Machine learning models enable data-based decision-making in many areas and have attracted extensive attention. By testing the factors that influence the adoption of machine learning models, this study expands the scope of machine learning models in information technology adoption research. Based on the machine learning background and Technology Acceptance Model, this study integrates the necessary external variables, proposes a research model, and further verifies the validity of the model through the survey of 192 users of machine learning models. The results showed that organizational factors, trust, perceived usefulness, and perceived ease of use are positively correlated with the attitude of machine learning models. Moreover, our findings show that the interpretability of the model has an important positive effect on trust. The factors examined in this study are the basis for the development and use of reliable machine learning models. And it has important practical significance for promoting user adoption of machine learning model. Meanwhile, these theoretical studies also provide a strong literature support for the adoption of machine learning models and fill the theoretical research gap in this field.

机器学习模型可在许多领域实现基​​于数据的决策，并已引起广泛关注。通过测试影响机器学习模型采用的因素，本研究扩展了信息技术采用研究中机器学习模型的范围。基于机器学习背景和技术接受模型，该研究整合了必要的外部变量，提出了一个研究模型，并通过对192个机器学习模型用户的调查进一步验证了该模型的有效性。结果表明，组织因素，信任，感知的有用性和感知的易用性与机器学习模型的态度呈正相关。此外，我们的发现表明，该模型的可解释性对信任具有重要的积极影响。本研究中检验的因素是开发和使用可靠的机器学习模型的基础。对于促进用户采用机器学习模型具有重要的现实意义。同时，这些理论研究也为采用机器学习模型提供了有力的文献支持，并填补了该领域的理论研究空白。