Application of Machine Learning in Analyzing the Impact of Organizational Culture on Employee Satisfaction: Predictive Model

In today's business world, job satisfaction is critical to employee motivation, engagement and productivity, and thus has a direct impact on the success and sustainability of an organization. This paper examines the influence of organizational culture on employee satisfaction. The aim is to understand the key variables that shape employees' attitudes and contribute to their satisfaction. The research focuses on the application of machine learning algorithms to predict employee satisfaction using data collected from 103 respondents. The model uses the supervised machine learning algorithm Random Forest to develop a prediction model, with the prediction results represented by the confusion matrix and the classification accuracy metric. To interpret the effects of each feature from the input dataset, SHapley Additive exPlanations (SHAP) analysis was applied, which combines the importance of the features with the effects of the features. The predictor variables included dimensions of organizational culture. Employee satisfaction was analyzed as an overall dependent variable and segmented into four dependent subvariables for a more detailed analysis, resulting in different reduced data sets. The results show that the proposed machine learning algorithm enables accurate prediction of employee satisfaction and provides detailed insights into the influence of each variable on satisfaction. The practical implications of this study include the development of effective strategies to strengthen a positive organizational culture, reduce employee turnover and improve the work environment. From a theoretical perspective, the study contributes to a deeper understanding of organizational culture and job satisfaction and highlights the importance of innovative techniques such as machine learning in human resource research. These findings provide a basis for further studies and possible directions for improving predictive models and understanding factors that influence employee satisfaction in different organizational contexts.