Structural Modeling of Article Sharing with Visual Abstract Design in Digital Journal Platforms

Abstract

The rapid digitization of scholarly publishing has accelerated the adoption of visual abstracts as critical dissemination tools on digital journal platforms and academic social networks. While visual abstracts are designed to summarize research findings efficiently, systematic empirical evidence examining how specific design characteristics influence academic article sharing remains scarce. This study develops and tests a comprehensive structural equation model to assess the relationships between visual abstract design attributes (visual clarity, aesthetic appeal, and information density) and users' cognitive states (cognitive load and perceived usefulness), and their subsequent sharing intentions on digital platforms. Data collected from 420 active researchers and academic professionals were analyzed using structural equation modeling. The empirical results demonstrate that visual clarity and aesthetic appeal significantly reduce cognitive load and enhance perceived usefulness, whereas excessively high information density increases cognitive load and hampers sharing intentions. Perceived usefulness and cognitive load were confirmed as vital mediators in the relationship between visual abstract design and content sharing. These findings provide actionable insights and design guidelines for digital journal editors, academic publishers, and researchers seeking to maximize the dissemination and societal impact of scholarly publications in the digital era.