Human Ecology Research

The Korean Home Economics Association (대한가정학회)
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Consumers' acceptance and resistance to virtual bank: views of non-users

인터넷전문은행 수용 의도와 저항에 관한 연구: 소비자, 혁신, 환경 특성을 중심으로

  • Kim, Hyo Jung (Konkuk University Social Science Research Institute) ;
  • Lee, Seung Sin (Konkuk University Department of Global Trade and Consumer)
  • 김효정 (건국대학교 사회과학연구소) ;
  • 이승신 (건국대학교 글로벌비지니스학부 글로벌통상.소비자전공)
  • Received : 2018.09.26
  • Accepted : 2019.02.01
  • Published : 2019.05.30
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Abstract

Convergence between technology and financial services is ubiquitous and widespread. Virtual banks represent an important aspect of financial markets that can generate value added for consumers and enhance the quality of financial services. This study explores the effect of innovation characteristics (relative advantage, compatibility, and perceived risk), consumer characteristics (status quo bias), and social mechanisms (network externality: complementarity, numbers of peers) on consumers' adoption intention and resistance to virtual banks. This study adopted an innovation resistance model with two dependent variables: adoption intention and resistance to virtual banks. An online self-administered survey was conducted and 532 or non-users of virtual banks aged 20 to 69 years old were analyzed. Frequency analysis, descriptive analysis, and hierarchical multiple regression indicated that status quo bias, relative advantage, perceived risk, complementarity, and number of peers insignificantly influence the adoption intention regarding virtual banks. Furthermore, status quo bias, relative advantage, perceived risk, and number of peers insignificantly influence the resistance to virtual banks. Female respondents have a lower adoption intention and higher resistance to virtual banks than male respondents. The findings suggest that the innovation resistance model can be useful in understanding consumers'adoption and resistance behavior as well as reveal that innovation characteristics, consumer characteristics, and social mechanism are important antecedent variables of the innovation adoption decision.

