TY  - JOUR
AU  - Jr, Daniel Dasig
AU  - Torres, Chester Bryan
AU  - Talion, Fernando
AU  - Ditan, Carolina
AU  - Siahan, Antonius
PY  - 2026
DA  - 2026/04/08
TI  - A TAM-based Human-Machine Synergy Model for Automation and Industry 4.0 Adoption
JO  - PWU Journal of Research, Innovation, and Transformation
T2  - PWU Journal of Research, Innovation, and Transformation
JF  - PWU Journal of Research, Innovation, and Transformation
VL  - 1
IS  - 2
SP  - 65
EP  - 72
DO  - 10.62762/JRIT.2026.810897
UR  - https://www.icck.org/article/abs/JRIT.2026.810897
KW  - technology acceptance model
KW  - automation adoption
KW  - industry 4.0
KW  - human–machine synergy
AB  - The acceleration of Industry 4.0 has compelled enterprises to integrate automation, cyber-physical systems, and analytics into everyday operations. Despite rapid digitalization, the effectiveness of these initiatives depends largely on the human capacity to accept and collaborate with machines. This study applies the Technology Acceptance Model (TAM) to examine behavioral and organizational factors influencing automation adoption among 900 professionals across manufacturing, logistics, and service sectors. Using descriptive-correlational design and partial least squares structural equation modeling (PLS-SEM), the study investigates how perceived usefulness (PU) and perceived ease of use (PEOU) shape attitude (ATT) and behavioral intention (BI). Findings reveal that PU strongly predicts ATT and BI, while PEOU enhances PU and positive attitude toward technology. The proposed Human–Machine Synergy Framework situates these constructs within a socio-technical context, showing that automation readiness in enterprises arises when human competence and institutional scaffolding evolve together. The study enriches local Industry 4.0 scholarship and provides actionable guidance for managers, policymakers, and educators promoting inclusive digital transformation.
SN  - 3071-4001
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Jr2026A,
  author = {Daniel Dasig Jr and Chester Bryan Torres and Fernando Talion and Carolina Ditan and Antonius Siahan},
  title = {A TAM-based Human-Machine Synergy Model for Automation and Industry 4.0 Adoption},
  journal = {PWU Journal of Research, Innovation, and Transformation},
  year = {2026},
  volume = {1},
  number = {2},
  pages = {65-72},
  doi = {10.62762/JRIT.2026.810897},
  url = {https://www.icck.org/article/abs/JRIT.2026.810897},
  abstract = {The acceleration of Industry 4.0 has compelled enterprises to integrate automation, cyber-physical systems, and analytics into everyday operations. Despite rapid digitalization, the effectiveness of these initiatives depends largely on the human capacity to accept and collaborate with machines. This study applies the Technology Acceptance Model (TAM) to examine behavioral and organizational factors influencing automation adoption among 900 professionals across manufacturing, logistics, and service sectors. Using descriptive-correlational design and partial least squares structural equation modeling (PLS-SEM), the study investigates how perceived usefulness (PU) and perceived ease of use (PEOU) shape attitude (ATT) and behavioral intention (BI). Findings reveal that PU strongly predicts ATT and BI, while PEOU enhances PU and positive attitude toward technology. The proposed Human–Machine Synergy Framework situates these constructs within a socio-technical context, showing that automation readiness in enterprises arises when human competence and institutional scaffolding evolve together. The study enriches local Industry 4.0 scholarship and provides actionable guidance for managers, policymakers, and educators promoting inclusive digital transformation.},
  keywords = {technology acceptance model, automation adoption, industry 4.0, human–machine synergy},
  issn = {3071-4001},
  publisher = {Institute of Central Computation and Knowledge}
}