1. Home
  2. Volume 4 • Issue 3
  3. Determinants of consumers’ intention to use credit card: a perspective of multifaceted perceived risk
New submission
Issues
Journal Information
Aims and ScopeEditorial BoardEditorial Advisory BoardJournal Policies
Resources
For Readers
For Authors
For Editors
For Reviewers
Publication EthicsCall for Special Issue
ISSN
ISSN 2615-9821
ISSN(Previous) 2588-1396

Volume 4 • Issue 3 | November 2020

Determinants of consumers’ intention to use credit card: a perspective of multifaceted perceived risk

Hoang Nam Trinh, Hong Ha Tran, and Duc Hoang Quan Vuong

20/12/2020 281 0 0
Like Buy now 20.000 đ
Vietnam Perceived risk Behavioral intention Credit card

Abstract:

Purpose – The purpose of this study is to develop a theoretical model for consumer behavioral intention by integrating the technology acceptance model (TAM) and the theory of perceived risk, which is tested on the intended use of credit cards in Vietnam.

Design/methodology/approach – The data were collected from 485 bank customers through a nationwide online survey. An exploratory and confirmatory factor analyzes were performed to validate the factor structure of the measurement items while structural equation modeling was used to validate the proposed model and testing the hypotheses.

Findings – The results of structural equation modeling reveal that perceived risk, perceived usefulness, social influence and perceived ease of use were significant determinants of consumer intention to use a credit card. Of them, only perceived risk discouraged the intended use of a credit card, which was synthesized from psychological, financial, performance, privacy, time, social and security risk.

Research limitations/implications – This study measured the first-order risk dimensions based on the payment function of the credit card only; these measurements missed potential losses relevant to credit function of credit cards. Practical implications – This study can be beneficial to banks enacting policies to attract more consumers and to help decide how to allocate resources to retain and expand their customer base.

Originality/value – The study adds value to the literature on consumer behavior by confirming the impact of second-order perceived risk on the intended use of credit cards, which most previous studies have not demonstrated. The research also provides an empirical evidence to the academic research platform on e-banking services in Vietnam, especially related to the credit card industry.

References:

