Huawei’s Leapfrogging to Overtake Ericsson

Chapter 8 explores how Huawei was able to emerge as the leader in the telecommunications system sector, overtaking the incumbent Swedish giant Ericsson. It answers this question by focusing on whether a latecomer firm trying to catch up uses technologies similar to or different from those of the forerunners. The study investigated patents by Huawei and Ericsson and found that Huawei relied on Ericsson as a knowledge source in its early days but subsequently reduced this reliance and increased its self-citation ratio to become more independent. The results of mutual citations, common citations, and self-citations provided strong evidence that Huawei caught up with or overtook Ericsson by taking a different technological trajectory. Huawei developed its technologies by relying on more recent and scientific knowledge; in terms of citations to scientific articles and citation lags, Huawei extensively explored basic research and up-to-date technologies to accomplish its technological catch-up. This study suggests that leapfrogging by exploring a new technological path is a possible and viable catch-up strategy for a latecomer. Moreover, Huawei’s case re-confirms the hypothesis that catch-up in technological capabilities tends to precede that in market share. Huawei overtook Ericsson in terms of quantity and quality of patents before annual sales. In summary, the results suggest that Huawei’s catch-up with Ericsson in the telecommunications equipment market is owing not only to its cost advantage, the large domestic market, or the Chinese government’s support but also more importantly to its technological leapfrogging based on its technological strength and independence.

Technological Innovation Research

The paper explores the relationship among technological innovation, technological trajectory transition, and firms’ innovation performance. Technological innovation is studied from the perspectives of innovation novelty and innovation openness. Technological trajectory transition is categorized into creative cumulative technological trajectory transition and creative disruptive technological trajectory transition. A structural equation model is developed and tested with data collected by surveying 366 Chinese firms. The results indicate that both innovation novelty and innovation openness positively affects creative cumulative technological trajectory transition as well as creative disruptive technological trajectory transition. Innovation openness and creative disruptive technological trajectory transition both positively affect firms’ innovation performance. However, neither innovation novelty nor creative cumulative technological trajectory transition positively affects firms’ innovation performance. Implications for managers and directions for future studies are discussed.

Technological Innovation research—a structural equation modelling approach

The paper explores the relationship among technological innovation, technological trajectory transition, and firms’ innovation performance. Technological innovation is studied from the perspectives of innovation novelty and innovation openness. Technological trajectory transition is categorized into creative cumulative technological trajectory transition and creative disruptive technological trajectory transition. A structural equation model is developed and tested with data collected by surveying 366 Chinese firms. The results indicate that both innovation novelty and innovation openness positively affects creative cumulative technological trajectory transition as well as creative disruptive technological trajectory transition. Innovation openness and creative disruptive technological trajectory transition both positively affect firms’ innovation performance. However, neither innovation novelty nor creative cumulative technological trajectory transition positively affects firms’ innovation performance. Implications for managers and directions for future studies are discussed.

An Empirical Study on Innovation Ecosystem, Technological Trajectory Transition, and Innovation Performance

This paper explores technological trajectory transition in the perspective of innovation ecosystem and their effect on innovation performance of latecomers in market. A structural equation model is developed and tested with data collected from 366 firms in China. In specific, this paper categories technological trajectory transition creative accumulative technological trajectory transition (CCT) and creative disruptive technological trajectory transition (CDT). The results indicate that firms' organizational learning ability positively affect their technological trajectory transition and innovation performance. Firms' network relationship strength negatively affects their technological trajectory transition and positively affect their innovation performance. Governments' environmental concerns positively affect firms' technological trajectory transition and their innovation performance, whereas firms' environmental concerns do not. CCT does not positively affect their innovation performance. In contrast, CDT positively affects their innovation performance.

Do main paths reflect technological trajectories? Applying main path analysis to the semiconductor manufacturing industry

It has been proposed that main path analysis can be used to identify technological trajectories in patent-citation networks. In this paper, the method is applied to a network composed of one million US patents and eight million citations in order to trace the backbone of the technological trajectory of the semiconductor manufacturing industry. An in depth discussion of the method is presented, focusing on the many parameters that can be adjusted while applying it and on the consequences of adjusting any of them. Moreover, and differently from other papers on the subject, the result of the algorithm is analysed to determine if it indeed represents the most important technological contributions to the trajectory or if it is merely a collection of relevant and connected patents. This is made easier by the fact that the semiconductor industry has a clear and widely known technological trajectory that spans more than 50 years, Moore's law.

AI and the resurrection of Technological Determinism

This paper elaborates on the connection between the AI regulation fever and the generic concept of Social Control of Technology. According to this analysis, the amplitude of the regulatory efforts may reflect the lock-in potential of the technology in question. Technological lock-in refers to the ability of a limited set of actors to force subsequent generations onto a certain technological trajectory, hence evoking a new interpretation of Technological Determinism. The nature of digital machines amplifies their lock-in potential as the multiplication and reuse of such technology is typically almost cost-free. I sketch out how AI takes this to a new level because it can be software and an autonomous agent simultaneously.

Choosing Technology: An Entrepreneurial Strategy Approach

A central premise of research in the strategic management of innovation is that start-ups are able to leverage emerging technological trajectories as a source of competitive advantage. But, if the potential for a technology is given by the fundamental character of a given technological trajectory, then why does entrepreneurial strategy matter? Or, put another way, if the evolution of technology is largely shaped by the strategic choices entrepreneurs make, then why do technological trajectories exhibit systematic patterns such as the technology S-curve? Taking a choice-based perspective, this paper illuminates the choices confronting a start-up choosing their technology by resolving the paradox of the technology S-curve through a reformulation of the foundations of the technology S-curve. Specifically, we reconceptualize the technology S-curve not as a technological given but as an envelope of potential outcomes reflecting differing strategic choices by the entrepreneur in exploration versus exploitation. Taking this lens, we are able to clarify the role of technological uncertainty on start-up strategy, the impact of constraints on technological evolution, and how technology choice is shaped by the possibility of imitation. Our findings suggest that staged exploration may stall innovation as a result of the replacement effect, increasing the strategic importance of commitment.