Knowledge Transfer Factors for Internal Combustion Engine (ICE) Industry to Electric Vehicle (EV) Industry by Artificial Intelligent: Machine Learning

Yinglak Dangjaroen, Mongkolchai Wiriyapinit, Sukree Sinthupinyo

2023

Abstract

This study aims to identify the factors influencing knowledge transfer within companies transitioning from the internal combustion engine (ICE) industry to the electric vehicle (EV) industry through an extensive literature review. In addition to summarizing findings and proposing strategies for utilizing artificial intelligence in knowledge transfer, our framework reveals the relevance of three key knowledge transfer factors and three distinct forms of artificial intelligence, including machine learning, in facilitating knowledge transfer. These insights can prove invaluable to entrepreneurs operating within the internal combustion engine automotive sector, offering essential guidance for enhancing the knowledge transfer process and navigating the transition to the electric vehicle industry. By implementing these strategies, businesses can maintain and support their competitiveness in this evolving business.

Paper Citation

in Harvard Style

Dangjaroen Y., Wiriyapinit M. and Sinthupinyo S. (2023). Knowledge Transfer Factors for Internal Combustion Engine (ICE) Industry to Electric Vehicle (EV) Industry by Artificial Intelligent: Machine Learning. In Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 3: KMIS; ISBN 978-989-758-671-2, SciTePress, pages 142-149. DOI: 10.5220/0012162800003598

in Bibtex Style

@conference{kmis23,
author={Yinglak Dangjaroen and Mongkolchai Wiriyapinit and Sukree Sinthupinyo},
title={Knowledge Transfer Factors for Internal Combustion Engine (ICE) Industry to Electric Vehicle (EV) Industry by Artificial Intelligent: Machine Learning},
booktitle={Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 3: KMIS},
year={2023},
pages={142-149},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012162800003598},
isbn={978-989-758-671-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 3: KMIS
TI - Knowledge Transfer Factors for Internal Combustion Engine (ICE) Industry to Electric Vehicle (EV) Industry by Artificial Intelligent: Machine Learning
SN - 978-989-758-671-2
AU - Dangjaroen Y.
AU - Wiriyapinit M.
AU - Sinthupinyo S.
PY - 2023
SP - 142
EP - 149
DO - 10.5220/0012162800003598
PB - SciTePress