Sun Wei, Tohirovich Dedahanov Alisher, Li Wei Ping, Young Shin Ho
Management School, Henan University of Urban Construction, Pingdingshan, China.
Business School, Central Asian University, Tashkent, Uzbekistan.
Heliyon. 2024 Aug 22;10(16):e36620. doi: 10.1016/j.heliyon.2024.e36620. eCollection 2024 Aug 30.
Due to sanctions, more Chinese high-tech SMEs are turning to rent AI computing power through cloud service providers. Therefore, it is necessary to give a variety of suggestions for China's high-tech SMEs to better develop AI applications through computing power leasing. Because traditional theories are difficult to explain this new technology adoption behavior, this research combines and extends TTF and UTAUT2 theories to take an empirical research. A total of 387 questionnaires were received, of which incomplete questionnaires and invalid questionnaires were issued, leaving 281 valid questionnaires. The results indicate that SME innovativeness, perceived risk, performance expectancy, price value and task technology fit are all significantly related to usage, whereas task technology fit moderates the other relationships significantly. Results give a variety of suggestions for China's high-tech SMEs to better develop AI applications through computing power leasing in the context of sanctions. This study not only suggests ways to increase the competitiveness of SMEs by optimizing leasing services but also give directions in investors' investment decisions. The findings are also applicable to the large-scale application of China's domestic AI chips in computing power leasing scenarios in the future.
由于制裁，越来越多的中国高科技中小企业转向通过云服务提供商租用人工智能算力。因此，有必要为中国高科技中小企业通过算力租赁更好地开发人工智能应用提供各种建议。由于传统理论难以解释这种新技术采用行为，本研究将TTF和UTAUT2理论结合并扩展以进行实证研究。共收到387份问卷，其中剔除了不完整问卷和无效问卷，剩下281份有效问卷。结果表明，中小企业创新性、感知风险、绩效期望、价格价值和任务技术匹配度均与使用显著相关，而任务技术匹配度对其他关系有显著的调节作用。研究结果为受制裁背景下中国高科技中小企业通过算力租赁更好地开发人工智能应用提供了各种建议。本研究不仅提出了通过优化租赁服务提高中小企业竞争力的方法，还为投资者的投资决策提供了方向。研究结果也适用于未来中国国产人工智能芯片在算力租赁场景中的大规模应用。
