Liu Xin, Qin Nan, Wei Xiaochong
School of Public Administration and Policy, Renmin University of China, Beijing 100872, China.
Behav Sci (Basel). 2025 Feb 26;15(3):276. doi: 10.3390/bs15030276.
In today's highly competitive and rapidly evolving work environment, employee job satisfaction is a crucial indicator of organizational success and employee well-being. Utilizing the Bayesian rule set (BRS) algorithm, this study systematically explored how multiple variables, such as sleep quality, autonomy, and working hours, interact to influence job satisfaction. Based on an analysis of 618 data points from the CGSS database, we found that a single variable alone is insufficient to significantly improve job satisfaction: instead, a combination of multiple factors can substantially enhance it. Specifically, individuals who are older, have medium to high levels of sleep quality, and work fewer hours report higher job satisfaction. Similarly, individuals with medium to high health levels, high autonomy, and shorter working hours also exhibit high job satisfaction. By employing a multivariable combination analysis approach, this study reveals the complex pathways that affect job satisfaction, providing new theoretical insights and practical guidance for organizations seeking to improve employee satisfaction.
在当今竞争激烈且快速发展的工作环境中，员工工作满意度是组织成功和员工福祉的关键指标。本研究运用贝叶斯规则集（BRS）算法，系统地探究了睡眠质量、自主性和工作时长等多个变量如何相互作用以影响工作满意度。基于对中国综合社会调查（CGSS）数据库中618个数据点的分析，我们发现单一变量不足以显著提高工作满意度：相反，多个因素的组合可大幅提升工作满意度。具体而言，年龄较大、睡眠质量处于中高水平且工作时长较短的个体报告的工作满意度更高。同样，健康水平处于中高水平、自主性高且工作时长较短的个体也表现出较高的工作满意度。通过采用多变量组合分析方法，本研究揭示了影响工作满意度的复杂路径，为寻求提高员工满意度的组织提供了新的理论见解和实践指导。
