
기술수용모델을 적용한 중국 무용 전공자의 AI 사용 태도 연구: AI 기술 불안, 지각된 용이성, 지각된 유용성, AI 지식 관여도를 중심으로
Copyright Ⓒ 2026 by the Korean Society of Dance Science
초록
본 연구는 기술수용모델을 활용하여 중국 무용 전공자의 AI 사용 태도 형성 과정을 살펴보고, 이 과정에서 AI 지식 관여도의 조절효과를 검증하고자 하였다. 이를 위해 AI 기술 불안을 외생변수로 설정하고, 지각된 용이성과 지각된 유용성이 AI 사용 태도에 미치는 영향과 지각된 용이성이 지각된 유용성에 미치는 영향을 분석하였다. 중국 무용 전공자를 대상으로 총 245부의 설문지를 배포하였으며, 그중 233부를 최종 분석에 활용하였다. 수집된 자료는 SPSS 26.0, R(lavaan) 및 PROCESS Macro를 활용하여 빈도분석, 상관관계분석, 확인적 요인분석, 매개효과 및 조절효과 분석을 실시하였다. 연구 결과, AI 기술 불안은 AI 사용 태도에 부정적인 영향을 미치는 것으로 나타났다. 또한 AI 기술 불안은 지각된 용이성과 지각된 유용성에 부정적 영향을 미쳤으며, 지각된 용이성은 지각된 유용성에 정적인 영향을 미치는 것으로 나타났다. 아울러 지각된 용이성과 지각된 유용성은 모두 AI 사용 태도에 정적인 영향을 미쳤다. 또한 지각된 용이성과 지각된 유용성은 AI 기술 불안과 AI 사용 태도 간의 관계에서 각각 매개효과를 보였으며, 지각된 용이성을 통해 지각된 유용성으로 이어지는 연쇄 매개효과 또한 확인되었다. 한편, AI 지식 관여도는 AI 기술 불안과 지각된 유용성 간의 관계에서 유의한 조절효과를 보였다. 반면, AI 기술 불안과 지각된 용이성 간의 관계에서는 AI 지식 관여도의 조절효과가 유의하지 않은 것으로 나타났다. 본 연구는 무용 창작 맥락에서 AI 사용 태도를 단순한 기능적 수용의 문제가 아니라, 창작 과정에서 나타나는 심리적 반응과 인지적 판단의 관점에서 이해하였다는 점에서 의의가 있다. 또한 중국 무용 전공자를 대상으로 예술 창작 분야에서의 AI 기술 수용 과정을 실증적으로 분석하였다는 점에서 학술적 의미를 가진다.
Abstract
Based on the Technology Acceptance Model (TAM), this study examined the process of attitude formation toward the use of artificial intelligence (AI) among Chinese dance majors and investigated the moderating effect of AI knowledge involvement in this process. To this end, AI anxiety was set as an exogenous variable, and the effects of perceived ease of use and perceived usefulness on Attitude Toward Use (ATU), as well as the effect of perceived ease of use on perceived usefulness, were analyzed. A total of 245 questionnaires were distributed to Chinese dance majors, of which 233 valid responses were used for the final analysis. The collected data were analyzed using SPSS 26.0, R (lavaan), and PROCESS Macro through frequency analysis, correlation analysis, confirmatory factor analysis, mediation analysis, and moderation analysis. The results showed that AI anxiety had a negative effect on Attitude Toward Use (ATU). In addition, AI anxiety negatively affected both perceived ease of use and perceived usefulness, while perceived ease of use positively affected perceived usefulness. Furthermore, both perceived ease of use and perceived usefulness positively influenced Attitude Toward Use. Perceived ease of use and perceived usefulness each mediated the relationship between AI anxiety and Attitude Toward Use, and a sequential mediation effect through perceived ease of use and perceived usefulness was also confirmed. Meanwhile, AI knowledge involvement showed a significant moderating effect on the relationship between AI anxiety and perceived usefulness. However, the moderating effect of AI knowledge involvement on the relationship between AI anxiety and perceived ease of use was not statistically significant. This study is meaningful in that it interprets Attitude Toward Use in the context of dance creation not merely as an issue of functional technology acceptance, but as a phenomenon involving psychological responses and cognitive judgments that emerge throughout the creative process. In addition, by empirically examining the process of AI technology acceptance among Chinese dance majors, this study contributes to a broader understanding of AI technology acceptance in the field of artistic creation.
