The number of theories of innovation systems has grown considerably over the past two decades and several innovation system approaches have been developed. The focus of most has been on the development of technological innovations which create economic value.However, addressing macro-level societal problems – sociotechnical and environmental challengesin which the production, dissemination and use of social and technical knowledge and technology can potentially resolve the problem – has been overlooked.
There is great untapped potential for farm mechanization to support rural development initiatives in low- and middle-income countries. As technology transfer of large machinery from high-income countries was ineffective during the 1980s and 90s, mechanization options were developed appropriate to resource poor farmers cultivating small and scattered plots. More recently, projects that aim to increase the adoption of farm machinery have tended to target service providers rather than individual farmers.
This article extends social science research on big data and data platforms through a focus on agriculture, which has received relatively less attention than other sectors like health. In this paper, I use a responsible innovation framework to move attention to the social and ethical dimensions of big data “upstream,” to decision-making in the very selection of agricultural data and the building of its infrastructures.
En el presente documento se realiza un análisis de la situación de los principales clusters de Argentina y cómo estas estructuras asociativas pueden mejorar la generación de innovaciones y generar externalidades positivas para la comunidad. Lo anterior se realiza a través de un análisis de tipo descriptivo, abordando el objeto de investigación mediante el estudio de casos. Asimismo se aplicó un modelo de gestión de conocimiento con el fin de evaluar los efectos de los clusters en la innovación y la creación de conocimiento valioso.
Social learning in multi-actor innovation networks is increasingly considered an important precondition for addressing sustainability in regional development contexts. Social learning is seen as a means for enabling stakeholders to take advantage of the diversity in perspectives, interests and values for generating more sustainable practices and policies. Although more and more research is done on the meaning and manifestations of social learning, particularly in the context of natural resource management, little is known about the social dynamics in the process of social learning.
This study examines the influence of an extra-curricular educational program on children's knowledge and cultural valuation of wild food plants, which are an important component of their diets. This program aims to reinforce children's traditional knowledge and values around biological resources in Wayanad, India's Western Ghats, encouraging tribal and non-tribal children to learn from each other and from their own communities. Results show that the educational program has enhanced children's ability to identify selected wild food plants.
This methodological guide was initially developed and used in Latin America and the Caribbean-LAC (Honduras, Nicaragua, Colombia, Peru, Venezuela, Dominican Republic), and was later improved during adaptation and use in eastern African (Uganda, Tanzania, Kenya, Ethiopia) through a South-South exchange of expertise and experiences. The aim of the methodological guide is to constitute an initial step in the empowerment of local communities to develop a local soil quality monitoring and decision-making system for better management of soil resources.
In this chapter the authors compute measures of total factor productivity (TFP) growth for developing countries and then contrast TFP growth with technological capital indexes. In developing these indexes, the authors incorporate schooling capital to yield two new indexes: Invention-Innovation Capital and Technology Mastery. They find that TFP performance is strongly related to technological capital and that technological capital is required for TFP and cost reduction growth.
Innovation systems can be defined in a variety of ways: they can be national, regional, sectoral, or technological. They all involve the creation, diffusion, and use of knowledge. Systems consist of components, relationships among these, and their characteristics or attributes. The focus of this paper is on the analytical and methodological issues arising from various system concepts. There are three issues that stand out as problematic. First, what is the appropriate level of analysis for the purpose at hand?
This paper argues that impact assessment research has not made more of a difference because the measurement of the economic impact has poor diagnostic power. In particular it fails to provide research managers with critical institutional lessons concerning ways of improving research and innovation as a process. Paper's contention is that the linear input-output assumptions of economic assessment need to be complemented by an analytical framework that recognizes systems of reflexive, learning interactions and their location in, and relationship with, their institutional context.