A dual-perspective innovation framework: quantifying novelty and diffusion of scientific publications

Purpose Evaluating the innovation of scientific publications remains a critical challenge, as traditional citation-based metrics often fail to capture both the novelty of research content and the dynamics of knowledge diffusion. This study aims to propose a dual-perspective framework that jointly measures how research draws upon prior knowledge and contributes to subsequent scientific development. Design/methodology/approach Using a data set of 193,193 papers in computer science and library and information science, this paper examines how research builds on existing knowledge and influences future work. A dual-perspective framework is proposed, incorporating four interpretable indicators: knowledge adoption novelty, knowledge diffusion breakthrough, knowledge duality index and knowledge innovation contribution ratio. Based on these indicators, a quadrant-based innovation typology is developed to classify research into four types of scientific innovation. Findings The analysis shows that innovation indicators vary across fields, reflecting differences in how knowledge is produced and diffused. Based on these indicators, research can be grouped into four types with distinct innovation profiles. While citation and usage patterns differ across types, some contrasts are modest and field-dependent. Research limitations/implications The paper observed temporal trends in indicator distributions, whereas the study did not apply temporal normalization to the innovation indicators. Practical implications This study offers a structured and interpretable framework for evaluating scholarly innovation. Social implications By addressing both how knowledge is built upon and how it is diffused, the proposed indicators provide a comprehensive perspective on research contribution. Originality/value This study proposes a dual-perspective framework that jointly captures the novelty and diffusion of scientific publications through four interpretable indicators.