The Assessment and Improvement of Mental Workload by Data Envelopment Analysis

• Main Authors: Tien-Hui Chen, 陳天惠
• Other Authors: Shiow-yun Chang
• Format: Others
• Language: zh-TW
• Published: 2006
• Online Access: http://ndltd.ncl.edu.tw/handle/61573382918453445705

博士 === 國立成功大學 === 工業管理科學系碩博士班 === 95 === Employees typically claim that their workloads are heavy and most firmly believe that there are no fair and equitable measures to evaluate how heavy a workload they are carrying. This study extends the data envelopment analysis (DEA) to the discrimination of relative workload among employees. The merits of DEA methodology are that the weights of the subscales are not assigned in advance and that it assigns all employees the most favorable weights in calculating their overall workload scores. All employees should accept the results of the assessment since they cannot find any other set of weights that gives them higher workload scores under the envelopment constraints. Therefore, employees cannot refute the objectivity of the DEA approach, even though their weighted overall workload score may indicate that, contrary to their subjective impression, they do not have a heavy workload. The characteristic of DEA allows individual employee to select the most favorable weights of subscales in calculating his/her workload score. However, this flexibility generally classifies many employees as heavy workloads. Due to the restrictions of resources, a company may not reduce all the workloads of the relative heavy workload employees at the same time, so that this study applies the slack analysis based on the peer-evaluation for further ranking of employees’ workloads. Moreover, for a multidimensional workload assessing approach, this study identifies the outstanding subscale for each heavy workload employee based on the characteristic of dual problem to improve his/her workload level. The proposed approach can be utilized to aid the manager in the decision making of human resource management (HRM) practices. Human resources are one of a firm’s most important assets. The ability to attract and retain talent is rapidly becoming one of the core competences of high performance organizations. Because heavy workload can influence an employee turnover and/or affect an employee’s physical or mental health, performance, or productivity, this study suggests analyzing the scatter diagram of workload score and performance to understand the working situation of each employee. Then the decision maker can apply appropriate HRM practices in retaining high performers and strengthening the capability of employees.