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This paper aims that privacy research is divided in distinct communities and rarely considered as a singular field, harming its disciplinary identity. The authors collected 119.810 publications and over 3 million references to perform a bibliometric domain analysis as a quantitative approach to uncover the structures within the privacy research field.

The bibliometric domain analysis consists of a combined directed network and topic model of published privacy research. The network contains 83,159 publications and 462,633 internal references. A Latent Dirichlet allocation (LDA) topic model from the same dataset offers an additional lens on structure by classifying each publication on 36 topics with the network data. The combined outcomes of these methods are used to investigate the structural position and topical make-up of the privacy research communities.

The authors identified the research communities as well as categorised their structural positioning. Four communities form the core of privacy research: individual privacy and law, cloud computing, location data and privacy-preserving data publishing. The latter is a macro-community of data mining, anonymity metrics and differential privacy. Surrounding the core are applied communities. Further removed are communities with little influence, most notably the medical communities that make up 14.4% of the network. The topic model shows system design as a potentially latent community. Noteworthy is the absence of a centralised body of knowledge on organisational privacy management.

This is the first in-depth, quantitative mapping study of all privacy research.

In organisational and innovation research, the term “open innovation” refers to the inflow and outflow of knowledge to and from organisations: with open innovation theory suggesting active exchanges of knowledge with external actors leads to the development of exploitable new ideas. In the field of education, however, the exchange of knowledge with external parties represents a paradigm shift. In response, this article presents findings from research design to explore the nature and composition of school innovation networks, and the effects of such these networks on knowledge mobilisation.

The study draws on data from a representative random sample of 411 German school leaders. Respondents were asked to detail their engagement in open and closed innovation activity and their school's external collaborations during the last 12 months. A latent class distal outcome model was developed to examine whether different types of collaboration associate with different knowledge mobilisation processes.

The study findings suggest that schools in Germany mainly use internal knowledge for innovation, with external knowledge exchange taking place on a very limited basis. Knowledge mobilisation varies depending on the innovation network. The authors use the findings to indicate new insights for how schools can further innovate learning and teaching in future.

Although there is increasing discussion on Professional Learning Networks in schools, the discourse on knowledge mobilisation within educational networks is limited, making concept of open innovation so far completely absent from discourses on school improvement. This paper initiates the population of this new research space.