Journal article
An individual-based description of learning within an organization
IEEE transactions on engineering management, Vol.47(3), pp.370-378
08/2000
DOI: 10.1109/17.865905
Abstract
The authors examine the problem of selecting a model for an individual-based representation of learning within a population of learners. Individual-based representations can be used to create distributions of learning patterns in the workplace. This is an alternate theoretical view of learning in organizations whereby performance is a unique attribute of each individual within the organization. Several published learning curve models are fitted to 3874 episodes of individual performance improvement. They conclude that a three-parameter hyperbolic function outperforms the other models for this application. This approach provides managers with: (1) distributions of between-worker variability with respect to rate of learning, prior learning, and steady-state production rates; (2) a quantitative measure of workforce learning that can provide information useful for workforce task-assignments; and (3) a methodological framework for selecting a most preferred individual model such that workforce distributions may be constructed and provide such information. Results indicate that workers perform in a region between the two extremes of fast improvement to a low level of productivity and slow improvement to a high level of productivity. Also, workers with more prior experience tend to have a higher steady-state productivity level.
Details
- Title: Subtitle
- An individual-based description of learning within an organization
- Creators
- D.A Nembhard - University of Wisconsin–MadisonM.V Uzumeri
- Resource Type
- Journal article
- Publication Details
- IEEE transactions on engineering management, Vol.47(3), pp.370-378
- Publisher
- IEEE
- DOI
- 10.1109/17.865905
- ISSN
- 0018-9391
- eISSN
- 1558-0040
- Language
- English
- Date published
- 08/2000
- Academic Unit
- Business Analytics; Industrial and Systems Engineering
- Record Identifier
- 9984187051002771
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