Information Systems Review (경영정보학연구)

The Korea Society of Management Information Systems (한국경영정보학회)
권호 표지
DOI QR코드

DOI QR Code

The Factors Influencing Value Awareness of Personalized Service and Intention to Use Smart Home: An Analysis of Differences between "Generation MZ" and "Generation X and Baby Boomers"

스마트홈 개인화 서비스에 대한 가치 인식 및 사용의도에의 영향 요인: "MZ세대"와 "X세대 및 베이비붐 세대" 간 차이 분석

  • Sang-Keul Lee (Department of Business Administration, Sangmyung University) ;
  • Ae Ri Lee (Department of Business Administration, Sangmyung University)
  • Received : 2021.07.05
  • Accepted : 2021.08.11
  • Published : 2021.08.31
Download PDF
⟨ Previous Next ⟩

Abstract

Smart home is an advanced Internet of Things (IoT) service that enhances the convenience of human daily life and improves the quality of life at home. Recently, with the emergence of smart home products and services to which artificial intelligence (AI) technology is applied, interest in smart home is increasing. To gain a competitive edge in the smart home market, companies are providing "personalized service" to users, which is a key service that can promote smart home use. This study investigates the factors affecting the value awareness of personalized service and intention to use smart home. This research focuses on four-dimensional motivated innovativeness (cognitive, functional, hedonic, and social innovativeness) and privacy risk awareness as key factors that influence the value awareness of personalized service of smart home. In particular, this study conducts a comparative analysis between the generation MZ (young people in late teens to 30s), who are showing socially differentiated characteristics, and the generation X and baby boomers in 40s to 50s or older. Based on the analysis results, this study derives the distinctive characteristics of generation MZ that are different from the older generation, and provides academic and practical implications for expanding the use of smart home services.

스마트홈(Smart Home)은 일상생활에서의 편의성을 높이고 가정에서의 삶의 질을 향상시킬 수 있도록 하는 첨단 사물인터넷(IoT) 서비스이다. 최근 인공지능(AI) 기술이 적용된 스마트홈 서비스가 등장하면서 그에 대한 관심이 더욱 증가하고 있다. 스마트홈 시장에서의 경쟁 우위 선점을 위해, 기업들은 사용자들에게 맞춤형 "개인화 서비스"를 제공하고 있으며, 이는 스마트홈 사용을 보다 촉진할 수 있는 핵심 서비스라 할 수 있다. 본 연구는 스마트홈 개인화 서비스에 대한 가치 인식과 사용의도에 대한 영향 요인을 고찰하고자 한다. 본 연구에서는 스마트홈 개인화 서비스에 대한 가치 인식에 영향을 주는 핵심 요인으로 4가지 차원의 동기화된 혁신성(인지적, 기능적, 쾌락적, 사회적 혁신성)과 프라이버시 위험 인식에 초점을 두고 그 영향력을 분석하였다. 특히 본 연구에서는 최근 사회형태적으로 차별화된 특징을 보이고 있는 MZ세대(10후반~30대의 젊은 층)와 40~50대 이상의 X세대 및 베이비붐 세대 간 차이점이 있는지 비교 분석하였다. 이를 통해, 기성세대와 다른 MZ세대가 보이는 차별적 특징을 도출하고, 스마트홈 서비스 사용 활성화를 위한 학문적·실무적 시사점을 제공하고자 한다.

