Modeling organizational performance with machine learning - Jozsef Pap; Csaba Makó; Miklos Illessy; Norbert Kis; Amir Mosavi (Journal of Open Innovation: Technology, Market, and Complexity, 2022)
Identifying the performance factors of organizations is of utmost importance for labor studies for both empirical and theoretical research. The present study investigates the essential intra- and extra-organizational factors in determining the performance of firms using the European Company Survey (ECS) 2019 framework. The evolutionary computation method of genetic algorithm and the machine learning method of Bayesian additive regression trees (BART), are used to model the importance of each of the intra- and extra-organizational factors in identifying the firms' performance as well as employee well-being. The standard metrics are further used to evaluate the accuracy of the proposed method. The mean value of the evaluation metrics for the accuracy of the impact of intra- and extra-organizational factors on firm performance are MAE = 0.225, MSE = 0.065, RMSE = 0.2525, and R2 = 0.9125, and the value of these metrics for the accuracy of the impact of intra- and extra-organizational factors on employee well-being are MAE = 0.18, MSE = 0.0525, RMSE = 0.2275, and R2 = 0.88. The low values of MAE, MSE and RMSE, and the high value of R2, indicate the high level of accuracy of the proposed method. The results revealed that the two variables of work organization and innovation are essential in improving firm performance well-being, and that the variables of collaboration and outsourcing, as well as job complexity and autonomy, have the greatest role in improving firm performance.
Keywords: 650; artificial intelligence; Bayesian additive regression trees; big data; deep learning; firm performance; machine learning; management; open innovation; organizational performance; social science