噗噗共乘LINE 預約系統使用者行為意向之研究

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：50

、訪客IP：3.138.34.93

姓名

傅錦玟(Chin-Wen Fu) 查詢紙本館藏

畢業系所

土木工程學系

論文名稱

噗噗共乘LINE 預約系統使用者行為意向之研究

相關論文

★ 圖書館系統通閱移送書籍之車輛途程問題	★ 起迄對旅行時間目標下高速公路匝道儀控之研究
★ 結合限制規劃法與螞蟻演算法求解運動排程問題	★ 共同邊界資料包絡分析法在運輸業之應用-以國內航線之經營效率為例
★ 雙北市公車乘客知覺服務品質、知覺價值、滿意度、行為意向路線與乘客之跨層次中介效果與調節式中介效果	★ Investigating the influential factors of public bicycle system and cyclist heterogeneity
★ A Mixed Integer Programming Formulation for the Three-Dimensional Unit Load Device Packing Problem	★ 高速公路旅行時間預測之研究--函數資料分析之應用
★ Behavior Intention and its Influential Factors for Motorcycle Express Service	★ Inferring transportation modes (bus or vehicle) from mobile phone data using support vector machine and deep neural network.
★ 混合羅吉特模型於運具選擇之應用-以中央大學到桃園高鐵站為例	★ Preprocessing of mobile phone signal data for vehicle mode identification using map-matching technique
★ 含額外限制式動態用路人均衡模型之研究	★ 動態起迄旅次矩陣推估模型之研究
★ 動態號誌時制控制模型求解演算法之研究	★ 不同決策變數下動態用路人均衡路徑選擇模型之研究

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

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。

摘要(英)

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.

關鍵字(中)

★ 噗噗共乘
★ 預約系統
★ 計畫行為理論
★ 科技接受模型
★ 偏最小平方結構模型
★ 多群組分析
★ 預測取向區隔

關鍵字(英)

★ BUBU Car Sharing service
★ theory of planned behavior
★ technology acceptance model
★ partial least squares structural equation modeling
★ PLS-MGA
★ PLS-POS

論文目次

摘要 II
ABSTRACT III
目錄 V
表目錄 VII
圖目錄 IX
第一章緒論 1
第二章現況分析與研究架構 4
2.1 噗噗共乘 4
2.2 研究架構 6
2.3 研究假設 9
第三章研究方法 13
3.1 偏最小平方結構方程模型 13
3.2 PLS-SEM異質性分析方法 17
3.2.1 多群組分析 17
3.2.2 預測取向區隔 18
第四章研究設計 20
4.1 問卷設計 20
4.2 抽樣方法 21
4.3 信效度衡量標準 24
4.3.1 信度分析 24
4.3.2 效度分析 26
第五章結果分析 30
5.1 受訪者社經分析 31
5.2 共同方法變異 32
5.3 信效度分析結果 33
5.3.1 信度分析結果 33
5.3.2 效度分析結果 34
5.4 假設驗證 35
5.5 重要性績效 38
5.6 異質性分析 39
5.6.1 多群體分析 39
5.6.2 預測取向區隔 47
5.7 管理意涵 52
第六章結論與建議 54
6.1 結論 54
6.2 建議 55
參考文獻 57
附錄A 敘述性統計 62
附錄B 問卷 64
附錄C 交叉負荷量 69

