Original scientific paper

https://doi.org/10.17559/TV-20200507022306

Comparison of Job Satisfaction Prediction Models for Construction Workers: CART vs. Neural Network

Tao Chen orcid.org/0000-0002-8450-8316 ; Wuhan University of Science and Technology, Wuhan 430081, Hubei, P. R. China
Zhonghong Cao* orcid.org/0000-0002-1967-940X ; School of Management, Wuhan University of Science and Technology, Wuhan 430081, Hubei, P. R. China
Yuqing Cao orcid.org/0000-0002-2147-5860 ; Huazhong University of Science and Technology, Wuhan 430074, Hubei, P. R. China

Abstract

To establish a suitable prediction model of construction workers' job satisfaction, this study chooses the widely used models CART (Classification and Regression Tree) and NN (Neural network) in the prediction model to make a comparison and finds out the main influencing factors of construction workers' job satisfaction in occupational health and safety training. Through the investigation and analysis of 280 cases of empirical data, it is found that the CART model based on Kappa value and Accuracy of categorical variables have a better prediction effect, and the main factors affecting job satisfaction are job categories, working days per week and the latest training time. The main innovation of this paper is to add the actual value set of empirical data on the basis of the usual training set, verification set, test set and prediction set, and draw a conclusion by comparing the predicted value with the actual value of kappa.

Keywords

CART; construction workers; job satisfaction; Kappa; neural network

Hrčak ID:

260784

URI

https://hrcak.srce.hr/260784

Publication date:

22.7.2021.

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