Discriminating relative workload level by data envelopment analysis

Shiow-Yun Chang, Tien Hui Chen

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)773-778
Number of pages6
JournalInternational Journal of Industrial Ergonomics
Volume36
Issue number9
DOIs
Publication statusPublished - 2006 Sep 1

All Science Journal Classification (ASJC) codes

  • Human Factors and Ergonomics
  • Public Health, Environmental and Occupational Health

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