An examination of behavioral intention to use contactless mobile payment: Rapid transit system in Thailand
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Published:
Aug 31, 2018
Keywords:
contactless mobile payment rapid transit payment TAM DOI Thailand
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Abstract
The purpose of this study is to examine the behavioral intention to use the contactless mobile payment for rapid transit passengers in Bangkok, Thailand. The theoretical background of this study is based on Technology Acceptance Model (TAM) and Diffusion of Innovation (DOI) theory including with five additional constructs i.e. personal innovativeness, social influence, trustworthiness, financial cost, and security risk. A sample of 342 participants who have experiences in purchasing with contactless mobile payment and commuting on rapid transit system was examined by using survey conducted in Thailand. Data was analyzed using the structural equation modeling (SEM). The empirical findings revealed that personal innovativeness, perceived usability, social influence, and perceived financial cost have significant effects over the behavioral intention to use contactless mobile payment whereas perception of trustworthiness and security risk showed insignificant influence. The conclusions of this study provides a basis for further refinement of mobile payment acceptance model which can be generalized to mobile payment study in the other contexts.
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How to Cite
Kaewratsameekul, W. (2018). An examination of behavioral intention to use contactless mobile payment: Rapid transit system in Thailand. Science, Engineering and Health Studies, 12(2), 85–101. https://doi.org/10.14456/sehs.2018.2
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Research Articles
References
Acton, T., Golden, W., Gudea, S., Scott, M., and Change, S. (2004). Usability and acceptance in small-screen information systems. In: 9th European Collaborative Electronic Commerce Technology and Research Conference.
Agarwal, R. and Prasad, J. (1998). A Conceptual and Operational Definition of Personal Innovativeness in the Domain of Information Technology. Information Systems Research, 9(2), 204-215.
Agarwal, S., Khapra, M., Menezes, B., and Uchat, N. (2007). Security Issues in Mobile Payment Systems. Security, 142-152.
Ajzen, I. (2011). The theory of planned behavior. Journal of studies on alcohol and drugs, 72(2), 322-332.
Albion, P. (1999). Heuristic evaluation of educational multimedia: from theory to practice. In: ASCILITE99 Conference Proceedings, (pp. 9-15). Brisbane, Australia.
Augsburg, C. and Hedman, J. (2014). Value Added Services and Adoption of Mobile Payments. ICEC '14 Proceedings of the Sixteenth International Conference on Electronic Commerce, 27-27.
Bank of Thailand (1999). Mobile Payment: New trend that everyone must know. Bank of Thailand [Online URL:www.bot.or.th/Thai/phrasiam/Documents/Phrasiam_4_2555/Prasiam_4_2555.pdf] accessed on October 4, 2016.
BTS. (2016). Company Annual Report 2016. BTS Holding Group [Online URL:https://bts.listedcompany.com/misc/ar/20160621-bts-ar201516-th-02.pdf] accessed on April 10, 2017.
Cabanillas. L. F, Fernández J. S. and Leiva F. M. (2014). Antecedents of the adoption of the new mobile payment systems: The moderating effect of age. Computers in Human Behavior, 35, 464-478.
Carter, L. and Belanger, F. (2003). The Influence of Perceived Characteristics of Innovating on e-Government Adoption. Electronic Journal of e-Government, 2(1), 11-20.
Carter, L. and Bélanger, F. (2005). The utilization of e-government services: Citizen trust, innovation and acceptance factors. Information Systems Journal, 15(1), 5-25.
Cheng, Y. H., and Huang, T. Y. (2013). High speed rail passengers' mobile ticketing adoption. Transportation Research Part C: Emerging Technologies, 30, 143-160.
Chong, A. Y. L., Chan, F. T. S., and Ooi, K. B. (2012) Predicting consumer decisions to adopt mobile commerce: Cross country empirical examination between China and Malaysia. Decision Support Systems, 53(1), 34-43.
Cocosila, M. and Trabelsi, H. (2016). An integrated value-risk investigation of contactless mobile payments adoption. Electronic Commerce Research and Applications, 20, 159-170.
Dahlberg, T., Guo, J., and Ondrus, J. (2015). A critical review of mobile payment research. Electronic Commerce Research and Applications, 14(5), 265-284.
Davis, F. D. (1989). Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319-340.
Di Pietro, L., Mugion, R. G., Mattia, G., Renzi, M. F., and Toni, M. (2015). The Integrated Model on Mobile Payment Acceptance (IMMPA): An empirical application to public transport. Transportation Research Part C-Emerging Technologies, 56, 463-479.
