DOI QR코드

DOI QR Code

인공지능(AI) 디바이스 이용 소비자의 사용행태 및 사용자 경험 분석

Analysis of User Experience and Usage Behavior of Consumers Using Artificial Intelligence(AI) Devices

  • 김준환 (성결대학교 파이데이아학부)
  • 투고 : 2021.03.13
  • 심사 : 2021.06.20
  • 발행 : 2021.06.28

초록

본 연구는 인공지능(AI) 디바이스가 차세대 정보통신기술(ICT)의 핵심 플랫폼으로 급부상하고 있고, 소비자들의 일상에 널리 적용되고 있는 인공지능 디바이스를 통해 소비자의 사용행태 및 사용자 경험에 대해 살펴보았다. 이를 위해 AI 디바이스 사용 경험이 있는 국내 소비자 600명을 대상으로 AI 디바이스의 속성 인식과 사용행태를 도출하였다. 분석결과는 다음과 같다. 첫째, 다양한 속성 중 음악청취를 가장 많이 이용하였고, 날씨 정보제공과 같은 단순한 기능을 유용하게 인식하는 것으로 나타났다. 둘째, AI 디바이스 사용자의 주요 사용기기는 AI 스피커, 스마트폰, PC, 노트북 등으로 확인되었다. 셋째, AI 디바이스에 대한 연상 이미지는 재미있는, 유용한, 신기한, 똑똑한, 혁신적인, 친근한 순으로 나타났다. 따라서 본 연구는 AI 디바이스의 특성을 반영한 사용 행태를 분석함으로써 향후 AI 디바이스를 활용한 사용자의 서비스 제공에 기여할 수 있다는 실무적 시사점을 갖는다.

Artificial intelligence (AI) devices are rapidly emerging as a core platform of next-generation information and communication technology (ICT), this study investigated consumer usage behavior and user experience through AI devices that are widely applied to consumers' daily lives. To this end, data was collected from 600 consumers with experience in using AI devices were derived to recognize the attributes and behavior of AI devices. The analysis results are as follows. First, music listening was the most used among various attributes and it was found that simple functions such as providing weather information were usefully recognized. Second, the main devices used by AI device users were identified as AI speakers, smartphone, PC and laptops. Third, associative images of AI devices appeared in the order of fun, useful, novel, smart, innovative, and friendly. Therefore, practical implications are suggested to contribute to provision of user services using AI devices in the future by analyzing usage behaviors that reflect the characteristics of AI devices.

키워드

참고문헌

  1. IBM research team. https://www.research.ibm.com/artificial-intelligence/tursted-ai/#introduction
  2. M. Arnold, R. K. Bellamy, M. Hind, S. Houde, S. Mehta, A. Mojsilovic & D. Reimer. (2019). FactSheets: Increasing trust in AI services through supplier's declarations of conformity. IBM Journal of Research and Development, 63(4/5), 6-1.
  3. H. T. Yang & D. B. Kim. (2017). Intelligent Personal Assistant Market Trend and Domestic Industry Impact Forecast. Science & Technology Policy, 35, 6-1.
  4. Gartner (2017. 10. 3). Artificial Intelligence, Immersive Experiences, Digital Twins, Event-thinking and Continuous Adaptive Security Create a Foundation for the Next Generation of Digital Business Models and Ecosystems. Gartner Top 10 Strategic Technology Trends for 2018. https://www.gartner.com/smarterwithgartner/gartner-top-10-strategic-technology-trends-for-2018/
  5. Statista (2020 3. 3). Smart speaker with intelligent personal assistant quarterly shipment share from 2016 to 2019, by vendor. https://www.statista.com/statistics/792604/worldwide-smart-speaker-market-share/
  6. J. H. Kim & N. Y. Lee. (2020). AI speakers!, Speak with feelings-Focusing on Analysis of SNS Comments. Journal of Digital Convergence, 18(7), 101-110. DOI : 10.14400/JDC.2020.18.7.101
  7. S. Andreev, O. Galinina, A. Pyattaev, M. Gerasimenko, T. Tirronen, J. Torsner & Y. Koucheryavy. (2015). Understanding the IoT Connectivity Landscape: A Contemporary M2M Radio Technology Roadmap. IEEE Communications Magazine, 53(9), 32-40. DOI : 10.1109/MCOM.2015.7263370
  8. G. Aloi, G. Caliciuri, G. Fortino, R. Gravina, P. Pace, W. Russo & C. Savaglio. (2017). Enabling IoT interoperability through opportunistic smartphone-based mobile gateways. Journal of Network and Computer Applications, 81(1), 74-84. DOI : 10.1016/j.jnca.2016.10.013
  9. H. Lee, C. H. Cho, S. Y. Lee & Y. H. Keel. (2019). A Study on Consumers' Perception of and Use Motivation of Artificial Intelligence (AI) Speaker. The Journal of the Korea Contents Association, 19(3), 138-154. DOI : 10.5392/JKCA.2019.19.03.138
  10. G. Lopez, L. Quesada & L. A. Guerrero. (2017, July). Alexa vs. Siri vs. Cortana vs. Google Assistant: a comparison of speech-based natural user interfaces. In International Conference on Applied Human Factors and Ergonomics (pp. 241-250). Springer, Cham.
  11. E. J. Lee & Y. J. Sung. (2020). "Hey Kakao!": A Qualitative study on the Interaction between AI devices and its Consumer. Korean Journal of Consumer and Advertising Psychology, 21(1), 21-53. DOI : 10.21074/kjlcap.2020.21.1.21
  12. Y. Kim, S. K. Han, Z. Yoon, E. Heo, J. W. Kim & J. Lee. (2017). Users' Perception and Behavioral Differences Depending on Chatbot Agent Identities. Journal of the HCI Society of Korea, 12(4), 45-55. DOI : 10.17210/jhsk.2017.11.12.4.45
  13. J. H. Park & J. W. Joo. (2018). A Behavioral Economic Approach to Increase Users' Intention to Continue to Use the Voice Recognition Speakers: Anthropomorphism, Design convergence study, 17(3), 41-53. DOI : 10.31678/SDC.70.3
  14. G. E. Jo & S. I. Kim. (2018). A study on User Experience of Artificial Intelligence speaker. Journal of the Korea Convergence Society, 9(8), 127-133. DOI : 10.15207/JKCS.2018.9.8.127
  15. H. J. Lee. (2018). A Ghost in the Shell? Influences of AI Features on Product Evaluations of Smart Speakers with Customer Reviews. Journal of Information Technology Services, 17(2), 191-205. DOI : 10.9716/KITS.2018.17.2.191
  16. R. W. Picard & J. Healey. (1997). Affective wearables. Personal Technologies, 1(4), 231-240. DOI : 10.1007/BF01682026