A novel approach for assessing academic journals: Application of integer DEA model for management science and operations research field

A twofold perspective on the quality of research publications: The use of ICTs and research activity models

Previous studies have highlighted the inherent subjectivity, complexity, and challenges associated with research quality leading to fragmented findings. We identified determinants of research publication quality in terms of research activities and the use of information and communication technologies by employing an interdisciplinary approach. We conducted web-based surveys among academic scientists and applied machine learning techniques to model behaviors during and after the COVID-19 pandemic. Using model-agnostic explanations, we identified the determinants of research publication quality across 66 activity models. These models reflect the variety of behaviors among academic scientists during and after the COVID-19 pandemic. Our two-fold perspective distinguishes between research activities of academic scientists who increase research publication quality and those who maintain it. Notably, our findings reveal a diversity within activity models in shaping research publication quality. Academic institutions can apply our approach to analyze research staff behavior, stimulate activities, and ensure alignment with institutional objectives, thereby fostering individual and team complementarity.

AIRank: An algorithm on evaluating the academic influence of papers based on heterogeneous academic network

Evaluation of papers’ academic influence is a hot issue in the field of scientific research management. Academic big data provides a data treasure with the coexistence of different types of academic entities, which can be used to evaluate academic influence from a more macro and comprehensive perspective. Based on academic big data, a heterogeneous academic network composed of links within and between three types of academic entities (authors, papers and venues) is constructed. In addition, a new academic influence ranking algorithm, AIRank, is proposed to evaluate papers’ academic influence. Different from the existing academic influence ranking algorithms, AIRank has made innovations in the following two aspects. (1) AIRank distinguishes the influence transmission intensity between different node pairs. Different from the strategy of evenly distributing influence among different node pairs, AIRank quantifies the intensity of influence transmission between node pairs based on investigating the citation emotional attribute, semantic similarity and academic quality differences between node pairs. Based on the intensity characteristics, AIRank realises the distribution and transmission of influence among different node pairs. (2) AIRank incorporates the influence transmission from heterogeneous neighbours in evaluating papers’ influence. According to the academic influence of author nodes and venue nodes, AIRank fine-tunes the iteration formula of paper influence to obtain the ranking of papers under the joint influence of homogeneous and heterogeneous neighbours. Experimental results show that, compared with the ranking results based on citation frequency and PageRank algorithm, AIRank algorithm can produce more differentiated and reasonable academic influence ranking results.