Fishbein, M., and Ajzen, I. (1975). Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research. Reading, MA: Addison-Wesley,
Hair, J. F., Jr., Anderson, R. E., Tatham, R. L., and Black, W. C. (1998). Multivariate data analysis, 5th ed., Prentice Hall, Upper Saddle River, New Jersey.
Hair, J. F., Black, W. C., Babin, B. J., and Anderson, R. E. (2010). Multivariate Data Analysis: a global perspective, 7th ed., Pearson Education, New Jersey.
Hu, L. T., and Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55.
Kaiser, H. F. (1970). A second generation little jiffy. Psychometrika, 35(4), 401-415.
Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31-36.
Karin, S. E. H. M. (2003). Evaluating the t of structural equation models: Tests of significance and descriptive goodness-of-t measures. Methods of Psychological Research Online, 8(23).
Kim, C., Mirusmonov, M., and Lee, I. (2010). An empirical examination of factors influencing the intention to use mobile payment. Computers in Human Behavior, 26(3), 310-322.
Kline, R. B., Hair, J. F., Anderson, R. E., Tatham, R. L., Black, W. C., and Babin, B. J. (2011). Principles and Practice of Structural Equation Modeling. Analysis, 77(27), 688-688.
Kuo, Y. F., and Yen, S. N. (2009). Towards an understanding of the behavioral intention to use 3G mobile value-added services. Computers in Human Behavior, 25(1), 103-110.
Leong, L. Y., Hew, T. S., Tan, G. W. H., and Ooi, K. B. (2013). Predicting the determinants of the NFC-enabled mobile credit card acceptance: A neural networks approach. Expert Systems with Applications, 40(14), 5604-5620.
Li, H., Liu, Y., and Heikkilä, J. (2014). Understanding the Factors Driving Nfc-Enabled Mobile Payment Adoption: an Empirical Investigation. PACIS (Pacific Asia Conference on Information Systems), 231.
Lu, Y., Yang, S., Chau, P. Y. K., and Cao, Y. (2011). Dynamics between the trust transfer process and intention to use mobile payment services: A cross-environment perspective. Information and Management, 48(8), 393-403.
Luarn, P., and Lin, H. H. (2005). Toward an understanding of the behavioral intention to use mobile banking. Computers in Human Behavior, 21(6), 873-879.
MacCallum, R. C., Browne, M. W., and Sugawara, H. M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1(2), 130-149.
Marett, K., Pearson, A. W., Pearson, R. A., and Bergiel, E. (2015). Using mobile devices in a high risk context: The role of risk and trust in an exploratory study in Afghanistan. Technology in Society, 41, 54-64.
Mayer, R. C., and Davis, J. H. (1995). An Integrative Model of Organizational. Academy of Management Review, 20(3), 709-734.
McGee, M., Rich, A., and Dumas, J. (2004). Understanding the Usability Construct: User-Perceived Usability. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 48(5), 907-911.
McQuitty, S. (2004). Statistical power and structural equation models in business research. Journal of Business Research, 57(2), 175-183.
Moore, G. C., and Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192-222.
Morosan, C., and DeFranco, A. (2016). It's about time: Revisiting UTAUT2 to examine consumers' intentions to use NFC mobile payments in hotels. International Journal of Hospitality Management, 53, 17-29.
NBTC. (2017). Mobile Market Report: November 2016. [Online URL: ttid.nbtc.go.th] accessed on April 20, 2017.
Neuman, W. L. (2014). Social Research Methods: Qualitative and Quantitative Approaches (7th Ed.), Pearson Education Limited, England.
Oliveira, T., Thomas, M., Baptista, G., and Campos, F. (2016). Mobile payment: Understanding the determinants of customer adoption and intention to recommend the technology. Computers in Human Behavior, 61, 404-414.
Ooi, K. B., and Tan, G. W. H. (2016). Mobile technology acceptance model: An investigation using mobile users to explore smartphone credit card. Expert Systems with Applications, 59, 33-46.
Pham, T. T. T., and Ho, J. C. (2015). The effects of product-related, personal-related factors and attractiveness of alternatives on consumer adoption of NFC-based mobile payments. Technology in Society, 43, 159-172.
Phonthanukitithaworn, C., Sellitto, C., and Fong, M. W. L. (2016). An investigation of mobile payment (m-payment) services in Thailand. Asia-Pacific Journal of Business Administration, 8(1), 37-54.
Rogers, E. M. (2003). Diffusion of Innovations. 5th ed., Free Press, New York.
