Enhancing Job Satisfaction Using an Adaptive Neuro-Fuzzy Inference System by Considering HSEE Factors

Abstract

Job satisfaction plays a crucial role in enhancing productivity and reveals intriguing insights that impact the operational effectiveness of organizations. Due to the importance of maintenance units, special attention should be paid to their employees. This study employs a machine learning approach to enhance the performance and job satisfaction of maintenance units through the focus on health, safety, environment, and ergonomics (HSEE). A standardized questionnaire is developed for on HSEE data. Within the neural-fuzzy inference network, inputs such as health and safety protocols, environmental data collection, and its reliability is assessed using Cronbach's alpha coefficient. Subsequently, various adaptive neuro fuzzy inference system (ANFIS) models are utilized to predict job satisfaction based factors, and ergonomics are considered, while job satisfaction serves as the output. Following the selection of the optimal model, individual efficiency levels are assessed and scrutinized based on the calculated error. The findings suggest that enhancing employee job satisfaction relies on prioritizing the enhancement of ergonomics and the work environment.

Keywords

• safety
• job satisfaction
• machine learning
• adaptive neuro fuzzy inference system