Fashion & Textile Research Journal (한국의류산업학회지)

The Society of Fashion and Textile Industry (한국의류산업학회)
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A Study on the Effect of the Internet Self-Efficacy of Generation MZ on Use Intention of Luxury Fashion Platform - Focusing on the New Exogenous Mechanism of Extended UTAUT -

MZ세대의 인터넷 자기 효능감이 명품 패션플랫폼 이용의도에 미치는 영향 - 확장된 통합기술수용모델의 새로운 외생 메커니즘을 중심으로 -

  • Received : 2022.08.15
  • Accepted : 2022.10.05
  • Published : 2022.10.31
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Abstract

This study aims to confirm which factors related to technology acceptance affect generation MZ's use of luxury fashion platforms. For this purpose, Internet self-efficacy and technology readiness were added as new exogenous and moderating variables using extended UTAUT. Then, performance expectancy, effort expectancy, social influence, and facilitating condition as a factor of UTAUT were set as mediating factors. Response data were collected through questionnaires for generation MZ with experience in using luxury fashion platforms, and a factor analysis, reliability analysis, correlation analysis, and path analysis were conducted using the SPSS and AMOS statistical programs. According to the results, the Internet self-efficacy of generation MZ significantly affected on performance expectancy, effort expectancy, social influence, and facilitating conditions on the luxury fashion platform. Moreover, performance expectancy and social influence had a significant effect on the use intention of luxury fashion platforms, but effort expectancy and facilitating condition did not have a significant effect on use intention and use behavior. Additionally, as the moderating effect of technology readiness affects only the relationship between social influence and behavioral intention, it was confirmed that social influence is an important variable with the characteristics of MZ consumers. Therefore, it is deemed necessary to recognize the importance of social influence when trying to use new technologies targeting generation MZ in the fashion industry.

