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

Xiaohang Zhang, Yuan Wang, Zhengren Li
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引用次数: 4

Abstract

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.
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用户对机器学习模型的接受度——将几个重要的外部变量与技术接受模型集成
机器学习模型在许多领域实现了基于数据的决策,并引起了广泛的关注。通过测试影响采用机器学习模型的因素,这是…
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来源期刊
International Journal of Electrical Engineering Education
International Journal of Electrical Engineering Education 工程技术-工程:电子与电气
CiteScore
2.90
自引率
0.00%
发文量
0
审稿时长
>12 weeks
期刊介绍: The International Journal of Electrical Engineering Education''s origins date back to 1948, when the world’s first stored-programme digital computer ran at the University of Manchester. In 1963, the Bulletin of Electrical Engineering Education evolved into the International Journal of Electrical Engineering Education (IJEEE).
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