Comparing the International Knowledge Flow of China’s Wind and Solar Photovoltaic (PV) Industries: Patent Analysis and Implications for Sustainable Development

Climate-relevant technologies, like wind and solar energy, are crucial for mitigating climate change and for achieving sustainable development. Recent literature argues that Chinese solar firms play more active roles in international knowledge flows, which may better explain their success in international markets when compared to those of Chinese wind firms; however, empirical evidence remains sparse. This study aims to explore to what extent and how do the international knowledge flows differ between China’s wind and solar photovoltaic (PV) industries? From a network perspective, this paper develops a three-dimensional framework to compare the knowledge flows in both explicit and tacit dimensions: (i) inter-country explicit knowledge clusters (by topological clustering of patent citation network); (ii) inter-firm explicit knowledge flow (patent citation network of key firms); and, (iii) inter-firm tacit knowledge flow (by desktop research and interviews). The results show that China’s PV industry has stronger international knowledge linkages in terms of knowledge clustering and explicit knowledge flow, but the wind power industry has a stronger tacit knowledge flow. Further, this study argues that the differences of global knowledge links between China’s wind and solar PV industries may be caused by technology characteristics, market orientation, and policy implementation. This suggests that these industries both have strong connections to global knowledge networks, but they may involve disparate catch-up pathways that concern follower-modes and leader-modes. These findings are important to help us understand how China can follow sustainable development pathways in the light of climate change

Indirect ties in knowledge networks:a social network analysis with ordered weighted averaging operators

This PhD thesis analyses networks of knowledge flows, focusing on the role of indirect ties in the knowledge transfer, knowledge accumulation and knowledge creation process. It extends and improves existing methods for mapping networks of knowledge flows in two different applications and contributes to two stream of research. To support the underlying idea of this thesis, which is finding an alternative method to rank indirect network ties to shed a new light on the dynamics of knowledge transfer, we apply Ordered Weighted Averaging (OWA) to two different network contexts. Knowledge flows in patent citation networks and a company supply chain network are analysed using Social Network Analysis (SNA) and the OWA operator. The OWA is used here for the first time (i) to rank indirect citations in patent networks, providing new insight into their role in transferring knowledge among network nodes; and to analyse a long chain of patent generations along 13 years; (ii) to rank indirect relations in a company supply chain network, to shed light on the role of indirectly connected individuals involved in the knowledge transfer and creation processes and to contribute to the literature on knowledge management in a supply chain. In doing so, indirect ties are measured and their role as means of knowledge transfer is shown. Thus, this thesis represents a first attempt to bridge the OWA and SNA fields and to show that the two methods can be used together to enrich the understanding of the role of indirectly connected nodes in a network. More specifically, the OWA scores enrich our understanding of knowledge evolution over time within complex networks. Future research can show the usefulness of OWA operator in different complex networks, such as the on-line social networks that consists of thousand of nodes

Dual Networks of Knowledge Flows: An Empirical Test of Complementarity in the Prepackaged Software Industry

In this paper, we develop a model of complementarity of knowledge flows in software ecosystems through two knowledge-acquisition mechanisms: a formal, fine-grained, contractual governance mechanism through inter- firm alliances and a nonformal, course-grained, noncontractual mechanism of spillover capture. In contrast to studies that focus solely on knowledge exchange in alliances, we focus on two mechanisms and test their additive and super-additive effects in the software sector. We examine the effect of a software firm’s position in the alliance network (formal, contractual mechanism) and patent citation network (nonformal, non- contractual mechanism) using two important network characteristics: reach and redundancy. We test our model using data on the packaged software industry during the period 1995 to 1999. Our results show that software firms’ sales performance is predicted by their positions within these two networks. Furthermore, we find that these network positions are additive and complementary in their impact on performance. Our results are potentially generalizable to other settings that have interdependent information and knowledge flows across organizational boundaries

Cross-country learning from patents: an analysis of citations flows in innovation trajectories

AbstractThis study proposes a methodological approach to investigate cross-country creativity/knowledge flows by analyzing patent citation networks, taking the aircraft, aviation and cosmonautics (AAC) industry as a case study. It aims at shedding some light on the following research questions: (a) how cross-country creative/learning flows can be investigated; (b) have countries of current patent owners benefited from patent acquisitions. In fact, despite the well-established economic interest for (analyzing and forecasting) innovation trajectories, this research area is still unexplored, thus, motivating the need for such study. Over 43,000,000 patents have been analyzed whereby: (a) owners have performed cross-country patent acquisitions; (b) acquired patents (granted within 2005–2009) are cited by subsequent patents (2010–2015). Methodology and results are scalable to other industries and can be exploited by managers and policy makers to: (a) help firms forecasting innovation trajectories; (b) support governments in designing/implementing measures nurturing patented innovations in industries deemed relevant to national interest

Features of the discipline knowledge network: evidence from China

Interdisciplinary knowledge exchange constitutes a network with discipline nodes and knowledge flow edges. Using data on Chinese academic literature, the current paper establishes a discipline knowledge network and analyses its structural features. Citation analysis is first used to measure the flow of knowledge between disciplines to build a discipline knowledge network. Subsequently, the features of the network, such as degree distribution, degree correlation, knowledge flow mode and other structure properties, are then analysed based on complex networks and social network theory. The tail of the degree distribution of this discipline knowledge network is in concordance with exponential distribution. The network has also a distinct hierarchical structure. Moreover, the knowledge flow between disciplines is directional. It flows from certain basic and academic disciplines to the applied disciplines. First published online: 28 Jan 201

Evolving the Knowledge Space: Towards a Selection Dynamics Model of Patent Classes

The current report seeks to understand the selection dynamics of patent classes (CPCs) in Europe by employing the methodology developed by Acemoglu et al. (2016) to predict future patenting. Their research focuses on citation networks measuring the knowledge flows across technologies and uses theses to estimate future volumes of patents per CPC during 1995-2004 in the United States. In our current analysis we replicate their results using the European patent database for the years 2005-2014, and likewise demonstrate that the innovation networks have significant predictive power over future patenting in Europe. Furthermore, we improve their methodology by accounting for more complex interactions between CPCs. Finally, we discuss their implications for developing a selection-dynamics model grounded in evolutionary theory

The Research Space: using the career paths of scholars to predict the evolution of the research output of individuals, institutions, and nations

In recent years scholars have built maps of science by connecting the academic fields that cite each other, are cited together, or that cite a similar literature. But since scholars cannot always publish in the fields they cite, or that cite them, these science maps are only rough proxies for the potential of a scholar, organization, or country, to enter a new academic field. Here we use a large dataset of scholarly publications disambiguated at the individual level to create a map of science-or research space-where links connect pairs of fields based on the probability that an individual has published in both of them. We find that the research space is a significantly more accurate predictor of the fields that individuals and organizations will enter in the future than citation based science maps. At the country level, however, the research space and citations based science maps are equally accurate. These findings show that data on career trajectories-the set of fields that individuals have previously published in-provide more accurate predictors of future research output for more focalized units-such as individuals or organizations-than citation based science maps