Productivity is an important issue in recent literature because it encourages cost savings and efficiency in the use of industrial resources in all countries. However, the study of the factors that explain the productivity levels reached by the companies presents controversy, and the existing research demands new analysis models that can more accurately identify the causes of industrial productivity. The present study aims to develop a new model that allows determining with high accuracy the factors that explain productivity in the construction industry. For this, an important sample of industrial companies and techniques of artificial neural networks has been used. The results obtained provide levels of accuracy that exceed those obtained by the previous literature, and have allowed us to identify that the aspects related to turnover, liquidity, and growth of companies provide an excellent strategy for promoting industrial productivity.

中文翻译：

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用人工神经网络识别工业生产率因素

生产率是最近文献中的一个重要问题，因为它鼓励在所有国家中节约成本和提高工业资源的利用效率。然而，对解释公司所达到的生产率水平的因素的研究存在争议，并且现有的研究要求新的分析模型可以更准确地确定工业生产率的原因。本研究旨在开发一种新模型，该模型可以高精度确定解释建筑业生产率的因素。为此，使用了重要的工业公司样本和人工神经网络技术。获得的结果所提供的准确性水平超过了以前的文献，并且使我们能够确定与营业额，流动性，