Keywords

References

  1. 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 Association, 19 (2), 91-104. https://doi.org/10.20462/tebs.2018.4.19.2.91
  2. Baker, R. K., & White, K. M. (2010). Predicting adolescents'use of social networking sites from an extended theory of planned behaviour perspective. Computers in Human Behavior, 26(6), 1591-1597. https://doi.org/10.1016/j.chb.2010.06.006
  3. Chaudhry, A. A., Parveiz, A., & Javed, Y. (2016). Determinants of users trust for branchless banking in Pakistan. The Journal of Internet Banking and Commerce, 21 (1). 1-15.
  4. Cho, B. J., & Lee, J. S. (2016). Adoption factors of smart watch: Focusing on moderate effects of innovation resistance. Journal of Broadcasting and Telecommunication Research, 93, 111-136.
  5. Choi, S. J., & Kang, S. Y. (2016). Consumers'intentions for the usage of mobile payments: Extending UTAUT with innovativeness, trust and network rffect. International Telecommunications Policy Review, 23 (4), 29-52.
  6. Choi, J. I. (2016). Introduction of the internet-only bank and development direction proposal. Journal of Digital Convergence, 14 (9), 139-147. https://doi.org/10.14400/JDC.2016.14.9.139
  7. Falk, T., Schepers, J., Hammerschmidt, M., & Bauer, H. H. (2007). Identifying cross-channel dissynergies for multichannel service providers. Journal of Service Research, 10 (2), 143-160. https://doi.org/10.1177/1094670507306683
  8. 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
  9. Hsieh, J. K., Hsieh, Y. C., Chiu, H. C., & Feng, Y. C. (2012). Post-adoption switching behavior for online service substitutes: A perspective of the push-pull-mooring framework. Computers in Human Behavior, 28 (5), 1912-1920. https://doi.org/10.1016/j.chb.2012.05.010
  10. 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
  11. Huh, K., & Park, K. Y. (2013). A study on the effect of risk tolerance and status quo bias on search behavior of financial information, financial management behavior and satisfaction for economic life of households. Financial Planning Review, 6 (1), 1-26.
  12. Hwang, K. D. (2018). FinTech and Koreanische banken in digital transition. Koreanische Zeitschrift fuer Wirtschaftswissenschaften, 36 (1), 23-41. https://doi.org/10.18237/KDGW.2018.36.1.023
  13. Joseph, R. C. (2010). Individual resistance to IT innovations. Communications of the ACM, 53 (4), 144-146. https://doi.org/10.1145/1721654.1721693
  14. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-292. https://doi.org/10.2307/1914185
  15. Kang, J. N. (2018. July 17). Relaxation of regulation of banking and commerce separation, The Weekly Donga, Retrieved October 22, 2018, from http://weekly.donga.com/3/all/11/1386668/1
  16. Katz, M. L., & Shapiro, C. (1985). Network externalities, competition, and compatibility. The American Economic Review, 75 (3), 424-440.
  17. Kim, H. W., & Kankanhalli, A. (2009). Investigating user resistance to information systems implementation: A status quo bias perspective. MIS Quarterly, 33 (3), 567-582. https://doi.org/10.2307/20650309
  18. Kim, D., & Kim, S. (2017). Factors to influence switching intention to Internet-only bank from legacy bank: Focused on financial consumers'asset management. Information Society & Media, 18 (1), 105-134.
  19. Kim, S. H., & Park, T. K. (2017). Acceptance factors of financial consumers on Internet Primary Banks. Journal of Industrial Economics and Business, 30 (2), 589-622. https://doi.org/10.22558/jieb.2017.4.30.2.589
  20. Kim, H. J., & Rha, J. Y. (2018). Consumers'adoption and resistance of branchless bank: Non-users perspective. Journal of Consumer Studies, 29 (4), 97-118. https://doi.org/10.35736/JCS.29.4.5
  21. Kim, H., Kim, J., Yun, S., & Lee, M. (2013). KakaoTalk and network externalities: Focusing on the mediation role of perceived interactivity and perceived risk. Korean Marketing Review, 28 (2), 17-38.
  22. Kim, H. J., Lee, J. M., & Rha, J. Y. (2017). Understanding the role of user resistance on mobile learning usage among university students. Computers & Education, 113, 108-118. https://doi.org/10.1016/j.compedu.2017.05.015
  23. Kurila, J., Lazuras, L., & Ketikidis, P. H. (2016). Message framing and acceptance of branchless banking technology. Electronic Commerce Research and Applications, 17, 12-18. https://doi.org/10.1016/j.elerap.2016.02.001
  24. Kwak, N. Y., Yoo, H., & Lee, C. C. (2018). Study on factors affecting financial customer's switching intention to internet only bank: Focus on Kakao bank. Journal of Digital Convergence, 16 (2), 157-167. https://doi.org/10.14400/JDC.2018.16.2.157
  25. Lee. K., & Kim, S. I. (2018). A study on the factors affecting the reliability of user's confidence in Korean internet professional bank: Focused on Kakao Bank and KBank. Journal of Korea Convergence Society, 9 (1), 277-282. https://doi.org/10.15207/JKCS.2018.9.1.277
  26. Lee, Y. H., Hsieh, Y. C., & Hsu, C. N. (2011). Adding innovation diffusion theory to the technology acceptance model: Supporting employees' intentions to use e-learning systems. Journal of Educational Technology & Society, 14 (4), 124-137.
