Paths to research-driven decision making in the realms of environment and water

Savannah Cooley, Amber Jenkins, Blake Schaeffer, Kat J. Bormann, Adel Abdallah, Forrest Melton, Stephanie Granger and Indrani Graczyk

Technology in Society, 2022, vol. 70, issue C

Abstract: Now more than ever it is critical for researchers and decision makers to work together to improve how we manage and preserve the planet's natural resources. Water managers in the western U.S., as in many regions of the world, are facing unprecedented challenges including increasing water demands and diminishing or unpredictable supplies. The transfer of knowledge (KT) and technology (TT) between researchers and entities that manage natural resources can help address these issues. However, numerous barriers impede the advancement of such transfer, particularly between organizations that do not operate in a profit-oriented context and for which best practices for university-industry collaborative engagement may not be sufficient. Frameworks designed around environmental KT – such as the recently-developed Research-Integration-Utilization (RIU) model – can be leveraged to address these barriers. Here, we examine two examples in which NASA Earth science satellite data and remote-sensing technology are used to improve the management of water availability and quality. Despite differences in scope and outcomes, both of these case studies adopt KT and TT best practices and can be further understood through the lens of the RIU model. We show how these insights could be adopted by NASA through a conceptual framework that charts individual- and organizational-level integration milestones alongside technical milestones. Environmental organizations can learn from this approach and adapt it to fit their own institutional needs, integrating KT/TT models and best practices while recognizing and leveraging existing institutional logics that suit their organization's unique history, technical capability and priorities.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:teinso:v:70:y:2022:i:c:s0160791x2200135x

DOI: 10.1016/j.techsoc.2022.101994

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