From Research Knowledge to Adoption-Ready Information: How Science Communication Supports the Diffusion of University Research Outputs

Abstract

Universities increasingly make research outputs searchable through repositories, patent databases, and digital matching platforms, yet informational availability does not necessarily make an innovation intelligible or evaluable to potential adopters. This article asks how organizationally embedded science communication transforms university research outputs into information that external actors can use in pre-adoption decision processes. Drawing on innovation diffusion theory and knowledge-boundary scholarship, we develop the concept of adoption-ready information: information that is traceable to evidence, organized around adoption-relevant attributes, situated in testable contexts, and connected to actors and procedures capable of carrying evaluation forward. We use an embedded comparative case design to examine four contrasting pathways: editorial mediation at Annual Reviews’ Knowable Magazine, exhibit-based translation at Chongqing University’s Science Communication Innovation Laboratory, standardized transfer-oriented communication at Shandong University, and an AI-assisted human-in-the-loop workflow at Hangzhou Dianzi University. EurekAlert!, Cell Press graphical abstracts, and two Chinese science communication initiatives provide supplementary mechanism evidence. Cross-case analysis identifies four mechanisms—evidence anchoring, attribute articulation, contextual enactment, and organizational relay. Together, they can reduce cognitive uncertainty and move potential adopters from general awareness toward evaluative engagement, testing, and negotiation. Science communication does not create technical performance or guarantee commercialization; rather, it functions as a cognitive and organizational interface between research dissemination and adoption. The article contributes a process explanation of how communication shapes adoption preparedness while remaining constrained by technical maturity, demand, absorptive capacity, intellectual property, and implementation conditions.