使用自動駕駛車輛的行為意向與相關影響因素之研究

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：13

、訪客IP：3.145.161.42

姓名

閻大維(Da-Wei Yen) 查詢紙本館藏

畢業系所

土木工程學系

論文名稱

使用自動駕駛車輛的行為意向與相關影響因素之研究
(Interrelationships between behavior intention and influential factors: An example of fully automated vehicles)

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)

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。

摘要(英)

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.

關鍵字(中)

★ 計畫行為理論
★ 自動駕駛車輛
★ 偏最小平方法
★ 異質性

關鍵字(英)

★ The theory of planned behavior
★ fully automated vehicle
★ PLS-SEM
★ heterogeneity

論文目次

Abstract iii
中文摘要 iv
致謝 v
Table of contents vi
List of figures viii
List of tables ix
1 Introduction 1
2 Theoretical background and hypotheses 4
2.1 Theory of planned behavior 4
2.2 The role of perceived risk, personal innovativeness and price sensitivity 4
2.3 Heterogeneity 6
2.3.1 Observed heterogeneity (due to gender or city) 6
2.3.2 Unobserved heterogeneity (due to latent variable) 6
2.4 Framework of research model 7
3 Research methods 11
3.1 Partial least squares structural equation modeling (PLS-SEM) 11
3.2 Partial least squares structural equation modeling (PLS-SEM) with considerations on heterogeneity 12
3.2.1 Partial least squares multi-group analysis (PLS-MGA) 12
3.2.2 Partial Least Squares-Prediction-Oriented Segmentation (PLS-POS) 13
4 Measures 15
5 Empirical results 17
5.1 Data collection 17
5.2 Descriptive statistics 17
5.3 Common method variance 18
5.4 Measurement model 19
5.4.1 Reliability 19
5.4.2 Validity 21
5.5 Test of hypotheses 22
5.6 Heterogeneous analysis 24
5.6.1 Analysis on observed heterogeneity 24
5.6.2 Analysis on unobserved heterogeneity 25
5.7 Summary 31
6 Discussion and practical implications 32
7 Contribution and limitation 34
7.1 Contribution 34
7.2 Research limitations and future research 34
Appendix A: TPB model 40
Appendix B: Procedure for applying PLS-SEM and PLS-POS 41
Appendix C: Measurement items 43
Appendix D: Descriptive statistics for measurement items 46
Appendix E: The significance level for outer loadings and weights 48
Appendix F: MICOM 49
Appendix G: PLS-MGA 52
Appendix H: Individual segment’s reliability and validity 54