Sangle, P. S., and Awasthi, P. (2011). Consumer's expectations from mobile CRM services: a banking context. Business Process Management Journal, 17(6), 898-918.
Schierz, P. G., Schilke, O., and Wirtz, B. (2010). Understanding Consumer Acceptance of Mobile Payment Services: An Empirical Analysis. Electronic Commerce Research and Applications, 9, 206-216.
Tan, G. W. H., Ooi, K. B., Chong, S. C., and Hew, T. S. (2014). NFC mobile credit card: The next frontier of mobile payment? Telematics and Informatics, 31(2), 292-307.
Tavilla, E. (2015). Transit Mobile Payments : Driving Consumer Experience and Adoption. Federal Reserve, Bank of Boston. [Online URL:www.bostonfed.org/-/media/Documents/Payment Strategies/publications/2015/transit-mobile-payments.pdf] accessed on October 20, 2016.
Teo, A. C., Tan, G. W. H., Ooi, K. B., and Lin, B. (2015a). Why consumers adopt mobile payment? A partial least squares structural equation modelling (PLS-SEM) approach. International Journal of Mobile Communications, 13(5), 478-478.
Teo, A. C., Tan, G. W. H., Ooi, K. B., Hew, T.-S., and Yew, K. T. (2015b). The effects of convenience and speed in m-payment. Industrial Management & Data Systems, 115(2), 311-331.
Thakur, R., and Srivastava, M. (2014). Adoption readiness, personal innovativeness, perceived risk and usage intention across customer groups for mobile payment services in India. Internet Research, 24(3), 369-392.
Tornatzky, L. G., and Klein, K. J. (1982). Innovation characteristics and innovation adoption-implementation: A meta-analysis of findings. IEEE Transactions on Engineering Management, EM-29(1), 28-45.
Venkatesh, V. (2000). Determinants of Perceived Ease of Use : Integrating Control , Intrinsic Motivation, and Emotion into the Technology Acceptance Model. Information System Research, 11(4), 342-365.
Venkatesh, V., Morris, M. G., Davis, G. B., and Davis, F. D. (2009). User Acceptance of Information Technology: Toward a Unified View. Management Information Systems, 27(3), 425-478.
Venkatesh, V., Thong, J. Y. L., and Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1 SRC-GoogleScholar FG-0), 157-178.
VGI. (2016). VGI-Global Media - Shareholder Meeting Report 2016. VGI Global Media [Online URL:vgi-th.listedcompany.com/misc/shareholder-meeting/agm-2016/20160715-vgi-agm-2016-minutes-th-02.pdf] accessed on May 1, 2017.
Yang, S., Lu, Y., Gupta, S., Cao, Y., and Zhang, R. (2012). Mobile payment services adoption across time: An empirical study of the effects of behavioral beliefs, social influences, and personal traits. Computers in Human Behavior, 1(28), 129-142.
Zhou, T. (2011). The effect of initial trust on user adoption of mobile payment. Information Development, 27(4), 290-300.
Zhou, T. (2013). An empirical examination of user adoption of location-based services. Electronic Commerce Research, 13(1), 25-39.
Agarwal, R. and Prasad, J. (1998). A Conceptual and Operational Definition of Personal Innovativeness in the Domain of Information Technology. Information Systems Research, 9(2), 204-215.
Agarwal, S., Khapra, M., Menezes, B., and Uchat, N. (2007). Security Issues in Mobile Payment Systems. Security, 142-152.
Ajzen, I. (2011). The theory of planned behavior. Journal of studies on alcohol and drugs, 72(2), 322-332.
Albion, P. (1999). Heuristic evaluation of educational multimedia: from theory to practice. In: ASCILITE99 Conference Proceedings, (pp. 9-15). Brisbane, Australia.
Augsburg, C. and Hedman, J. (2014). Value Added Services and Adoption of Mobile Payments. ICEC '14 Proceedings of the Sixteenth International Conference on Electronic Commerce, 27-27.
Bank of Thailand (1999). Mobile Payment: New trend that everyone must know. Bank of Thailand [Online URL:www.bot.or.th/Thai/phrasiam/Documents/Phrasiam_4_2555/Prasiam_4_2555.pdf] accessed on October 4, 2016.
BTS. (2016). Company Annual Report 2016. BTS Holding Group [Online URL:https://bts.listedcompany.com/misc/ar/20160621-bts-ar201516-th-02.pdf] accessed on April 10, 2017.
Cabanillas. L. F, Fernández J. S. and Leiva F. M. (2014). Antecedents of the adoption of the new mobile payment systems: The moderating effect of age. Computers in Human Behavior, 35, 464-478.
