There has been an increasing interest in science, technology and innovation policy studies in the topic of policy mixes. While earlier studies conceptualised policy mixes mainly in terms of combinations of instruments to support innovation, more recent literature extends the focus to how policy mixes can foster sustainability transitions.

While national governments are the main actors in innovation policy, it is observed a proliferation of challenge-oriented innovation policies both at the subnational and the supranational level. This begs the question about subsidiarity: what innovation policies for societal challenges should be organized at subnational, national and supranational levels?

In the past 15 years, Tanzania has made considerable progress in the fight against child undernutrition. This paper analyses in what respects an enabling environment for nutrition action in Tanzania has emerged. It critically investigates the nature of government political commitment and assesses the breadth and depth of a range of public policies, initiatives and actions within and across nutrition-specific and nutrition-sensitive sectors, and at the national, sub-national and community levels.

Despite the key role of actor networks in progressing new sustainable technologies, there is a shortage of conceptual knowledge on how policy can help strengthen collaborative practices in such networks. The objective of this paper is to analyze the roles of such policies – so-called network management – throughout the entire technological development processes.

Science, technology and innovation (STI) policy is shaped by persistent framings that arise from historical context. Two established frames are identified as co-existing and dominant in contemporary innovation policy discussions. The first frame is identified as beginning with a Post-World War II institutionalisation of government support for science and R&D with the presumption that this would contribute to growth and address market failure in private provision of new knowledge.

This article conceptualizes the diffusion of user innovations from a service ecosystem perspective. With the focus on sustainable innovations, the service ecosystem is evaluated, along with other systemic innovation concepts, as a possible theoretical basis for explaining the first adoption and diffusion of user innovations.

Rather than merely supporting R&D and strengthening innovation systems, the focus of innovation policy is currently shifting towards addressing societal challenges by transforming socio-economic systems. A particular trend within the emerging era of transformative innovation policy is the pursuit of challenge-based innovation missions, such as achieving a 50 % circular economy by 2030. By formulating clear and ambitious societal goals, policy makers are aiming to steer the directionality and adoption of innovation.

So far, numerous studies have exhibited Silicon Valley and other thriving innovation ecosystems by distinguishing special characteristics in which their survival rely on sustaining activities that convert them to specific regions. These regions provide ready-made grounds for networking to be innovative. Meantime, it is struggling for innovations to be transformed into measurable economic results if players encounter a weak network of collaborative relationships in the ecosystem.

Brazil’s influence in agricultural development in Africa has become noticeable in recent years. South–South cooperation is one of the instruments for engagement, and affinities between Brazil and African countries are invoked to justify the transfer of technology and public policies. In this article, examines the case of one of Brazil’s development cooperation programs, More Food International (MFI), to illustrate why policy concepts and ideas that emerge in particular settings, such as family farming in Brazil, do not travel easily across space and socio-political realities.

Research on next generation agricultural systems models shows that the most important current limitation is data, both for on-farm decision support and for research investment and policy decision making. One of the greatest data challenges is to obtain reliable data on farm management decision making, both for current conditions and under scenarios of changed bio-physical and socio-economic conditions.