Investigating the influential factors of public bicycle system and cyclist heterogeneity

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：32

、訪客IP：18.225.254.81

姓名

陳敬典(Ching-Tien Chen) 查詢紙本館藏

畢業系所

土木工程學系

論文名稱

(Investigating the influential factors of public bicycle system and cyclist heterogeneity)

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	★ 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	★ 動態人口分布最佳化控制之研究-雙層規劃模型之應用

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]

至系統瀏覽論文 ( 永不開放)

摘要(中)

隨著環境議題備受重視，公共自行車系統(public bicycle system, PBS)在世界各國逐漸流行。本研究調查了影響使用公共自行車系統之因素及其相互關係，以計畫行為理論(theory of planned behavior, TPB)與科技接受模式(technology acceptance model, TAM)作為研究的基礎框架，利用 (1) 偏最小平方結構方程模式(partial least squares structural equation modeling, PLS-SEM)探究因素間之路徑關係； (2) 偏最小平方多群組分析(partial least squares multi-group analysis, PLS-MGA)闡述可觀測的異質性，如：城市與性別的調節效果，以及 (3) 有限混合偏最小平方法(finite mixture partial least squares, FIMIX-PLS)探索不可觀測的異質性。為了進行實證分析，本研究在台北及台中蒐集了520個有效樣本，結果顯示： (1) 在計畫行為理論與科技接受模式中，除了主觀規範外，其餘的因子均會直接或間接影響行為意向； (2) 其他納入的影響因素，如：習慣、環境關懷以及公共自行車系統的基礎設施滿意度皆顯著正影響行為意向； (3) 異質性分析表明，城市與性別對模型中部分路徑存有調節效果，並於使用者中辨別出兩潛在類別。最後，提出研究結論、意涵與未來研究方向。

摘要(英)

As environmental concern increases, public bicycle system (PBS) is becoming more and more popular worldwide. This research investigates interrelationships of factors influencing PBS. The research framework is constructed mainly based on theory of planned behavior (TPB) and technology acceptance model (TAM). The research framework is then analyzed with (1) partial least squares structural equation modeling (PLS-SEM) for exploring path relationships of influential factors of PBS, (2) partial least squares multi-group analysis (PLS-MGA) for elaborating observed heterogeneity, like city and gender, and (3) finite mixture partial least squares (FIMIX-PLS) for unobserved heterogeneity. To conduct the empirical study, we collected a sample of 520 respondents from Taipei and Taichung cities in Taiwan. The result shows: (1) Except for subjective norm, all factors drawn from TPB and TAM can exert influence and explain, either directly or indirectly, the effect on the behavior intention of using PBS, (2) Additional factors, such as habit, environmental concern, and satisfaction with PBS infrastructure, indicate positive effect on behavior intention of using PBS, (3) Heterogeneous analysis reveals that city and gender exert partial moderation effect, and two latent classes can be identified among cyclists. In the end, discussion and implications for future research are given.

Highlights:

►Include TPB, TAM and some added relevant factors in the proposed research framework.
►Investigate the interrelationships among influential variables using PLS-SEM model.
►Elaborate observed heterogeneity caused by city and gender using PLS-MGA model.
►Explore unobserved heterogeneity of PBS cyclists, two latent classes, with FIMIX-PLS.
►Use FIMIX-PLS rather than traditional clustering that ignores path model relations.

關鍵字(中)

★ 公共自行車系統
★ 異質性
★ 多群組
★ 潛在類別

關鍵字(英)

