Organizational learning: Approximation of multiple-level learning and forgetting by an aggregated single-level model

In a large organization, learning and forgetting may occur at different rates at the various levels of the organization. Recently, it has been shown that a multiple-level learning model works effectively for the accurate measurement and prediction of learning and forgetting in such an organization. Due to a lack of sufficiently detailed data at each organizational level, however, it is often necessary to use the conventional aggregated single-level model to estimate the learning and forgetting of the entire organization. In such an approximation, the potentially different impacts of learning and forgetting at different levels of the organization is not explicitly considered. This paper investigates the accuracy of this single-level approximation. The single-level approximation, of course, cannot be used to explain how the learning and forgetting occur at various levels of an organization. However, numerical experiments based upon the Liberty ships dataset show that the single-level approximation can provide surprisingly good estimates of the organization’s key performance measure, e.g., production time per unit. It can therefore yield good estimates of the learning and forgetting rates aggregated for the entire organization, and these estimates can be used to compare the performance of one organization to another. The single-level approximation is shown to perform particularly well when the data exhibit a large amount of dispersion, the number of units used for fitting is large, the learning occurs slowly, or the forgetting rate is high.

在一个大型组织中，学习和遗忘在组织的各个层面可能以不同的速率发生。最近，已经表明多层次学习模型对于此类组织中学习和遗忘的准确测量和预测是有效的。然而，由于在每个组织层面缺乏足够详细的数据，通常有必要使用传统的聚合单层次模型来估计整个组织的学习和遗忘情况。在这种近似中，没有明确考虑组织不同层面学习和遗忘的潜在不同影响。本文研究了这种单层次近似的准确性。当然，单层次近似不能用于解释学习和遗忘是如何在组织的各个层面发生的。然而，基于“自由轮”数据集的数值实验表明，单层次近似能够对组织的关键绩效指标（例如单位生产时间）提供令人惊讶的良好估计。因此，它能够对整个组织汇总的学习和遗忘速率给出良好的估计，并且这些估计可用于比较一个组织与另一个组织的绩效。当数据呈现大量离散、用于拟合的单元数量较大、学习发生缓慢或遗忘速率较高时，单层次近似表现得特别好。