Keywords

References

  1. ChosunBiz(조선비즈), "스마트홈'으로 눈 돌린 IT 기업들", Chosun Daily News, 2016, Available at http://biz.chosun.com/site/data/html_dir/2016/11/10/2016111002262.html.
  2. IT조선(아이티조선), "애플, 가족 목소리 구분하는 AI 스피커 '홈팟 미니' 공개", IT Chosun, 2020, Available at http://it.chosun.com/site/data/html_dir/2020/10/14/2020101400350.html.
  3. 강승미, 김영욱, "개인정보 유출 위험에 대한 개인의 인지된 통제성이 위치 기반 서비스 이용 의도에 미치는 영향: 위험 인식의 매개효과 및 프라이버시 염려의 조절 효과 중심", 홍보학 연구, 제23권, 제4호, 2019, pp. 69-99. https://doi.org/10.15814/jpr.2019.23.4.69
  4. 강필균, 서동백, "라이브 커머스: MZ 세대의 관점에서 호스트적, 시스템적, 서비스적 특성이 시청의도 및 구매의도에 미치는 영향", 한국경영정보학회 학술대회, 2021, pp. 242-242.
  5. 김경희, 김광재, 이숙정, "모바일 환경에서의 미디어 리터러시 구성 요소와 세대 간 미디어 리터러시 격차", 한국방송학보, 제33권, 제4호, 2019, pp. 5-36. https://doi.org/10.22876/KAB.2019.33.4.001
  6. 김민정, 신동주, "배달 플랫폼의 e-서비스 품질이 지각된 유용성 및 이용자 만족에 미치는 영향: 인구통계학적 특성의 조절효과를 중심으로", 관광연구, 제33권, 제2호, 2018, pp. 83-101. https://doi.org/10.21719/IJTMS.33.2.5
  7. 김종기, 김상희, "프라이버시 염려와 정보제공행동 간의 프라이버시 역설에 관한 연구: 프라이버시 계산 이론을 중심으로", Entrue Journal of Information Technology, 제13권, 제3호, 2014, pp. 139-152.
  8. 박유진, 이보성, 김범수, 이애리, "온라인 중고물품 재구매의도 영향요인 분석: 중고나라 사이트를 중심으로", e-비즈니스연구, 제18권, 제1호, 2017, pp. 123-140. https://doi.org/10.20462/TeBS.2017.02.18.1.123
  9. 박현지, "ICT 기반 관광옴니채널에 대한 고객행동분석-인구통계학적 특성에 따른 통합기술수용모형의 변수를 중심으로", 디지털융복합연구, 제16권, 제6호, 2018, pp. 95-104. https://doi.org/10.14400/JDC.2018.16.6.095
  10. 성례아, "애니메이션의 재미: 감각적 재미, 인지적 재미, 심리적 재미의 상관관계", 만화애니메이션 연구, 통권, 제33호, 2013, pp. 99-126. https://doi.org/10.7230/KOSCAS.2013.33.099
  11. 위키피디어(Wikipedia), "X세대", 2021, Available at https://ko.wikipedia.org/wiki/X%EC%84%B8%EB%8C%80.
  12. 위키피디어(Wikepedia), 2021, "베이비붐 세대", 2021, Available at https://ko.wikipedia.org/wiki/%EB%B2%A0%EC%9D%B4%EB%B9%84%EB%B6%90_%EC%84%B8%EB%8C%80
  13. 유재호, 이애리, 김경규, "페이스북에서의 프라이버시 패러독스 현상 연구: 듀얼팩터이론을 중심으로", 지식경영연구, 제17권, 제1호, 2016, pp. 17-47. https://doi.org/10.15813/KMR.2016.17.1.002
  14. 이만, 김맹호, "스마트홈(Smart Home) 앱 디자인의 사용성 평가를 위한 평가지표 개발", 한국콘텐츠학회논문지, 제19권, 제5호, 2019, pp. 249-258. https://doi.org/10.5392/JKCA.2019.19.05.249
  15. 이선민, 진민정, 이봉현, "밀레니얼 세대의 뉴스 이용에 대한 탐색적 연구", 한국방송학보, 제34권, 제4호, 2020, pp. 80-115. https://doi.org/10.22876/KAB.2020.34.4.003
  16. 이애리, 김범수, 장재영, "사물인터넷(IoT) 환경에 서의 개인정보 위험 분석 프레임워크", 한국IT서비스학회지, 제15권, 제4호, 2016, pp. 41-62. https://doi.org/10.9716/KITS.2016.15.4.041
  17. 전해영, "사물인터넷(IoT) 관련 유망산업 동향 및 시사점", VIP Report, 662, 2016, pp. 1-16.
  18. 최명경, 이준성, "스포츠 소비자의 시간조망과 윤리 감정이 스포츠 조직의 윤리성 판단에 미치는 영향", 체육과학연구, 제32권, 제1호, 2021, pp. 51-64. https://doi.org/10.24985/KJSS.2021.32.1.51
  19. 최모세, 김상진, "MZ 세대의 틱톡 이용 동기가 만족도와 챌린지 참여의도에 미치는 영향", 상품문화디자인학연구(KIPAD 논문집), 제62권, 2020, pp. 21-30.
  20. 프리그레이스(Freegrace), "세대 총정리", 2020, Available at https://blog.naver.com/barbie1712/222068943974.
  21. 한경경제용어사전, "MZ세대", 2021, Available at https://terms.naver.com/entry.naver?docId=6084621&cid=42107&categoryId=42107.
  22. 한국 스마트홈 산업협회, 스마트홈(홈IOT) 생태계 6대 구성요소, 한국 스마트홈 산업협회, 2014.
  23. Agarwal, R. and J. Prasad, "A conceptual and operational definition of personal innovativeness in the domain of information technology", Information Systems Research, Vol.9 No.2, 1998, pp. 204-215. https://doi.org/10.1287/isre.9.2.204
  24. Ahn, M., J. Kang, and G. Hustvedt, "A model of sustainable household technology acceptance", International Journal of Consumer Studies, Vol.40, No.1, 2016, pp. 83-91. https://doi.org/10.1111/ijcs.12217
  25. Aldossari, M. Q. and A. Sidorova, "Consumer acceptance of Internet of Things (IoT): Smart home context", Journal of Computer Information Systems, Vol.60, No.6, 2020, pp. 507-517. https://doi.org/10.1080/08874417.2018.1543000
