%0 Journal Article
%T 融合心理属性的家政员工隐性缺勤预测研究<br>A Study on the Prediction of Implicit Absenteeism of Domestic Employees by Integrating Psychological Attributes
%A 曹艳霞
%A 刘峰涛
%J Service Science and Management
%P 81-92
%@ 2324-7916
%D 2024
%I Hans Publishing
%R 10.12677/SSEM.2024.131012
%X 本文提出融合心理属性的家政服务员隐性缺勤预测模型，以Y企业作为研究平台，设计心理量表进行问卷调查，基于心理弹性理论、心理契约理论和职业倦怠理论构建家政服务员心理属性，进行相关性分析与心理属性筛选，确定了用于预测家政服务员隐性缺勤的心理属性。结合企业内数据，进一步构建XGBoost家政服务员隐性缺勤预测模型，将基于基础属性和行为属性的模型与融合心理属性的模型预测结果进行对比。结果表明，融合心理属性的预测模型在准确率、精确率、召回率和F1值等指标上都优于基于基础属性和行为属性的预测模型，其中预测准确率达到了93.78%，证明了融合心理属性的预测模型的优越性。<br />
This article proposes a prediction model for implicit absenteeism of domestic helpers that integrates psychological attributes. Using Y Enterprise as the research platform, a psychological scale is designed for questionnaire survey. Based on the theory of psychological resilience, psychological contract theory, and occupational burnout theory, the psychological attributes of domestic helpers are constructed, and correlation analysis and psychological attribute screening are conducted to determine the psychological attributes used to predict implicit absenteeism of domestic helpers. Based on data within the enterprise, further construct an XGBoost model for predicting implicit absenteeism of domestic service staff, and compare the prediction results of the model based on basic and behavioral attributes with the model integrating psychological attributes. The results show that the prediction model integrating psychological attributes is superior to the prediction model based on basic and behavioral attributes in terms of accuracy, accuracy, recall, and F1 value, with a prediction accuracy of 93.78%, proving the superiority of the prediction model integrating psycho-logical attributes.
%K XGBoost，隐性缺勤，心理属性，机器学习<br>XGBoost
%K Implicit Absenteeism
%K Psychological Attributes
%K Machine Learning
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=78893