DOI QR코드

DOI QR Code

Predicting intention to adopt mobile card payment service

모바일 카드 결제서비스 수용 의도의 결정 요인

  • Kim, Hyo-Jung (Chungnam National University, Human Ecology Research Institute) ;
  • Lee, Jin-Myong (Chungnam National University, Consumer Science)
  • 김효정 (충남대학교 생활과학연구소) ;
  • 이진명 (충남대학교 소비자학과)
  • Received : 2019.03.06
  • Accepted : 2020.07.23
  • Published : 2020.11.30

Abstract

The use of mobile payment services has recently increased in South Korea. Mobile payments allow consumers to purchase items digitally, using a mobile card in an app affiliated with a payment service. This study explores the predictors of intention to adopt mobile payment services. The study employed an A(affective)-B(behavioral)-C(cognitive) model with two antecedent variables: cognitive (perceived usefulness, perceived risk, perceived ease of use, and perceived herding behavior) and affective (satisfaction with the status quo, innovation resistance) responses. An online survey of 405 non-users of mobile payment services aged 20 to 49 years was conducted. The study used SPSS 23.0 for descriptive analysis and Amos 23.0 for confirmatory factor analysis and structural equation modelling. The results are as follows. First, perceived usefulness, perceived risk, and perceived herding behavior significantly influenced innovation resistance. Second, perceived herding behavior significantly influenced subjective norms. Third, innovation resistance and subjective norms significantly influenced the intention to adopt mobile payment services. The findings suggest that the A-B-C model can be useful in understanding consumers' adoption and resistance behaviors and that cognitive and affective responses are important antecedent variables affecting the decision to adopt mobile payment services.