★ public bicycle system
★ heterogeneity
★ multi-group
★ latent class

論文目次

Table of contents
Abstract i
中文摘要 ii
誌謝 iii
Table of contents iv
List of figures vi
List of tables vii
1. Introduction 1
2. Theoretical background and hypotheses 3
2.1 Theoretical background 4
2.2 Hypotheses 4
2.2.1 Interrelationship determined by Theory of Planned Behavior and its extension 4
2.2.2 Interrelationship determined by the Technology Acceptance Model 5
2.2.3 Interrelationship between habit and other relevant factors 6
2.2.4 Interrelationship between environmental concern and other relevant factors 7
2.2.5 Interrelationship between satisfaction with PBS infrastructure and other relevant factors 8
2.2.6 Moderation effect caused by heterogeneous travelers 8
2.2.6.1 Moderation effect caused by travelers with observed heterogeneity 8
2.2.6.2 Moderation effect caused by travelers with unobserved heterogeneity 9
2.2.7 Framework of research model 10
3. Research methods 14
3.1 Partial least squares structural equation modeling (PLS-SEM) versus covariance-based structural equation modeling (CB-SEM) 14
3.2 Partial least squares structural equation modeling (PLS-SEM) with considerations on heterogeneity 16
3.2.1 Partial least squares multi-group analysis (PLS-MGA) 16
3.2.2 Finite mixture partial least squares (FIMIX-PLS) 16
4. Measures 18
5. Empirical results 20
5.1 Data collection 20
5.2 Descriptive statistics 20
5.3 Common method variance 22
5.4 Measurement model 23
5.4.1 Reliability 23
5.4.2 Validity 25
5.5 Test of hypotheses 28
5.6 Heterogeneous analysis 30
5.6.1 Analysis on observed heterogeneity 30
5.6.2 Analysis on unobserved heterogeneity 32
5.7 Summary 37
6. Discussion and practical implications 37
7. Contribution and limitation 39
7.1 Contribution 39
7.2 Research limitations and future research 41
References 43
Appendix A: Structural configuration for the TPB, TAM and Combined TPB and TAM model 51
Appendix B: Procedure for applying PLS-SEM and FIMIX-PLS 52
Appendix C: Measurement items 54
Appendix D: Descriptive statistics for measurement items 58
Appendix E: The significance level for outer loadings and outer weights 60

