publications

2026

1. JOI

   Does the Matthew effect exist in open peer review? An empirical study of institutional prestige effects on review outcomes

   Jeongyeon Choi, Yi Zhao, and Yongjun Zhu

   Journal of Informetrics, Sep 2026

   Abs DOI

   The phenomenon of cumulative advantage, known as the Matthew Effect, has been extensively observed in the scientific community. However, less is known about how cumulative advantage manifests through institutional prestige during the evaluative phase of open peer review (OPR). In this study, we investigate whether authors’ institutional prestige influences review outcomes in OPR and, if so, the extent of that influence. Examining all research and review articles published on the open-access publishing platform F1000Research from 2012 to 2025, we assess the effects of institutional prestige on OPR review outcomes using coarsened exact matching (CEM). To isolate the effect of institutional prestige, we controlled for authors’ scholarly impact using the h-index and article-level differences using measures of novelty and readability. The results indicate that (1) institutional prestige is positively associated with peer review evaluations, (2) its effects vary across review criteria, (3) it leads to shorter review periods, and (4) its effects weaken as authors accumulate greater scholarly impact. This study advances the scholarly understanding of the Matthew Effect in OPR and devises recommendations for reviewers, editors, and policymakers.

2. ASIS&T

   Developing an affordance-based feature framework for depression-focused mental health applications

   Jeongyeon Choi and Shengang Wang

   Proceedings of the Association for Information Science and Technology, Oct 2026

   Abs

   This study investigates the features presented in developers’ descriptions of depression-focused mental health applications (MHapps) to develop an affordance-based feature framework. To achieve this, we collected developer descriptions from 10 top depression-focused MHapps on Google Play and utilized a stratified sampling strategy to gather user reviews across different rating scores. Through analysis of developer descriptions, we identified four major affordance-based feature groups or categories: monitoring & self-management, educational support, self-reflection, and decision-making support. After analyzing feature prevalence, we found automated support and mood tracking to be widespread, while relationship reflection was relatively scarce. The findings contribute to an affordance-based understanding of MHapps and provide a framework for future analyses of user evaluations of MHapp features.