This study aims to examine people intention in choosing the transport mode and identify predictors of using MRT for daily activities. A set of hypotheses is developed from the literature review and observation to determine predictors on people intention in using MRT consist of socio-demographic aspect, service quality aspect, the theory of planned behaviour and external factor. Taipei Metro is chosen as a research setting with Taiwanese, Indonesian and Foreigner in Taiwan as our main respondent. A survey using a questionnaire has been done to 276 respondents gathered in Taipei. The data gathered is examined using descriptive analysis, correlation and hierarchical regression analysis to explain people intention in using MRT.
The study revealed that 5 predictors consist of Subjective Norm (B=0.185, p<0.05), Perceived Behavioral Control (B=0.427, p<0.05), Walking Distance (B=0.102, p<0.05), Weather Condition (B=0.119, p<0.05) and Parking Issue (B=0.111, p<0.05) could explain Behavior Intention in using MRT. From the data analysis, the Author learn that TPB antecedents took a big portion in explaining people intention using MRT. The result of the predictor can be used as a suggestion to transport agency in fixing operation strategy of rail-based transportation. The findings can be an input for governments to increase people intention in using MRT and switch from their current transport mode.

ABSTRACT

This study aims to examine people intention in choosing the transport mode and identify predictors of using MRT for daily activities. A set of hypotheses is developed from the literature review and observation to determine predictors on people intention in using MRT consist of socio-demographic aspect, service quality aspect, the theory of planned behavior and external factor. Taipei Metro is chosen as a research setting with Taiwanese, Indonesian and Foreigner in Taiwan as our main respondent. A survey using a questionnaire has been done to 276 respondents gathered in Taipei. The data gathered is examined using descriptive analysis and hierarchical regression analysis to explain people intention in using MRT.
The study revealed that 5 predictors consist of Subjective Norm (B=0.185, P<0.05), Perceived Behavioral Control (B=0.427, P<0.05), Walking Distance (B=0.102, P<0.05), Weather Condition (B=0.119, P<0.05) and Parking Issue (B=0.111, P<0.05) could explain Behavior Intention in using MRT. From the data analysis, the Author learn that TPB antecedents took a big portion in explaining people intention using MRT. The result of the predictor can be used as a suggestion to transport agency in fixing operation strategy of rail-based transportation. The findings can be an input for governments to increase people intention in using MRT and switch from their current transport mode.

