The European Union (EU) promotes collaboration across functions and borders in its funded innovation projects, which are seen as complex collaboration to co-create knowledge. This requires the engagement of multiple stakeholders throughout the duration of the project. To probe complexity in EU-funded innovation projects the research question is: How does complexity affect the co-creation of knowledge in innovation projects, according to project participants?
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.
There is a broad consensus that farmers are not simply recipients of promoted techniques: rather, they are also an important source of agricultural innovations. They invent farm tools and equipment, develop new crop varieties, and add value to externally promoted technologies. When scouting, documenting and promoting such farmer-generated innovations, the thorny issue of intellectual property rights (IPRs) often emerges.
Individuals from a diverse range of backgrounds are increasingly engaging in research and development in the field of artificial intelligence (AI). The main activities, although still nascent, are coalescing around three core activities: innovation, policy, and capacity building. Within agriculture, which is the focus of this paper, AI is working with converging technologies, particularly data optimization, to add value along the entire agricultural value chain, including procurement, farm automation, and market access.
Agricultural Internet of Things (IoT) has brought new changes to agricultural production. It not only increases agricultural output but can also effectively improve the quality of agricultural products, reduce labor costs, increase farmers' income, and truly realize agricultural modernization and intelligence. This paper systematically summarizes the research status of agricultural IoT. Firstly, the current situation of agricultural IoT is illustrated and its system architecture is summarized. Then, the five key technologies of agricultural IoT are discussed in detail.