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Finding new industry partners poses a challenge to many public research organizations. This article explores how statistical classification can support partner selection at the example of the Fraunhofer Society in Germany, Europe's largest public organization for applied research. We use internal cooperation data and feature sets based on unstructured data, i.e., text and industry codes, both of which describe business activities of firms. An important advantage of this data is that it is available for most companies in Germany, even small and medium enterprises, which allows for an almost complete screening of the market, in contrast to using other data sources, e.g., patents. In addition, we also include economic variables linked to firms, as turnover, number of employees/managers and firm age. We report the performance of various classification techniques such as logistic regression, support vector machines, and random forests in our dataset for diverse combinations of feature sets. Results show that simple methods with fewer parameters remain competitive in comparison to complex ones. Overall, the performance of most classifiers is high enough to support the decision process of finding new industry partners for public research. [ABSTRACT FROM AUTHOR]

Researchers have debated the relative importance of technology push and demand pull policies in the adoption of environmental technologies. Here, we examine how internationally distributed technology push and demand pull policies interact to support the diffusion of a renewable energy technology. We analyze 40 years (1974–2013) of solar photovoltaic policies in three countries: USA, Germany, and China. We identify four phases of international policy interactions: in Phase 1 (1974–1990), the USA launched technology push policies; in Phase 2 (1991–2003), Germany pioneered demand pull policies; in Phase 3 (2004–2008), China responded to international market incentive programs with a scaling up of manufacturing; and in Phase 4 (2008–2013), Germany reduced whereas China increased demand policies. Our contributions are threefold: First, we demonstrate the importance of “manufacturing push” alongside the standard technology push and demand pull factors; second, we show how global adoption was spurred by the complementarity of policy efforts across national boundaries, while considering the tradeoffs in climate change policy between increasing global environmental welfare and improving national economic development; third, we demonstrate three generic patterns of cross-national policy complementarity. [ABSTRACT FROM AUTHOR]