Keywords

References

  1. Al-Gahtani, S. S., & King, M. (1999). Attitudes, satisfaction and usage: Factors contributing to each in the acceptance of information technology. Behaviour & Information Technology, 18(4), 277-297. https://doi.org/10.1080/014492999119020
  2. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T
  3. Bae, J. K. (2018). A study on the determinant factors of innovation resistance and innovation acceptance on internet primary bank services: Combining the theories of innovation diffusion and innovation resistance. The e-Business Studies, 19(2), 91-104. https://doi.org/10.20462/TeBS.2018.4.19.2.91
  4. Bikhchandani, S., Hirshleifer, D., & Welch, I. (1992). A theory of fads, fashion, custom, and cultural change as informational cascades. Journal of Political Economy, 100(5), 992-1026. http://dx.doi.org/10.1086/261849
  5. Bonabeau, E. (2004). The perils of the imitation age. Harvard Business Review, 82(6), 45-54.
  6. Breckler, S. J. (1984). Empirical validation of affect, behavior, and cognition as distinct components of attitude. Journal of Personality and Social Psychology, 47(6), 1191-1205. https://doi.org/10.1037/0022-3514.47.6.1191
  7. Chang, E. J., & Kim, J. K. (2017). What makes people keep using Fintech payment service? In the perspective of herding behavior theory and trust. The e-Business Studies, 18(2), 197-212. http://dx.doi.org/10.20462/TeBS.2017.04.18.2.197
  8. Chen, Y. F. (2008). Herd behavior in purchasing books online. Computers in Human Behavior, 24 (5), 1977-1992. https://doi.org/10.1016/j.chb.2007.08.004
  9. Chen, Y. F., & Wang, Y. J. (2010). Effect of herd cues and product involvement on bidder online choices. Cyberpsychology, Behavior, and Social Networking, 13(4), 423-428. https://doi.org/10.1089/cyber.2009.0304
  10. Choi, B. K. (2008). The effects of coffee shop image and perceived value on customer switching intentions and repurchasing intentions: Focused on the mediating effects of customer satisfaction (Unpublished master's thesis). Saejong University, Seoul, Korea.
  11. 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. https://doi.org/10.1287/mnsc.35.8.982
  12. de Luna, I. R., Liebana-Cabanillas, F., Sanchez-Fernandez, J., & MunozLeiva, F. (2019). Mobile payment is not all the same: The adoption of mobile payment systems depending on the technology applied. Technological Forecasting and Social Change, 146, 931-944. https://doi.org/10.1016/j.techfore.2018.09.018
  13. Ding, A. W., & Li, S. (2019). Herding in the consumption and purchase of digital goods and moderators of the herding bias. Journal of the Academy of Marketing Science, 47(3), 460-478. https://doi.org/10.1007/s11747-018-0619-0
  14. Elkaseh, A. M., Wong, K. W., & Fung, C. C. (2016). Perceived ease of use and perceived usefulness of social media for e-learning in Libyan higher education: A structural equation modeling analysis. International Journal of Information and Education Technology, 6(3), 192-199. https://doi.org/10.7763/IJIET.2016.V6.683
  15. Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley.
  16. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18 (1), 39-50. https://doi.org/10.1177/002224378101800104
  17. Gupta, A., Su, B. C., & Walter, Z. (2004). Risk profile and consumer shopping behavior in electronic and traditional channels. Decision Support Systems, 38(3), 347-367. https://doi.org/10.1016/j.dss.2003.08.002
  18. Ha, J., Park, K., & Park, J. (2016). Which restaurant should I choose? Herd behavior in the restaurant industry. Journal of Foodservice Business Research, 19(4), 396-412. https://doi.org/10.1080/15378020.2016.1185873
  19. Ha, L. D., & Lee, H. S. (2015). Perceived risk and user resistance of mobile wallet service. Entrue Journal of Information Technology, 14(3), 115-129.
  20. Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis. Upper Saddle River, NJ: Pearson Prentice Hall
  21. Hsu, C. L., & Lin, J. C. C. (2016). An empirical examination of consumer adoption of internet of things services: Network externalities and concern for information privacy perspectives. Computers in Human Behavior, 62, 516-527. https://doi.org/10.1016/j.chb.2016.04.023
  22. Huang, J. H., & Chen, Y. F. (2006). Herding in online product choice. Psychology & Marketing, 23(5), 413-428. https://doi.org/10.1002/mar.20119
  23. Hwang, H. J., & Kim, J. K. (2018). The study on the user resistance intention of mobile easy money transfer service. The e-Business Studies, 19(1), 135-153. https://doi.org/10.20462/TeBS.2018.2.19.1.135
  24. Jiang, Y., Ho, Y. C., Yan, X., & Tan, Y. (2018). Investor platform choice: Herding, platform attributes, and regulations. Journal of Management Information Systems, 35(1), 86-116. https://doi.org/10.1080/07421222.2018.1440770
  25. Johnson, V. L., Kiser, A., Washington, R., & Torres, R. (2018). Limitations to the rapid adoption of m-payment services: Understanding the impact of privacy risk on m-payment services. Computers in Human Behavior, 79, 111-122. https://doi.org/10.1016/j.chb.2017.10.035
  26. Jung, J. Y., Jeong, H. Y., & Jo, H. (2018). An empirical study on the acceptance-resistance motivation to use a mobile payment service: Applying multivariate discriminant analysis. The Journal of Information Systems, 27(3), 115-134. https://doi.org/10.5859/KAIS.2018.27.2.115
