自動駕駛計程車使用行為意向之關鍵影響因素分析

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洪維廷(Wei-Ting Hung) 查詢紙本館藏

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土木工程學系

論文名稱

自動駕駛計程車使用行為意向之關鍵影響因素分析
(Key Factors Contributing to Ride Hailing Behavior Intention of Robo-Taxi)

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摘要(中)

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。

摘要(英)

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.

關鍵字(中)

★ 整合科技接受模型
★ 科技接受模型(TAM)-計劃行為理論(TPB)整合模型
★ 大眾運輸
★ 偏最小平方多群組分析
★ 汽車共享
★ 重要性和績效矩陣分析
★ 科技接受模型(TAM)
★ 偏最小平方結構方程模式
★ 異質性
★ PLS預測取向分組

關鍵字(英)

★ UTAUT
★ C-TAM-TPB
★ TAM
★ ride hailing service
★ PLS-SEM
★ heterogeneity

論文目次

Abstract i
中文摘要 ii
Table of content iii
List of figures vi
List of tables vii
1.Introduction………………………………. 1
2.Illustrations between models and hypotheses 6
2.1 Illustrations between models 6
2.1.1Combined TAM and TPB (C-TAM-TPB)…………… 6
2.1.2 Technology Acceptance Model (TAM) 6
2.1.3 The Unified Theory of Acceptance and Use of Technology (UTAUT) 7
2.1.4 Brief summary between the three models 7
2.2 Hypotheses 7
2.2.1 Interrelationship determined by Unified Theory of Acceptance and Use of Technology and its extension 8
2.2.2 Interrelationship determined by the Technology Acceptance Model 9
2.2.3 Interrelationship between Combined TAM and TPB and other relevant factors 10
2.2.4 Interrelationship between perceived risk 12
2.2.5 Interrelationship between environmental concern, hailing, trust 13
2.2.6 Moderation effect caused by heterogeneous travelers 13
2.2.6.1 Moderation effect caused by travelers with observed heterogeneity 13
2.2.6.2 Moderatingcaused by travelers with unobserved heterogeneity 14
2.2.7 The proposed conceptual framework 15
2.2.8 The comparison result of research model 18
3. Research methods 19
3.1 Partial least squares structural equation modeling (PLS-SEM) versus covariance-based structural equation modeling (CB-SEM) 19
3.2 Partial least squares structural equation modeling (PLS-SEM) with considerations on heterogeneity 21
3.2.1 Partial least squares multi-group analysis (PLS-MGA) 21
3.2.2 Partial least squares-Prediction-Oriented Segmentation (PLS-POS) with considerations for unobserved heterogeneity 22
3.2.3 Importance-performance map analysis (IPMA) 23
4. Measures 24
5.Empirical results…………………………. 27
5.1 Data collection 27
5.2 Descriptive statistics 28
5.3 Common method variance 30
5.4Measurement model 31
5.4.1 Reflective measurement model assessment 31
5.5 Structural measurement model assessment 33
5.6 Summaryof heterogeneity 34
5.7 Heterogeneous analysis 38
5.7.1 Analysis on observed heterogeneity 38
5.7.2 Analysis on unobserved heterogeneity 39
5.8 Importance-Performance Map Analysis 44
5.9 Comparison results between the models 46
5.10 Summary 49
6. Discussion and practical implications 49
7.Contribution and limitation……………….. 51
7.1 Contribution 51
7.2 Research limitations and future research 52
References 53
Appendix A: Structural frameworkfor the UTAUT, TAM and Combined TPB and TAM model 59
Appendix B: Measurement items 60
Appendix C: Operationalization of constructs & Measurement items 66
Appendix D: Outer Loading & Cross Loading 68
Appendix E: Inner VIF for structural model 71
Appendix F: AVE, Composite Reliability &Fornell-Lacker criterion 72
Appendix G: Q Square for structural model 73
Appendix H: PLS-MGA 74
Appendix I: IPMA 91
Appendix J: Results of structural models 92