Keywords:
Technology Acceptance Model (TAM), Chinese Dance Majors, AI Anxiety, Attitude Toward Use, AI Knowledge Involvement키워드:
기술수용모델, 중국 무용 전공자, AI 기술 불안, AI 사용 태도, AI 지식 관여도References
- 김동규, 이해준, 최은지(2025). 숫자를 활용한 무용창작 프로그램 시안. 한국무용학회지, 24(4), 197-207.
- 김미진(2024). 무용을 배운 기계: 창작의 주체와 책임. 움직임의 철학: 한국체육철학회지, 32(3), 67-79.
- 태혜신, 김선영(2019). 인공지능과 예술의 융합 양상에 관한 탐색적 고찰. 한국무용과학회지, 36(2), 27-42.
-
Aldlaigan, A. H., & Buttle, F. A. (2001). Consumer involvement in financial services: An empirical test of two measures. International Journal of Bank Marketing, 19(6), 232-245.
[https://doi.org/10.1108/EUM0000000006022]
- Alrajawy, I., Isaac, O., Ghosh, A., Nusari, M., Al-Shibami, A. H., & Ameen, A. A. (2018). Determinants of student's intention to use mobile learning in Yemeni public universities: Extending the technology acceptance model (TAM) with anxiety. International Journal of Management and Human Science, 2, 1-9.
-
Baía Reis, A., Vašků, P., & Solmošiová, S. (2025). Artificial intelligence in dance choreography: a practice-as-research exploration of human–AI co-creation using ChatGPT-4. International Journal of Performance Arts and Digital Media, 21(2), 284-304.
[https://doi.org/10.1080/14794713.2025.2515754]
- Crnkovic-Friis, L., & Crnkovic-Friis, L. (2016). Generative choreography using deep learning. arXiv preprint arXiv:1605.06921, .
- Cyberspace Administration of China. (2023). Interim Measures for the Management of Generative Artificial Intelligence Services.
-
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
[https://doi.org/10.2307/249008]
-
Foxall, G. R., & Pallister, J. G. (1998). Measuring purchase decision involvement for financial services: Comparison of the Zaichkowsky and Mittal scales. International Journal of Bank Marketing, 16(5), 180-194.
[https://doi.org/10.1108/02652329810228181]
-
Heldal, F., & Dehlin, E. (2021). In search of autonomy: Dancing with rules. Frontiers in Psychology, 12, 717590.
[https://doi.org/10.3389/fpsyg.2021.717590]
-
Igbaria, M., & Parasuraman, S. (1989). A path analytic study of individual characteristics, computer anxiety and attitudes toward microcomputers. Journal of Management, 15(3), 373-388.
[https://doi.org/10.1177/014920638901500302]
-
Iqbal, J., & Sidhu, M. S. (2022). Acceptance of dance training system based on augmented reality and technology acceptance model (TAM). Virtual Reality, 26(1), 33-54.
[https://doi.org/10.1007/s10055-021-00529-y]
-
Johnson, B. T., & Eagly, A. H. (1989). Effects of involvement on persuasion: A meta-analysis. Psychological Bulletin, 106(2), 290-314.
[https://doi.org/10.1037/0033-2909.106.2.290]
-
Kyle, G., Absher, J., Norman, W., Hammitt, W., & Jodice, L. (2007). A modified involvement scale. Leisure Studies, 26(4), 399–427.
[https://doi.org/10.1080/02614360600896668]
-
Kaya, F., Aydin, F., Schepman, A., Rodway, P., Yeti¸sensoy, O., & Demir Kaya, M. (2024). The roles of personality traits, AI anxiety, and demographic factors in attitudes toward artificial intelligence. Int. J. Human–Computer Interact. 40, 497–514.
[https://doi.org/10.1080/10447318.2022.2151730]
-
Li, X. (2021). The art of dance from the perspective of artificial intelligence. Journal of Physics: Conference Series, 1852(4), 042011.
[https://doi.org/10.1088/1742-6596/1852/4/042011]
-
Liu, Y., & Sra, M. (2024). DanceGen: Supporting choreography ideation and prototyping with generative AI. In Proceedings of the 2024 ACM Designing Interactive Systems Conference (pp. 920-938).
[https://doi.org/10.1145/3643834.3661594]
-
Lottridge, D., Weber, R., McLean, E. R., Williams, H., Cook, J., & Bai, H. (2022). Exploring the design space for immersive embodiment in dance. In 2022 IEEE Conference on Virtual Reality and 3D User Interfaces (VR) (pp. 93-102). IEEE.
[https://doi.org/10.1109/VR51125.2022.00027]
-
López-Bonilla, J. M., & López-Bonilla, L. M. (2011). Validation of an information technology anxiety scale in undergraduates. British Journal of Educational Technology, 43(2), E56-E58.
