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As Industry 4.0 reshapes modern manufacturing, the integration of smart mechanical technologies, such as AI-powered CNC machines, IoT-connected systems, and machine vision, has become essential in engineering education. This study explores the behavioral factors influencing students’ intention to use these technologies, focusing on perceived usefulness, perceived ease of use, perceived enjoyment, job replacement threats, and personal innovativeness. Data were collected from 587 undergraduate mechanical engineering students at Thai Nguyen University of Technology (TNUT) and analyzed using a dual-path approach combining Partial Least Squares Structural Equation Modeling (PLS-SEM) and Artificial Neural Network (ANN) analysis. The results show that perceived usefulness, ease of use, enjoyment, and personal innovativeness significantly affect students’ behavioral intention, while job replacement threat does not. These findings contribute to the extension of the Technology Acceptance Model (TAM) in the context of smart mechanical tools and offer practical guidance for educational institutions aiming to foster more meaningful technology adoption among future engineers.

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Predictive Modeling of Smart Mechanical Technology Adoption: An Integrated PLS-SEM and ANN Approach

  • 会议论文

  • 发表日期：

    2026年

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  • Tran Quang Huy,
  • Hoang Xuan Tu,
  • Tran Thi Phuong Thao

摘要

As Industry 4.0 reshapes modern manufacturing, the integration of smart mechanical technologies, such as AI-powered CNC machines, IoT-connected systems, and machine vision, has become essential in engineering education. This study explores the behavioral factors influencing students’ intention to use these technologies, focusing on perceived usefulness, perceived ease of use, perceived enjoyment, job replacement threats, and personal innovativeness. Data were collected from 587 undergraduate mechanical engineering students at Thai Nguyen University of Technology (TNUT) and analyzed using a dual-path approach combining Partial Least Squares Structural Equation Modeling (PLS-SEM) and Artificial Neural Network (ANN) analysis. The results show that perceived usefulness, ease of use, enjoyment, and personal innovativeness significantly affect students’ behavioral intention, while job replacement threat does not. These findings contribute to the extension of the Technology Acceptance Model (TAM) in the context of smart mechanical tools and offer practical guidance for educational institutions aiming to foster more meaningful technology adoption among future engineers.