptance: A general acceptance factor predicting intentions to use shared autonomous vehicles

IF 3.5 2区工程技术 Q1 PSYCHOLOGY, APPLIED

Transportation Research Part F-Traffic Psychology and Behaviour Pub Date : 2024-11-01 DOI:10.1016/j.trf.2024.10.025

Ole Aasvik , Pål Ulleberg , Marjan Hagenzieker

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引用次数: 0

Abstract

The primary aim of this study was to develop an accurate measure of acceptance for shared autonomous vehicles (SAVs) and to assess whether this measure can predict intentions to use SAVs. One leading model for explaining technology uptake is the UTAUT (Unified theory of acceptance and use of technology). This model is extensive and has received numerous suggested extensions and revisions, even being developed into a Multi-Level Model of Autonomous Vehicle Acceptance (MAVA). The challenge is to consolidate a model that effectively measures SAV acceptance and to determine which extensions capture the unique social situation within SAVs.

The current study used survey data from 1902 respondents. The sample was split into two: one half underwent a principal component analysis (PCA) and the other half a confirmatory factor analysis (CFA). We found that the 24 items we included were reducible to a single general acceptance factor (GAF), with three additional factors measuring interpersonal security, sociability, and attractivity. The GAF was, by a large margin, the most efficacious predictor of intention to use SAVs. The GAF could be further reduced to as little as two predictors, trust and usefulness, accounting for over 70 % of the variance in intention to use. However, there is also an argument to be made that the other components of SAV acceptance may capture different nuances of the service, particularly relating to the social situation. Interaction terms show differences between genders in their rating of sociability and how this impacts intentions to use SAVs.

Our findings carry significant implications for future research in this field. They underscore the pivotal roles of trust and usefulness while corroborating the notion that SAV acceptance is best represented by a single latent component. However, further investigation is warranted to explore individual-level moderating effects on the other components, potentially offering novel insights for the design of future SAV services.

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简化接受度：预测共享自动驾驶汽车使用意向的一般接受因素

本研究的主要目的是开发一种准确的共享自动驾驶汽车（SAV）接受度测量方法，并评估该测量方法能否预测使用 SAV 的意向。解释技术吸收的一个主要模型是UTAUT（技术接受和使用统一理论）。该模型内容广泛，并得到了许多扩展和修订建议，甚至被发展成为自主车辆接受的多层次模型（MAVA）。目前的挑战是整合一个能有效衡量 SAV 接受度的模型，并确定哪些扩展模型能捕捉到 SAV 独特的社会状况。样本被一分为二：一半进行主成分分析（PCA），另一半进行确证因子分析（CFA）。我们发现，我们所包含的 24 个项目可还原为一个单一的总体接受因子（GAF），另外还有三个衡量人际安全感、交际能力和吸引力的因子。GAF 是最有效的预测 SAV 使用意向的因素。GAF 可以进一步缩减到只有两个预测因子，即信任度和有用性，占使用意向差异的 70% 以上。不过，也有一种观点认为，SAV 接受度的其他组成部分可能捕捉到了服务的不同细微差别，尤其是与社会环境有关的细微差别。我们的研究结果对该领域未来的研究具有重要意义。我们的研究结果对这一领域的未来研究具有重要意义。研究结果强调了信任和有用性的关键作用，同时证实了SAV接受度最好由一个潜在成分来代表的观点。然而，我们还需要进一步研究个人层面对其他因素的调节作用，从而为未来SAV服务的设计提供新的见解。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Transportation Research Part F-Traffic Psychology and Behaviour Multiple-

CiteScore

7.60

自引率

14.60%

发文量

239

审稿时长

71 days

期刊介绍： Transportation Research Part F: Traffic Psychology and Behaviour focuses on the behavioural and psychological aspects of traffic and transport. The aim of the journal is to enhance theory development, improve the quality of empirical studies and to stimulate the application of research findings in practice. TRF provides a focus and a means of communication for the considerable amount of research activities that are now being carried out in this field. The journal provides a forum for transportation researchers, psychologists, ergonomists, engineers and policy-makers with an interest in traffic and transport psychology.