Keywords: sustainable transportation, MRT, intention using public transport

References

1. Achuthan, K., Titheridge, H., & Mackett, R. L. (2010). Mapping accessibility differences for the whole journey and for socially excluded groups of people. Journal of Maps, 6(1), 220-229.
2. Aiken, R.L., 2000. Psychological Testing and Assessment. 10th Edn., Allyn and Bacon, Boston, pp: 360.
3. Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211.
4. Ajzen, I. (2005). Attitudes, personality, and behavior. McGraw-Hill Education (UK).
5. Ajzen, I. (2006). Constructing a theory of planned behaviour questionnaire.
6. Bakti, I. G. M. Y., & Sumaedi, S. (2015). P-TRANSQUAL: a service quality model of public land transport services. International Journal of Quality & Reliability Management, 32(6), 534-558.
7. Bakti, I. G. M. Y., Rakhmawati, T., Damayanti, S., Sumaedi, S., & Yarmen, M. (2018, December). Testing and Extending P-Transqual Public Transport Service Quality Model: A Causal Approach. In 2018 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) (pp. 1563-1567). IEEE.
8. Bamberg, S., Hunecke, M., & Blöbaum, A. (2007). Social context, personal norms and the use of public transportation: Two field studies. Journal of environmental psychology, 27(3), 190-203.
9. Behrens, J. T. (1997). Principles and procedures of exploratory data analysis. Psychological Methods, 2(2), 131.
10. Brundtland, G. H., Khalid, M., Agnelli, S., Al-Athel, S., & Chidzero, B. (1987). Our common future. New York.
11. Boschmann, E. E., & Brady, S. A. (2013). Travel behaviors, sustainable mobility, and transit-oriented developments: a travel counts analysis of older adults in the Denver, Colorado metropolitan area. Journal of Transport Geography, 33, 1-11.
12. Burchardt, T., & Le Grand, J. (2002). Degrees of Exclusion: Developing a Dynamic, Multidimensional. Understanding social exclusion, 30.
13. 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. Transportation research part F: traffic psychology and behaviour, 14(2), 128-137.
14. Chen, C. F., & Lai, W. T. (2011). The effects of rational and habitual factors on mode choice behaviors in a motorcycle-dependent region: Evidence from Taiwan. Transport Policy, 18(5), 711-718.
15. Chetty, R., Hendren, N., Kline, P., & Saez, E. (2014). Where is the land of opportunity? The geography of intergenerational mobility in the United States. The Quarterly Journal of Economics, 129(4), 1553-1623.
16. Chowdhury, S., & Ceder, A. A. (2016). Users’ willingness to ride an integrated public-transport service: A literature review. Transport Policy, 48, 183-195.
17. Clark, B., Chatterjee, K., & Melia, S. (2016). Changes to commute mode: The role of life events, spatial context and environmental attitude. Transportation Research Part A: Policy and Practice, 89, 89-105.
18. Coakes, S. J., & Steed, L. (2009). SPSS: Analysis without anguish using SPSS version 14.0 for Windows. John Wiley & Sons, Inc.
19. Cohen, R.J. and M. Swerdlik, 2001. Psychological Testing and Assessment: An Introduction to Tests and Measurement. 5th Edn., McGraw-Hill, Boston, ISBN: 10: 0767421574, pp: 800.
20. Crandal, R., 1973. The Measurement of Self-Esteem and Related Constructs. In: Measures of Social Psychological Attitudes, Robinson, J.P. and P.R. Shaver (Eds.). Institute for Social Research, Arbor, MI., pp: 80-82.
21. De Gruyter, C., Currie, G., & Rose, G. (2016). Sustainability Measures of Urban Public Transport in Cities: A World Review and Focus on the Asia/ Middle East Region. Sustainability, 9(1), 43.
22. De Oña, J., de Oña, R., Eboli, L., Forciniti, C., & Mazzulla, G. (2016). Transit passengers’ behavioural intentions: the influence of service quality and customer satisfaction. Transportmetrica A: Transport Science, 12(5), 385-412.
23. DeGood, K., & Schwartz, A. (2016). Can New Transportation Technologies Improve Equity and Access to Opportunity?
24. Ettema, D., Friman, M., Gärling, T., & Olsson, L. E. (2016). Travel mode use, travel mode shifts and subjective well-being: Overview of theories, empirical findings and policy implications. In Mobility, sociability and well-being of urban living (pp. 129-150). Springer, Berlin, Heidelberg.
25. Godin, G., Valois, P., & Lepage, L. (1993). The pattern of influence of perceived behavioral control upon exercising behavior: An application of Ajzen's theory of planned behavior. Journal of behavioral medicine, 16(1), 81-102.
26. Green, S. B. (1991). How many subjects does it take to do a regression analysis? Multivariate behavioral research, 26(3), 499-510.
27. Gregory, R.J., 2003. Psychological Testing: History, Principles and Applications. 5th Edn., Pearson Education, Boston, ISBN: 10: 0205354726, pp: 654.
28. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2013). Multivariate data analysis: Pearson new international edition. Pearson Higher Ed.
29. Griffiths, B., & Curtis, C. (2017). Effectiveness of transit oriented development in reducing car use: Case Study of Subiaco, Western Australia. Urban Policy and research, 35(4), 391-408.
30. Haron, S., Nasir, M. S., & Mohamad, S. S. (2016, October). Rail transport service performance indicators in Klang Valley. In AIP Conference Proceedings (Vol. 1774, No. 1, p. 030022). AIP Publishing.
31. He, Y., Zhao, Y., & Tsui, K. L. (2018, November). An Analysis of Factors Influencing Metro Station Ridership: Insights from Taipei Metro. In 2018 21st International Conference on Intelligent Transportation Systems (ITSC) (pp. 1598-1603). IEEE.
32. Heath, Y., & Gifford, R. (2002). Extending the theory of planned behavior: Predicting the use of public transportation 1. Journal of Applied Social Psychology, 32(10), 2154-2189.