參考文獻

Agarwal, R., Prasad, J., 1998. A conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research, Vol. 9, No. 2, pp. 204–215.
Ajzen, I., 1985. From intentions to actions: A theory of planned behavior, Springer, Heidelberg, Germany.
Ajzen, I., 1991. The theory of planned behavior. Organizational Behavior. and Human Decision Processes, Vol. 50, No. 2, pp. 179-211.
Aldás-Manzano, J., Ruiz-Mafé, C., Sanz-Blas, S., 2009. Exploring individual personality factors as drivers of M-shopping acceptance. Industrial Management and Data Systems, Vol. 109, No. 6, pp. 739–757.
Baron, R. M., & Kenny, D. A., 1986. The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, Vol. 51, No. 6, pp. 1173-1182.
Bauer, R. A., 1960. Consumer behavior as risk taking, In: Hancock, R.S., Dynamic marketing for a Changing World, American Marketing Association, Chicago, pp. 389-398.
Bielefeld, F., Ajzen, I., and Schmidt, P., 2003. Choice of travel mode in the theory of planned behavior: The roles of past behavior, habit and reasoned action, Basic and Applied Social Psychology, Vol. 25, No. 3, pp. 175-187.
Chang, H.-L., Yeh, T.-H., 2007. Motorcyclist accident involvement by age, gender, and risky behaviors in Taipei, Taiwan. Transportation Research Part F: Traffic Psychol. and Behaviors, Vol.10, No. 2, pp. 109-122.
Chen, C. T., 2016. Investigating the Influential Factors of Public Bicycle System and Cyclist Heterogeneity. Master’s Thesis, National Central University, Taiwan.
Chin, W. W., 1998. The partial least squares approach to structural equation modeling. Marcoulides, George A. (Ed). Modern Methods for Business Research, Mahwah, NJ, US: Lawrence Erlbaum Associates Publishers, viii, 1998, pp. 295-336.
Chin, W.W., & Newsted, P.R., 1999. Structural equation modeling analysis with small samples using partial least squares. In Hoyle, R (ed.) Statistical Strategies for Small Sample Research, Thousand Oaks, CA: Sage Publications, 1999, pp. 307-341.
Cronbach, L. J., 1951. Coefficient alpha and the internal structure of tests. Psychometrika, Vol. 16, No. 3, pp. 297-334.
Department of Statistics, Ministry of the Interior (Taiwan, ROC) (2015):
Dowling, G. R., & Staelin, R., 1994. A model of perceived risk and intended risk-handling activity. Journal of Consumer Research, Vol. 21, No. 1, pp. 119-134.
Dudenhöffer, K., 2013. Why electric vehicles failed: An experimental study with PLS approach based on the Technology Acceptance Model. Journal of Management Control, Vol. 24, No. 20, pp. 95-124.
Eggers, F., & Eggers, F. 2011.Where have all the flowers gone? Forecasting green trends in the automobile industry with a choice-based conjoint adoption model. Technological Forecasting & Social Change, Vol. 78, No. 1, pp. 51-62.
Feng, Y. T., 2016. Research on the Behavioral Intention of Electric Vehicle Sharing. Master’s Thesis, National Central University, Taiwan.
Fishbein, M., Ajzen, I., 1975. Belief, attitudes, intention, and behavior. An introduction to theory and research. Addison-Wesley, Boston, MA.
Garson, G. D. 2016. Partial least squares: Regression and structural equation models. Asheboro, NC: Statistical Associates Publishers.
Haenlein, M., & Kaplan, A. M. 2004. A beginner′s guide to partial least squares analysis. Understanding statistics, Vol. 3, No. 4, pp. 283-297.
Hair, J. F., Sarstedt, M., Matthews, L. M., Ringle, C. M., 2016. Identifying and treating unobserved heterogeneity with FIMIX-PLS: Part I–method. Eurpoean Business Review, Vol. 28, No. 1, pp. 63-76.
Hair, J. F., Hult, G. T. M., Ringle, C., Sarstedt, M., 2014. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), Sage, Thousand Oaks, CA.
Harman, H. H., 1976. Modern Factor Analysis, 3rd ed. University of Chicago Press, Chicago.
Henseler, J., 2012. PLS-MGA: A non-parametric approach to partial least squares-based multi-group analysis. In: W. A. Gaul, A. Geyer-Schulz, L. Schmidt-Thieme, and J. Kunze (Eds.) Challenges at the Interface of Data Analysis, Computer Science, and Optimization - Proceedings of the 34th Annual Conference of the Gesellschaft für Klassifikation, Karlsruhe, July 21-23, 2010, Springer, Berlin/Heidelberg, pp. 495-501.
Henseler, J., Ringle, C. M., Sarstedt, M., 2015. A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal Academy of Marketing, Vol. 43, No. 1, pp. 115-135.
Hui, B. S., & Wold, H., 1982. Consistency and consistency at large of partial least squares estimates. Systems under Indirect Observation, Part II, Amsterdam: North-Holland.