  26. Alexander, D. L., J. G. Lynch, and Q. Wang, "As time goes by: Do cold feet follow warm intentions for really new versus incrementally new products?", Journal of Marketing Research, Vol.45, No. 3, 2008, pp. 307-319. https://doi.org/10.1509/jmkr.45.3.307
  27. Awad, N. F. and M. S. Krishnan, "The personalization privacy paradox: an empirical evaluation of information transparency and the willingness to be profiled online for personalization", MIS Quarterly, Vol.30, No.1, 2006, pp. 13-28. https://doi.org/10.2307/25148715
  28. Bolton, R. N. and J. H. Drew, "A multistage model of customers' assessments of service quality and value", Journal of Consumer Research, Vol.17, No.4, 1991, pp. 375-384. https://doi.org/10.1086/208564
  29. Brown, S. A. and V. Venkatesh, "Model of adoption of technology in households: A baseline model test and extension incorporating household life cycle", MIS Quarterly, Vol.29, No.3, 2005, pp. 399-426. https://doi.org/10.2307/25148690
  30. Carman, J., "Values and consumption patterns: A closed loop", Advances in Consumer Research, Vol.15, 1978, pp. 403-407.
  31. Chin, W. W., B. L. Marcolin, and P. R. Newsted, "A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study", Information Systems Research, Vol.14, No.2, 2003, pp. 189-217. https://doi.org/10.1287/isre.14.2.189.16018
  32. Cho, H., J. S. Lee, and S. Chung, "Optimistic bias about online privacy risks: Testing the moderating effects of perceived controllability and prior experience", Computers in Human Behavior, Vol.26, No,5, 2010, pp. 987-995. https://doi.org/10.1016/j.chb.2010.02.012
  33. Chung, T., R. Rust, and M. Wedel, "My mobile music: An adaptive personalization system for digital audio players", Marketing Science, Vol.28, No.1, 2009, pp. 52-68. https://doi.org/10.1287/mksc.1080.0371
  34. Dinev, T. and P. Hart, "An extended privacy calculus model for e-commerce transactions", Information Systems Research, Vol.17, No.1, 2006, pp. 61-80. https://doi.org/10.1287/isre.1060.0080
  35. Fornell, C. and D. F. Larcker, "Evaluating structural equation models with unobservable variables and measurement error", Journal of Marketing Research, Vol.18, No.1, 1981, pp. 39-50. https://doi.org/10.1177/002224378101800104
  36. Gefen, D., D. Straub, and M. C. Boudreau, "Structural equation modeling and regression: Guidelines for research practice", Communications of the Association for Information Systems, Vol.4, No.1, 2000, pp. 1-77. https://doi.org/10.17705/1CAIS.00407
  37. Goswami, S. and B. Chandra, "Convergence dynamics of consumer innovativeness vis-a-vis technology acceptance propensity: An empirical study on adoption of mobile devices", IUP Journal of Marketing Management, Vol.12, No.3, Vol.12, No.3, 2013, pp. 63-87.
  38. Guo, X., X. Zhang, and Y. Sun, "The privacy-personalization paradox in mHealth services acceptance of different age groups. Electroni", Commerce Research and Applications, Vol.16, 2016, pp. 55-65. https://doi.org/10.1016/j.elerap.2015.11.001
  39. Hess, T. J., M. Fuller, and D. E. Campbell, "Designing interfaces with social presence: Using vividness and extraversion to create social recommendation agents", Journal of the Association for Information Systems, Vol.10, No.12, 2009, pp. 889-919. https://doi.org/10.17705/1jais.00216
  40. Hwang, J., S. Park, and I. Kim, "Understanding motivated consumer innovativeness in the context of a robotic restaurant: The moderating role of product knowledge", Journal of Hospitality and Tourism Management, Vol. 44, 2020, pp. 272-282. https://doi.org/10.1016/j.jhtm.2020.06.003