參考文獻

Abrahamse, W., Steg, L., Gifford, R., Vlek, C., 2009. Factors influencing car use for commuting and the intention to reduce it: A question of self-interest or morality? Transport. Res. Part F: Traffic Psychol. and Behav., 12(4), 317-324.
Ajzen, I., 1985. From intentions to actions: A theory of planned behavior. Springer, Heidelberg, Germany.
Ajzen, I., 1989. Attitude structure and behavior, In: A. R. Pratkanis, S. J. Breckler, A.G. Greenwald (Eds.), Attitude Structure and Function, Lawrence Erlbaum, Hillsdale, NJ, pp. 241– 274.
Ajzen, I., 1991. The theory of planned behavior. Organizational Behav. and Human Decision Processes, 50(2), 179-211.
Ajzen, I., Fishbein, M., 1980. Understanding Attitudes and Predicting Social Behavior., Prentice-Hall, Englewood Cliffs, NJ.
Akaike, H., 1987. Factor analysis and AIC. Psychometrika, 52(3), 317-332.
Akar, G., Clifton, K., 2009. Influence of individual perceptions and bicycle infrastructure on decision to bike. Transport. Res. Record: J. Transport. Res. Board, 2140, 165-172.
Bagozzi, R. P., Yi, Y., 1991. Multitrait-multimethod matrices in consumer research. J. Consumer Res., 17(4), 426-439
Becker, J.-M., Rai, A., Ringle, C. M., Völckner, F., 2013. Discovering unobserved heterogeneity in structural equation models to avert validity threats. MIS Quarterly, 37(3), 665-694.
Beroud ,B., Clavel, R., Le Vine, S., 2010. Perspectives on the growing marking for public bicycles focus on France and United Kingdom, European Transport Conference, London:Imperial College, pp. 1-25.
Bhattacherjee, A., 2000. Acceptance of e-commerce services: The case of electronic brokerages. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 30(4), 411-420.
Bhattacherjee, A., 2001. Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351-370.
Bozdogan, H., 1987. Model selection and Akaike′s information criterion (AIC): The general theory and its analytical extensions. Psychometrika, 52(3), 345-370.
Bozdogan, H., 1994. Mixture-model cluster analysis using model selection criteria and a new informational measure of complexity. Proceedings of the First US/Japan Conference on the Frontiers of Statistical Modeling: An Informational Approach, Dordrecht, Netherlands, pp 69-113.
Bruijn, G.-J. d., Kremers, S. P., Singh, A., Van den Putte, B., Van Mechelen, W., 2009. Adult active transportation: Adding habit strength to the theory of planned behavior. Am. J. Prev. Med., 36(3), 189-194.
Celeux, G., Soromenho, G., 1996. An entropy criterion for assessing the number of clusters in a mixture model. J. Classification, 13(2), 195-212.
Chang, H.-L., Yeh, T.-H., 2007. Motorcyclist accident involvement by age, gender, and risky behaviors in Taipei, Taiwan. Transport. Res. Part F: Traffic Psychol. and Behav., 10(2), 109-122.
Chang, S.-H., 2010. An Alternative Strategy for Developing Urban Bicycle Transportation. Transportation Planning J., 39(4), 359-380. (in Chinese)
Chen, C.-F., Chao, W.-H., 2011. Habitual or reasoned? Using the theory of planned behavior, technology acceptance model, and habit to examine switching intentions toward public transit. Transport. Res. Part F: Traffic Psychol. and Behav., 14(2), 128-137.
Chen, H.-K., 2016. Investigating Structural Interrelationships between Group-level Service Quality, Customer Satisfaction, and Behavioral Intentions for Bus Passengers in Taiwan. Int. J. Sustain. Transport., DOI: 10.1080/15568318.2014.965372. (in press)
Chen, P.-C., 2014. User′s Intention of Using the Public Bicycle System in Taipei City. Master′s Thesis, National Taiwan University, Taiwan. (in Chinese)
Cheng, Y.-H., Huang, T.-Y., 2013. High speed rail passengers’ mobile ticketing adoption. Transport. Res. Part C: Emerging Technol., 30, 143-160.
Chin, W. W., 1998. The partial least squares approach to structural equation modeling. Modern Methods for Bus. Res., 295(2), 295-336.
Cronbach, L. J., 1951. Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297-334.
Curran, A., 2008. TransLink Public Bike System Feasibility Study. Quay Communications Inc., Vancouver.
Davis, F. D., 1989. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
Fishbein, M., Ajzen, I., 1975. Belief, attitudes, intention, and behavior. An introduction to theory and research. Addison-Wesley, Boston, MA.
Fransson, N., Gärling, T., 1999. Environmental concern: Conceptual definitions, measurement methods, and research findings. J. Environ. Psychol., 19(4), 369-382.
Gatersleben, B., Appleton, K. M., 2007. Contemplating cycling to work: Attitudes and perceptions in different stages of change. Transport. Res. Part A: Policy and Practice, 41(4), 302-312.
