員工們經常主觀地認定他們的工作負荷重，但是在一工作團隊中，究竟哪些人的相對工作負荷較重？哪些人的相對工作負荷較輕呢？在以往的研究中，很難找到一種能令每位受評者皆信服的衡量相對工作負荷方法。本文應用資料包絡分析（data envelopment analysis，DEA）模式，來區別一個組織中員工的相對工作負荷水準。此方法在包絡限制條件下，每位員工可以選擇對他們最有利的一組權數，來計算他們的工作負荷分數。每位受評者均以對其最有利的工作負荷分數作評比，即使部分受評者被評為相對工作負荷輕，他們也不能否認此方法的客觀性。因為在包絡限制條件下，他們找不到可以再增加他們工作負荷分數的另一組權數。
應用資料包絡分析模式區別一團體成員之相對工作負荷水準時，經常會歸類出多個相對工作負荷水準重的受評者。在公司資源有限的情況下，改善工作負荷水準的措施，應針對工作負荷水準最重的受評員工優先著手。故本研究採用交叉評估的方式，並援引差額分析法，針對工作負荷水準較重的員工作進一步排序。再透過對偶問題的特性，對工作負荷較重者，在多構面之工作負荷評估效標中，界定凸顯效標（outstanding subscale）。決策者針對凸顯效標著手加以改善之，可使改善工作負荷水準的措施，達到事半功倍之效。
本研究成果亦可以應用於人力資源管理實務，因為人力資源是一組織企業中最重要的資產之一。管理者能否留住高績效人才已成為該組織企業是否仍保有核心競爭力的重要因素。因為工作負荷水準會影響員工的離職與身體健康、工作績效和生產力。所以，本文透過分析每位員工的工作負荷與績效水準，以瞭解每位員工的績效水準與工作負荷之情況，可協助決策者作為實施人力資源管理措施之參考。

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.

中文文獻

1. 李再長，黃雪玲，李永輝，王明揚，人因工程，華泰，台北，頁288（2005）。
2. 高強，黃旭男，Sueyoshi, T.，管理績效評估—資料包絡分析法，華泰，台北，頁20（2003）。
3. 薄喬萍，績效評估之資料包絡分析法，五南，台北，頁59（2005）。

