A comprehensive hybrid Ndea- Bsc model and a new neural network for predicting organizational performance indicators

Strategy is the main source of long-term growth of organizations and if the strategy is not successfully implemented, even if the appropriate strategies are adopted, this process is useless. The purpose of this paper is to propose a comprehensive hybrid model for predicting organizational performance indicators.

In order to achieve the research goal, first, a balanced scorecard as a tool for designing performance evaluation indicators and network data envelopment analysis as a tool for performance evaluation has been used. Then, by matching the Malmquist productivity index with the mentioned hybrid model, the model of progress and regression of organizations in two consecutive periods is presented. Finally, by combining the proposed models and artificial neural networks, a solution is presented to evaluate the performance of 500 bank branches and also to identify their progress and regression.

The obtained results show good accuracy and less computational time of the proposed hybrid models.

The present study can add to the existing knowledge on performance appraisal of enterprises by providing a hybrid model using network data envelopment analysis and balanced scorecard; And the proposed methods can be promising tools for evaluating the performance of organizations, especially big data.