Gefen, D., Straub, D., Boudreau, M.-C., 2000. Structural equation modeling and regression: Guidelines for research practice. Communications of the Assoc. for Inf. Syst., 4(7), 1-70.
Haenlein, M., Kaplan, A. M., 2004. A beginner′s guide to partial least squares analysis. Understanding Statistics, 3(4), 283-297.
Hahn, C. H., Johnson, M. D., Herrmann, A., Huber, F., 2002. Capturing customer heterogeneity using a finite mixture PLS approach. Schmalenbach Bus. Rev., 54(3), 243-269.
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.
Hair, J. F., Ringle, C. M., Sarstedt, M., 2011. PLS-SEM: Indeed a silver bullet. J. Mark. Theory and Practice, 19(2), 139-152.
Hair, J. F., Sarstedt, M., Matthews, L. M., Ringle, C. M., 2016. Identifying and treating unobserved heterogeneity with FIMIX-PLS: Part I–method. Eur. Bus. Rev., 28(1), 63-76.
Harman, H. H., 1976. Modern factor analysis, 3rd ed. University of Chicago Press, Chicago.
Haustein, S., Klöckner, C. A., Blöbaum, A., 2009. Car use of young adults: The role of travel socialization. Transport. Res. part F: Traffic Psychol. and Behav., 12(2), 168-178.
Heinen, E., Maat, K., Van Wee, B., 2011. The role of attitudes toward characteristics of bicycle commuting on the choice to cycle to work over various distances. Transport. Res. Part D: Transport and Environ., 16(2), 102-109.
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. J. Academy of Mark. Sci., 43(1), 115-135.
Henseler, J., Ringle, C. M., Sarstedt, M., 2016. Testing measurement invariance of composites using partial least squares. Int. Mark. Rev., 33(3), DOI: 10.1108/IMR-09-2014-0304. (in press)
Henseler, J., Ringle, C. M., Sinkovics, R. R., 2009. The use of partial least squares path modeling in international marketing. Advances in Int. Mark., 20(1), 277-319.
Hong, J.-C., Fu, H.-Y., 2012. A Study on the Pro-Environmental Behavioral Intention of Post-Secondary School Students. Bull. of Educational Psychol., 44(2), 373-387. (in Chinese)
Hsiao, C.-H., Yang, C., 2010. Predicting the travel intention to take High Speed Rail among college students. Transport. Res. Part F: Traffic Psychol. and Behav., 13(4), 277-287.
Hsieh, H.-N., Chang, H.-W., 2011. A study on Public Bike Scheme - Hsinchu Technopolis case. J. Architecture and Planning, 12(3), 237-263.(in Chinese)
Hu, S.-R., Liu, C.-T., 2014. An optimal location model for the Bicycle Sharing System: A case study of the Kaohsiung City-bike System. Transportation Planning J., 43(4), 367-392. (in Chinese)
Hunecke, M., Haustein, S., Grischkat, S., Böhler, S., 2007. Psychological, sociodemographic, and infrastructural factors as determinants of ecological impact caused by mobility behavior. J. Environ. Psychol., 27(4), 277-292.
Jöreskog, K.G., 1978), Structural analysis of covariance and correlation matrices. Psychometrika, 43(4), 443-477.
Kaplan, S., Manca, F., Nielsen, T. A. S., Prato, C. G., 2015. Intentions to use bike-sharing for holiday cycling: An application of the Theory of Planned Behavior. Tourism Management, 47, 34-46.
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.
Klöckner, C. A., Blöbaum, A., 2010. A comprehensive action determination model: Toward a broader understanding of ecological behaviour using the example of travel mode choice. J. Environ. Psychol., 30(4), 574-586.
Laroche, M., Bergeron, J., Barbaro-Forleo, G., 2001. Targeting consumers who are willing to pay more for environmentally friendly products, J. Consumer Mark., 18(6), 503-520.
Lerman, S. R., 1984. Recent advances in disaggregate demand modeling. In: M. Florian (Ed.), Transportation Planning Models, North-Holland, Amsterdam.
Li, Z., Wang, W., Yang, C., Ragland, D. R., 2013. Bicycle commuting market analysis using attitudinal market segmentation approach. Transport. Res. Part A: Policy and Practice, 47, 56-68.
Liang, Z., Jaszczak, R. J., Coleman, R. E., 1992. Parameter estimation of finite mixtures using the EM algorithm and information criteria with application to medical image processing. IEEE Transactions on Nucl. Sci., 39(4), 1126-1133.
Lohmöller, J.-B., 1989. Latent Variables Path Modeling with Partial Least Squares. Physica-Verlag, Heidelberg.
Lubke, G. H., Muthén, B., 2005. Investigating population heterogeneity with factor mixture models. Psychol. Methods, 10(1), 21-39.
Maldonado-Hinarejos, R., Sivakumar, A., Polak, J. W., 2014. Exploring the role of individual attitudes and perceptions in predicting the demand for cycling: A hybrid choice modelling approach. Transport., 41(6), 1287-1304.
Matthews, L. M., Sarstedt, M., Hair, J. F., Ringle, C. M., 2016. Identifying and treating unobserved heterogeneity with FIMIX-PLS: Part II–a case study. Eur. Bus. Rev., 28(2), 208-224.
