A study on computer intelligent evaluation of employment quality based on binary logistic model and big data technology

This paper adopts the binary logistic model to study the impact of migrant workers' employment quality on job satisfaction with a special focus on gender differences. Data used in the model are obtained from a survey on the topics of employment quality and job satisfaction of the new generation migrant workers conducted during winter vacation 2020. Analysis suggests that monthly salary income, job types, satisfaction with opportunity for training, satisfaction with working environment, satisfaction with colleague relationship as well as satisfaction with higher and lower ranks all have significant impact on job satisfaction, while the rest of the employment quality indicator shows negligible correlation. Subjective index variables show significant influence over migrant workers’ job satisfaction. Meanwhile, gender differences exist in impact of employment quality on job satisfaction. Satisfaction with opportunity for training and satisfaction with colleague relationship significantly impact female migrant workers’ job satisfaction while satisfaction with colleague relationship and satisfaction with higher and lower ranks significantly impact male migrant workers’ job satisfaction. Among migrant workers’ individual characteristics, education level and work location have an impact on job satisfaction, but the impact is not significant compared to the employment quality indicators.