Discriminating relative workload level by data envelopment analysis

Shiow-Yun Chang, Tien Hui Chen

研究成果: Article同行評審

8 引文 斯高帕斯(Scopus)

摘要

The study investigates a method of the extended data envelopment analysis (DEA) model to discriminate relative workload level within a group of employees, in which subjective subscales are introduced. The model chooses the most favorable set of weights under the constraint for each employee. Hence, the workload scores calculated by the extended DEA model are more considerate and friendly to employees than the method in which weights are determined by individual judgment. The proposed approach eliminates the need to specify a priori weights for each assessing factor, and it can discriminate relative workload among employees and help managers making decisions with regard to suitable human resource practices to strengthen employee capability and achieve higher performance. Relevance to industry: Assessing workload is an important issue in the management and health of employees. A long-term heavy workload can affect an employee's physical or mental health, performance, or productivity. A heavy workload is a component of job stress and has a negative impact on turnover. This loss of employees and their output leads to higher costs for an organization. After understanding the relative overall workload level within a group of employees, the manager can reassign tasks to level the workload and make informed decisions about human resource practices, such as in-service education or job rotation to strengthen employee capability and achieve higher performance.

原文English
頁(從 - 到)773-778
頁數6
期刊International Journal of Industrial Ergonomics
36
發行號9
DOIs
出版狀態Published - 2006 九月 1

All Science Journal Classification (ASJC) codes

  • 人因工程和人體工學
  • 公共衛生、環境和職業健康

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