  27. Jung, B. N., Kim, S. W., & Kang, H. T. (2012). An empirical study on the use of and resistance to an enterprise resources planning system: Focused on the public company A. Management Education Review, 27(3), 397-420.
  28. Jung, Y. H., Kim, G., & Lee, C. J. (2015). Factors influencing user satisfaction and continuous usage intention on mobile credit card: Based on innovation diffusion theory and post acceptance model. The Journal of Society for e-Business Studies, 20(3), 11-28. https://doi.org/10.7838/jsebs.2015.20.3.011
  29. Kaushik, A. K., Agrawal, A. K., & Rahman, Z. (2015). Tourist behaviour towards self-service hotel technology adoption: Trust and subjective norm as key antecedents. Tourism Management Perspectives, 16, 278-289. https://doi.org/10.1016/j.tmp.2015.09.002
  30. Kim, D., & Kim, S. (2011). Factors influencing users' resistance to location based SNS application for smart phones. Korean Journal of Broadcasting and Telecommunication Studies, 25(3), 133-166.
  31. Kim, H. J. (2010). Study on healthcare service quality in health examination center affecting on switching intention (Unpublished master's thesis). Kyunghee University, Seoul, Korea.
  32. Kim, H. J., & Lee, S. S. (2019). Consumers' acceptance and resistance to virtual bank: views of non-users. Family and Environment Research, 57(2), 171-183. https://doi.org/10.6115/fer.2019.012
  33. Kim, H. J., & Rha, J. Y. (2017). Consumer resistance to smartwatches: Gender and age differences. The Journal of the Korea Contents Association, 17(12), 447-460. https://doi.org/10.5392/JKCA.2017.17.12.447
  34. Kim, H. W., Noh, S. E., Lee, Y. L., & Kwahk, K. Y. (2009). The effect of switching costs on user resistance in the adoption of open source software. Information Systems Review, 11(3), 125-146.
  35. Kim, J. K., & Kim, J. S. (2012, 06). The relationship of innovation diffusion: Characteristics of social construct which is Smartphone based mobile banking service. Paper presented at the 14th Conference of the Information Systems Review, Seoul, Korea.
  36. Kim, M. S., Kim, H. J., Kim, M. O., & Kim, H. J. (2010). A study on the user resistance to IPTV. The Journal of Society for e-Business Studies, 15(2), 205-217.
  37. Koenig-Lewis, N., Marquet, M., Palmer, A., & Zhao, A. L. (2015). Enjoyment and social influence: Predicting mobile payment adoption. The Service Industries Journal, 35(10), 537-554. https://doi.org/10.1080/02642069.2015.1043278
  38. Kwon, S. H., & Lim, Y. W. (2012). A study for rejection and acceptance for information technology innovative products: Based on Smart phone usage intention of General mobile phone users. Journal of the Korea Society of Computer and Information, 17(1), 219-226. https://doi.org/10.9708/jksci.2012.17.1.219
  39. Lazarus, R. S. (1984). On the primacy of cognition. American Psychologist, 39(2), 124-129. https://doi.org/10.1037/0003-066X.39.2.124
  40. Lee, S. Y., & Park, J. W. (2016). A study on the intention of the use of mobile payment services: Application of the technology acceptance model. Korean Management Science Review, 33(2), 65-74. https://doi.org/10.7737/KMSR.2016.33.2.065
  41. Lee, H., Lee, S. H., & Chang, B. H. (2012). Factors affecting the resistance of DTV adoption; Combining the theory of diffusion of innovation and innovation resistance model. Journal of Broadcasting and Telecommunications Research, 2012(10) 78-111.
  42. Lee, J., & Hong, I. B. (2016). Predicting positive user responses to social media advertising: The roles of emotional appeal, informativeness, and creativity. International Journal of Information Management, 36(3), 360-373. https://doi.org/10.1016/j.ijinfomgt.2016.01.001
  43. Lee, S. B., Wang, Y. Q., & Suh, Y. H. (2015). Editorial: General quality research; Factors affecting the mobile instant messenger satisfaction, loyalty, and switching intention. Journal of Korean Society for Quality Management, 43(4), 545-558. https://doi.org/10.7469/JKSQM.2015.43.4.545
  44. Liebana-Cabanillas, F., Marinkovic, V., de Luna, I. R., & Kalinic, Z. (2018). Predicting the determinants of mobile payment acceptance: A hybrid sem-neural network approach. Technological Forecasting and Social Change, 129, 117-130. https://doi.org/10.1016/j.techfore.2017.12.015
  45. Liu, L. (2019). An empirical study on consumption propensity, selective attribute of mobile payment services and behavior intention: Focusing on Chinese consumers. The e-Business Studies, 20(2), 3-17.
  46. Liu, D., Brass, D., Lu, Y., & Chen, D. (2015). Friendships in online peerto-peer lending: Pipes, prisms, and relational herding. Mis Quarterly, 39(3), 729-742. http://dx.doi.org/10.2139/ssrn.2251155
  47. Liu, Y., Zhang, X., Zhang, Y., & Qiub, C. (2018, November). Research on influencing factors of consumer shopping behavior in online shopping festival . Paper presented at the 4th International Conference on Management Science and Engineering (MSE2018), Chongqing, Tianjin Normal University, China.
  48. Min, Q., & Kim, E. H. (2019). A study on factors influencing the continuous use intention of mobile easy payment service: Integration of information system post acceptance model and value model. The Journal of Information Systems, 28(1), 155-181. https://doi.org/10.5859/KAIS.2019.28.1.155
  49. Moon, M. A., Khalid, M. J., Awan, H. M., Attiq, S., Rasool, H., & Kiran, M. (2017). Consumer's perceptions of website's utilitarian and hedonic attributes and online purchase intentions: A cognitive-affective attitude approach. Spanish Journal of Marketing-ESIC, 21(2), 73-88. https://doi.org/10.1016/j.sjme.2017.07.001