Keywords

References

  1. Bandura, A. (1977). Self-efficacy - Toward unifying theory of behavior change. American Psychologist, 37, 122-147. doi:10.1037//0033-295x.84.2.191
  2. Bandura, A., Caprara, G. V., Barbaranelli, C., Gerbino, M., & Pastorelli, C. (2003). Role of affective self-regulatory efficacy in diverse spheres of psychosocial functioning. Child development, 74(3), 769-782. doi:10.1111/1467-8624.00567
  3. Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen and J. S. Long (Eds.), Testing structural equation models (pp. 136-162). Newbury Park, CA: Sage.
  4. Cha, Y., Kim, S., & Kim, J. (2022, May). [럭럭셔리 시장을 이끄는 뉴럭셔리 비즈니스 트렌드 [New luxury business trends leading the luxury market. KPMG Samjong]. Retrieved September 9, 2022, from https://assets.kpmg/content/dam/kpmg/kr/pdf/2022/issue-monitor/kr-im-new-luxury-business-trends-20220520.pdf
  5. Chung, I. G., Sun, Z., & Yoon, S. (2020). A study on the customer's intention to accept the innovative products - Focused on unified theory of acceptance and use of technology(UTAUT), technology readiness index(TRI) and value-based acceptance model(VAM). Journal of Marketing Management Research, 25(4), 89-121. doi:10.37202/KMMR.2020.25.4.89
  6. Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy - Development of a measure and initial test. MIS Quarterly, 19(2), 189-211. doi:10.2307/249688
  7. Danziger, P. N. (2018, February 26). 5 Brands that reveal the future of luxury online. Forbes. Retrieved September 6, 2022, from https://www.forbes.com/sites/pamdanziger/2018/02/26/5-brands-thatreveal-the-future-of-luxury-online/?sh=1d7a94053c7e
  8. Dash, S., & Saji, K. B. (2008). The role of consumer self-efficacy and website social presence in customers' adoption of B2C online shopping. Journal of International Consumer Marketing, 20(2), 33-48. doi:10.1300/J046v20n02_04
  9. DeAcetis, J. (2020, July 9). Digitize or die - Technology makes luxury brands more accessible. Forbes. Retrieved September 7, 2022, from https://www.forbes.com/sites/josephdeacetis/2020/07/09/digitize-or-die-technology-makes-luxury-brands-more-accessible/?sh=399d859169e5
  10. Dimock, M. (2019, January 17). Defining generations - Where millennials end and generation z begins. Pew Research Center. Retrieved February 2, 2021, from https://www.pewresearch.org/fact-tank/2019/01/17/where-millennials-end-and-generation-z-begins/
  11. Eastin, M. S., & LaRose, R. (2000). Internet self-efficacy and the psychology of the digital divide. Journal of Computer-Mediated Communication, 6(1). doi:10.1111/j.1083-6101.2000.tb00110.x
  12. Faccioli, G., & Sheehan, E. (2021). Global powers of luxury goods 2021 - Breakthrough luxury[PDF]. Deloitte. Retrieved August 10, 2022, from https://www2.deloitte.com/global/en/pages/consumerbusiness/articles/gx-cb-global-powers-of-luxury-goods.html
  13. Francis, T., & Hoefel, F. (2018, November 12). 'True gen' - Generation Z and its implications for companies. Mckinsey & Company. Retrieved February 2, 2021, from https://www.mckinsey.com/industries/consumer-packaged-goods/our-insights/true-gengeneration-z-and-its-implications-for-companies
  14. 'From the baby boomer to the post-millennial generations - 50 years of change'. (n. d.). Iberdrola. Retrieved February 2, 2021, from https://www.iberdrola.com/talent/generation-x-y-z
  15. 'Generation Z'. (n. d.). Wikipedia. Retrieved February 2, 2021, from https://en.wikipedia.org/wiki/Generation_Z
  16. Glassman, M., & Kang, M. J. (2012). Intelligence in the internet age - The emergence and evolution of open source intelligence (OSINT). Computers in Human Behavior, 28(2), 673-682. doi:10.1016/j.chb.2011.11.014.
  17. Gunasinghe, A., Abd Hamid, J., Khatibi, A., & Azam, SM. F. (2018). Does the lecturer's innovativeness drive VLE adoption in higher education institutes?. Journal of Information Technology Management, 10(3), 20-42. doi:10.22059/jitm.2019.285648.2382
  18. Guo, Y., & Barnes, S. (2011). Purchase behavior in virtual worlds: -An empirical investigation in second life. Information & Management, 48(7), 303-312. doi:10.1016/j.im.2011.07.004
  19. Hooper, D., Coughlan, J., & Mullen, M. R. (2008). Structural equation modelling - Guidelines for determining model fit. The Electronic Journal of Business Research Methods, 6(1), 53 - 60. doi: 10.21427/D7CF7R
  20. Hsu, M. H., & Chiu, C.-M. (2004). Internet self-efficacy and electronic service acceptance. Decision Support System, 38(2), 369-381. doi:10.1016/j.dss.2003.08.001