Mattila, A. S., Enz, C. A., 2002. The role of emotions in service encounters. J. Serv. Res., 4(4), 268-277.
McLachlan, G.J., Peel, D., 2000. Finite Mixture Models. Wiley, New York.
Moan, I. S., Rise, J., 2011. Predicting intentions not to “drink and drive” using an extended version of the theory of planned behaviour. Accid. Anal. & Prev., 43(4), 1378-1384.
Molin, E., Mokhtarian, P., Kroesen, M., 2016. Multimodal travel groups and attitudes: A latent class cluster analysis of Dutch travelers. Transport. Res. Part A: Policy and Practice, 83, 14-29.
Monecke, A. Leisch, F., 2012. SemPLS: Structural equation modeling using partial least squares. J. Statistical Softw., 48(3), 1-32.
Mooi, E., Sarstedt, M., 2011. A Concise Guide to Market Research: The Process, Data, and Methods using IBM SPSS Statistics. Springer, Berlin.
Neal, D. T., Wood, W., Drolet, A., 2013. How do people adhere to goals when willpower is low? The profits (and pitfalls) of strong habits. J. Personality and Soc. Psychol., 104(6), 959-975.
Nunnally, J., Bernstein, I., 1994. Psychometric theory, 3rd ed. McGraw-Hill, New York.
Olson, J. M., Zanna, M. P., 1993. Attitudes and attitude change. Annual Review of Psychol., 44(1), 117-154.
Ouellette, J. A., Wood, W., 1998. Habit and intention in everyday life: the multiple processes by which past behavior predicts future behavior. Psychol. Bull., 124(1), 54-74.
Ozanne, L., Mollenkopf, D., 1999. Understanding consumer intentions to carpool: A test of alternative models. In Proceedings of the 1999 annual meeting of the Australian & New Zealand Marketing Academy (Vol. 8081), New Zealand.
Pai, J.-T., Pai, S.-Y., 2015. User behaviour analysis of the Public Bike System in Taipei. Int. Rev. for Spatial Planning and Sustain. Dev., 3(2), 39-52.
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. J. Applied Psychol., 88(5), 879-903.
Podsakoff, P. M., Organ, D. W., 1986. Self-reports in organizational research: Problems and prospects. J. Management, 12(4), 531-544.
Ramaswamy, V., DeSarbo, W. S., Reibstein, D. J., Robinson, W. T., 1993. An empirical pooling approach for estimating marketing mix elasticities with PIMS data. Mark. Sci., 12(1), 103-124.
Rhodes, N., Pivik, K., 2011. Age and gender differences in risky driving: The roles of positive affect and risk perception. Accid. Anal. & Prev., 43(3), 923-931.
Rigdon, E. E., Ringle, C. M., Sarstedt, M., 2010. Structural modeling of heterogeneous data with partial least squares. Rev. of Mark. Res., 7(7), 255-296.
Rigdon, E. E., Ringle, C. M., Sarstedt, M., Gudergan, S. P., 2011. Assessing heterogeneity in customer satisfaction studies: across industry similarities and within industry differences. Advances in Int. Mark,, 22, 169-194.
Ringle, C. M., 2006. Segmentation for path models and unobserved heterogeneity: The finite mixture partial least squares approach, Research Papers on Marketing and Retailing, 35, University of Hamburg, Hamburg.
Ringle, C. M., Sarstedt, M., Schlittgen, R., 2014. Genetic algorithm segmentation in partial least squares structural equation modeling. OR Spectrum, 36(1), 251-276.
Ringle, C. M., Sarstedt, M., Straub, D., 2012. A critical look at the use of PLS-SEM in MIS Quarterly. MIS Quarterly, 36(1), iii–xiv.
Ringle, C.M., Wende, S., Becker, J.-M., 2015. SmartPLS 3, SmartPLS GmbH, Bönningstedt (available at http://www.smartpls.com).
Ringle, C. M., Wende, S., Will, A., 2005. Customer segmentation with FIMIX-PLS. In: Aluja, T., Casanovas, J., Esposito Vinzi, V., Morineau, A. and Tenenhaus, M. (Eds.), PLS and Related Methods: Proceedings of the PLS’05 International Symposium. Decisia, Paris, pp. 507-514.
Ringle, C.M., Wende, S., Will, A., 2010. Finite mixture partial least squares analysis:Methodology and numerical examples. In: Esposito Vinzi, V., Chin, W.W., Henseler, J.and Wang, H., (Eds), Handbook of Partial Least Squares: Concepts, Methods and Applications, Springer Handbooks of Computational Statistics Series, (Vol. 2), Springer, Heidelberg,Dordrecht, London, NY, pp. 195-218.
Roberts, J. A., Bacon, D. R., 1997. Exploring the subtle relationships between environmental concern and ecologically conscious consumer behavior. J. Bus. Res., 40(1), 79-89.
Ryley, T., 2006. Use of non-motorised modes and life stage in Edinburgh. J. Transport Geogr., 14(5), 367-375.
Sarstedt, M., 2008. A review of recent approaches for capturing heterogeneity in partial least squares path modelling. J. Modelling in Management, 3(2), 140-161.
Sarstedt, M., Becker, J.-M., Ringle, C. M., Schwaiger, M., 2011. Uncovering and treating unobserved heterogeneity with FIMIX-PLS: Which model selection criterion provides an appropriate number of segments? Schmalenbach Bus. Rev., 63(1), 34-62.
