Production economics and the learning curve: A meta-analysis

Eric H. Grosse, Christoph H. Glock and Sebastian Müller

International Journal of Production Economics, 2015, vol. 170, issue PB, 401-412

Abstract: 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.

Keywords: Learning; Learning curve; Experience curve; Curve fitting; Literature survey; Literature review; Meta-analysis (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (13)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:proeco:v:170:y:2015:i:pb:p:401-412

DOI: 10.1016/j.ijpe.2015.06.021

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