User acceptance of machine learning models – Integrating several important external variables with technology acceptance model

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.