Jashapara, A., and Tai, W.C., 2006. Understanding the complexity of human characteristics on e-learning systems: an integrated study of dynamic individual differences on user perceptions of easy of use. Knowledge Management Research & Practice, Vol.4, No. 3, pp. 227-39.
JM Becker, A. Rai, CM Ringle, F. Völckner, 2013. Discovering unobserved heterogeneity in structural equation models to avert validity threats. MIS Quarterly, Vol. 37, No. 3, pp. 665-694.
Jöreskog, K.G., 1978, Structural analysis of covariance and correlation matrices. Psychometrika, Vol. 43, No. 4, pp. 443-477.
Kang, C., 2016. ”Self-Driving Cars Gain Powerful Ally: The Government”. The New York Times. ISSN 0362-4331. Retrieved 2016-09-28.
Keil, M., Tan, B. C., Wei, K.-K., Saarinen, T., Tuunainen, V., Wassenaar, A., 2000. A cross-cultural study on escalation of commitment behavior in software projects. MIS Quarterly, Vol. 24, No. 2, pp. 299-325.
Kim, D.J., Ferrin, D.L., Rao, H.R., 2008. A trust-based consumer decision-making model in electronic commerce: the role of trust, perceived risk, and their antecedents. Decision Support Systems, Vol. 44, No. 2, pp. 544-564.
Lin, P., 2014. What if your autonomous car keeps routing you past Krispy Kreme. The Atlantic, 22.
Moan, I. S., Rise, J., 2011. Predicting intentions not to “drink and drive” using an extended version of the theory of planned behaviour. Accident Analysis & Prevention, Vol. 43, No.4, pp. 1378-1384.
Molin, E., Mokhtarian, P., Kroesen, M., 2016. Multimodal travel groups and attitudes: A latent class cluster analysis of dutch travelers. Transportation Research Part A: Policy and Practice, Vol. 83, No. 2016, pp. 14-29.
Mooi, E., Sarstedt, M., 2011. A Concise Guide to Market Research: The Process, Data, and Methods using IBM SPSS Statistics. Springer, Berlin.
NHTSA, 2013. U.S. Department of transportation releases policy on automated vehicle development. NHTSA 14-13, Thursday, May 30, 2013. Available online:.
Nunnally, J., Bernstein, I., 1994. Psychometric Theory, 3rd ed. McGraw-Hill, New York.
O’Fallon, C., Sullivan, C., and Hensher, D. A., 2004. Constraints affecting mode choices by morning car commuters. Transport Policy, Vol. 11, No. 1, pp. 17-29.
Petit, J., & Shladover, S. E., 2015. Potential cyberattacks on automated vehicles. IEEE Transactions on Intelligent Transportation Systems, 16(2), 546-556.
Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P., 2003. Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of Applied Psychology, Vol. 88, No. 5, pp. 879-903.
Reinartz, W., Haenlein, M., & Henseler, J., 2009. An empirical comparison of the efficacy of covariance-based and variance-based SEM. International Journal of Research in Marketing, Vol. 26, No. 4, pp. 332-344.
Rhodes, N., Pivik, K., 2011. Age and gender differences in risky driving: The roles of positive affect and risk perception. Accident Analysis & Prevention, Vol. 43, No. 3, pp. 923-931.
Rogers, E.M., 1995. Diffusion of Innovations. New York, Free Press.
Sánchez-Prieto, J.C., Olmos-Migueláñez, S., García-Pe~nalvo, F. J., 2016. MLearning and pre- service teachers: An assessment of the behavioral intention using an expanded TAM model. Computers in Human Behavior. No. 72, pp.644-654
Sheppard, B.H., Hartwick, J. & Warshaw, P.R., 1988. The theory of reasoned action: A meta-analysis of past research with recommendations for modifications and future research. Journal of Consumer Research, Vol. 15, No. 3, pp. 325–343.
Smith, B. W., 2014. Automated vehicles are probably legal in the United States.
Strader, T. J., & Shaw, M. J., 1999. Consumer cost differences for traditional and internet markets. Internet Research, Vol. 9, No. 2, pp. 82-92.
Straub, D., Boudreau, M.-C., Gefen, D., 2004. Validation guidelines for IS positivist research. Communications of the Association for Information Systems, Vol. 13, No. 1, pp. 380- 427.
Wadud, Z., MacKenzie, D., Leiby, P., 2016. Help or hindrance? The travel, energy and carbon impacts of highly automated vehicles. Transportation Research Part A: Policy and Practice, Vol. 86, pp. 1-18.
Werts, C. E., Linn, R. L., Jöreskog, K. G., 1974. Intraclass reliability estimates: Testing structural assumptions. Educational and Psychol Measurtment, Vol. 34, No. 1, pp. 25- 33.

指導教授

陳惠國(Huey-Kuo Chen)

審核日期

2017-7-28

推文