  1. Ajzen, I. (1991), “The theory of planned behavior”, Organizational Behavior and Human Decision Processes, Vol. 50 No. 2, pp. 179-211.
  2. Alalwan, A., Dwivedi, Y. and Rana, N. (2017), “Factors influencing adoption of mobile banking by Jordanian bank customers: extending UTAUT2 with trust”, International Journal of Information Management, Vol. 37 No. 3, pp. 99-110, doi: 10.1016/j.ijinfomgt.2017.01.002.
  3. Ali, M., Raza, S. and Puah, C. (2017), “Factors affecting to select Islamic credit cards in Pakistan: the TRA model”, Journal of Islamic Marketing, Vol. 8 No. 3, pp. 330-344, doi: 10.1108/JIMA-06-2015-0043.
  4. Amin, H. (2013), “Factors influencing Malaysian bank customers to choose Islamic credit cards: empirical evidence from the TRA model”, Journal of Islamic Marketing, Vol. 4 No. 3, pp. 245-263, doi: 10.1108/JIMA-02-2012-0013.
  5. Anderson, J.C. and Gerbing, D.W. (1991), “Predicting the performance of measures in a confirmatory factor analysis with a pretest assessment of their substantive validities”, Journal of Applied Psychology, Vol. 76 No. 5, pp. 732-740.
  6. Bauer, R.A. (1960), Consumer Behavior as Risk Taking, Dynamic Marketing for a Changing World, American Marketing Association, Chicago.
  7. Byrne, B.M. (2010), Structural Equation Modeling with Amos: Basic Concepts, Applications and Programming, 2th ed. Taylor and Francis Group, New Jersey.
  8. Cao, Q. and Niu, X. (2019), “Integrating context-awareness and UTAUT to explain Alipay user adoption”, International Journal of Industrial Ergonomics, Vol. 69, pp. 9-13, doi: 10.1016/j.ergon.2018.09.004.
  9. Cattell, R.B. (1978), “Matched determiners vs factor invariance: a reply to Korth”, Multivariate Behavioral Research, Vol. 13 No. 4, pp. 431-448.
  10. Chahal, H., Sahi, G. and Rani, A. (2014), “Moderating role of perceived risk in credit card usage and experience link”, Journal of Indian Business Research, Vol. 6 No. 4, pp. 286-308, doi: 10.1108/JIBR-06-2014-0034.
  11. Chhonker, M., Verma, D. and Kar, A. (2017), “Review of technology adoption frameworks in mobile commerce”, Procedia Computer Science, Vol. 122, pp. 888-895.
  12. Dali, N., Yousafzai, S. and Hamid, H. (2015), “Credit cards preferences of Islamic and conventional credit card”, Journal of Islamic Marketing, Vol. 6 No. 1, pp. 72-94, doi: 10.1108/JIMA-05-2013-0039.
  13. Davis, F.D., Bagozzi, R.P. and Warshaw, P.R. (1989), “User acceptance of computer technology: a comparison of two theoretical models”, Management Science, Vol. 35 No. 8, pp. 982-1003.
  14. Dewri, L., Islam, R. and Saha, N. (2016), “Behavioral analysis of credit card users in a developing country: a case of Bangladesh”, International Journal of Business and Management, Vol. 11 No. 4, p. 299, doi: 10.5539/ijbm.v11n4p299.
  15. Featherman, M.S. and Pavlou, P.A. (2003), “Predicting e-services adoption: a perceived risk facets perspective”, International Journal of Human-Computer Studies, Vol. 59 No. 4, pp. 451-474, doi: 10.1016/S1071-5819(03)00111-3.
  16. Fishbein, M. and Ajzen, I. (1975), Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research, Addison-Wesley, Reading.
  17. Fornell, C. and Larcker, D.F. (1981), “Evaluating structural equation models with unobservable variables and measurement error”, Journal of Marketing Research, Vol. 18 No. 1, pp. 39-50.
  18. Foscht, T., Maloles, C., Swoboda, B. and Chia, S. (2010), “Debit and credit card usage and satisfaction: who uses which and why – evidence from Austria”, International Journal of Bank Marketing, Vol. 28 No. 2, pp. 150-165, doi: 10.1108/02652321011018332.
  19. Hair, J., Black, W., Babin, B. Anderson, R. and Tatham, R. (2014), Multivariate Data Analysis, 7th ed. Pearson Education, Hoboken, NJ.
  20. Hanafizadeh, P. and Khedmatgozar, H. (2012), “The mediating role of the dimensions of the perceived risk in the effect of customers’ awareness on the adoption of internet banking in Iran”, Electronic Commerce Research, Vol. 12 No. 2, pp. 151-175, doi: 10.1007/s10660-012-9090-z.
  21. Khare, A., Khare, A. and Singh, S. (2012), “Factors affecting credit card use in India”, Asia Pacific Journal of Marketing and Logistics, Vol. 24 No. 2, pp. 236-256, doi: 10.1108/13555851211218048.
  22. Leong, L., Hew, T., Tan, G. and Ooi, K. (2013), “Predicting the determinants of the NFC-enabled mobile credit card acceptance: a neural networks approach”, Expert Systems with Applications, Vol. 40 No. 14, pp. 5604-5620, doi: 10.1016/j.eswa.2013.04.018.
  23. Liébana, F., Luna, I. and Montoro, F. (2017), “Intention to use new mobile payment systems: a comparative analysis of SMS and NFC payments”, Economic Research-Ekonomska Istrazivanja, Vol. 30, pp. 892-910, doi: 10.1080/1331677X.2017.1305784.
  24. Liu, Z., Ben, S. and Zhang, R. (2019), “Factors affecting consumers’ mobile payment behavior: a meta-analysis”, Electronic Commerce Research, Vol. 19 No. 3, pp. 575-601, doi: 10.1007/s10660-019-09349-4.
