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Cities are host to many of the world’s knowledge intensive research and innovation clusters. As such, they are likely to be attractive locations for emerging market multinational enterprises (MNEs) seeking to engage in knowledge seeking “springboard” type firm-level catch-up strategies. The purpose of this study is to therefore explore whether city-based research-intensive clusters containing deep pools of location bounded (i.e. “sticky”) knowledge are a stronger driver for greenfield research and development (R&D)-related FDI projects for Chinese MNEs than they are for developed market MNEs.

The authors use logistic modelling on 97,163 worldwide greenfield FDI projects to explore the relative likelihoods of Chinese MNEs engaging in R&D-related greenfield (i.e. “strategic asset seeking”) FDI projects as well as how city type (global or research-intensive cluster city) moderates this relationship for Chinese MNEs.

The authors find that Chinese MNEs are more likely to engage in overseas R&D FDI projects (compared with other types of project) than DMNEs and that research-intensive city clusters hold a stronger attraction for Chinese MNEs than developed market MNEs.

The authors discuss how the research contributes to the debate on emerging market MNE catch-up theory, as well as that on sub-national city location choice, by highlighting the growing importance of sub-national geography to understanding strategic asset seeking related greenfield FDI.

Sub-national city location choice is an important driver of strategic asset seeking FDI for Chinese MNEs, one that both national and local city level policymakers should pay attention to.

Chinese FDI via aggressive mergers and acquisitions to acquire key technologies has been restricted in recent years. Policymakers must consider whether they may also wish to restrict Chinese greenfield FDI in R&D-related projects, which now exhibit a pronounced upward trend.

The authors highlight the growing importance of sub-national geography to understanding strategic asset seeking related greenfield FDI in Chinese MNEs (and how it plays, more generally, a central role in their strategies).

This study researched how corruption affects the attraction of foreign direct investment (FDI). With the help of a qualitative methodology, the results of the analysis show that firms headquartered in countries where corruption is high have an advantage when operating in a foreign country with a similar institutional environment. The reason for this advantage is that such firms may possess knowledge of how to cope with the arbitrary and pervasive dimensions of corruption at home. On the other hand, firms from countries with lower corruption levels than the host country are more affected by corruption in a highly corrupt host country. Finally, even though this study found evidence that all firms operating in a highly corrupt country might participate in corrupt deals, those headquartered in highly corrupt countries are more willing to do so. This claim is based on the fact that firms from less corrupt countries might face stronger pressures from their headquarters to not engage in corrupt deals, whereas firms from more corrupt countries might not encounter such pressures.

An accurate detection of overhead ground wire under open surroundings with varying illumination is the premise of reliable line grasping with the off-line arm when the inspection robot cross obstacle automatically. This paper aims to propose an improved approach which is called adaptive homomorphic filter and supervised learning (AHSL) for overhead ground wire detection.

First, to decrease the influence of the varying illumination caused by the open work environment of the inspection robot, the adaptive homomorphic filter is introduced to compensation the changing illumination. Second, to represent ground wire more effectively and to extract more powerful and discriminative information for building a binary classifier, the global and local features fusion method followed by supervised learning method support vector machine is proposed.

Experiment results on two self-built testing data sets A and B which contain relative older ground wires and relative newer ground wire and on the field ground wires show that the use of the adaptive homomorphic filter and global and local feature fusion method can improve the detection accuracy of the ground wire effectively. The result of the proposed method lays a solid foundation for inspection robot grasping the ground wire by visual servo.

This method AHSL has achieved 80.8 per cent detection accuracy on data set A which contains relative older ground wires and 85.3 per cent detection accuracy on data set B which contains relative newer ground wires, and the field experiment shows that the robot can detect the ground wire accurately. The performance achieved by proposed method is the state of the art under open environment with varying illumination.