參考文獻

Ajzen, I., 1985. From intentions to actions: A theory of planned behavior, Springer, Heidelberg, Germany.
Ajzen, I., 1991. The theory of planned behavior.OrganizationalBehaviorand Human Decision Processes,50 (2):179-211.doi: 10.1016/0749-5978(91)90020-T.
Aldas- Manzano, J., Ruiz-Mafe, C& Sanz-Blas, S, 2009.Exploring individual personality factors as drivers of M-shopping acceptance.Industrial Management and Data Systems, 109 (6): 739–757.
Al-Gahtani, S. S;Hubona, G.S& Wang, J., 2007.Information technology (IT) in Saudi Arabia: Culture and the acceptance and use of IT.Information&Management, 44 : 681–691.
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, 51 (6): 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 & 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, 25(3): 175-187.
Burke, C., Jarvis, S.B & Mackenzie, P.M., 2003.A Critical Review of Construct Indicators and Measurement Model Misspecification in Marketing and Consumer Research.Journalof Consumer Research, 30(2):199-218.
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, 10(2): 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.
Chin, W. W., 2010. ‘How to write up and report PLS analyses.’ In: Handbook of Partial Least Squares: Concepts, Methods and Applications (Springer Handbooks of Computational Statistics Series, Vol II). Vincenzo Esposito Vinzi, Wynne W. Chin, JorgHenseler, Huiwen Wang (Eds): 655-690, Heidelberg, Dordrecht, London, New York: Springer. doi: 10.1007/978-3-540-32827-8_29.
Chin, W.W., Thatcher, J., & Wright, R.T., 2012. Assessing Common Method Bias: Problems with the ULMC Technique.MIS Quarterly,36(3): 1003-1019.
Cronbach, L. J., 1951. Coefficient alpha and the internal structure of tests.Psychometrika, 16(3): 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, 21(1): 119-134.
Dudenhoffer, K., 2013. Why electric vehicles failed: An experimental study with PLS approach based on the Technology Acceptance Model. Journal of Management Control, 24(20): 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,78(1): 51-62.
Feng, Y. T., 2016. Research on the Behavioral Intention of Electric Vehicle Hailing.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,3(4): 283-297.
Hair, J. F., Hult, G. T. M., Ringle, C., Sarstedt, M., 2011. PLS-SEM: Indeed a Silver Bullet. Journal of Marketing Theory and Practice,19(2): 139–151.
Hair, J. F., &Sarstedt, M., &Ringle, C., 2011. An assessment of the use of partial least squares structural equation modeling in marketing research. Academy of Marketing Science,40: 414-433.
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 fur Klassifikation, Karlsruhe, July 21-232010, Springer, Berlin/Heidelberg, pp. 495-501.
Henseler, J., C. M. Ringle., &M. Sarstedt., 2015.“A new criterion for assessingdiscriminant validity in variance-based structural equation modeling.”Journal of the Academy of Marketing Science,43 (1): 115-135.doi:10.1007/s11747-014-0403-8.
Henseler, J., Ringle, C. M., Sinkovics, R. R., 2009. The use of partial least squares path modeling in international marketing. New Challenges to International Marketing Advances in International Marketing, 20: 277–319.
Hock, T. T., Lih, B. O., &Chunhui, L., Kwok, K. W., 2003.An empirical study of the effects of interactivity on web user attitude.Int. J. Human-Computer Studies,58: 281–305.
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 ease of use.Knowledge Management Research & Practice,4(3): 227-39.
Jen, W., Lu, T., & Liu, P.T., 2009. An Integrated Analysis of Technology AcceptanceBehavior Models: Comparison of Three Major Models.MIS Review,15(1): 89-121.
JM Becker., A. Rai.,CM Ringle., F. Volckner., 2013.Discovering unobserved heterogeneity in structural equation models to avert validity threats.MIS Quarterly, 37(3): 665-694.
Joreskog, K.G., 1978. Structural analysis of covariance and correlationmatrices.Psychometrika, 43(4): 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, 24(2): 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, 44(2): 544-564.
Liang, H., Saraf, N., & Hu, Q &Xue,Y., 2007. Assimilation of Enterprise Systems: The Effect of Institutional Pressures and the Mediating Role of Top Management.MIS Quarterly, 31(1): 59-87.
Limayem, M., Hirt, S.G., & Cheung, C. M. K., 2007. How habit limits the predictive power of intention: The case of information systems continuance. MIS Quarterly, 31(4): 705-737.
Lee, J. D., 1994. Trust, self-confidence, and operators’ adaptation to automation.Battelle Seattle Research Center, 40: 153-184.