  41. Kim, D., K. Park, Y. Park, and J. H. Ahn, "Willingness to provide personal information: Perspective of privacy calculus in IoT services", Computers in Human Behavior, Vol.92, 2019, pp. 273-281. https://doi.org/10.1016/j.chb.2018.11.022
  42. Kim, M. S. and S. Kim, "Factors influencing willingness to provide personal information for personalized recommendations", Computers in Human Behavior, Vol. 88, 2018, pp. 143-152. https://doi.org/10.1016/j.chb.2018.06.031
  43. Kim, M., The Impacts of Privacy Rules on Users' Perception on Internet of Things (IoT) Applications: Focusing on Smart Home Security Service (Master's Thesis), Graduate School of UNIST, 2017.
  44. Kwon, O., K. Choi, and M. Kim, "User acceptance of context-aware services: Self-efficacy, user innovativeness and perceived sensitivity on contextual pressure", Behaviour & Information Technology, Vol.26, No.6, 2007, pp. 483-498. https://doi.org/10.1080/01449290600709111
  45. Li, H., R. Sarathy, and H. Xu, "The role of affect and cognition on online consumers' decision to disclose personal information to unfamiliar online vendors", Decision Support Systems, Vol.51, No.3, 2011, pp. 434-445. https://doi.org/10.1016/j.dss.2011.01.017
  46. Lin, H. T., "Implementing smart homes with open source solutions", International Journal of Smart Home, Vol.7, No.4, 2013, pp. 289-295.
  47. Mohamed, N. and I. H. Ahmad, "Information privacy concerns, antecedents and privacy measure use in social networking sites: Evidence form Malaysi", Computers in Human Behavior, Vol.28, No.6, 2012. pp. 2366ᐨ2375.
  48. Mowad, M. A. E. L., A. Fathy, and A. Hafez, "Smart home automated control system using android application and microcontroller", International Journal of Scientific & Engineering Research, Vol.5, No.5, 2014, pp. 935-939.
  49. Pal, D., C. Arpnikanondt, and M. A. Razzaque, "Personal information disclosure via voice assistants: The personalization-privacy paradox", SN Computer Science, Vol.1, No.5, 2020, pp. 1-17. https://doi.org/10.1007/s42979-020-00287-9
  50. Roehrich, G., "Consumer innovativeness: Concepts and measurements", Journal of Business Research, Vol.57, No.6, 2004, pp. 671-677. https://doi.org/10.1016/S0148-2963(02)00311-9
  51. Rogers, E. M., Diffusion of Innovations (4th Ed.), Free Press, New York, NY, 1995.
  52. Seyed Esfahani, M. and N. Reynolds, "Impact of consumer innovativeness on really new product adoption", Marketing Intelligence & Planning, Vol.39, No.4, 2021, pp. 589-612. https://doi.org/10.1108/MIP-07-2020-0304
  53. Sheng, H., F. F. H. Nah, and K. Siau, "An Experimental study on ubiquitous commerce adoption: Impact of personalization and privacy concerns", Journal of the Association for Information Systems, Vol.9 No.6, Article 15. 2008.
  54. Simonson, I. and S. M. Nowlis, "The role of explanations and need for uniqueness in consumer decision making: Unconventional choices based on reasons", Journal of Consumer Research, Vol.27, No.1, 2000, pp. 49-68. https://doi.org/10.1086/314308
  55. Statista, "Forecast market size of the global smart home market from 2016 to 2022," 2017, Available at https://www.statista.com/statistics/682204/global-smart-home-market-size/.
  56. Statista, "Number of digital voice assistants in use worldwide from 2019 to 2023", 2019, Available at https://www.statista.com/statistics/ 973815/worldwide-digital-voice-assistant-in-use/.
