Analyzing the Impact of HSEE, Internet of Things, and Resilience Engineering on Job Satisfaction (Case Study: Tehran Electricity Company)

Abstract

AbstractJob satisfaction is a significant matter for managers and employees. Job satisfaction denotes the interest of individuals in fulfilling job responsibilities. Job satisfaction in employees increases profitability in the company, loyalty to the company, and work efficiency. Promotion, reciprocal respect, job security, salary, benefits, etc. are some factors increasing job satisfaction in employees. One of the items that affect job satisfaction is the Internet of Things (IoT), which improves the work environment in the organization. Further, resilience engineering (RE) improves the ability of an organization to tackle hazards and threats. In addition to the aforementioned, HSEE has proven significant in organizations and it is beneficial in increasing efficiency. Considering the potential hazards in organizations, focusing on the actions and decisions of employees of organizations is crucial. Therefore, it is essential to analyze health, safety, environment, and ergonomics (HSEE) in the workplace. In this study, a neural network (AI) and an adaptive network-based inference system (ANFIS) have been suggested to analyze the effects of IoT, HSEE, and RE on the job satisfaction of the employees of Tehran Electricity Company. For better training of the recommended networks, a metaheuristic genetic algorithm has been utilized to choose a model with the lowest error. Therefore, a standard questionnaire with IoT, HSEE, and RE as input and job satisfaction as output was prepared. The efficiency of the questionnaire was examined using Cronbach’s alpha. The questionnaire was then distributed among the employees of Tehran Electricity Company. The suggested network was solved in different modes, and the network with the lowest error was selected and combined with metaheuristic algorithms for better training. Lastly, employees with low efficiency were identified, and several solutions were recommended to improve the Maintenance Sector.