崔馨玥,赵胜川.考虑细化用户群体的全自动驾驶汽车使用意愿[J].,2022,62(5):476-484 |
考虑细化用户群体的全自动驾驶汽车使用意愿 |
Behavioral intention of fully autonomous vehicles considering user group segmentation |
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DOI:10.7511/dllgxb202205005 |
中文关键词:全自动驾驶汽车使用意愿偏最小二乘法结构方程模型模糊C均值聚类潜在剖面分析群体差异 |
英文关键词:fully autonomous vehiclebehavioral intentionpartial least square structural equation modelfuzzy C-means clusteringlatent profile analysisgroup differences |
基金项目:国家自然科学基金资助项目(51478085);交通运输部公路科学研究院智能交通技术交通运输行业重点实验室开放基金课题F20211745;中央高校基本科研业务费专项资金资助项目DUT22GJ203. |
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中文摘要: |
为探究公众中不同典型群体间的异构性和影响选择全自动驾驶汽车(SAE,L5)的因素,基于技术接受模型,对不同公众群体的特征和使用意愿的差异进行分析.在对公众进行全自动驾驶汽车使用意愿线上与线下调查的基础上,采用偏最小二乘法结构方程模型、模糊C均值聚类及潜在剖面聚类,对204名受访者进行了分析及群体聚类.结果显示:(1)感知收益内部有影响关系,社会影响是关键因素,可以通过中介效应影响使用意愿.(2)受访者可分为消极组、摇摆组及积极组3个代表性群体.不同群体间,教育程度、是否有驾照、社会影响和使用意愿表现出了显著差异.积极组最可能成为早期消费者.(3)根据不同群组间的方差分析结果,可以通过社会影响方面的措施改变摇摆组的态度.研究成果有助于更好理解市场细分后用户的使用意愿,为制定针对特定群体的全自动驾驶汽车发展策略提供参考依据. |
英文摘要: |
In order to explore the heterogeneity among typical groups of the public and the factors influencing the selection of fully autonomous vehicles (SAE, L5), the differences in characteristics and behavioral intention of different public groups are analyzed based on the technology acceptance model. Based on the online and offline survey of the behavioral intention to fully autonomous vehicles, 204 respondents are analyzed and clustered by partial least square structural equation model, fuzzy \%C\%-means clustering and latent profile clustering. The results show that: (1)Perceived benefit has an internal influence relationship. Social influence is the key factor, which can influence behavioral intention through mediating effects. (2)Respondents can be divided into three representative groups - negative group, ambivalent group and positive group. Educational level, driver′s license, social influence and behavioral intention show significant differences among different groups. The positive group is most likely to be the pioneer. (3) According to the results of ANOVA between different groups, the attitude of the ambivalent group can be changed through social influence. The results contribute to understanding the behavioral intention after market segmentation, and provide references for the fully autonomous vehicle development strategies for different potential groups. |
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