Sarstedt, M., Ringle, C. M., 2010. Treating unobserved heterogeneity in PLS path modeling: A comparison of FIMIX-PLS with different data analysis strategies. J. Applied Statistics, 37(8), 1299-1318.
Sarstedt, M., Schwaiger, M., Ringle, C. M., 2009. Do we fully understand the critical success factors of customer satisfaction with industrial goods? -Extending Festge and Schwaiger’s model to account for unobserved heterogeneity. J. Bus. Mark. Management, 3(3), 185-206.
Schiffman, L. G., Kanuk, L. L., 2000. Consumer behavior, 7th. Prentice Hall, New Jersey.
Schwarz, G., 1978. Estimating the dimension of a model. The Annals of Statistics, 6(2), 461-464.
Shinar, D., Schechtman, E., Compton, R., 2001. Self-reports of safe driving behaviors in relationship to sex, age, education and income in the US adult driving population. Accid. Anal. & Prev., 33(1), 111-116.
Şimşekoğlu, Ö., Nordfjærn, T., Rundmo, T., 2015. The role of attitudes, transport priorities, and car use habit for travel mode use and intentions to use public transportation in an urban Norwegian public. Transport Policy, 42, 113-120.
Spector, P. E., 1987. Method variance as an artifact in self-reported affect and perceptions at work: Myth or significant problem? J. Applied Psychol., 72(3), 438.
Straub, D., Boudreau, M.-C., Gefen, D., 2004. Validation guidelines for IS positivist research. Communications of the Assoc. for Inf. Syst., 13(1), 380-427.
Straughan, R.D., Roberts, J.A., 1999. Environmental segmentation alternatives: A look at green consumer behavior in the new millennium, J. Consumer Mark., 16(6), 558-575.
Taipei Youbike Website, 2016. http://taipei.youbike.com.tw/en/index.php (Accessed 6/22/2016)
Taiwan Environmental Protection Administration Website, 2014. http://www.epa.gov.tw/ct.asp?xItem=10052&ctNode=31352&mp=epa (Accessed 8/1/2016)
Taylor, S., Todd, P. A., 1995a. Understanding information technology usage: A test of competing models. Inf. Syst. Res., 6(2), 144-176.
Taylor, S., Todd, P. A., 1995b.Decomposition and crossover effects in the theory of planned behavior: A study of consumer adoption intentions. Int. J. Res. in Mark., 12(2), 137-155.
Tsai, L.-D., 2013. A study on the influencing intention of using public bicycles-A case study of Kaohsiung citizens. Master′s Thesis, National Kaohsiung University of Applied Sciences, Taiwan. (in Chinese)
Ulleberg, P., 2001. Personality subtypes of young drivers. Relationship to risk-taking preferences, accident involvement, and response to a traffic safety campaign. Transport. Res. Part F: Traffic Psychol. and Behav., 4(4), 279-297.
Vandenbulcke, G., Dujardin, C., Thomas, I., de Geus, B., Degraeuwe, B., Meeusen, R., et al., 2011. Cycle commuting in Belgium: Spatial determinants and ‘re-cycling’strategies. Transport. Res. Part A: Policy and Practice, 45(2), 118-137.
Venkatesh, V., 2000. Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Inf. Syst. Res., 11(4), 342-365.
Verplanken, B., Orbell, S., 2003. Reflections on past behavior: A self‐report index of habit strength. J. Applied Social Psychol., 33(6), 1313-1330.
Wang, Y.-C., Lin, Y.-H., 2010. Analysis of Bicycle Sharing System. Urban Traffic Biannually, 25(2), 20-40.(in Chinese)
Wen, C.-H., Wang, W.-C., Fu, C., 2012. Latent class nested logit model for analyzing high-speed rail access mode choice. Transport. Res. Part E: Logistics and Transport. Rev., 48(2), 545-554.
Werts, C. E., Linn, R. L., Jöreskog, K. G., 1974. Intraclass reliability estimates: Testing structural assumptions. Educational and Psychol. Meas., 34(1), 25-33.
Wetzels, M., Odekerken-Schröder, G., Van Oppen, C., 2009. Using PLS path modeling for assessing hierarchical construct models: Guidelines and empirical illustration. MIS Quarterly, 33(1), 177-195.
Wold, H., 1966. Estimation of principal components and related models by iterative least squares. In: Krishnaiah, P.R. (Ed.), Multivariate Analysis. Academic Press, New York, pp. 391-420.
Wold, H., 1982. Soft modeling: The basic design and some extensions. In: J Loreskog, K.G., Wold, H. (Eds.),Systems under Indirect Observation, Part 2. North-Holland, Amsterdam, pp. 1-54.
Wold, H., 1985. Partial least squares. In: Kotz, S., Johnson, N.L. (Eds.), Encyclopedia of Statistical Sciences,Vol. 6. Wiley, New York, pp. 581-591.
Wood, W., Neal, D. T., 2007. A new look at habits and the habit-goal interface. Psychol. Review, 114(4), 843-863.
Young, W., Hwang, K., McDonald, S., Oates, C.J., 2010. Sustainable Consumption: Green Consumer Behaviour When Purchasing Products, Sustainable Development, 18(1), 20-31.

指導教授

陳惠國(Huey-Kuo Chen)

審核日期

2016-8-3

推文