Modeling the impact of Covid-19’s stress and resilience on job burnout using Radial Basis Functions - Artificial Neural NetworkThe case of knowledge-based companies

This study aims to model the impact of Covid-19’s stress and resilience on job burnout in knowledge-based companies.

Design/methodology/approach:

 This study is typically quantitative and cross-sectional and in terms of purpose it is applied research. The statistical population included the managers of the first and second-tier and the employees of the knowledge-based companies. Based on the equation of the statistical sample size of the unlimited population, 384 were examined.  The standard questionnaires of Maslach and Brief Resilient Coping Scale (BRCS) and Covid-19 researcher-made questionnaires were used for data collection. Radial Basis Functions - Artificial Neural Network (RBF-ANN) was used for data analysis.

65% of the managers and employees of knowledge-based companies were at moderate and lower resilience levels and 61% of the statistical sample had job burnout. Also, the amount of stress caused by Covid-19 was higher among married women compared to others. The RBF method was used to design the ANN. Accordingly, the number of neurons in the input layer was equal to 10, the number of neurons in the single hidden layer was equal to 35, the number of neurons in the output layer was equal to 1, and  was equal to 10. 70% and 30% of the data were used for training and testing, respectively. In the designed ANN, all but one of the test data, and all but two of the experimental data were correctly predicted and the Root Mean Square Error (RMSE) error was less than 0.3. Finally, based on the obtained results, the proposed model was confirmed.

Research limitations/implications:

 The difficulty of accessing statistical samples in Covid-19 conditions and the resulting limitations along with the lack of relevant research background were among the limitations of the present study. For future research, similar comparative studies are suggested to be conducted in the manufacturing knowledge-based companies for modeling and adapting the results and conducting a study using other methods of ANN design, including multilayer perceptron (MLP). Also, separating the areas of activity of knowledge-based companies and comparing the results are suggested as the subjects of study on the variables of this research.

Practical implications:

 Since in the research related to social sciences and humanities, less use is made of engineering methods such as neural network design, the present study seems innovative in terms of subject and methodology and the researchers and experts who are interested in the subject of this study can benefit from the findings. Business and entrepreneurship and organizational behavior, engineering sciences and sustainability issues, students and managers, and employees of technology and knowledge-based companies are the other beneficiaries of this study.

Social implications: 

Since there is no immediate and definitive solution to reduce the stress and burnout of managers and employees of the startups, constant pressure has created a long-term detrimental situation for startup companies. Addressing this issue is necessary because the performance and productivity of a company require the physical and mental health of its managers and employees; stress and resilience are also the two factors affecting job burnout which have been exacerbated by the Covid-19 crisis over the past two years.

Originality/value:

 Because dealing with complex relationships between research variables requires the use of precise and in-depth analytical methods, in this study, an ANN was used to predict their behavior and the impact of variables on each other. Therefore, the attempt made to reduce the theoretical gap and the contribution made in theory based on innovation in the subject and research variables and the analysis method has led this paper to have an interdisciplinary approach.