디지털융복합연구 (Journal of Digital Convergence)

  • 제20권3호
  • /
  • Pages.241-249
  • /
  • 2022
  • /
  • 2713-6434(pISSN)
  • /
  • 2713-6442(eISSN)
한국디지털정책학회 (The Society of Digital Policy and Management)
권호 표지
DOI QR코드

DOI QR Code

인공지능 사전경험 무시 현상과 수용에 관한 연구: AI Effect를 중심으로

A study on Discount in Prior Experience of AI and Acceptance: Focusing on AI Effect

  • 이정선 (숙명여자대학교 대학 IR 센터)
  • Lee, JeongSeon (Center for Institutional Research, Sookmyung Women's University)
  • 투고 : 2022.01.24
  • 심사 : 2022.03.20
  • 발행 : 2022.03.28
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

인공지능은 개인의 일상생활뿐 아니라 전 산업 분야에 적용되며 인공지능 시대라 해도 과언이 아닌 시기가 도래하였다. 그러므로 인공지능 수용에 영향을 주는 요인 파악은 중요하다. 본 연구는 상용화되거나 익숙해진 인공지능은 더는 인공지능이라 인식하지 못하는 AI Effect 현상으로 인공지능 사전경험이 무시되었을 때 인공지능 수용에 어떠한 영향을 미치는지를 분석하였다. 이를 위해 두 번의 실험을 수행하였다. 105명의 성인을 대상으로 한 첫 번째 실험 결과는 실험 대상자 중 32.4%(34명)가 AI Effect가 존재하였고, 이 중 여성이 43.6%(24명), 남성은 20%(10명)가 AI Effect가 존재하는 것을 나타나 여성이 약 2배 정도 높았고, 인공지능 지식 정도가 낮을수록 AI Effect가 존재하는 것으로 나타났다. 두 번째 실험 결과는 성인 240명의 참가자 중 AI Effect가 존재하는 85명만이 대상이었고, 인공지능 경험인지는 인공지능을 적극적으로 수용하게 하는 것으로 나타났다. 본 연구를 통한 AI Effect 이해는 기업에 인공지능의 적극적 수용방안 설정에 도움을 줄 수 있을 것이라 기대된다. 더불어 사용자의 개인 차이와 AI Effect의 관계 규명, AI Effect가 다양한 수용 태도에 미치는 영향 등을 고려한 연구로의 확장을 기대한다.

Artificial intelligence is applied not only to the daily life of individuals but also to all industries, and it is no wonder that the age of artificial intelligence has arrived. Therefore it is important to understand the factors that influence the acceptance of AI. This study analyzes whether "AI Effect" which recognizes that commercialized or familiar artificial intelligence is no longer artificial intelligence, affects the acceptance of artificial intelligence and proposes an acceptance plan based on the results. Two experiments were conducted. The first experiment was conducted on 105 adults in the result it was found that 32.4% (34 people) had AI Effect, AI Effect existed in 43.6% (24 people) of women and 20% (10 people) of men, that is, the proportion of AI Effect exsitence in women is about twice as high.and AI Effect exists when the level of AI knowledge is low. The second experiment was conducted 240 adults and 85 participants with AI Effect were selected. We found the group that recognized experience of AI accepted AI more actively. Understanding of AI Effect is expected to suggest companies' views in order to enhance AI capabilities and acceptance. In addition, future studies are expected on considering individual differences or related to acceptance attitudes.

