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Correlations of workload with personality traits, perceived negative and positive behaviors, and organizational factors: A meta-analysis
期刊文章   同儕審查

Correlations of workload with personality traits, perceived negative and positive behaviors, and organizational factors: A meta-analysis

Work (Reading, Mass.), 卷.83(1), 頁碼.227-241
01/01/2026
PMID: 40808613
Web of Science ID: WOS:001549900900001

摘要

Burnout, Professional - psychology Humans Job Satisfaction Occupational Stress - psychology Organizational Culture Perception Personality Workload - psychology Workload - standards Workplace - psychology Workplace - standards Taiwan
BackgroundExcessive workload, a key workplace stressor, occurs when job demands exceed employees' physical and mental capacities because of limited resources and time.ObjectiveThrough a meta-analysis, we examined the correlations of workload with personality traits, perceived negative and positive behaviors, and organizational factors.MethodsWe conducted a meta-analysis of 106 Taiwanese studies on workload published from 1999 to 2022.ResultsWorkload was positively correlated with personality traits and organizational factors; individuals with certain traits or organizational environments experienced elevated workload. Furthermore, workload was positively correlated with perceived negative behaviors, such as job stress and burnout, but negatively correlated with perceived positive behaviors, such as job satisfaction and well-being. These results highlight the complex and multidimensional effects of workload on employees and organizations.ConclusionsThis study offers a comprehensive econometric analysis of workload in the Taiwanese context and underscores the importance of effective workload management in enhancing employee well-being and organizational efficiency. Practical recommendations include optimizing task allocation on the basis of individual personality traits, implementing supportive organizational policies, and fostering a positive work culture. Future studies should explore additional moderating variables and compare domestic and international data to gain deeper insights into workload dynamics.

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