Assessing emerging sustainability-oriented technologies: the case of precision agriculture

Abstract

The pressing need for a transition towards a more sustainable economy has given rise to sustainability-oriented innovations (SOIs). The development of SOIs involves a wide range of emerging technologies, some of which are highly uncertain and may have the potential to transform an existing industry. Thus, the identification and assessment of emerging technologies is pivotal for established companies to evaluate business opportunities as well as for researchers and policy makers to support the transition to a more sustainable economy. Therefore, we aim to contribute by offering a new approach that seeks to assess such emerging technologies that contribute to sustainability transition (i.e., sustainability-oriented technologies (SOTs)) by means of spectral cluster analysis based on the semantic similarities of scientific research articles in the field of precision agriculture (PA); a case example where multiple SOIs occur. Our analyses reveal that spectral cluster analysis is a suitable approach for assessing emerging SOTs. In addition, multi-perspective assessments based on, inter alia, related United Nations Sustainable Development Goals, Web of Science subject categories, strategic diagrams, and business models allows to render a holistic assessment of a scientific research area (in this case PA) and its emerging SOTs as well as their evolution into commercial SOIs.