Managing Open Innovation
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Managing Open Innovation

Connecting the Firm to External Knowledge

André Spithoven, Peter Teirlinck and Dirk Frantzen

Open innovation is about firms’ external relations with other firms and organisations. It is a topic which has attracted an immense amount of attention, but which has also been heavily criticised due to the diversity of the ideas and fuzziness of its key concepts. To date, the bulk of the literature on open innovation draws on case study material to illustrate the operation of firms in an anecdotal way. By contrast, this book examines open innovation practices by using large-scale datasets and stresses their impact on firm performance. The authors examine four key issues: differences between firms in open innovation practices, public funding to enhance external relations, R & D outsourcing of firms, and the role of human resources in R & D and innovation.
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Chapter 1: Open Innovation and the Relation between Innovative Activities and Firm Performance

André Spithoven, Peter Teirlinck and Dirk Frantzen


1.1 INTRODUCTION The way firms manage innovation has changed profoundly in recent years (Chesbrough, 2003a; Chesbrough and Crowther, 2006). They used to invest heavily in specialised in-house R&D departments in order to generate innovation. This model has recently ceded ground to a more ‘open’ model, in which the research strategies of innovative firms increasingly rely on external knowledge. This encourages cooperation and implies a reorganisation of the research process, to allow for the internalisation of external ideas. Open innovation also implies a changing attitude to the appropriability of new ideas, since this may occur not only through product innovation, but through selling these ideas in disembodied form, through licensing, trademarks, and so on. It might therefore be of great interest to reconsider the relation between innovative effort and firm performance. Recent econometric studies on the relation between innovation and firm performance have estimated empirical structural models to explain the input and output stages of the innovation process as well as its impact on productivity (Crépon et al., 1998; Lööf and Heshmati, 2002, 2006; Griffith et al., 2006; Van Leeuwen and Klomp, 2006), or on turnover and employment growth (Klomp and Van Leeuwen, 2001). Special care was taken to make the models suitable for drawing statistical inferences from a population larger than that of the research-active firms. This was done by adding a probit equation to explain the probability of doing research and so obtain a generalised tobit model. One drawback with these studies, however, is that they...

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