  57. Sung, J. and J. Jo, "The influence of perceived risk and consumer innovativeness on intention to use of internet of things service", Journal of Theoretical & Applied Information Technology, Vol.96, No.4, 2018, pp. 1008-1017.
  58. Sweeney, J. and G. Soutar, "Consumer perceived value: The development of a multiple item scale", Journal of Retailing, Vol.77, No.2, 2001, pp. 203-220. https://doi.org/10.1016/S0022-4359(01)00041-0
  59. Tuunainen, V. K., O. Pitkanen, and M. Hovi, "Users' awareness of privacy on online social networking sites-case Facebook", Bled 2009 Proceedings, 42, 2009, pp. 1-17.
  60. Um, M. and M. Kim, "An exploratory study on factors affecting efforts for information protection in cyber space", Informatization Policy, NIA, Vol.14, No.1, 2007, pp. 125-143.
  61. Usak, M., M. Kubiatko, M. S. Shabbir, O. Viktorovna Dudnik, K. Jermsittiparsert, and L. Rajabion, "Health care service delivery based on the Internet of things: A systematic and comprehensive study", International Journal of Communication Systems, Vol.33 No.2, 2020, e4179.
  62. Vallerand, R. J., "Toward a hierarchical model of intrinsic and extrinsic motivation", Advances in Experimental Social Psychology, Vol.29, 1997, pp. 271-360. https://doi.org/10.1016/S0065-2601(08)60019-2
  63. Vandecasteele, B. and M. Geuens, "Motivated consumer innovativeness: Concept, measurement, and validation", International Journal of Research in Marketing, Vol.27, No.4, 2010, pp. 308-318. https://doi.org/10.1016/j.ijresmar.2010.08.004
  64. Venkatesh, V., S. A. Brown, L. M. Maruping, and H. Bala, "Predicting different conceptualizations of system use: the competing roles of behavioral intention, facilitating conditions, and behavioral expectation", MIS Quarterly, Vol.32, No.3, 2008, pp. 483-502. https://doi.org/10.2307/25148853
  65. Venkatraman, M. P., "The impact of innovativeness and innovation type on adoption", Journal of Retailing, Vol.67, No.1, 1991, pp. 51-67.
  66. Voss, K. E., E. R. Spangenberg, and B. Grohmann, "Measuring the hedonic and utilitarian dimensions of consumer attitude", Journal of Marketing Research, Vol.40, No.3, 2003, pp. 310-320. https://doi.org/10.1509/jmkr.40.3.310.19238
  67. Wang, T., T. D. Duong, and C. C. Chen, "Intention to disclose personal information via mobile applications: A privacy calculus perspective", International Journal of Information Management, Vol.36, No.4, 2016, pp. 531-542. https://doi.org/10.1016/j.ijinfomgt.2016.03.003
  68. Xu, H. and S. Gupta, "The effects of privacy concerns and personal innovativeness on potential and experienced customers' adoption of location-based services", Electronic Markets, Vol.19, 2009, pp. 137-149. https://doi.org/10.1007/s12525-009-0012-4
  69. Xu, H., X. R. Luo, J. M. Carroll, and M. B. Rosson, "The personalization privacy paradox: An exploratory study of decision making process for location-aware marketing", Decision Support Systems, Vol.51, No.1, 2011, pp. 42-52. https://doi.org/10.1016/j.dss.2010.11.017
  70. Yan, Y., C. Huang, Q. Wang, and B. Hu, "Data mining of customer choice behavior in internet of things within relationship network", International Journal of Information Management, Vol.50, 2020, pp. 566-574. https://doi.org/10.1016/j.ijinfomgt.2018.11.013
  71. Yao, L., Q. Z. Sheng, B. Benatallah, S. Dustdar, X. Wang, A. Shemshadi, and S. S. Kanhere, "WITS: An IoT-endowed computational framework for activity recognition in personalized smart homes", Computing, Vol.100, No.4, 2018, pp. 369-385. https://doi.org/10.1007/s00607-018-0603-z