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Production economics and the learning curve: A.meta-analysis

机译:生产经济学和学习曲线:A。元分析

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For almost a century, researchers and practitioners have studied learning curves in production economics. Learning, in this context, refers to performance improvements of individuals, groups or organizations over time as a result of accumulated experience. Various learning curves, which model this phenomenon, have been developed and applied in the area of production economics in the past. When developing planning models in production economics, the question arises which learning curve should be used to best describe the learning process. In the past, the focus of the literature has been on empirical studies that investigated learning processes in laboratory settings or in practice, but no effort has been undertaken so far to compare existing learning curves on a large set of empirical data to assess which learning curve should be used for which application. This study systematically collected empirical data on learning curves, which resulted in a large database of empirical data on learning. First, the data contained in the database is categorized with the help of meta-tags along different characteristics of the studies the data was taken from. Second, a selection of well-known learning curves is fitted to the empirical datasets and analyzed with regard to goodness of fit and data characteristics. We identify a set of data/task characteristics that are important for selecting an appropriate learning curve. The results of the paper may be used in production economics to assist researchers to select the right learning curve for their modeling efforts. (C) 2015 Elsevier B.V. All rights reserved.
机译:近一个世纪以来,研究人员和从业人员研究了生产经济学中的学习曲线。在这种情况下,学习是指由于积累的经验而随着时间的推移而提高个人,团体或组织的绩效。过去,已经开发出各种模拟这种现象的学习曲线,并将其应用于生产经济学领域。在生产经济学中开发计划模型时,出现了一个问题,即应该使用哪种学习曲线来最好地描述学习过程。过去,文献的重点一直放在对实验室环境或实践中的学习过程进行调查的实证研究上,但是迄今为止,尚未进行任何努力来比较大量实证数据上的现有学习曲线,以评估哪个学习曲线。应该用于哪个应用程序。这项研究系统地收集了学习曲线上的经验数据,从而建立了一个庞大的学习经验数据数据库。首先,借助元标签,根据包含数据的研究的不同特征对数据库中包含的数据进行分类。其次,将一系列著名的学习曲线拟合到经验数据集,并就拟合优度和数据特征进行分析。我们确定了一组数据/任务特征,这些特征对于选择合适的学习曲线很重要。论文的结果可用于生产经济学,以帮助研究人员为他们的建模工作选择正确的学习曲线。 (C)2015 Elsevier B.V.保留所有权利。

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