Data-driven modeling of technology acceptance: A machine learning perspective
作者：
Highlights：
• This research highlights the impact of machine learning on technology acceptance.
• 37 constructs predicted personal technology acceptance within consumer-use context.
• Past behavior was found the most influential determinant of technology use.
• Support vector regression-polynomial created a unique paradigm improvement.
• Sensitivity analysis-partial derivatives improved evaluation of constructs’ ranking.
摘要
•This research highlights the impact of machine learning on technology acceptance.•37 constructs predicted personal technology acceptance within consumer-use context.•Past behavior was found the most influential determinant of technology use.•Support vector regression-polynomial created a unique paradigm improvement.•Sensitivity analysis-partial derivatives improved evaluation of constructs’ ranking.
论文关键词：Machine learning,Technology acceptance,Predictive analytics,UTAUT,Support vector regression,Structural equation modeling
论文评审过程：Received 7 November 2019, Revised 4 July 2021, Accepted 8 July 2021, Available online 17 July 2021, Version of Record 10 August 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2021.115584
