BibTex RIS Kaynak Göster

DETERMINANTS OF CONTACTLESS CREDIT CARDS ACCEPTANCE IN TURKEY

Yıl 2017, , 331 - 346, 01.04.2017
https://doi.org/10.17130/ijmeb.2017228687

Öz

The purpose of the study is to provide an insight into the determinants of individuals’ contactless credit cards acceptance. We developed a theoretical model based on the Technology Acceptance Model TAM with added constructs perceived risk and perceived playfulness empirically, and tested its ability in predicting individuals’ behavioral intention to use contactless credit cards. We designed a survey and obtained 695 usable responses. We analyzed the data using Structured Equation Modeling SEM to evaluate the strength of the causal relationships. The results indicate that perceived ease of use has a direct and perceived usefulness has an indirect effect on the acceptance of contactless credit cards. Also perceived risk and perceived playfulness have an effect but it is insignificant

Kaynakça

  • Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179–211.
  • Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs, NJ: Prentice-Hall.
  • Bagozzi, R., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74-94.
  • Calli, L., Balcikanli, C., Calli, F., Cebeci, H. I., & Seymen, O. F. (2013). Identifying factors that contribute to the satisfaction of students in e-learning. Turkish Online Journal of Distance Education, 14(1), 85-101.
  • Christiansen, P. (2011). Four important trends shaping the future of credit cards. First Data White Paper. First Data Corporation, 10p.
  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
  • Davis, F. D., Bagozzi, R. P. & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003.
  • Featherman, M. S. & Pavlou, P. A. (2002). Predicting e-services adoption: A perceived risk facets perspective. Proceedings of the eighth Americas conference on information systems, Dallas.
  • Fiedler, M., & Ozturen, A. (2014). Online behavior and loyalty program participation-parameters influencing the acceptance of contactless payment devices. Research Journal of Applied Sciences Engineering and Technology, 7(15), 3188-3197.
  • Gefen, D., Karahanna, E., & Straub, D. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51-90.
  • Haberturk Newspaper. (2015). 04.01.2015, page 9/BKM-Interbank Crd Center-CEO Soner CANKO (Interview).
  • Hair J. F., Anderson R. E., Tatham, R. L. & Black V. C. (1995). Multivarite data analysis with reading. Prentice Hall, International Inc, Viacorn Company, New Jersey.
  • Hair, J., Anderson, R., Tatham, R., & Black, W. (1998). Multivariate data analysis. Upper Saddle River, NJ: Prentice Hall.
  • Handschuh, H. (2004). Contactless technology security issues. Information Security Bulletin, 9, 95-100.
  • Harper, A. (2014). Case study of the impact on businesses and society by mobile contactless card technology. North central University Graduate Faculty of the School of Business and Technology Management, PhD dissertation, 126p.
  • Ho, E., Apostu, S., Michahelles, F., & Ilic, A. (2013). Digital receipts: Fostering mobile payment adoption. Ambient Intelligence Lecture Notes, Computer Science, 8309, 140- 149
  • Hooper, D., Coughlan, J., & Mullen R. M. (2008). Structural equation modeling: Guidelines for determining model fit. The Electronic Journal of Business Research Methods, 6(1), 53-60.
  • Karaiskos, D. C., Kourouthanassis, P., & Giaglis, G. M. (2007). User acceptance of pervasive information systems: Evaluating an RFID ticketing system. Proceedings of the 15th European Conference on Information Systems (ECIS), Athens, Greece, 1910-1921.
  • Grabner–Krauter, S., & Faullant R. (2008). Consumer acceptance of internet banking: The influence of internet trust. International Journal of Bank Marketing, 26(7), 483-504.
  • Lee, Y., Kozar, K. A., & Larsen, K. R. T. (2003). The technology acceptance model: Past, present, and future. Communications of AIS, 12, 752-780.
  • Lin, C. S., Wu, S., & Tsai, R. J. (2005). Integrating perceived playfulness into expectation- confirmation model for web portal context. Information & Management, 42, 683-693.
  • Mathieson, K. (1991), Predicting user intentions: comparing the technology acceptance model with the theory of planned behavior. Information Systems Research, 2(3), 173-191.
  • Moon, J., & Kim, Y. (2001). Extending the TAM for a world-wide-web context, Information & Management. 38, 217-230.
  • Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory. New York: McGrawHill Inc.
  • Pikkarainen, T., Pikkarainen, K., Karjjaluoto, H., & Pahnila, S. (2004). Consumer acceptance of online banking: An extension of the technology acceptance model. Internet Research, 14(3), 224–235.
  • Polasik, M., Wisniewski, T. P., & Lightfoot, G. (2010). Modeling customers’ intentions to use contactless cards. International Journal of Banking, Accounting and Finance, 4(3), 203–231.
  • Reisinger, Y., & Mavondo, F. (2006). Structural equation modeling: Critical issues and new developments. Journal of Travel & Tourism Marketing, 21(4), 41-71.
  • Shin, S., & Lee, W. (2014). The effects of technology readiness and technology acceptance on NFC mobile payment services in Korea. The Journal of Applied Business Research, 30(6), 1615-1626.
  • Smart Card Alliance. (2007). Contactless payments: Frequently asked questions. A Smart Card Alliance Contactless Payments Council Publication, 10p.
  • Taherdoost, H., & Masrom, M. (2009). An examination of smart card technology acceptance using adoption model. 31st International Conference on Information Technology Interfaces (ITI), Cavtat, Croatia, 329-334.
  • Teo, T. (2009). Evaluating the intention to use technology among student teachers: Structural equation modeling approach. International Journal of Technology in Teaching and Learning, 5(2), 106-118.
  • Trutsch, T. (2014). The impact of contactless payment on spending. International Journal of Economic Sciences, 3(4), 70-98.
  • Wang, Y. (2008). Determinants affecting consumer adoption of contactless credit card: An empirical study. Cyber Psychology & Behavior, 11(6), 687-689.
  • Wu, M. Y., Yu, P. Y., & Weng, Y. C. (2012). A study on user behavior for I pass by UTAUT: Using Taiwan’s MRT as an example. Asia Pacific Management Review, 17(1), 91-111.
  • Yap, B. W. & Khong, K. W. (2006). Examining the effects of customer service management on perceived business performance via structural equation modeling. Applied Stochastic Models in Business and Industry, 22(5-6), 587-605.
  • Yen, D. C., Wu, C., Cheng, F., & Huang, Y. (2010). Determinants of users’ intention to adopt wireless technology: An empirical study by integrating TTF with TAM. Computers in Human Behavior, 26(5), 906-915.
  • Zheng, L., Favier, M., Huang, P., & Coat, F. (2012). Chinese consumer perceived risk and risk relievers in e-shopping for clothing. Journal of Electronic Commerce Research, 13(3), 255-274.