參考文獻

[1] 中華民國交通部。2009，公路公共運輸發展計畫。臺北市。
[2] 中華民國交通部。2016，智慧運輸系統發展建設計畫 (106-109年)。臺北市。
[3] 中華民國行政院。2021，改善偏鄉交通—為偏鄉完成最後一哩路。臺北市。
擷自：https://www.ey.gov.tw/Page/5A8A0CB5B41DA11E/cfdb2443-fffe-4da7-a697-1cc7cd86fa63
[4] 高雄市政府交通局。2023，公車式小黃服務計畫簡介。高雄市。
擷自：https://www.tbkc.gov.tw/Service/PublicTransport/Taxis?ID=db7194a9-88c3-4277-9343-6e080d7493e8
[5] 陳惠國。2019，研究分析方法教學講義，國立中央大學土木工程學系，桃園市。
[6] 科技顧問室。2023，交通部噗噗共乘幸福巴士2.0，臺北市。擷自：https://www.bubumotc.net/CarSharing_RWD/WebPage/PublicWebModule/pagBubumotc.aspx　
[7] Ajzen, I., and Fishbein, M., 1980. Understanding Attitudes and Predicting Social Behavior. Prentice-Hall, New Hersey. (ISBN：978-0139364358)
[8] Becker, J.M., Rai, A., Ringle, C.M., and Völckner, F., 2013. Discovering Unobserved Heterogeneity in Structural Equation Models to Avert Validity Threats. MIS Quarterly 37(3), 665-694. http://www.jstor.org/stable/43825995.
[9] Chang, C.M., Liu, L.W., Huang, H.C., and Hsieh, H.H. 2019. Factors Influencing OnLINE Hotel Booking: Extending UTAUT2 with Age, Gender, and Experience as Moderators. Information. 10. 281. 10.3390/info10090281.
[10] Canada Victoria Transport Policy Institute. 2023. Evaluating Transportation Equity. Victoria, Canada.
[11] Chin, W.W., and Marcoulides, G., 1998. The Partial Least Squares Approach to Structural Equation Modeling. Modern Methods for Business Research, 295-336.
[12] Davis, F.D., 1985. A Technology Acceptance Model for Empirically Testing New End-User Information Systems. Massachusetts Institute of Technology, Massachusetts.
[13] Davis, F.D., 1989. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly 13(3), 319-340. Doi:https://doi.org/10.2307/249008.
[14] Davis, F.D., Bagozzi, R.P., and Warshaw, P.R., 1989. User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Management Science 35(8), 982-1003. http://www.jstor.org/stable/2632151.
[15] Fornell, C., and Larcker, D.F., 1981. Structural Equation Models with Unobservable Variables and Measurement Error: Algebra and Statistics. Journal of Marketing Research 18(3), 382-388. Doi:https://doi.org/10.2307/3150980.
[16] Harman, H.H., 1976. Modern Factor Analysis. The University of Chicago Press, Chicago. (ISBN：978-0226316529)
[17] Henseler, J., 2010. PLS-MGA: A Non-Parametric Approach to Partial Least Squares-based Multi-Group Analysis. Part of Challenges at the Interface of Data Analysis, Computer Science, and Optimization. 495-501. Doi:http://doi.org/10.1007/978-3-642-24466-7_50.
[18] Hale, J., Householder, B., and Greene, K., 2003. The Theory of Reasoned Action. Part of The Persuasion Handbook: Developments in Theory and Practice, 259-286. Doi: http://doi.org/10.4135/9781412976046.n14.
[19] Ilham, S., and Mat, N. 2012. Examining a Theory of Planned Behavior (TPB) and Technology Acceptance Model (TAM) in Internet Purchasing Using Structural Equation Modeling. Journal of Arts, Science & Commerce. 3. 62-77.
[20] Jöreskog, K.G., 1978. Structural Analysis of Covariance and Correlation Matrices. Psychometrika 43, 443-477. Doi:http://doi.org/10.1007/BF02293808.
[21] Lohr, S. 2019. Sampling: Design and Analysis. Duxbury Press. London, England.
[22] Lohmöller, J.-B. 1989. Latent Variables Path Modeling with Partial Least Squares. Heidelberg: Physica-Verlag.
[23] Rutherford, G.S.W., 1988. Multivariate Data Analysis with Readings. Journal of the Royal Statistical Society Series D: The Statistician 37(4-5), 484.Doi:https://doi.org/10.2307/2348783
[24] Mattila, A.S., and Enz, C.A., 2002. The Role of Emotions in Service Encounters. Journal of Service Research 4(4), 268-277. Doi:http://doi.org/10.1177/1094670502004004004.
[25] McMullan, R., and Gilmore, A. 2008. Customer Loyalty: An Empirical Study. European Journal of Marketing. 42. 1084-1094. 10.1108/03090560810891154.
[26] Muhammad Z. I., Faza F. B., and Sigit P. 2022. Using an Integrated Model of TPB and TAM to Analyze the Pandemic Impacts on the Intention to Use Bicycles in the Post-COVID-19 Period. IATSS Research, Volume 46, Issue 3, p.p. 380-387
[27] Nadlifatin, R., Miraja, B., Persada, S., Belgiawan, P., Redi, A.A.N., and Lin, S.C. 2020. The Measurement of University Students’ Intention to Use Blended Learning System through Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB) at Developed and Developing Regions: Lessons Learned from Taiwan and Indonesia. International Journal of Emerging Technologies in Learning 15 (9), pp. 219-230. Kassel, Germany: International Journal of Emerging Technology in Learning.
[28] Podsakoff, P., and Organ, D.W., 1986. Self-Report in Organizational Research: Problems and Prospects. Journal of Management 12(4), 531-544. Doi:http://doi.org/10.1177/014920638601200408.
[29] Podsakoff, P.M., MacKenzie, S.B., Lee, J.Y., and Podsakoff, N.P., 2003. Common Method Biases in Behavioral Research: A Critical Review of the Literature and Recommended Remedies. The Journal of Applied Psychology 88(5). 879-903. Doi:http://doi.org/10.1037/0021-9010.88.5.879.
[30] Ringle, C.M., Wende, S., and Becker, J.M, 2022. SmartPLS 4. Oststeinbek: SmartPLS GmbH, http://www.smartpls.com.
[31] Schwab, D.P., 1980. Construct Validity in Organizational Behavior. Research in Organizational Behavior: An Annual Series of Analytical Essays and Critical Reviews 2, 3-43.
[32] Sethi, V., and Carraher, S., 1993. Developing Measures for Assessing the Organizational Impact of Information Technology: A Comment on Mahmood and Soon′s Paper. Decision Sciences 24(4), 867-877. Doi: https://doi.org/10.1111/j.1540-5915.1993.tb00493.x.
[33] Taylor, S., and Todd, P., 1995. Decomposition and Crossover Effects in the Theory of Planned Behavior: A Study of Consumer Adoption Intentions. International Journal of Research in Marketing 12(2), 137-155. Doi: https://doi.org/10.1016/0167-8116(94)00019-K.
[34] United State Department of Transportation, 2023. U.S. Department of Transportation Equity Action Plan. Washington, DC, USA
[35] Venkatesh, V., Thong, J., and Xu, X. 2012. Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly. 36. 157-178. 10.2307/41410412.

指導教授

陳惠國(Heuy-Kuo Chen)

審核日期

2023-8-21

推文