英文文獻

1. Allen, D. G. and Griffeth, R. W., Job performance and turnover: a review and integrative multi-route model, Human Resource Management Review 9, 525-548 (1999).
2. Amit, R. and Belcourt, M., Human resources management processes: A value-creating source of competitive advantage, European management Journal 7, 174-181 (1999).
3. Bartel, A. P., Productivity gains from the implementation of employee training programs, Industrial Relationships 33, 411-425 (1994).
4. Boussofiane A, Dyson, R.G. and Thanassoulis, E., Applied data envelopment analysis, European Journal of Operational Research, 52, 1-15 (1991).
5. Charnes, A., Cooper, W. W., Huang, Z. M. and Sun, D. B., Polyhedral cone-ratio DEA models with an illustrative application to large commercial banks, Journal of Econometrics 46, 73-91 (1990).
6. Charnes, A., Cooper, W. W., Lewin, A. Y. and L.M. Seiford, Data Envelopment Analysis: Theory, Methodology and Application, Boston: Kluwer Academic Publishers (1994).
7. Charnes, A., Cooper, W. W. and Rhodes, E., Measuring the efficiency of decision making units, European Journal of Operational Research 2, 429-444 (1978).
8. Charnes, A., Cooper, W. W. and Rhodes, E., Short communication: measuring the efficiency of decision making units, European Journal of Operational Research 3, 339 (1979).
9. Charnes, A., Cooper, W. W. , Wei, Q. L. and Huang, Z. M., Cone ratio data envelopment analysis and multi-objective programming, International Journal of Systems Science 20, 1099-1118 (1989).
10.Chen, L. H., Liaw, S. Y. and Lee, T. Z., Using an HRM pattern approach to examine the productivity of manufacturing firm – an empirical study, International Journal of Manpower 24, 299-320 (2003).
11.Cooper, G.. and Harper, R., The use of pilot ratings in the evaluation of aircraft handling qualities, NASA Ames Technical Report, NASA TN-D-5153, NASA Ames Research Center, California (1969).
12.Doyle, J., Multifactor choice for the lazy decision maker: let the alternatives decide, Organizational Behavior and Human Decision Process 62, 87-100 (1995).
13.Doyle, J. and R. Green, Efficiency and cross-efficiency in DEA: derivations, meanings and uses, Journal of the Operational Research Society 45, 567-578 (1994).
14.Dyson, R.G., Allen, R., Camanho, V. V., Podinovski, V. V. and Sarrico, C. S., Pitfalls and protocols in DEA, European Journal of Operational Research 132, 245-259 (2001).
15.Dyson, R.G. and Thanassoulis, E., Reducing weight flexibility in data envelopment analysis, Journal of the Operational Research Society 39, 563-576 (1988).
16.Farrell, M. J., The measurement of productive efficiency. Journal of Royal Statistical Society, Series A 120, 253-281 (1957).
17.Firth, L., Mellor, D. J., Moore, K. A. and Loquet, C., How can managers reduce employee intention to quit? Journal of Managerial Psychology 19, 170-187 (2004).
18.Franco, L. M., Bennett, S. and Kanfer, R., Health sector reform and public sector health worker motivation: a conceptual framework, Social Science & Medicine 54, 1255-1266 (2002).
19.Frayne, C. A. and Geringer, J. M., Self-management training for improving job performance: A field experiment involving salespeople, Journal of Applied Psychology 85, 361-372 (2000).
20.Golany B., An interactive MOLP procedure for the extension of DEA to effectiveness analysis. Journal of the Operational Research Society 39, 725-734 (1988).
21.Green, R. and Doyle, J., Improving discernment in DEA using profiling: a comment, Omega 24, 365-366 (1995).
22.Hart, S. G.., Theory and Measurement of Human Workload. In J. Zeidner (ED.). Human Productivity Enhancement: Training and Human Factors in Systems Design, Praeger, New York, 396-456 (1986).
23.Hart, S. G. and Staveland, L. E., Development of the NASA task load index (TLX): results of experimental and theoretical research. In: Hancoke, P. A. Meshkati, N. (Eds.), Human Workload, Amsterdam, North Holland, 138-183 (1988).
24.Helmer, F. T. and McKnight, P., Management strategies to minimize nursing turnover, Health Care Management Review 14, 73-80 (1989).
25.Im, J. H., Hartman, S. J. and Bondi, P. J., How do JIT systems affect human resource management? Production and Inventory Management Journal 35, 1-4 (1994).
26.Iverson, R. D. and Pullman, J. A., Determinants of voluntary turnover and layoffs in an environment of repeated downsizing following a merger: an event history analysis, Journal of Management 26, 977-1003 (2000).
27.Jolma, D. J., Relationship between nursing work load and turnover, Nursing Economics 8, 110-114 (1990).
28.Jung, H. S. and Jung, H., Establishment of overall workload assessment technique for various task and workplaces. International Journal of Industrial Ergonomics 28, 341-353 (2001).
29.Kantowitz, B. H., Workload. In P. A. Hancock (Ed.). Human Factors Psychology, Amsterdam: Elsevier (1988).
30.Kao C. and Hung, H. T., Data envelopment analysis with common weights: the compromise solution approach, Journal of the Operational Research Society 56, 1196-1203 (2005).
31.Kawada, T. and Ooya, M., Workload and health complaints in overtime workers: A survey, Archives of Medical Research 36, 594-597 (2005).
32.Lee, H., Song, R., Cho, Y. S., Lee, G. Z. and Daly, B., A comprehensive model for predicting burnout in Korean nurses, Journal of Advanced Nursing 44,534-545 (2003).
33.Li, X. and Reeves, G. R., A multiple criteria approach to data envelopment analysis, European Journal of Operational Research 115, 507-517 (1999).