  21. Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis - Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1-55. doi:10.1080/10705519909540118
  22. IGI Global. (2021). What is internet self-efficacy? IGI Global Publisher of Timely Knowledge. Retrieved August 09, 2022, from https://www.igi-global.com/dictionary/internet-self-efficacy/37397
  23. Jang, S. (2022). A study on the influence of service and platform attribute, satisfaction and continuance usage intention in OTT - Focus on MZ generation. Journal of Speech, Media and Communication Research, 21(3), 71-114. doi:10.51652/ksmca.2022.21.3.3
  24. Jena, R. K. (2022). Exploring antecedents of peoples' intentions to use smart services in a smart city environment - An extended UTAUT model. Journal of Information Systems, 36(1), 133-149. doi:10.2308/ISYS-2020-050
  25. Jeong, D. (2022). A study on consumer behavior on online luxury platforms using the unified theory of acceptance and use of technology - Focusing on the extended UTAUT(2) theory. Fashion & Textile Research Journal, 24(4), 386-398. doi:10.5805/SFTI.2022.24.4.386
  26. Joyce, M. (2013). Development of a general internet attitude scale and internet self-efficacy scale. Unpublished doctoral dissertation, University College Cork, Ireland.
  27. Kang, Y. R., & Kim, M. Y. (2022). An exploratory study on the lifestyle characteristics of the MZ generation - A focus on the 2010-2020 studies. Fashion & Textile Research Journal, 24(1), 81-94. doi:10.5805/SFTI.2022.24.1.81
  28. Kapferer, J. N., & Bastien, V. (2012). The luxury strategy - Break the rules of marketing to build luxury brands. London, UK: Kogan page publishers.
  29. KDI. (2021, December). [온라인 명품 플랫폼으로 보는 소비 트렌드의 변화] Changes in consumption trends as online luxury platforms. Korea Development Institute. Retrieved August 12, 2022, from https://eiec.kdi.re.kr/material/pageoneView.do?idx=1501
  30. Kim, Y., & Glassman, M. (2013). Beyond search and communication: Development and validation of the Internet Self-efficacy Scale (ISS). Computers in Human Behavior, 29(4), 1421-1429. doi:10.1016/j.chb.2013.01.018
  31. Lee, H. B., & Kwon, N. K. (2006). The role of internet self-efficacy in Internet shopping. Asia Marketing Journal, 8(2), 27-62. UCI:G704-001118.2006.8.2.004
  32. Lee, J. S. (2020). The factors affecting MZ generation's acceptance of YouTube based beauty health information - Focusing on the application of the technology readiness and acceptance model. Journal of Investigative Cosmetology, 16(4), 367-375. doi:10.15810/jic.2020.16.4.007
  33. Lee, S. H., & Park, C. W. (2021). A study of the intention to use the key drivers and user acceptance on omnichannel service based on unified theory of acceptance and use of technology(UTAUT). The Journal of Internet Electronic Commerce Research, 21(2), 1-34. doi:10.37272/JIECR.2021.04.21.2.1
  34. Lian, J. W., & Yen, D. C. (2014). Online shopping drivers and barriers for older adults - Age and gender differences. Computers in Human Behavior, 37, 133-143. doi:10.1016/j.chb.2014.04.028
  35. Liljander, V., Gillberg, F., Gummerus, J., & Van Riel, A. (2006). Technology readiness and the evaluation and adoption of selfservice technologies. Journal of Retailing and Consumer Services, 13(3), 177-191. doi:10.1016/j.jretconser.2005.08.004
  36. MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1(2), 130-49. doi:10.1037/1082-989X.1.2.130
  37. Meola, A. (2022, January 5). Generation Z news - Latest characteristics, research, and facts. Insider Intelligence. Retrieved August 12, 2022, from https://www.insiderintelligence.com/insights/generation-z-facts/
  38. 'Millennials'. (n. d.). Wikipedia. Retrieved February 2, 2021, from https://en.wikipedia.org/wiki/Millennials
  39. Miltiadou, M., & Yu, C. Y. (2000). Validation of the online technologies self-efficacy scale (OTSES). Retrieved June 2, 2022, from https://www.researchgate.net/publication/228467955_Validation_of_the_online_technologies_self-efficacy_scale_OTSES
  40. Moran, K. (2022, May 29). Why so many luxury brands are terrible at ecommerce. Niel Norman Group. Retrieved September 8, 2022, from https://www.nngroup.com/articles/luxury-terrible-ecommerce/
  41. Parasuraman, A. (2000). Technology Readiness Index(Tri) - A multiple-item scale to measure readiness to embrace new technologies. Journal of Service Research, 2(4), 307-320. doi:10.1177/109467050024001
