| 摘要: | 本研究旨在探討未來民眾使用以自駕車為基底的Ride Hailing 服務作為現行大眾運輸旅次之行為意向，結合情境式問卷進行方便抽樣。伴隨科技的進步，Robo-Taxi將於2018年底實行。儘管尚未實行於台灣區域，然而作為研究目的，探索人們對其使用之意向是極具研究價值。本研究架構是以比較整合科技接受模型(UTAUT)、科技接受模型(TAM)-計劃行為理論(TPB)整合模型及科技接受模型(TAM)等三個模型為基礎並加入知覺風險、環境問題、信任等影響因素，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。(4)重要性和績效矩陣分析(Importance-performance map analysis, IPMA)檢視外生潛在變數對內生潛在變數的相對重要性等。
本研究期望探討:(1)其他影響因素(例如: 知覺風險、環境問題等)是否會影響行為意向；(2)是否存在其他不可觀測的異質性。本研究分別於北部 (台北車站及台灣大學)、南部蒐集359個有效樣本，實證結果顯示：(1)理論之因子均顯著影響行為意向；(2)在其他影響因素中，除主觀規範、知覺風險及環境問題外，均顯著影響行為意向；(3)異質性分析結果中，性別及地區存在部分路徑的調節效果；另外透過PLS-POS和IPMA找出樣本存有兩個潛在類別及相對重要性與表現性。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 ride hailing service on autonomous vehicle as the substitute vehicles for the current mass transit. With the advancement of technology, the ride hailing service will implement by the end of 2018. Although not being introduced in Taiwan, it is still worthy discussing the intention to use the ride hailing service. The research framework is constructed based on the comparison of C-TAM-TPB, TAM and UTAUT and additional influencing factors such as perceived risk, environmental concern, ride hailing and trust. With the sample data of 359 respondents collected from three areas, i.e., northern area (107 respondents): Taipei Station and Taiwan University, southern areas (252 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.(4) Importance-performance map analysis(IPMA) to present path information like importance and performance.
The empirical results showed that:(1)perceived risk, environmental concern,trustindicate positive effect on behavior intention of using ride hailing service.(2)for observed variables, heterogeneity does exist by gender and city. (3) for unobserved variables, amongfew numbers of segments, two segments can be best identified by PLS-POS. (4)for further analysis, we use IPMA as our means to implement in accord with data. Finally, a few remarks about the comparison of the selected three models are providedin the end. |