34.LINDO Systems Inc., LINGO User’s Guide, LINDO Systems Inc., Chicago (1999).
35.Matthews, R., Legg, S. and Charlton, S., The effect of cell phone type on drivers subjective workload during concurrent driving and conversing, Accident Analysis and Prevention 35, 451-457 (2003).
36.Mayes, D. K., Sims, V. K. and Koonce, J. M., Comprehension and workload differences for VDT and paper-based reading, International Journal of Industrial Ergonomics 28, 367-378 (2001).
37.Miyake, S., Multivariate workload evaluation combining physiological and subjective measure, International Journal of Psychophysiology 40, 233-238 (2001).
38.Nygren, T. E., Psychometric properties of subjective workload measurement techniques for their use in the assessment of perceived workload, Human Factors 33, 17-31 (1991).
39.Parasuraman, S., Nursing turnover: an integrated model, Research in Nursing and Health 12, 267-277 (1989).
40.Pfeffer, J., Seven practices of successful organizations, California Management Review 40, 96-124 (1998).
41.Reid, G., Shingledecker, C., and Eggemeier, F., Application of conjoint measurement to workload scale development, Proceedings of the Human Factors Society-25th Annual Meeting, Human Factors Society, CA, 522-526 (1981).
42.Retzlaff-Roberts, D.L., Relating discriminant analysis and DEA to one another. Computers and Operations Research 4, 311-322 (1996).
43.Roll, Y., Cook, W. D. and Golany, B., Controlling factor weights in data envelopment analysis, IIE Transactions 23, 2-9 (1991).
44.Roll, Y. and Golany, B., Alternate methods of treating factor weights in DEA, Omega 21, 99-109 (1993).
45.Sato, N., Kamada, T., Miyake, S., Kumashiro, M. and Kume, Y., Subjective workload in Type A woman. International Journal of Industrial Ergonomics 24, 331-336 (1999).
46.Seiford, L. M., Data envelopment analysis: the evolution of state of the art (1978-1995), Journal of Productivity Analysis 7, 99-137 (1996).
47.Sexton, T. R., Silkman, R. H. and Hogan, A. J., Data Envelopment Analysis: Critique and Extensions. In: Silkman, R.H. (Ed.), Measuring Efficiency: An Assessment of Data Envelopment Analysis. Jossey-Bass, San Francisco, CA (1986).
48.Sinuany-Stern, Z. and Friedman, L., DEA and the discriminant analysis of ratios for ranking units, European Journal of Operational Research 111, 470-478 (1998).
49.Sinuany-Stern, Z., Mehrez, A. and Barboy, A., Academic departments efficiency in DEA. Computers and Operations Research 21, 543-556 (1994).
50.Sinuany-Stern Z., Mehrez, A. and Hada, Y., An AHP/DEA methodology for ranking decision making units, International Transactions in Operational Research 7, 109-124 (2000).
51.Smith, P., Data envelopment analysis applied to financial statements, Omega 18, 131-38 (1990).
52.SPSS Inc. (2000), SPSS 1999 – Statistic modeling for windows 95/98/2000/NT, SPSS Inc., Chicago.
53.Stewart, T. J., Data envelopment analysis and multiple criteria decision making: a response, Omega 22, 205-206 (1994).
54.Sueyoshi, T., DEA-discriminant analysis in the view of goal programming, European Journal of Operational Research 115, 564-582 (1999).
55.Sueyoshi, T., Mixed integer programming approach of extended DEA-discriminant analysis, European Journal of Operational Research 152, 45-55 (2004).
56.Talluri, S. and Yoon, K. P., A cone-ratio DEA approach for AMT justification, International Journal of Production Economics 66, 119-129 (2000).
57.Tasang, P., Wilson, G. F. Workload. In: Salvendy, G. (ed.). Handbook of Human Factors and Ergonomics. Wiley, New York, 417-449 (1997).
58.Thanassoulis, E., Boussofiance, A. and Dyson, R.G., Exploring output quality targets in the provision of perinatal care in England using DEA, European Journal of Operational Research 60, 588-608 (1995).
59.Thompson, R. G., Langemeier, L. N., Lee, C. T. and Thrall, R. M., The role of multiplier bounds in efficiency analysis with application to Kansas farming, Journal of Econometrics 46, 93-108 (1990).
60.Tsang, P. S. and Vidulich, M. A., The roles of immediacy and redundancy in relative subjective workload assessment, Human Factors 36, 503-513 (1994).
61.Veltman, J. A. and Gaillard, A. W. K., Physiological indices of workload in a simulated flight task, Biological Psychology 42, 323-42 (1996).
62.Vidulich, M. A. and Wickens, C. D., Causes of dissociation between subjective workload measures and performance: caveats for the use of subjective assessments, Applied Ergonomics 17, 291-296 (1986).
63.Weiner, J. S., The measurement of human workload. Ergonomics 25: 953-965 (1982).
64.Wickens, C. D., Engineering Psychology and Human Performance, 2nd Edition, Harper Collins Publishers, New York, 560 (1992).
65.Wierwille, W. W. and Casali, J. G., A validated rating scale for global workload measurement applications, Proceedings of the Human Factors Society-27th Annual Meeting, 129-133 (1983).
66.Wong, Y. H. B. and Beasley, J. E., Restricting weight flexibility in data envelopment analysis, Journal of the Operational Research Society 41, 829-835 (1990).
67.Wright, P. M. and Boswell, W. R., Desegregating HRM: A review and synthesis of micro and macro human resource management research, Journal of Management 28, 247-276 (2002).
68.Wu, X. J., Zhang, X. J. and Gao, F. L., A study on the relationship between nurses’ intention to quit and work stress, Chinese Journal of Nursing 35, 197-99 (2000).
69.Yin, J. C. T. and Yang, K. P. A., Nursing turnover in Taiwan: a meta-analysis of related factors, International Journal of Nursing Studies 39, 573-81 (2002).