Formation of Intellectual Capital Post Covid-19

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Abstract

In social sciences, the meta-analytical fixed effects models have gained special relevance due to their predictive capacity of a scenario, context and process, although they have focused on the estimation and prediction of simple variables, avoiding the effects of diffuse variables such as those emerging in processes Training and research. The objective of this work was to establish fixed effects models to explain the influence of diffuse variables in the formation of intellectual capital, considering contextual, educational, academic and professional variables. A retrospective study was conducted with literature from 2019 to 2021, as well as an exploratory study with variables that have been conceptualized, but not empirically tested and correlational with an intentional selection of six studies that used diffuse variables to explain attrition. The results show that the model with the greatest adjustment is the one where the emergence of anti-plagiarism software and new editorial provisions explain the dropout, although the research design limited the results to the study scenario, suggesting its extension and sophistication with other statistical techniques.
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新冠肺炎后知识资本的形成
在社会科学中,元分析固定效应模型由于其对情景、背景和过程的预测能力而获得了特殊的相关性,尽管它们侧重于简单变量的估计和预测,避免了诸如过程培训和研究中出现的扩散变量的影响。这项工作的目的是建立固定效应模型来解释扩散变量在智力资本形成中的影响,考虑到背景,教育,学术和专业变量。对2019年至2021年的文献进行了回顾性研究,并对已概念化但未经实证检验的变量进行了探索性研究,并有意选择了六项研究,这些研究使用弥散变量来解释人员流失。结果显示,调整最大的模型是出现反剽窃软件和新的编辑条款来解释退学的模型,尽管研究设计将结果限制在研究场景中,表明其与其他统计技术的延伸和复杂性。
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