키워드

참고문헌

  1. IDC. (2020). Korea Artificial Intelligence Forecast, 2019-2023.
  2. D. Leslie. (2019). Understanding artificial intelligence ethics and safety: A guide for the responsible design and implementation of AI systems in the public sector. The Alan Turing Institute. DOI : 10.5281/zenodo.3240529
  3. H. J. Wilson & P. R. Daugherty. (2018). Collaborative Intelligence: Humans and AI Are Joining Forces. Harvard Business Review. July/August Issue, 114-123.
  4. The Presidential Committee on the 4th industrial revolution. (2021). Public perception survey on the use of artificial intelligence.
  5. McCorduck, Pamela (2004), Machines Who Think (2nd ed.), Natick, MA: A. K. Peters, Ltd., ISBN 1-56881-205-1.
  6. Haenlein, Michael; Kaplan, Andreas (2019). "A Brief History of Artificial Intelligence: On the Past, Present, and Future of Artificial Intelligence". California Management Review. 61 (4): 5-14. doi:10.1177/0008125619864925. S2CID 199866730.
  7. Phillips, E. M. (1999). If it works, it's not AI: a commercial look at artificial intelligence startups (Doctoral dissertation, Massachusetts Institute of Technology).
  8. Hofstadter, Douglas (1980), Godel, Escher, Bach: an Eternal Golden Braid.
  9. ARM. (2017). AI Today, AI Tomorrow : Awareness, acceptance and anticipation of AI : A global consumer perspective.
  10. PEGA. (2017). What Consumers Really Think About AI: A Global Study.
  11. H. Choi., Kim, Y. Kim & J. Kim. (2010). An acceptance model for an internet protocol television service in korea with prior experience as a moderator. Service Industries Journal, 30(11), 1883-1901. DOI : 10.1080/02642060802627178
  12. M. L. Ashour & R. M. Al-qirem. (2021). Consumer Adoption of Self-Service Technologies: Integrating the Behavioral Perspective with the Technology Acceptance Model. The Journal of Asian Finance, Economics and Business, 8(3), 1361-1369. DOI : 10.13106/jafeb.2021.vol8.no3.1361
  13. P. Hanafizadeh, S. Ghandchi & M. Asgarimehr. (2017). Impact of Information Technology on Lifestyle: A Literature Review and Classification. Int. J. Virtual Communities Soc. Netw., 9, 1-23. DOI : 10.4018/IJVCSN.2017040101
  14. S. Taylor & P.A. Todd. (1995). Assessing IT usage: the role of prior experience. Management Information Systems Quarterly, 19, 561-570. DOI : 10.2307/249633
  15. D. Gefen. E. Karahanna & D. W. Straub. (2003a). Trust and TAM in online shopping: An integrated model. MIS quarterly, 27(1), 51-90. https://doi.org/10.2307/30036519
  16. D. Gefen, E. Karahanna & D.W. Straub. (2003b). Inexperience and experience with online stores: The importance of TAM and trust. IEEE Transactions on engineering management, 50(3), 307-321. https://doi.org/10.1109/TEM.2003.817277
  17. S. Bonaccio & R. S. Dalal. (2006). Advice taking and decision-making: An integrative literature review, and implications for the organizational sciences. Organizational Behavior and Human Decision Processes,101(2), 127-151. DOI : 10.1016/j.obhdp.2006.07.001
  18. B. Smith, P. Caputi & Rawstorne, P. (2000). Differentiating Computer Experience And Attitudes Toward Computers: An Empirical Investigation. Computer in Behavior, 16(1), 59-81. https://doi.org/10.1016/S0747-5632(99)00052-7
  19. V. Venkatesh. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research,11(4), 342-365. DOI ; 10.1287/isre.11.4.342.11872
  20. V. Venkatesh & F. D. Davis. (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
  21. Y. Y. Kim, S. J. Oh, J. H. Ahn & J. J. Jang. (2008). What happens after IT adoption?: Role of habits, confirmation, and computer self-efficacy formed by the experiences of use. Asia Pacific Journal of Information Systems, 18(1), 25-52.
  22. H. Shengnan. (2003). Individual adoption of IS in organisations: A literature review of technology acceptance model (TUCS Technical Report, No. 540, 1-45). Finland: Turku Centre for Computer Science, ISBN: 952-12-1189-X.
  23. S. Chang & D. Youm. (2018). The Effects of User Experience on Facebook Acceptance Behavior and Advertising Acceptance Behavior. Journal of Digital Convergence, 16(3), 169-179. DOI : 10.14400/JDC.2018.16.3.169
  24. J. Y. Sung & K. H. Park. (2011). A Study on Influence of Smart-Phone User Interface upon Brand Loyalty : with a focus on coordinating role of perceived skills on the device. Journal of Basic Design & Art 12(1), 311-322.
  25. M. K. Kang & J. S. Lee.(2019). The Influence of Podcast Motivation on Use and Satisfaction :Focusing on the Moderation Role of Radio Preference and Participation Experience. Korean Journal of Broadcasting and Telecommunication Studies, 33(2), 5-34.
  26. J. I. Kim & I. S. Kim. (2014). A Study on how Smartphone Users' Experiences Affect Consumer Loyalty Affect. Global Business Administration Review, 11(1), 179-203. DOI : 10.17092/jibr.2014.11.1.179
  27. P.Stone et al. (2016). Artificial Intelligence and Life in 2030. One Hundred Year Study on Artificial Intelligence: Report of the 2015-2016 Study Panel. Stanford :Stanford University. Doc: http://ai100.stanford.edu/2016-report.
  28. M. Yutaka.(2015).Artificial Intelligence and Deep Learning: Changes and Innovations in the Industrial Structure of Artificial Intelligence, Seoul: Dong-a mnb.
  29. I. Yaniv.(2004). The Benefit of Additional Opinions. Current Directions in Psychological Science, 13(2),. 75-78. DOI : 10.1111/j.0963-7214.2004.00278.x
  30. F. Gino & D. A. Moore. (2007). Effects of Task Difficulty on Use of Advice. Journal of Behavioral Decision Making, 20(1) 21-35. DOI : 10.1002/bdm.539
  31. F. Gino. (2008). Do we listen to advice just because we paid for it? The impact of advice cost on its use. Organizational Behavior and Human Decision Processes, 107(2), 234-245. DOI : 10.1016/j.obhdp.2008.03.001
  32. J. M.. Logg, J. A. Minson & D. A. Moore. (2019). Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes, 151, 90-103. DOI : 10.1016/j.obhdp.2018.12.005
  33. K.H.Lee (2019). The Digital Divide and Challenges in Intelligence Information Society. Health and welfare policy forum, 274,16-28