  25. McDonald, R.P. and Ho, M.R. (2002), “Principles and practice in reporting structural equation analyses”, Psychological Methods, Vol. 7 No. 1, pp. 64-82, doi: 10.1037/1082-989X.7.1.64.
  26. Malaquias, R. and Hwang, Y. (2019), “Mobile banking use: a comparative study with Brazilian and US participants”, International Journal of Information Management, Vol. 44, pp. 132-140, doi: 10.1016/j.ijinfomgt.2018.10.004.
  27. Martins, C., Oliveira, T. and Popovič, A. (2014), “Understanding the internet banking adoption: a unified theory of acceptance and use of technology and perceived risk application”, International Journal of Information Management, Vol. 34 No. 1, pp. 1-13, doi: 10.1016/j.ijinfomgt.2013.06.002.
  28. Mitchell, V.W. (1999), “Consumer perceived risk conceptualizations and models”, European Journal of Marketing, Vol. 33 Nos 1/2, pp. 163-195.
  29. Mutahar, A., Daud, N., Ramayah, T., Isaac, O. and Aldholay, A. (2018), “The effect of awareness and perceived risk on the technology acceptance model (TAM): mobile banking in Yemen”, International Journal of Services and Standards, Vol. 12 No. 2, pp. 180-204, doi: 10.1504/IJSS.2018.091840.
  30. Nguyen, O.D.Y. and Cassidy, J.F. (2018), “Consumer intention and credit card adoption in Vietnam”, Asia Pacific Journal of Marketing and Logistics, Vol. 30 No. 4, pp. 779-796, doi: 10.1108/APJML-01-2017-0010.
  31. Ooi, K. and Tan, G. (2016), “Mobile technology acceptance model: an investigation using mobile users to explore smartphone credit card”, Expert Systems with Applications, Vol. 59, pp. 33-46, doi: 10.1016/j.eswa.2016.04.015.
  32. Patil, P., Rana, N., Dwivedi, Y. and Hamour, H. (2018), “The role of trust and risk in mobile payments adoption: a meta-analytic review”, Pacific Asia Conference on Information Systems.
  33. Pelaez, A., Chen, C., Chen, Y.X. and Pelaez, A. (2019), “Effects of perceived risk on intention to purchase: a meta-analysis”, Journal of Computer Information Systems, Vol. 59 No. 1, pp. 73-84, doi: 10.1080/08874417.2017.1300514.
  34. Porto, N. and Xiao, J. (2019), “Credit card adoption and usage in China: urban-rural comparisons”, The Singapore Economic Review, Vol. 64 No. 01, pp. 41-56, doi: 10.1142/S021759081743010X.
  35. Qureshi, J., Baqai, S. and Qureshi, M. (2018), “Consumers’ attitude towards usage of debit and credit cards: evidences from the digital economy of Pakistan”, International Journal of Economics and Financial Issues, Vol. 8, pp. 220-228.
  36. Roy, S., Balaji, M., Kesharwani, A. and Sekhon, H. (2017), “Predicting internet banking adoption in India: a perceived risk perspective”, Journal of Strategic Marketing, Vol. 25 Nos 5/6, pp. 418-438, doi: 10.1080/0965254X.2016.1148771.
  37. Sripalawat, J., Thongmak, M. and Ngramyarn, A. (2011), “M-banking in metropolitan Bangkok and a comparison with other countries”, Journal of Computer Information Systems, Vol. 51, pp. 67-76, doi: 10.1080/08874417.2011.11645487.
  38. Tam, C. and Oliveira, T. (2017), “Literature review of mobile banking and individual performance”, International Journal of Bank Marketing, Vol. 35 No. 7, pp. 1044-1067, doi: 10.1108/ijbm-09-2015-0143.
  39. Tan, G., Ooi, K., Chong, S. and Hew, T. (2014), “NFC mobile credit card: the next frontier of mobile payment?”, Telematics and Informatics, Vol. 31 No. 2, pp. 292-307, doi: 10.1016/j.tele.2013.06.002.
  40. Tandon, U., Kiran, R. and Sah, A. (2016), “Understanding online shopping adoption in India: unified theory of acceptance and use of technology 2 (UTAUT2) with perceived risk application”, Service Science, Vol. 8 No. 4, pp. 420-437, doi: 10.1287/serv.2016.0154.
  41. Trinh, H.N. and Vuong, D.H.Q. (2017), “Developing credit card market from Vietnamese consumers’ perspective”, Journal of Science Ho Chi Minh City Open University, Vol. 21 No. 1, pp. 61-75.
  42. Tseng, S. (2016), “Bringing enjoy shopping by using credit cards: the antecedents of internal beliefs”, Journal of Economics and Economic Education Research, Vol. 17, p. 16.
  43. Varaprasad, G., Chandran, K., Sridharan, R. and Unnithan, A. (2013), “An empirical investigation on credit card adoption in India”, International Journal of Service Science, Management, Engineering, and Technology, Vol. 4, pp. 13-29, doi: 10.4018/jssmet.2013010102.
  44. Venkatesh, V., Morris, M., Davis, G. and Davis, F. (2003), “User acceptance of information technology: toward a unified view”, MIS Quarterly: Management Information Systems, Vol. 27, pp. 425-478, doi: 10.2307/30036540.
  45. Yang, Y., Liu, Y., Li, H. and Yu, B. (2015), “Understanding perceived risks in mobile payment acceptance”, Industrial Management and Data Systems, Vol. 115 No. 2, pp. 253-269, doi: 10.1108/IMDS-08-2014-0243.
  46. Zhang, Y., Weng, Q. and Zhu, N. (2018), “The relationships between electronic banking adoption and its antecedents: a meta-analytic study of the role of national culture”, International Journal of Information Management, Vol. 40, pp. 76-87, doi: 10.1016/j.ijinfomgt.2018.01.015.

Related articles