Exploring collaborative innovation networks and knowledge spillovers in the agriculture sector: A social network analysis approach

Most knowledge producing institutions in South Africa have vast knowledge repositories and are strategically located near some formal agricultural businesses (e.g. University of Stellenbosch located in the vicinity of wine farms). As a result, most of these agricultural businesses are well positioned to benefit from the acquisition and use of knowledge developed by universities, government research agencies, and private research institutes. Furthermore, by virtue of engaging in collaboration innovation networks, agricultural businesses may benefit from new technologies currently in use in other agricultural enterprises to support their innovation efforts as well as effective technological catch-up and long-term growth of the sector.

Given that knowledge spreads best in the vicinity of businesses engaged in similar types of innovation activities, the tacit nature of technological knowledge typically necessitates cooperation for innovation in order for knowledge spillovers to occur between knowledge producing institutions and businesses that use that knowledge for innovation. When knowledge spillovers occur, this knowledge may be transformed through these collaborative innovation networks into tangible innovations (product, process, organisational and marketing) for the benefit of businesses. On the other hand, knowledge producers may be incentivised for their efforts in developing that knowledge through intellectual property rights and patent licensing. In sum, collaborative innovation networks are therefore critical for technological learning since they promote direct interactions and knowledge spillovers between businesses and knowledge producers.

This study developed a set of directly observable measures that can be used by policy makers and researchers alike to better analyze and understand the interactions between businesses and knowledge producing institutions for innovation purposes. The SNA approach used in this study can be replicated at in other sectors of the economy to explore and visualise how collaborative innovation networks and how knowledge spillovers emerge and maintained. The use of innovation data from CIS-like questionnaires is especially relevant and efficient for this kind of study since it helps determining the required level of rigour to allow validation to take place. As such, the research approach used in this study should contribute to further theory based research in the area of collaboration for innovation and knowledge spillovers in the agricultural sector.