  50. Mowen, J. C., & Minor, M. (1995). Customer behavior. Upper Saddle River, New Jersey: Prentice Hall.
  51. Mzoughi, N., & M'Sallem, W. (2013). Predictors of internet banking adoption: Profiling Tunisian postponers, opponents and rejectors. International Journal of Bank Marketing, 31(5), 388-408. https://doi.org/10.1108/IJBM-10-2012-0105
  52. Ooi, K. B., & 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. https://doi.org/10.1016/j.eswa.2016.04.015
  53. Park, S. C., & Chae, S. W. (2014). A study on user's resist and productivity using smart device in the smartwork context. The Journal of Information Systems, 23(3), 143-164. https://doi.org/10.5859/KAIS.2014.23.3.143
  54. Qasim, M., Hussain, R., Mehboob, I., & Arshad, M. (2019). Impact of herding behavior and overconfidence bias on investors' decisionmaking in Pakistan. Accounting, 5(2), 81-90. https://doi.org/10.5267/j.ac.2018.7.001
  55. Ram, S., & Sheth, J. N. (1989). Consumer resistance to innovations: The marketing problem and its solutions. Journal of Consumer Marketing, 6(2), 5-14. https://doi.org/10.1108/EUM0000000002542
  56. Rao, H., Greve, H. R., & Davis, G. F. (2001). Fool's gold: Social proof in the initiation and abandonment of coverage by wall street analysts. Administrative Science Quarterly, 46(3), 502-526. https://doi.org/10.2307/3094873
  57. Raza, S. A., Umer, A., & Shah, N. (2017). New determinants of ease of use and perceived usefulness for mobile banking adoption. International Journal of Electronic Customer Relationship Management, 11(1), 44-65. https://doi.org/10.1504/IJECRM.2017.086751
  58. Rivera, M., Gregory, A., & Cobos, L. (2015). Mobile application for the timeshare industry: The influence of technology experience, usefulness, and attitude on behavioral intentions. Journal of Hospitality and Tourism Technology, 6(3), 242-257. https://doi.org/10.1108/JHTT-01-2015-0002
  59. Rogers, E. M. (2010). Diffusion of Innovation. New York, The Free Press.
  60. Ryu, S., Hong, M. G., & Lee, J. K. (2018). A study on factors influencing the use intention of mobile payment service based biometric authentication. Korean Management Science Review, 35(4), 65-86. https://doi.org/10.7737/KMSR.2018.35.4.065
  61. Salawu, K. J., Hammedi, W., & Castiaux, A. (2019). What about passive innovation resistance? Exploring user's resistance to technology in the healthcare sector. Journal of Innovation Economics Management, 30(3), 17-37.
  62. Sharma, S. K., Mangla, S. K., Luthra, S., & Al-Salti, Z. (2018). Mobile wallet inhibitors: Developing a comprehensive theory using an integrated model. Journal of Retailing and Consumer Services, 45, 52-63. https://doi.org/10.1016/j.jretconser.2018.08.008
  63. Slade, E. L., Williams, M. D., & Dwivedi, Y. K. (2013). Mobile payment adoption: Classification and review of the extant literature. The Marketing Review, 13(2), 167-190. https://doi.org/10.1362/146934713X13699019904687
  64. Solomon, M. R., & Rabolt, N. J. (2007). Consumer behavior: In fashion. London: Prentice Hall.
  65. Song, H., Jung, J. W., & Jung, J. (2016). Factors affecting web developers' resistance to HTML5 adoption. Korean Management Review, 45(3), 925-945. http://dx.doi.org/10.17287/kmr.2016.45.3.925
  66. Song, J., & Qu, H. (2019). How does consumer regulatory focus impact perceived value and consumption emotions? International Journal of Contemporary Hospitality Management, 31(1), 285-308. https://doi.org/10.1108/IJCHM-03-2017-0136
  67. Sun, H. (2013). A longitudinal study of herd behavior in the adoption and continued use of technology. Mis Quarterly, 37(4), 1013-1041. https://doi.org/10.25300/MISQ/2013/37.4.02
  68. Sung, S. J. (2019, April 17). Increasing easy payment. Digital Times. Retrieved January 20, 2020, from http://www.dt.co.kr/contents.html?article_no=2019041802100151029001
  69. Talke, K., & Heidenreich, S. (2014). How to overcome pro change bias: Incorporating passive and active innovation resistance in innovation decision models. Journal of Product Innovation Management, 31(5), 894-907. https://doi.org/10.1111/jpim.12130
  70. Taylor, S., & Todd, P. (1995). Decomposition and crossover effects in the theory of planned behavior: A study of consumer adoption intentions. International Journal of Research in Marketing, 12(2), 137-155. https://doi.org/10.1016/0167-8116(94)00019-K
  71. The Bank of Korea. (2019). Retrieve January 20, 2020, from http://www.bok.or.kr/portal/main/main.do
  72. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. http://doi.org/10.2307/30036540
  73. Wang, G., Dou, W., & Zhou, N. (2008). Consumption attitudes and adoption of new consumer products: A contingency approach. European Journal of Marketing, 42(1/2), 238-254. https://doi.org/10.1108/03090560810840998
  74. Yang, S., Lu, Y., Gupta, S., Cao, Y., & 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, 28(1), 129-142. https://doi.org/10.1016/j.chb.2011.08.019
  75. Yoon, S. K., Kim, M. J., & Choi, J. H. (2014). Effects of innovation characteristics and user characteristics on the adopting e-books: Focused on innovation resistance model. The Journal of the Korea Contents Association, 14(8), 61-73. https://doi.org/10.5392/JKCA.2014.14.08.061
  76. Zhang, T. L., & Lee, J. H. (2016). A study on the use intention of easy mobile payment services. The e-Business Studies, 17(6), 203-218. https://doi.org/10.20462/tebs.2016.12.17.6.203