application of data mining in assessing the level of corporate social responsibility disclosure compliant with financial performance and accounting criteria

Utilizing new models instead of traditional statistical models can be quite useful in this field. examples include data mining, which possesses both high speed and accuracy as well as nonlinear and non-parametric properties therefore, in the present study, the effects of performance criteria on social responsibility disclosure level was investigated and analyzed via utilization of data mining, henceforth, a model will be presented aiming to estimate/project the optimal level of social responsibility disclosure grounded on performance criteria. the present study is applied in terms of its nature and purpose/objective as well as based on the field research method of data collection. the statistical population are companies listed on the tehran stock exchange and the required data were collected and analyzed using six data mining methods during the 2013-2019 period. the findings reveal it is possible to classify and predict the optimal level of corporate social responsibility disclosure within the iranian economic circumstances/environment. moreover, the utilization of the classification algorithm in the vicinity of the nearest neighbor can accurately predict the optimal level of corporate social responsibility disclosure based on performance criteria. the findings of this study indicate that performance metrics can be a positive predictor of the optimal level of corporate social responsibility disclosure. in addition, due to the potent strength of the proposed model, the used model in this research can be utilized to rank/rate the level of corporate social responsibility disclosure.