Development of Evaluation Framework for Adopting of a Cloud-based Artificial Intelligence Platform

클라우드 기반 인공지능 플랫폼 도입 평가 프레임워크 개발

  • 서광규 (상명대학교 경영공학과)
  • Received : 2023.09.10
  • Accepted : 2023.09.15
  • Published : 2023.09.30

Abstract

Artificial intelligence is becoming a global hot topic and is being actively applied in various industrial fields. Not only is artificial intelligence being applied to industrial sites in an on-premises method, but cloud-based artificial intelligence platforms are expanding into "as a service" type. The purpose of this study is to develop and verify a measurement tool for an evaluation framework for the adoption of a cloud-based artificial intelligence platform and test the interrelationships of evaluation variables. To achieve this purpose, empirical testing was conducted to verify the hypothesis using an expanded technology acceptance model, and factors affecting the intention to adopt a cloud-based artificial intelligence platform were analyzed. The results of this study are intended to increase user awareness of cloud-based artificial intelligence platforms and help various industries adopt them through the evaluation framework.

Keywords

Acknowledgement

본 논문은 2023년 상명대학교 교내연구비를 지원받아 수행하였음.

References

  1. H. C. Jung, K.-K. Seo. Data Standardization Method for Quality Management of Cloud Computing Services using Artificial Intelligence, Journal of the Semiconductor & Display Technology, Vol. 21, No. 2, pp. 133-137, 2022. 
  2. K.-K. Seo, A Study on Innovation Types of Cloud Companies based on a Meta Model, Journal of the Semiconductor & Display Technology, Vol. 20, No. 3, pp. 87-92, 2021. 
  3. B. S. Kim, H. J. Woo. A Study on the Intention to Use AI Speakers: focusing on extended technology acceptance model, The Journal of the Korea Contents Association, Vol.19, No.9, pp. 1-10, 2019. 
  4. S. Ahn, W. Jo. D, Chung, Factors Affecting Users to Adopt Voice Shopping: Empirical evidence from the UTAUT model, Journal of Technology Innovation, Vol.27, No.4, pp.111-144, 2019.  https://doi.org/10.14386/SIME.2019.27.4.111
  5. W. Zhang, A Study on the User Acceptance Model of Artificial Intelligence Music Based on UTAUT, Journal of the Korea Society of Computer and Information, Vol. 25, No. 6, pp. 25-33, 2020. 
  6. T. Son, J. Goo, D. Ahn, Understanding Elementary School Teachers' Intention to Use Artificial Intelligence in Mathematics Lesson Using TPACK and Technology Acceptance Model, Education of Primary School Mathematics, Vol. 26, No. 3, pp. 163-180, 2023. 
  7. W. H. Delone and E. R. McLean, Information System Success: The Quest for the Dependent Variable, Information Systems Research, Vol. 3, No. 1, pp. 60-95, 1992.  https://doi.org/10.1287/isre.3.1.60
  8. F. D. Davis, Perceived usefulness, perceived ease of use, and user acceptance of information technology, MIS Quarterly, Vol. 13, No. 3, pp. 319-340, 1989.  https://doi.org/10.2307/249008
  9. B. -S. Kang, Research Methodology for the Analysis of Causal, Trade Management Corp., Seoul. 2002. 
  10. P. M. Bentler, Comparative Fit Indices in Structural Models. Psychological Bulletin, Vol.107, No. 2, pp. 238-246, 1990. https://doi.org/10.1037/0033-2909.107.2.238