DETERMINANTS OF CONTACTLESS CREDIT CARDS ACCEPTANCE IN TURKEY

Yıl 2017, , 331 - 346, 01.04.2017
https://doi.org/10.17130/ijmeb.2017228687

Öz

The purpose of the study is to provide an insight into the determinants of individuals’contactless credit cards acceptance. We developed a theoretical model based on theTechnology Acceptance Model TAM with added constructs perceived risk and perceivedplayfulness empirically, and tested its ability in predicting individuals’ behavioral intentionto use contactless credit cards. We designed a survey and obtained 695 usable responses. Weanalyzed the data using Structured Equation Modeling SEM to evaluate the strength of thecausal relationships. The results indicate that perceived ease of use has a direct and perceivedusefulness has an indirect effect on the acceptance of contactless credit cards. Also perceivedrisk and perceived playfulness have an effect but it is insignificant.

Kaynakça

  • Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179–211.
  • Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs, NJ: Prentice-Hall.
  • Bagozzi, R., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74-94.
  • Calli, L., Balcikanli, C., Calli, F., Cebeci, H. I., & Seymen, O. F. (2013). Identifying factors that contribute to the satisfaction of students in e-learning. Turkish Online Journal of Distance Education, 14(1), 85-101.
  • Christiansen, P. (2011). Four important trends shaping the future of credit cards. First Data White Paper. First Data Corporation, 10p.
  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
  • Davis, F. D., Bagozzi, R. P. & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003.
  • Featherman, M. S. & Pavlou, P. A. (2002). Predicting e-services adoption: A perceived risk facets perspective. Proceedings of the eighth Americas conference on information systems, Dallas.
  • Fiedler, M., & Ozturen, A. (2014). Online behavior and loyalty program participation-parameters influencing the acceptance of contactless payment devices. Research Journal of Applied Sciences Engineering and Technology, 7(15), 3188-3197.
  • Gefen, D., Karahanna, E., & Straub, D. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51-90.
  • Haberturk Newspaper. (2015). 04.01.2015, page 9/BKM-Interbank Crd Center-CEO Soner CANKO (Interview).
  • Hair J. F., Anderson R. E., Tatham, R. L. & Black V. C. (1995). Multivarite data analysis with reading. Prentice Hall, International Inc, Viacorn Company, New Jersey.
  • Hair, J., Anderson, R., Tatham, R., & Black, W. (1998). Multivariate data analysis. Upper Saddle River, NJ: Prentice Hall.
  • Handschuh, H. (2004). Contactless technology security issues. Information Security Bulletin, 9, 95-100.
  • Harper, A. (2014). Case study of the impact on businesses and society by mobile contactless card technology. North central University Graduate Faculty of the School of Business and Technology Management, PhD dissertation, 126p.
  • Ho, E., Apostu, S., Michahelles, F., & Ilic, A. (2013). Digital receipts: Fostering mobile payment adoption. Ambient Intelligence Lecture Notes, Computer Science, 8309, 140- 149
  • Hooper, D., Coughlan, J., & Mullen R. M. (2008). Structural equation modeling: Guidelines for determining model fit. The Electronic Journal of Business Research Methods, 6(1), 53-60.
  • Karaiskos, D. C., Kourouthanassis, P., & Giaglis, G. M. (2007). User acceptance of pervasive information systems: Evaluating an RFID ticketing system. Proceedings of the 15th European Conference on Information Systems (ECIS), Athens, Greece, 1910-1921.
