Bin Guo - Böcker

Huawei has become China's most prominent multinational company and a leader in the ICT sector. Given unprecedented access to the company, the authors of this book examine the management transformation of Huawei from its inception in 1987 until 2019, observing in detail not only the creation of its organizational routines but also the breaking of routines across most major functional areas: Management, Product Development, HR, Supply Chain, Finance, R&D, Intellectual Property, and International Business. 'Dynamic capabilities' are central to theories of competitive advantage and this book highlights Huawei as an ideal case study for the successful implementation of change routines and change-supporting values. The chapters cover all the major change initiatives the firm has undertaken since 1996 to import best practices from the West, with the help of consultants. The insights presented in the book will be particularly interesting for academics in the field of strategy, management, and business history.

Huawei has become China's most prominent multinational company and a leader in the ICT sector. Given unprecedented access to the company, the authors of this book examine the management transformation of Huawei from its inception in 1987 until 2019, observing in detail not only the creation of its organizational routines but also the breaking of routines across most major functional areas: Management, Product Development, HR, Supply Chain, Finance, R&D, Intellectual Property, and International Business. 'Dynamic capabilities' are central to theories of competitive advantage and this book highlights Huawei as an ideal case study for the successful implementation of change routines and change-supporting values. The chapters cover all the major change initiatives the firm has undertaken since 1996 to import best practices from the West, with the help of consultants. The insights presented in the book will be particularly interesting for academics in the field of strategy, management, and business history.

This book provides a novel theoretical framework to explain the real source of competitive advantage of Chinese manufacturing. More importantly, such a framework can be generalized to analyze the potential of catch-up for large emerging economies in the globalization era. The book also provides insights for policy makers to rethink their design of policies.The rise of Made-in-China products has been widely attributed to low labour cost advantage and imitation advantage. However, as these two advantages are nearly innate to all late-developing countries, they cannot be regarded as the key factors that drive the rapid growth of China's manufacturing industry, or China's economy, over the past few decades. In this book, the author proposed a theory — "the catch-up ladders theory", to explain the rise of China's manufacturing industry. The manufacturing advancement of any country is in essence a process of catching-up in both market and technology, during which enterprises will form a ladder-like holistic structure due to their differences in capabilities, technology and market positioning. In light of this, the continuity of the catch-up ladder will greatly determine the catch-up efficiency of an industry and even a country at large. Such a perspective is more applicable to large emerging economies, especially those with over one hundred million population and thus huge potential domestic market demand.

This two-volume set (CCIS 1879 and 1880) constitutes the refereed proceedings of the 9th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2023 held in Harbin, China, during September 22–24, 2023.The 52 full papers and 14 short papers presented in these two volumes were carefully reviewed and selected from 244 submissions. The papers are organized in the following topical sections:Part I:  Applications of Data Science, Big Data Management and Applications, Big Data Mining and Knowledge Management, Data Visualization, Data-driven Security, Infrastructure for Data Science, Machine Learning for Data Science and Multimedia Data Management and Analysis.Part II: Data-driven Healthcare, Data-driven Smart City/Planet, Social Media and Recommendation Systems and Education using big data, intelligent computing or data mining, etc.