[https://doi.org/10.1111/j.1467-8535.2011.01256.x]
- Lv Yisheng. (2000). On the Normativity of Dance Education. Ethnic Art Studies, (05), 29-35. [in Chinese]
-
Ma, Q. (2025). Harnessing generative neural networks to fuse traditional Tujia Baishou dance with contemporary choreography: Enhancing creativity and aesthetic experience in dance students. Acta Psychologica, 258, 105178.
[https://doi.org/10.1016/j.actpsy.2025.105178]
-
Mikalonytė, E. S., & Kneer, M. (2022). Can artificial intelligence make art?: Folk intuitions as to whether AI-driven robots can be viewed as artists and produce art. ACM Transactions on Human-Robot Interaction, 11(4), 1-19.
[https://doi.org/10.1145/3530875]
-
Mok, P. Y., Chuang, H. H., Cheng, M. M., & Smith, T. J. (2025). Artificial intelligence assisted creativity: Conceptualization, instrument development and validation. The Journal of Creative Behavior, 59(1), e70004.
[https://doi.org/10.1002/jocb.70004]
-
Nogueira, M. R., Menezes, P., & Maçãs de Carvalho, J. (2024). Exploring the impact of machine learning on dance performance: a systematic review. International Journal of Performance Arts and Digital Media, 20(1), 60-109.
[https://doi.org/10.1080/14794713.2024.2338927]
-
Pang, Y., & Niu, Y. (2023). Dance video motion recognition based on computer vision and image processing. Applied Artificial Intelligence, 37(1), 2226962.
[https://doi.org/10.1080/08839514.2023.2226962]
-
Pataranutaporn, P., Mano, P., Bhongse-Tong, P., Chongchadklang, T., Archiwaranguprok, C., Hantrakul, L., … & Klunchun, P. (2024). Human-AI co-dancing: Evolving cultural heritage through collaborative choreography with generative virtual characters. In Proceedings of the 9th International Conference on Movement and Computing (pp. 1-10).
[https://doi.org/10.1145/3658852.3661317]
-
Teo, T. (2010). Validation of the technology acceptance measure for pre-service teachers (TAMPST) on a Malaysian sample: A cross-cultural study. Multicultural Education & Technology Journal, 4(3), 163-172.
[https://doi.org/10.1108/17504971011075165]
- Qu Meijie. (2024). Research on the Construction of Technology Acceptance Expansion Model for Normal University Students: Based on AI Anxiety (Master's Thesis). Northwest Normal University. [in Chinese]
- Rao Hua. (2006). Uniformity and Consistency: The Core of Group Dance Rehearsal. Journal of Xianning University, (05), 249-250. [in Chinese]
- Ren Dongsheng. (2024). Creative Practice of Dance Digital Human Technology: Taking the Digital Dancer for the 70th Anniversary of Beijing Dance Academy as an Example. Journal of Beijing Dance Academy, (06), 55-63.[in Chinese]
- The State Council of the People's Republic of China. (2025). Opinions on Deepening the Implementation of the ‘Artificial Intelligence Plus (+)’ Action Plan.
-
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204.
[https://doi.org/10.1287/mnsc.46.2.186.11926]
-
Wang, Y.-Y., & Wang, Y.-S. (2022). Development and validation of an artificial intelligence anxiety scale: An initial application in predicting motivated learning behavior. Interactive Learning Environments, 30, 619-634.
[https://doi.org/10.1080/10494820.2019.1674887]
- Yu Daxue. (2024). Research on the Mechanism of AI Digital Dance Art Creation. Journal of Beijing Dance Academy, (06), 74-81.[in Chinese]
-
Zaichkowsky, J. L. (1985). Measuring the involvement construct. Journal of Consumer Research, 12(3), 341-352.
[https://doi.org/10.1086/208520]
-
Zaichkowsky, J. L. (1994). The personal involvement inventory: Reduction, revision, and application to advertising. Journal of Advertising, 23(4), 59-70.
[https://doi.org/10.1080/00913367.1943.10673459]
-
Zeng, D. (2025). AI-Powered Choreography Using a Multilayer Perceptron Model for Music-Driven Dance Generation. Informatica, 49(20).
[https://doi.org/10.31449/inf.v49i20.8103]
-
Zhong, Y., Fu, X., Liang, Z., Chen, Q., Yao, R., & Ning, H. (2025). The application of artificial intelligence technology in the field of dance. Applied System Innovation, 8(5), 127.
[https://doi.org/10.3390/asi8050127]