Carter, L. and Belanger, F. (2003). The Influence of Perceived Characteristics of Innovating on e-Government Adoption. Electronic Journal of e-Government, 2(1), 11-20.
Carter, L. and Bélanger, F. (2005). The utilization of e-government services: Citizen trust, innovation and acceptance factors. Information Systems Journal, 15(1), 5-25.
Cheng, Y. H., and Huang, T. Y. (2013). High speed rail passengers' mobile ticketing adoption. Transportation Research Part C: Emerging Technologies, 30, 143-160.
Chong, A. Y. L., Chan, F. T. S., and Ooi, K. B. (2012) Predicting consumer decisions to adopt mobile commerce: Cross country empirical examination between China and Malaysia. Decision Support Systems, 53(1), 34-43.
Cocosila, M. and Trabelsi, H. (2016). An integrated value-risk investigation of contactless mobile payments adoption. Electronic Commerce Research and Applications, 20, 159-170.
Dahlberg, T., Guo, J., and Ondrus, J. (2015). A critical review of mobile payment research. Electronic Commerce Research and Applications, 14(5), 265-284.
Davis, F. D. (1989). Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319-340.
Di Pietro, L., Mugion, R. G., Mattia, G., Renzi, M. F., and Toni, M. (2015). The Integrated Model on Mobile Payment Acceptance (IMMPA): An empirical application to public transport. Transportation Research Part C-Emerging Technologies, 56, 463-479.
Fishbein, M., and Ajzen, I. (1975). Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research. Reading, MA: Addison-Wesley,
Hair, J. F., Jr., Anderson, R. E., Tatham, R. L., and Black, W. C. (1998). Multivariate data analysis, 5th ed., Prentice Hall, Upper Saddle River, New Jersey.
Hair, J. F., Black, W. C., Babin, B. J., and Anderson, R. E. (2010). Multivariate Data Analysis: a global perspective, 7th ed., Pearson Education, New Jersey.
Hu, L. T., and Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55.
Kaiser, H. F. (1970). A second generation little jiffy. Psychometrika, 35(4), 401-415.
Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31-36.
Karin, S. E. H. M. (2003). Evaluating the t of structural equation models: Tests of significance and descriptive goodness-of-t measures. Methods of Psychological Research Online, 8(23).
Kim, C., Mirusmonov, M., and Lee, I. (2010). An empirical examination of factors influencing the intention to use mobile payment. Computers in Human Behavior, 26(3), 310-322.
Kline, R. B., Hair, J. F., Anderson, R. E., Tatham, R. L., Black, W. C., and Babin, B. J. (2011). Principles and Practice of Structural Equation Modeling. Analysis, 77(27), 688-688.
Kuo, Y. F., and Yen, S. N. (2009). Towards an understanding of the behavioral intention to use 3G mobile value-added services. Computers in Human Behavior, 25(1), 103-110.
Leong, L. Y., Hew, T. S., Tan, G. W. H., and Ooi, K. B. (2013). Predicting the determinants of the NFC-enabled mobile credit card acceptance: A neural networks approach. Expert Systems with Applications, 40(14), 5604-5620.
Li, H., Liu, Y., and Heikkilä, J. (2014). Understanding the Factors Driving Nfc-Enabled Mobile Payment Adoption: an Empirical Investigation. PACIS (Pacific Asia Conference on Information Systems), 231.
Lu, Y., Yang, S., Chau, P. Y. K., and Cao, Y. (2011). Dynamics between the trust transfer process and intention to use mobile payment services: A cross-environment perspective. Information and Management, 48(8), 393-403.
Luarn, P., and Lin, H. H. (2005). Toward an understanding of the behavioral intention to use mobile banking. Computers in Human Behavior, 21(6), 873-879.
MacCallum, R. C., Browne, M. W., and Sugawara, H. M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1(2), 130-149.
Marett, K., Pearson, A. W., Pearson, R. A., and Bergiel, E. (2015). Using mobile devices in a high risk context: The role of risk and trust in an exploratory study in Afghanistan. Technology in Society, 41, 54-64.
Mayer, R. C., and Davis, J. H. (1995). An Integrative Model of Organizational. Academy of Management Review, 20(3), 709-734.
McGee, M., Rich, A., and Dumas, J. (2004). Understanding the Usability Construct: User-Perceived Usability. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 48(5), 907-911.
McQuitty, S. (2004). Statistical power and structural equation models in business research. Journal of Business Research, 57(2), 175-183.
Moore, G. C., and Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192-222.