  • Grabner–Krauter, S., & Faullant R. (2008). Consumer acceptance of internet banking: The influence of internet trust. International Journal of Bank Marketing, 26(7), 483-504.
  • Lee, Y., Kozar, K. A., & Larsen, K. R. T. (2003). The technology acceptance model: Past, present, and future. Communications of AIS, 12, 752-780.
  • Lin, C. S., Wu, S., & Tsai, R. J. (2005). Integrating perceived playfulness into expectation- confirmation model for web portal context. Information & Management, 42, 683-693.
  • Mathieson, K. (1991), Predicting user intentions: comparing the technology acceptance model with the theory of planned behavior. Information Systems Research, 2(3), 173-191.
  • Moon, J., & Kim, Y. (2001). Extending the TAM for a world-wide-web context, Information & Management. 38, 217-230.
  • Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory. New York: McGrawHill Inc.
  • Pikkarainen, T., Pikkarainen, K., Karjjaluoto, H., & Pahnila, S. (2004). Consumer acceptance of online banking: An extension of the technology acceptance model. Internet Research, 14(3), 224–235.
  • Polasik, M., Wisniewski, T. P., & Lightfoot, G. (2010). Modeling customers’ intentions to use contactless cards. International Journal of Banking, Accounting and Finance, 4(3), 203–231.
  • Reisinger, Y., & Mavondo, F. (2006). Structural equation modeling: Critical issues and new developments. Journal of Travel & Tourism Marketing, 21(4), 41-71.
  • Shin, S., & Lee, W. (2014). The effects of technology readiness and technology acceptance on NFC mobile payment services in Korea. The Journal of Applied Business Research, 30(6), 1615-1626.
  • Smart Card Alliance. (2007). Contactless payments: Frequently asked questions. A Smart Card Alliance Contactless Payments Council Publication, 10p.
  • Taherdoost, H., & Masrom, M. (2009). An examination of smart card technology acceptance using adoption model. 31st International Conference on Information Technology Interfaces (ITI), Cavtat, Croatia, 329-334.
  • Teo, T. (2009). Evaluating the intention to use technology among student teachers: Structural equation modeling approach. International Journal of Technology in Teaching and Learning, 5(2), 106-118.
  • Trutsch, T. (2014). The impact of contactless payment on spending. International Journal of Economic Sciences, 3(4), 70-98.
  • Wang, Y. (2008). Determinants affecting consumer adoption of contactless credit card: An empirical study. Cyber Psychology & Behavior, 11(6), 687-689.
  • Wu, M. Y., Yu, P. Y., & Weng, Y. C. (2012). A study on user behavior for I pass by UTAUT: Using Taiwan’s MRT as an example. Asia Pacific Management Review, 17(1), 91-111.
  • Yap, B. W. & Khong, K. W. (2006). Examining the effects of customer service management on perceived business performance via structural equation modeling. Applied Stochastic Models in Business and Industry, 22(5-6), 587-605.
  • Yen, D. C., Wu, C., Cheng, F., & Huang, Y. (2010). Determinants of users’ intention to adopt wireless technology: An empirical study by integrating TTF with TAM. Computers in Human Behavior, 26(5), 906-915.
  • Zheng, L., Favier, M., Huang, P., & Coat, F. (2012). Chinese consumer perceived risk and risk relievers in e-shopping for clothing. Journal of Electronic Commerce Research, 13(3), 255-274.
Toplam 37 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Research Article
Yazarlar

Kemal Eyuboglu Bu kişi benim

Uğur Sevim Bu kişi benim

Yayımlanma Tarihi 1 Nisan 2017
Yayımlandığı Sayı Yıl 2017

Kaynak Göster

APA Eyuboglu, K., & Sevim, U. (2017). DETERMINANTS OF CONTACTLESS CREDIT CARDS ACCEPTANCE IN TURKEY. Uluslararası Yönetim İktisat Ve İşletme Dergisi, 13(2), 331-346. https://doi.org/10.17130/ijmeb.2017228687