Prediction of implementing ISO 14031 guidelines using a multilayer perceptron neural network approach

The purpose of this study was to model the link between the implementation of ISO 14031 and ISO 14001. This study connects ISO 14031’s guidelines as independent variables to a dependent variable expressed by the ISO 14001 certification situation of industrial organizations based on the judgments of environmental managers in Saudi Arabia. Applying the quantitative approach using a survey with 596 responses from organizations functioning in 30 economic activities, a multi-layered neural network was trained to examine the relationship between standards and predict whether the organization is ISO 14001 certified in addition to testing the developed network on a group of collected cases. The results demonstrated the ability of the network to classify the organization’s certification status by 94.00% according to the training sample and its ability to predict 91.00% of the test sample, with an overall prediction efficiency of 91.30%. This work provides insights and adds to the environmental performance evaluation literature providing a neural network model based on ISO 14031 guidelines that can be extended to include other international standards such as ISO 9001. This study supports the merging of ISO 14001 with ISO 14031 into a binding standard.