Morosan, C., and DeFranco, A. (2016). It's about time: Revisiting UTAUT2 to examine consumers' intentions to use NFC mobile payments in hotels. International Journal of Hospitality Management, 53, 17-29.
NBTC. (2017). Mobile Market Report: November 2016. [Online URL: ttid.nbtc.go.th] accessed on April 20, 2017.
Neuman, W. L. (2014). Social Research Methods: Qualitative and Quantitative Approaches (7th Ed.), Pearson Education Limited, England.
Oliveira, T., Thomas, M., Baptista, G., and Campos, F. (2016). Mobile payment: Understanding the determinants of customer adoption and intention to recommend the technology. Computers in Human Behavior, 61, 404-414.
Ooi, K. B., and Tan, G. W. H. (2016). Mobile technology acceptance model: An investigation using mobile users to explore smartphone credit card. Expert Systems with Applications, 59, 33-46.
Pham, T. T. T., and Ho, J. C. (2015). The effects of product-related, personal-related factors and attractiveness of alternatives on consumer adoption of NFC-based mobile payments. Technology in Society, 43, 159-172.
Phonthanukitithaworn, C., Sellitto, C., and Fong, M. W. L. (2016). An investigation of mobile payment (m-payment) services in Thailand. Asia-Pacific Journal of Business Administration, 8(1), 37-54.
Rogers, E. M. (2003). Diffusion of Innovations. 5th ed., Free Press, New York.
Sangle, P. S., and Awasthi, P. (2011). Consumer's expectations from mobile CRM services: a banking context. Business Process Management Journal, 17(6), 898-918.
Schierz, P. G., Schilke, O., and Wirtz, B. (2010). Understanding Consumer Acceptance of Mobile Payment Services: An Empirical Analysis. Electronic Commerce Research and Applications, 9, 206-216.
Tan, G. W. H., Ooi, K. B., Chong, S. C., and Hew, T. S. (2014). NFC mobile credit card: The next frontier of mobile payment? Telematics and Informatics, 31(2), 292-307.
Tavilla, E. (2015). Transit Mobile Payments : Driving Consumer Experience and Adoption. Federal Reserve, Bank of Boston. [Online URL:www.bostonfed.org/-/media/Documents/Payment Strategies/publications/2015/transit-mobile-payments.pdf] accessed on October 20, 2016.
Teo, A. C., Tan, G. W. H., Ooi, K. B., and Lin, B. (2015a). Why consumers adopt mobile payment? A partial least squares structural equation modelling (PLS-SEM) approach. International Journal of Mobile Communications, 13(5), 478-478.
Teo, A. C., Tan, G. W. H., Ooi, K. B., Hew, T.-S., and Yew, K. T. (2015b). The effects of convenience and speed in m-payment. Industrial Management & Data Systems, 115(2), 311-331.
Thakur, R., and Srivastava, M. (2014). Adoption readiness, personal innovativeness, perceived risk and usage intention across customer groups for mobile payment services in India. Internet Research, 24(3), 369-392.
Tornatzky, L. G., and Klein, K. J. (1982). Innovation characteristics and innovation adoption-implementation: A meta-analysis of findings. IEEE Transactions on Engineering Management, EM-29(1), 28-45.
Venkatesh, V. (2000). Determinants of Perceived Ease of Use : Integrating Control , Intrinsic Motivation, and Emotion into the Technology Acceptance Model. Information System Research, 11(4), 342-365.
Venkatesh, V., Morris, M. G., Davis, G. B., and Davis, F. D. (2009). User Acceptance of Information Technology: Toward a Unified View. Management Information Systems, 27(3), 425-478.
Venkatesh, V., Thong, J. Y. L., and Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1 SRC-GoogleScholar FG-0), 157-178.
VGI. (2016). VGI-Global Media - Shareholder Meeting Report 2016. VGI Global Media [Online URL:vgi-th.listedcompany.com/misc/shareholder-meeting/agm-2016/20160715-vgi-agm-2016-minutes-th-02.pdf] accessed on May 1, 2017.
Yang, S., Lu, Y., Gupta, S., Cao, Y., and Zhang, R. (2012). Mobile payment services adoption across time: An empirical study of the effects of behavioral beliefs, social influences, and personal traits. Computers in Human Behavior, 1(28), 129-142.
Zhou, T. (2011). The effect of initial trust on user adoption of mobile payment. Information Development, 27(4), 290-300.
Zhou, T. (2013). An empirical examination of user adoption of location-based services. Electronic Commerce Research, 13(1), 25-39.