Abstract
Collaborative knowledge creation processes have received increasing attention in recent years, both in the scientific domain as well as in the policy realm. Collaborations in Research & Development (R&D) – in the literature often referred to as R&D networks – have become key for successfully generating new knowledge as a basis for innovation. Although a strong interconnectedness between national R&D actors supports the intra-regional knowledge diffusion, aiming for international embeddedness is an important pillar of policy and corporate strategies, since it allows firms to tap into different sources of knowledge. Hence, we propose an empirical agent-based simulation model of knowledge creation in a characteristic knowledge-driven industry, namely the semiconductor sector. With a special emphasis on collaborative knowledge creation, we investigate the effects of R&D networks on knowledge output, especially accounting for the role of international collaboration links.
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Notes
- 1.
However, it is argued that inter-organizational network channels are by no means sufficient but rather considered complementary to internal capabilities, since similar internal capabilities are necessary to evaluate research done by collaboration partners (e.g. Inkpen and Tsang 2005; Cowan and Jonard 2009).
- 2.
It is important to note that the model processes, if not otherwise stated, refer to the national industry firm population, since only they are qualified to actively perform research (with the possibility to engage with the other agent populations – international industry firms, national/international research universities and research institutes).
- 3.
The collaboration memory is implemented by means of an ordered list of length ten, representing equally preferred collaboration partners. The most recent collaboration partner is put in the first position in the list. Hence, with each new collaboration a former partner is pushed back on the next position in the list – eventually, dropping out of the collaboration memory after – at least ten – ticks, representing ten quarters of a year.
- 4.
Research organisations, such as universities, are – in contrast to firms – dedicated to R&D and usually exhibit a broad variety of topics and personnel for research projects, making it easy for firms to find suitable collaboration partners. Moreover, the focus of the model is to simulate collaborations by firms dependent on their research strategies; modelling in detail public knowledge infrastructure, such as universities, would go beyond the scope of this simulation model.
- 5.
To measure technological proximity between technology classes, we employ the Jaccard index based on the number of co-references of technology classes on patent documents that are derived from the empirical patent stock of the firm sample.
- 6.
Upon request the code and detailed model documentation can be obtained from the authors.
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Neuländtner, M., Paier, M., Unger, A. (2019). Modelling Collaborative Knowledge Creation Processes: An Empirical Application to the Semiconductor Industry. In: Payne, D., et al. Social Simulation for a Digital Society. SSC 2017. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-030-30298-6_14
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