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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Preliminary Version Terms of use: Documents in EconStor may AbstractGovernment subsidies for R&D are intended to promote projects with high returns to society but too little private returns to be beneficial for private investors. This may be caused by spillovers or a low appropriability rate. Apart from the direct funding of these projects, government grants may serve as a signal for good investments for private investors. We use a simple signaling model with different types of R&D projects to capture this phenomenon. In a setup where the subsidy can only be used to distinguish between high and low risk projects, government agency's signal is not very helpful for banks. However, if the subsidy is accompanied by a quality signal, it can lead to increased or better selected private investments.

During the three decades since its inception in 1984, the JPIM has shaped the evolution of innovation research as a scientific field. It helped create a topic landscape that is not only more diverse and rich in insights, but also more complex and fragmented in structure than ever before. We seek to map this landscape and identify salient development trajectories over time. In contrast to prior citation-based studies covering the first two decades of JPIM research, we benefit from recent advances in natural language processing and rely on a topic modeling algorithm to extract 57 distinct topics and the corresponding most common words, terms, and phrases from the entire full-text corpus of 1008 JPIM articles published between 1984 and 2013. Estimating the development trajectory of each topic based on yearly publication counts in JPIM allows us to identify "hot," "cold," "revival," "evergreen," and "wall-flower" topics. We map these topics onto the Product Development and Management Association (PDMA) Body of Knowledge categories and discover that these categories differ significantly not only in terms of their internal topic diversity and relative prevalence, but also-and arguably more importantly-in terms of their publication and citation trajectories over time. For instance, the PDMA category "Codevelopment and Alliances" exhibits only moderate topic diversity (7 out of 57 topics) and prevalence in JPIM (161 out of 1008 articles). That said, it is among the most dynamic categories featuring two evergreen topic ("Users and Innovation" and "Tools and Systems for Technology Transfer") and three hot topics ("Open Innovation," "Alliances and Cooperation," and "Networks and Network Structure") as well as a sharply growing annual number of citations received. Our findings are likely to be of interest to all those who are keen to (re)discover JPIM's topic landscape in search of hidden structures and development trajectories. Practitioners Points• We provide a map of the topic landscape in JPIM that enables practitioners and researchers to navigate the field more intuitively. • Using this map, practitioners can also identify experts in specific areas of innovation management. • We identify five articles per innovation management topic that are most strongly associated with the respective topic to provide a fast and efficient way to dive into a topic.• We show how to apply text mining methods to structure large collections of text documents and analyze their content automatically.