| 摘要: | 噗共乘服務滿足了偏鄉最後一哩路的乘車需求，相較於幸福巴士與幸福小黃，噗噗共乘不具有固定路線及班次，而是採用預約制，提供了更高的彈性，目前的預約管道有單位預約、通報網路及LINE預約平台，其中LINE預約平台在空間及時間上較少限制，應當更具優勢，然而透過訪談卻發現LINE預約平台是三個管道中最少被採用的。
為了解民眾對於噗噗共乘LINE預約平台的使用者行為意向，本研究於噗噗共乘實施過的地區進行配額抽樣，蒐集了405個有效樣本，以計畫行為理論(theory of planned behavior, TPB)結合科技接受模型(technology acceptance model, TAM)為本研究之理論基礎，並利用偏最小平方結構方程模型(partial least squares structural equation modeling, PLS-SEM)分析噗噗共乘LINE預約平台各因素交互影響關係。
本研究結果顯示：(1)各假設的直接路徑中，除了主觀規範及態度對於行為意向的影響不顯著外，其餘皆有顯著影響；(2)可觀測異質性的部分利用多群組分析(partial least squares multi-group analysis, PLS-MGA)發現地區間存在區隔效果，在某些地區，使用經驗具有區隔效果；(3)不可觀測異質性的部分則是利用PLS-POS(partial least squares prediction-oriented segmentation)找出了「系統功能派」及「自身能力派」潛在的兩個類別，並進行PLS-MGA也呈現顯著的效果。
最後針對分析結果探討推廣噗噗共乘LINE預約平台的管理意涵及策略，並提出結論與建議。
;BUBU Car Sharing service fulfills the last-mile transportation needs in rural areas. In comparison to Happy Bus and Happy Taxi, BUBU Car Sharing offers greater flexibility as it operates on a reservation basis, without fixed routes or schedules. Currently, reservation channels include institutional booking, onLINE reporting, and the LINE reservation platform. Among these, the LINE reservation platform appears to have a spatial and temporal advantage, but interviews revealed it to be the least utilized channel.
To understand the behavioral intentions of users towards the BUBU Car Sharing LINE reservation platform, this study employed quota sampling in areas where the service has been implemented, resulting in 405 valid responses. Drawing on the Theory of Planned Behavior (TPB) and Technology Acceptance Model (TAM), Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to examine the significance of the paths and analyze the interactive effects of factors affecting the BUBU Car Sharing LINE reservation platform.
The findings of this study reveal the following: (1) In the direct paths of each hypothesis, except for subjective norms and attitudes which do not significantly influence behavioral intention, all others have a significant impact; (2) In the portion related to observable heterogeneity, the use of Partial Least Squares Multi-Group Analysis (PLS-MGA) identifies a segmentation effect among regions, where experiential factors exhibit a discernible distinction in certain areas; (3) For the unobservable heterogeneity, Partial Least Squares Prediction-Oriented Segmentation (PLS-POS) is employed to identify two latent categories, "Functionalists" and "Self-Empowerment Advocates," and conducting PLS-MGA also demonstrates significant effects.
Finally, based on the analysis results, management implications and strategies for promoting the BUBU Car Sharing LINE reservation platform are discussed, and conclusions and recommendations are provided. |
