隨著科技的進步，在自動駕駛的相關技術方面也日漸成熟。本研究旨在探討未來民眾使用自動駕駛車輛之行為意向，結合情境式問卷進行方便抽樣，以計畫行為理論(theory of planned behavior, TPB)為基礎並加入知覺風險、個人創新特質等影響因素，設計本研究框架。研究方法利用：(1)偏最小平方結構方程模式(partial least squares structural equation modeling, PLS-SEM)檢驗路徑關係；(2)偏最小平方多群組分析(partial least squares multi-group analysis, PLS-MGA)解釋可觀測異質性的調節效果。(3) PLS預測取向分組(PLS prediction oriented segmentation, PLS-POS)探索不可觀測的異質性。本研究期望探討:(1)計畫行為理論因子是否會影響行為意向；(2)其他影響因素(例如價格敏感度等)是否會影響行為意向；(3)是否存在其他不可觀測的異質性。本研究分別於台北及台中地區蒐集574個有效樣本，實證結果顯示：(1)計畫行為理論之因子均顯著影響行為意向；(2)在其他影響因素中，除知覺風險外，均會顯著影響行為意向。另外，個人創新特質與知覺風險並無顯著關係；(3)異質性分析結果中，性別存在部分路徑的調節效果；另外透過PLS-POS找出樣本存有兩個潛在類別。最後基於分析結果提出研究結論與意涵。;The aim of this study is to explore the behavior intention of people to use the fully automatic vehicle (FAV). FAV has not been commercialized at present. However, with the progress of science and technology, FAV has become increasingly sophisticated and matured. The research framework is constructed based on the theory of planning behavior (TPB) and additional influencing factors such as perceived risk, personal innovativeness and price sensitivity. With the sample data of 574 respondents collected from two cities, i.e., Taipei (276 respondents) and Taichung (298 respondents), we perform analysis with: (1) partial least squares structural equation modeling (PLS-SEM) to examine path relationships. (2) partial least squares multi-group analysis (PLS- MGA) to elaborate observable heterogeneity. (3) PLS prediction orientation segmentation (PLS-POS) to study unobservable heterogeneity. The empirical results showed that: (1) except for the effect of personal innovativeness on perceived risk and perceived risk on behavior intention, all other direct effects on behavior intention are significant. (2) for observed variables, heterogeneity does exist by gender, but not by city. (3) for unobserved variables, among few numbers of segments, two segments can be best identified by PLS-POS. Finally, a few remarks are provided in the end.
