TY - GEN
T1 - Exploring the Impact of Artificial Intelligence on Knowledge Management in Automotive Manufacturing within Different Cultures
T2 - 25th European Conference on Knowledge Management, ECKM 2024
AU - Wang, Rui
AU - Yin, Yifen
N1 - Publisher Copyright:
© 2024 Academic Conferences Limited. All rights reserved.
PY - 2024
Y1 - 2024
N2 - This study explores the impact of artificial intelligence (AI) on knowledge management (KM) in the automotive manufacturing industry with a focus on different cultural contexts in China and Germany. The role of cultural factors on the effectiveness of AI in KM practices is explored by comparing automobile manufacturers in China and Germany. This study uses case studies to compare, and contrast leading automotive manufacturers in both countries and combines industry reports, papers journals, and other digital resources on the Internet to explore how the manufacturing industry can use AI technology to improve efficiency in the KM process. In addition, the study explores the impact of culture on organizational structure, decision-making, and employee engagement with new technologies within a company. The preliminary findings suggest differences in the understanding and use of AI and KM between China and Germany due to their different history, culture, and level of economic development. In China, the integration of AI into KM is driven by rapid technological advances and strong government support, focusing on efficiency and scalability. In contrast, German companies show more caution, emphasizing accuracy, reliability, and augmentation of human expertise. These differences reflect broader cultural attitudes toward technology and innovation in both countries. The study contributes to the understanding of the interaction between AI and KM in the context of cultural differences. The findings will have important implications for subsequent AI research and policy development.
AB - This study explores the impact of artificial intelligence (AI) on knowledge management (KM) in the automotive manufacturing industry with a focus on different cultural contexts in China and Germany. The role of cultural factors on the effectiveness of AI in KM practices is explored by comparing automobile manufacturers in China and Germany. This study uses case studies to compare, and contrast leading automotive manufacturers in both countries and combines industry reports, papers journals, and other digital resources on the Internet to explore how the manufacturing industry can use AI technology to improve efficiency in the KM process. In addition, the study explores the impact of culture on organizational structure, decision-making, and employee engagement with new technologies within a company. The preliminary findings suggest differences in the understanding and use of AI and KM between China and Germany due to their different history, culture, and level of economic development. In China, the integration of AI into KM is driven by rapid technological advances and strong government support, focusing on efficiency and scalability. In contrast, German companies show more caution, emphasizing accuracy, reliability, and augmentation of human expertise. These differences reflect broader cultural attitudes toward technology and innovation in both countries. The study contributes to the understanding of the interaction between AI and KM in the context of cultural differences. The findings will have important implications for subsequent AI research and policy development.
KW - Artificial Intelligence
KW - Automotive Manufacturing Industry
KW - Knowledge Management
UR - http://www.scopus.com/inward/record.url?scp=85206672788&partnerID=8YFLogxK
U2 - 10.34190/eckm.25.1.2415
DO - 10.34190/eckm.25.1.2415
M3 - Conference contribution
AN - SCOPUS:85206672788
T3 - Proceedings of the European Conference on Knowledge Management, ECKM
SP - 1051
EP - 1061
BT - Proceedings of the 25th European Conference on Knowledge Management, ECKM 2024
A2 - Obermayer, Nora
A2 - Bencsik, Andrea
PB - Academic Conferences and Publishing International Limited
Y2 - 5 September 2024 through 6 September 2024
ER -