Lee, J. D., 2004.,& See, K. A., 2004. Trust in Automation: Designing for Appropriate Reliance. University of Iowa, Iowa City, 46(1): 50-80.
Lin, P., 2014. What if your autonomous car keeps routing you past Krispy Kreme.The Atlantic, 22.
Marcoulides, G.A., & Saunders, C., 2006. PLS: A Silver Bullet? MIS Quarterly, 30(2): iii-ix.
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, 43(4): 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, 83: 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, 11(1): 17-29.
Petit, J., &Shladover, S. E., 2015. Potential cyber attacks 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, 88(5): 879-903.
Premkumar, G &Bhattacherjee, A., 2008.Explaining information technology usage:A test of competing models.The International Journal of Research in Management Science Omega, 36: 64-75.
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, 26(4): 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, 43(3): 923-931.
Richter, N. F., G. CepedaCarrion.,Roldan, J. L., Ringle, C.M. 2016. European management “European management research using partial least squares structural equation modeling (PLS-SEM): Editorial.”European Management Journal, 34 (6): 589-597.doi:10.1016/j-emj.2016.08.001.
Ringle, C.M., &Sarstedt, M.,& Straub, D.W., 2012. A Critical Look at the Use of PLS-SEM in MIS Quarterly.MIS Quarterly, 36(1)l: iii-xiv.
Ringle, C. M., & M. Sarstedt.,2016.“Gain more insight from your PLS-SEM results: The importance-performance map analysis.”Industrial Management & Data Systems 116 (9): 1865-1886. doi: 10.1108/JMDS-10-2015-0449.
Samaradiwakara, G, D, M, N., Gunawardena, C, G., 2014. Comparison of Existing Technology Acceptance Theories and Models to Suggest A Well Improved Theory/ Model. International Technical Sciences Journal (ITSJ).1(1).
Sanchez-Prieto, J.C.,Olmos-Miguelanez, S., Garcia-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, 72: 644-654.
Sarstedt, M., &Henseler, J., &Ringle, C.M., 2011.Multigroupanalysisinpartialleast squares (PLS) path modeling: Alternative methods and empirical results.Measurement and Research Methods in International Marketing Advances in International Marketing.22: 195-218.
Sarstedt, M., C. M. Ringle., &J. F. Hair., 2017.‘Partial least squares structural equation modeling.’In: Handbook of Market Research. Christian Homburg, MartinKlarmann, ArndVomberg (Eds). Heidelberg: Springer, in press. doi:10.1007/978-3-319-05542-8_15-1.
Schloderer, M. P., M. Sarstedt, and C. M. Ringle. 2014.‘The relevance of reputation in the nonprofit sector: The moderating effect of socio-demographiccharacteristics.’ International Journal of Nonprofit and Voluntary Sector Marketing 19 (2): 110-126. doi:10.1002/nvsm.1491.
Shmueli, G., S. Ray., J. M. Velasquez Estrada.,& S. B. Chatla., 2016.“The elephant tin the room: Evaluating the predictive performance of PLS models.” Journal of Business Research 69 (10): 4552-4564. doi:10.1016/j-jbusres.2016.03.049.
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, 15(3): 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, 9(2): 82-92.
Straub, D., Boudreau, M.-C., Gefen, D., 2004. Validation guidelines for IS positivist research. Communications of the Association for Information Systems, 13(1): 380-427.
Shaheen, S., Sperling, D & Wagner, C 1998.Carsharing in Europe and North America: Past, Present, and Future.Transportation Quarterly, 52(3): 35-52.
Thongrattana, P.T., 2010. Assessing reliability and validity of a measurement instrument for studying uncertain factors in Thairice supply chain.SBS HDR Student Conference, University of Wollongong.
Urbach, N&Ahlemann, F., 2010.Structural Equation Modeling in Information Systems Research Using Partial Least Squares.Journal of information technology theory and application, 11: 5-40.
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, 86: 1-18.
Werts, C. E., Linn, R. L., Joreskog, K. G., 1974. Intraclass reliability estimates: Testing structural assumptions. Educational and Psychol Measurtment, 34(1): 25-33.
Ylitalo, J., 2009. Controlling for Common Method Variance with Partial Least Squares Path modeling: A Monte Carlo Study.Helsinki University of Technology Faculty of Information and Natural Sciences Department of Mathematics and Systems Analysis Mat-2.4108 Independent Research Projects in Applied Mathematics.
Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles,2016. https://www.sae.org/standards/content/j3016_201609/ (Accessed 5/3/2018)

指導教授

陳惠國

審核日期

2018-7-18

推文