33. Irtema, H. I. M., Ismail, A., Borhan, M. N., Das, A. M., & Alshetwi, A. B. (2018). Case study of the behavioural intentions of public transportation passengers in Kuala Lumpur. Case Studies on Transport Policy, 6(4), 462-474.
34. ITDP.org. (n.d.). What is TOD? Retrieved August 7, 2019, from https://www.itdp.org/library/standards-and-guides/tod3-0/what-is-tod/
35. Jen, W., Tu, R., & Lu, T. (2011). Managing passenger behavioral intention: an integrated framework for service quality, satisfaction, perceived value, and switching barriers. Transportation, 38(2), 321-342.
36. Joewono, T. B., & Kubota, H. (2007). User satisfaction with paratransit in competition with motorization in indonesia: anticipation of future implications. Transportation, 34(3), 337-354.
37. Khoo, H. L., & Ong, G. P. (2015). Understanding sustainable transport acceptance behavior: A case study of Klang valley, Malaysia. International Journal of Sustainable Transportation, 9(3), 227-239.
38. Kotval-K, Z., & Vojnovic, I. (2015). The socio-economics of travel behavior and environmental burdens: A Detroit, Michigan regional context. Transportation Research Part D: Transport and Environment, 41, 477-491.
39. Lai, W. T., & Chen, C. F. (2011). Behavioral intentions of public transit passengers—The roles of service quality, perceived value, satisfaction and involvement. Transport policy, 18(2), 318-325.
40. Lehman, R. S. (1995). Statistics in the behavioral sciences: A conceptual introduction. Brooks/Cole Publishing Company.
41. Litman, T. (2007). Evaluating rail transit benefits: A comment. Transport Policy, 14(1), 94-97.
42. Litman, T. (2015). Evaluating public transit benefits and costs. Victoria, BC, Canada: Victoria Transport Policy Institute.
43. Nasri, A., & Zhang, L. (2014). The analysis of transit-oriented development (TOD) in Washington, DC and Baltimore metropolitan areas. Transport policy, 32, 172-179.
44. National Development Council, Taiwan. (n.d.). National Development Council-Taiwan Statistical Data Book. Retrieved June 27, 2019, from https://www.ndc.gov.tw/en/News_Content.aspx?n=607ED34345641980&sms=B8A915763E3684AC&s=B3B7911F9063C75F
45. Nunnally, J., & Bernstein, I. (1994). Psychometric Theory 3rd edition (MacGraw-Hill, New York).
46. Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). Servqual: A multiple-item scale for measuring consumer perc. Journal of retailing, 64(1), 12.
47. Polk, M. (2003). Are women potentially more accommodating than men to a sustainable transportation system in Sweden? Transportation Research Part D: Transport and Environment, 8(2), 75-95.
48. Roughneen. (2017, May 25). Booming Southeast Asian vehicle sales drive urban congestion. Retrieved July 29, 2019, from https://asia.nikkei.com/Economy/Booming-Southeast-Asian-vehicle-sales-drive-urban-congestion
49. Scharlach, A. E., & Lehning, A. J. (2013). Ageing-friendly communities and social inclusion in the United States of America. Ageing & Society, 33(1), 110-136.
50. Shi, Y., & Yang, X. (2013). The public transportation system of high quality in Taiwan. Procedia-Social and Behavioral Sciences, 96, 1350-1361.
51. 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.
52. Snaw, M.E. and J.M. Wright, 1967. Scale for Measurement of Attitude. 1st Edn., McGraw Hill Inc., New York, pp: 108.
53. Steg, L., & Gifford, R. (2005). Sustainable transportation and quality of life. Journal of transport geography, 13(1), 59-69.
54. Steinmetz, H., Davidov, E., & Schmidt, P. (2011). Three approaches to estimate latent interaction effects: Intention and perceived behavioral control in the theory of planned behavior.
55. Tabachnick, B. G., Fidell, L. S., & Ullman, J. B. (2007). Using multivariate statistics (Vol. 5). Boston, MA: Pearson.
56. Taipei Rapid Transit Corporation (2018). (2019, July 15). Ridership Statistics. Retrieved July 27, 2019, from https://english.metro.taipei/cp.aspx?n=78C1963041B43CBF
57. Tirachini, A., Hensher, D. A., & Jara-Díaz, S. R. (2010). Comparing operator and user’s costs of light rail, heavy rail and bus rapid transit over a radial public transport network. Research in transportation economics, 29(1), 231-242.
58. Titheridge, H., Achuthan, K., Mackett, R., & Solomon, J. (2009). Assessing the extent of transport social exclusion among the elderly.
59. Traffic congestion ranking: TomTom Traffic Index. (2018, August 03). Retrieved July 17, 2019, from https://www.tomtom.com/en_gb/traffic-index/ranking/?country=CN,HK,IN,ID,IL,JP,KW,MY,SA,SG,TW,TH,AE
60. UN.Org. (2016). Mobilizing Sustainable Transportation for Development. Retreived June 20, 2019, from https://sustainabledevelopment.un.org/content/documents/2375Mobilizing%20Sustainable%20Transport.pdf
61. Van Lierop, D., & El-Geneidy, A. (2016). Enjoying loyalty: The relationship between service quality, customer satisfaction, and behavioral intentions in public transit. Research in Transportation Economics, 59, 50-59.
62. Vujičić, M., & Prester, J. (2019). Assessing service quality of public tram transport in Zagreb city using P-TRANSQUAL model. Zbornik Ekonomskog fakulteta u Zagrebu, 17(1), 19-31.
63. Wang, S., Fan, J., Zhao, D., Yang, S., & Fu, Y. (2016). Predicting consumers’ intention to adopt hybrid electric vehicles: using an extended version of the theory of planned behavior model. Transportation, 43(1), 123-143.
64. Wen, C. H., Lan, L. W., & Cheng, H. L. (2005). Structural equation modeling to determine passenger loyalty toward intercity bus services. Transportation Research Record, 1927(1), 249-255.
65. Yang-Wallentin, F., Schmidt, P., Davidov, E., & Bamberg, S. (2004). Is there any interaction effect between intention and perceived behavioral control? Methods of psychological research online, 8(2), 127-157.