  42. Park, H. H. (2019). A study on the consumer use effect of AR fashion retail technology - Moderating effect of technology readiness. Fashion & Textile Research Journal, 21(6), 730-742. doi:10.5805/SFTI.2019.21.6.730
  43. Park, S., & Yang, J. (2021). 세대별 온라인 소비행태 변화와 시사점 [Changes in online consumption behavior by generation and implications]. Hanaif. Retrieved May 13, 2022, from http://www.hanaif.re.kr/boardDetail.do?hmpeSeqNo=34896
  44. Roumeliotis, M., & Maria, T. (2014). Perception and adoption of technology based services by students of higher education. International Journal of Scientific and Research Publications, 4(3) 1-5.
  45. Song, E. (2021). A study on the perception of fashion platforms and fashion smart factories using big data analysis. Fashion & Textile Research Journal, 23(6), 799-809. doi:10.5805/SFTI.2021.23.6.799
  46. Statista. (2022, March). Consumer markets - Luxury goods. Statista. Retrieved May 13, 2022, from https://www.statista.com/outlook/cmo/luxury-goods/worldwide
  47. Statistics Korea. (2020, October 7). 통계로 보는 MZ 세대 트렌드 [Statistics on MZ generation trends]. Statistics Korea. Retrieved August 12, 2022, from https://blog.naver.com/hi_nso/222109361424
  48. Sun, Y., Liu, L., Peng, X. M., Dong, Y., & Barnes, S. J. (2014). Understanding Chinese users' continuance intention toward online social networks - An integrative theoretical model. Electronic Markets, 24(1), 57-66. doi:10.1007/s12525-013-0131-9
  49. Tamilmani, K., Rana, N. P., Wamba, S. F., & Dwivedi, R. (2021). The extended Unified Theory of Acceptance and Use of Technology (UTAUT2) - A systematic literature review and theory evaluation. International Journal of Information Management, 57, 102269. doi:10.1016/j.ijinfomgt.2020.102269
  50. 'The state of smooth - Unpacking luxury in 2022'. (n. d.). Klarna. Retrieved August 10, 2022, from https://www.klarna.com/assets/sites/2/2022/01/18104911/LuxuryReport_US-2.pdf
  51. Tsai, M. J., & Tsai, C. C. (2003). Information searching strategies in web-based science learning - The role of Internet self-efficacy. Innovations in Education and Teaching International, 40, 43-50. doi:10.1080/1355800032000038822
  52. Tsai, C. C., Chuang, S. C., Liang, J. C., & Tsai, M. J. (2011). Selfefficacy on internet-based learning environment - A literature review. Journal of Educational Technology & Society, 14(4), 222-240. https://www.jstor.org/stable/jeductechsoci.14.4.222
  53. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model - Four longitudinal field studies. Management Science, 46(2), 186-204. doi:10.1287/mnsc.46.2.186.11926
  54. Venkatesh, V., Moris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology - Toward a unified view. MIS Quarterly, 27(3), 425-478. doi:10.2307/30036540
  55. Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology - Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157-178. doi:10.2307/41410412
  56. Venkatesh, V., Thong, J. Y. L., & Xu, X. (2016). Unified theory of acceptance and use of technology - A synthesis and the road ahead. Journal of the Association for Information Systems, 17(5), 328-376. doi:10.17705/1jais.00428
  57. Wang, T., Jung, C. H., Kang, M. H., & Chung, Y. S. (2014). Exploring determinants of adoption intentions towards enterprise 2.0 applications - An empirical study. Behaviour & Information Technology, 33(10), 1048-1064. doi:10.1080/0144929X.2013.781221
  58. Wang, Y. S., & Wang, H. Y.(2008). Developing and validating an instrument for measuring mobile computing self-efficacy. Cyber Psychology & Behavior, 11(4), 405-413. doi:10.1089/cpb.2007.0061
  59. Watanabe, C., Akhtar, W., Tou, Y., & Neittaanmaki, P. (2021). Amazon's new supra-omnichannel - Realizing growing seamless switching for apparel during COVID-19. Technology in Society, 66, 1-24. doi:10.1016/j.techsoc.2021.101645
  60. Xin, T. R., & Yoo, H. J. (2021). Chinese consumers intention to use paid online video services - Focused on E-TAM model. Journal of Human Ecology, 25(1), 1-21. doi:10.36357/johe.2021.25.1.1
  61. Yoo, S. J. (2015). New fashion platform - Technology & connecting. Fashion Information and Technology, 12, 80-88. UCI(KEPA):I410-ECN-0102-2016-590-000704527
  62. Yoo, S. J., Han, S. H., & Huang, W. H. (2012). The roles of intrinsic motivators and extrinsic motivators in promoting e-learning in the workplace - A case from south Korea. Computers in Human Behavior, 28(3), 942-950. doi:10.1016/j.chb.2011.12.015