  27. Lee, J. M. (2018). The effects of consumers'perceived value and network externality on continuous use intention of internet primary bank. Journal of Consumer Studies, 29 (4), 139-159. https://doi.org/10.35736/JCS.29.4.7
  28. Lee, E. J., Kwon, K. N., & Schumann, D. W. (2005). Segmenting the nonadopter category in the diffusion of internet banking. International Journal of Bank Marketing, 23 (5), 414-437. https://doi.org/10.1108/02652320510612483
  29. Lin, C. P., & Bhattacherjee, A. (2008). Elucidating individual intention to use interactive information technologies: The role of network externalities. International Journal of Electronic Commerce, 13 (1), 85-108. https://doi.org/10.2753/JEC1086-4415130103
  30. Lin, C. A., & Jeffres, L. W. (1998). Factors influencing the adoption of multimedia cable technology. Journalism & Mass Communication Quarterly, 75 (2), 341-352. https://doi.org/10.1177/107769909807500209
  31. Lin, H. F. (2011). An empirical investigation of mobile banking adoption: The effect of innovation attributes and knowledge-based trust. International Journal of Information Management, 31 (3), 252-260. https://doi.org/10.1016/j.ijinfomgt.2010.07.006
  32. Mallat, N. (2007). Exploring consumer adoption of mobile payments-A qualitative study. The Journal of Strategic Information Systems, 16 (4), 413-432. https://doi.org/10.1016/j.jsis.2007.08.001
  33. Moon, Y. H. (2017). Factors affecting intention to use Internet Primary Bank : An exploratory difference of demographic characteristics. The Journal of Business Education, 31 (6), 95-108. https://doi.org/10.34274/KRABE.2017.31.6.005
  34. Morrison, E. W. (2002). Newcomers' relationships: The role of social network ties during socialization. Academy of Management Journal, 45 (6), 1149-1160. https://doi.org/10.5465/3069430
  35. Mustafa, K., & Waheed, A. (2016). Impact of alternative factors on usage of telco-led branchless banking services: Empirical evidence from Parkistan. The Journal of Internet Banking and Commerce, 21(3), 1-19.
  36. Nam, S. W., & Hong, E. J. (2018). Gender difference on trust in Internetonly Banks using the multi-group path analysis. Journal of Convergence for Information Technology, 8 (3), 99-105. https://doi.org/10.22156/CS4SMB.2018.8.3.099
  37. Nam, S. T., Shin, S. Y., & Jin, C. Y. (2014). A meta-analysis and review of external factors based on the technology acceptance model: Focusing on the journals related to smartphone in Korea. Journal of the Korea Institute of Information and Communication Engineering, 18 (4), 848-854. https://doi.org/10.6109/jkiice.2014.18.4.848
  38. Oh, H. (2015). A study of factors affecting the adoption intention of mobile easy payment service. Journal of Financial Consumers, 5 (1), 33-64.
  39. Park, C., & Ryu, D. (2018). Internet-only banks: An introduction overview. Korean Management Review, 47 (3), 549-576. https://doi.org/10.17287/kmr.2018.47.3.549
  40. Park, S. D., & Sung, B. S. (2017). A study on the factors influencing the social adoption diffusion of innovative technology: Focuse on smart molility, Management & Information Systems Review, 36 (2), 239-260. https://doi.org/10.29214/damis.2017.36.2.013
  41. Polites, G. L., & Karahanna, E. (2012). Shackled to the status quo: The inhibiting effects of incumbent system habit, switching costs, and inertia on new system acceptance. MIS Quarterly, 36 (1) , 21-42. https://doi.org/10.2307/41410404
  42. Qasim, H., & Abu-Shanab, E. (2016). Drivers of mobile payment acceptance: The impact of network externalities. Information Systems Frontiers, 18 (5), 1021-1034. https://doi.org/10.1007/s10796-015-9598-6
  43. Ram, S. (1987). A model of innovation resistance. ACR North American Advance, 14, 208-212.
  44. Reaves, B., Scaife, N., Bates, A. M., Traynor, P., & Butler, K. R. (2015, August). Mo (bile) money, mo (bile) problems: Analysis of branchless banking applications in the developing world. Paper presented at the 24th Conference of USENIX Security Symposium. Whasington, D.C., USA.
  45. Rogers, E. M. (2003). The diffusion of innovation. New York: The Free Press.
  46. Samuelson, W., & Zeckhauser, R. (1988). Status quo bias in decision making. Journal of Risk and Uncertainty, 1 (1), 7-59. https://doi.org/10.1007/bf00055564
  47. 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
  48. 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. https://doi.org/10.2307/30036540
  49. Vijayasarathy, L. R. (2004). Predicting consumer intentions to use online shopping: the case for an augmented technology acceptance model. Information & Management, 41 (6), 747-762. https://doi.org/10.1016/j.im.2003.08.011
  50. 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
  51. Zhang, C. B., Li, Y. N., Wu, B., & Li, D. J. (2017). How WeChat can retain users: Roles of network externalities, social interaction ties, and perceived values in building continuance intention. Computers in Human Behavior, 69, 284-293. https://doi.org/10.1016/j.chb.2016.11.069