Further Resources

Abstract

Abstract

An overly favorable narrative has developed around the role played by digital technologies in containing Covid-19, which oversimplifies the complexity of technology adoption. This narrative takes sociomaterial arrangements for granted and conceptualizes technology affordance - the problem-solving capability of a technology - as a standard built-in feature that automatically activates during technology deployment, leading to undiversified and predetermined collective benefits. This paper demonstrates that not everything is as it seems; implementing a technology is a necessary but insufficient condition for triggering its potential problem-solving capability. The potential affordance and effects of a technology are mediated by the sociomaterial arrangements that users assemble to connect their goals to the materiality of technological artifacts and socio-organizational context in which technology deployment takes place. To substantiate this argument and illustrate the mediating role of sociomaterial arrangements, we build on sociomateriality and technology affordance theory, and we present the results of a systematic review of Covid-19 literature in which 2187 documents are examined. The review combines text data mining, co-occurrence pattern recognition, and inductive coding, and it focuses on four digital technologies that public authorities have deployed as virus containment measures: infrared temperature-sensing devices; ICT-based surveillance and contact-tracing systems; bioinformatic tools and applications for laboratory testing; and electronic mass communications media. Reporting on our findings, we add nuances to the academic debate on sociomateriality, technology affordance, and the governance of technology in public health crises. In addition, we provide public authorities with practical recommendations on how to strengthen their approach to digital technology deployment for pandemic control.

Abstract

Abstract

Agricultural technologies strengthen and streamline Food Value Chains (FVCs) while improving the lives and livelihoods of smallholder farmers and entrepreneurs. Technologies such as greenhouses, solar food dryers, threshers, grinders, and storage and packaging equipment can help increase the efficiency and sustainability of food value chain activities in emerging economies. However, there are a myriad of technological, infrastructural, and operational challenges that hinder the successful design and sustainable commercialisation or deployment of such products. After over a decade of research, experience, and consultation in the field, we present here an initial taxonomy of potential failure modes during the design, implementation, and maturity phases of agricultural technologies ventures. We argue that consideration of these failure modes early in the design process will assist agricultural technology designers and entrepreneurs in avoiding pitfalls later in the venture lifecycle. Part 1 (of 2) in this article series presents this rationale and development as well as the early, design-phase pitfalls. Together with Part 2 (implementation and maturity failure modes), this taxonomy aims to inform innovators and entrepreneurs seeking to launch successful and sustainable agricultural technology ventures in the developing world.

Abstract

Can STS offer a response to “alternative facts” without falling back into naive positivism? Can STS help to make science accountable to society and make it work—make it function in our democracies and let it produce scientific knowledge? In his valedictory lecture, Wiebe Bijker looks back upon three decades of STS research in general, and upon engaging STS with questions of democratization and development in particular. He starts from the question how to study technological cultures and ends with the question how to construct them. The argument moves from the social construction of technology to constructing socio-technical worlds. Finally, when trying to understand this construction work, the analysis zooms in on the constructing worlds: on the institutions in which this construction work takes place. 

Abstract

Making innovation happen is central to what many engineers do. However, when we finish our training most of us believe that it is our job to conceptualize designs, develop products and worry little about what happens after they have been introduced. Our courses are generally too practical to bother with theories about how innovation occurs, who it affects and how we might better manage the process. Diesel, inventor of the diesel engine, distinguished between two phases in technological progress: the conception and carrying out of the idea, which is a happy period of creative mental work in which technical challenges are overcome, and the introduction of the innovation, which is a “struggle against stupidity and envy, apathy and evil, secret opposition and open conflict of interests, a horrible period of struggle with man, a martyrdom even if success ensues.”1 Diesel is perhaps overstating the difficulties of managing innovation, but nevertheless as engineers we are still taught to prefer technical “invention” and leave dealing with people and the “innovation” side to others. However, engineers ignore the innovation process at their peril. Enabling innovation means building on peoples’ ingenuity and motivations, rather than working against them. In this paper I describe the learning selection approach to enabling innovation that capitalizes on the complexity of social systems at different scales of analysis. In the first part of the paper I describe the approach and how it can be used to guide the early stages of setting up a “grassroots” innovation process. In the second part of the paper I look at how the learn selection model can be used “top-down” to guide research investments to trigger large-scale systemic change.

Abstract

The greatest challenge now facing agricultural science is not how to increase production overall but how to enable resource-poor farmers to produce more. With the transfer-of-technology (TOT) model of agricultural research - part of the normal professionalism of agricultural scientists - scientists largely determine research priorities, develop technologies in controlled conditions, and then hand them over to large agricultural extension to transfer to farmers. Although strong interests sustain this model, many now recognise its bad fit with the needs of hundreds of millions of resource-poor farm (RPF) families. In response to this problem, the TOT model has been adapted and extended through multi-disciplinary farming systems research (FSR) and on-farm trials. These responses retain power in the hands of scientists. In contrast, the farmer-first-and-last (FFL) model transfers initiative to farmers, especially RPFs. The authors argue that FFL fits the diverse and complex conditions and needs of RPFs better than TOT, and makes more sparing and cost-effective use of scarce scientists. A parsimonious form of FFL avoids multidisciplinary teams and much data gathering and analysis by trusting farmers' knowledge and self-interest, and encouraging and enabling them to identify priorities for research.