Review of Knowledge Economy

Published by: PAK Publishing Group
Online ISSN: 2409-9449
Print ISSN: 2412-3668
Total Citation: 3

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A Model on Knowledge Workers Performance Evaluation

Pages: 1-13
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A Model on Knowledge Workers Performance Evaluation

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DOI: 10.18488/journal.67/2016.3.1/

Mahmoud Dehghan Nayeri , Malihe Rostami

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  1. Agarwall, S.C., 1980. A study of productivity measures for improving benefit-cost ratios of operating organizations. International Journal of Production Research, 18(1): 83-102.
  2. Bok, H.S. and K.S. Raman, 2000. Software engineering productivity measurement using function points: A case study. Journal of Information Technology, 15(1): 79-101.
  3. Bumbarger, W.B., 1984. Operation function analysis: Do it yourself productivity improvement. New York: Van Nostrand Reinhold, NY.
  4. Davenport, T., 2002. Can you boost knowledge work’s impact on the bottom line? Management Update, 7(11): 3-5.
  5. Davenport, T. and L. Prusak, 2000. Working knowledge: How organizations manage what they know. Boston: Harvard Business School Press.
  6. Davis, T., 1991. Information technology and white-collar productivity. Academy of Management Executive, 5(1): 55-68.
  7. Dove, R., 1998. The knowledge worker. Automotive Manufacturing & Production, 110(6): 26-28.
  8. Dreger, B., 1989. Function point analysis. Englewood Cliffs, NJ.: Prentice-Hall.
  9. Drucker, P., 1959. The landmarks of tomorrow. New York: Harper & Row.
  10. Drucker, P., 1968. The practice of management. London: Pan.
  11. Drucker, P., 1988. The coming of the new organization. Harvard Business Review, 66(1): 45-53.
  12. Drucker, P., 1994. Adventures of a bystander. New Brunswick: Transaction Publishers.
  13. Drucker, P., 1999. Knowledge-worker productivity: The biggest challenge. California Management Review, 41(2): 79-94. DOI 10.2307/41165987.
  14. Ebrahim, S., 2003. Towards a TQM-driven HR performance evaluation: An empirical study. Employee Relations, 25(4): 347-370. DOI
  15. Ghorpade, J., M.M. Chen and J. Caggiano, 1995. Creating quality-driven performance appraisal systems. Academy of Management Executive, 9(1): 32-40.
  16. Green, R.G. and M. Secret, 1996. Publishing by social work scholars in social work and non-social work journals. Social Work Research, 20: 31-42.
  17. Helton, R., 1988. The ‘best work’ method of knowledge worker assessment. Industrial Management, 30(5): 19-22.
  18. Horvath, D., 2001. Knowledge worker definition. Search CRM Technical Dictionary by Tech Target. Available from [Accessed 2002].
  19. Huang, S., J. Dismukes, J. Shi, Q. Su, M. Razzak, R. Bodhale and D.E. Robinson, 2003. Manufacturing productivity improvement using effectiveness metrics and simulation analysis. International Journal of Production Research, 41(3): 500-513. DOI
  20. Josu, T., S. Udomsak and P. Kongkiti, 2006. A proposed white-collar workforce performance measurement framework. Industrial Management & Data Systems, 106(5): 644-662.
  21. Montgomery, D., 1997. Introduction to statistical quality control. New York: Wiley.
  22. Mundel, M.E., 1975. Measuring and enhancing the productivity of service and government organizations. Tokyo: Asian Productivity Organization.
  23. Naisbitt, J., 1982. Megatrends. New York: Warner Books.
  24. Nickols, F., 2000. What is’ in the world of work and working: Some implications of the shift to knowledge work. Butterworth-Heinemann Yearbook of Knowledge Management,: 1-7.
  25. Overby, M., 1983. Technique for group time measurement simplifies indirect labor observations. Industrial Engineering, 15(7): 34-40.
  26. Pepitone, J.S., 2002. A case for humaneering. IIE Solutions, 34(5): 39-44.
  27. Ram?´rez, Y.W. and D.A. Nembhard, 2004. Measuring knowledge worker productivity. Journal of Intellectual Capital, 5(4): 602-628.
  28. Ray, P.K. and S. Sahu, 1989. The measurement and evaluation of white-collar productivity. International Journal of Operations & Production Management, 9(4): 28-48.
  29. Salleh, Y. and G. Wee-Keart, 2002. Manageing human resource toward achieving knowledge management. Journal of Knowledge Management, 6(5): 457-468.
  30. Schroeder, R., J. Anderson and G. Scudder, 1985. Measurement of white collar productivity. International Journal of Operations & Production Management, 5(2): 25-34.
  31. Sink, S., 1985. Productivity management: Planning, measurement and evaluation. Control and improvement. New York: John Wiley & Sons.
  32. Soliman, F. and K. Spooner, 2000. Strategies for implementing knowledge management: Role of human resource management. Journal of Knowledge Management, 4(4): 337-345.
  33. Soltani, E.J., R.B. Gennard, M. Vander and T. Williams, 2004. HR performance evaluation in the context of TQM a review of the literature. International Journal of Quality & Reliability Management, 21(4): 377-396.
  34. Takala, J., U. Suwansaranyu and K. Phusavat, 2006. A proposed white-collar workforce performance measurement framework. Industrial Management & Data Systems, 106(5): 644-662. DOI 10.1108/02635570610666421.
  35. Thomas, B.E. and J.P. Baron, 1994. Evaluating knowledge worker productivity: Literature review. Interim Report, No. FF-94/27, USACERL,1-27.
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(2016). A Model on Knowledge Workers Performance Evaluation. Review of Knowledge Economy, 3(1): 1-13. DOI: 10.18488/journal.67/2016.3.1/
The economic structure of society is constantly changing, whereas companies were dependent on manual labor in the past, today they are dependent on the knowledge worker. Since that knowledge workers make up two-thirds of the labor force, the focus of strategic plans nowadays are to improve their efficiency. Currently, there is not any adopted method for evaluating the performance of the knowledge workers. But the fundamental change in the nature of the labor force necessitates this issue. This paper aims to develop a model for evaluating the performance of knowledge workers in an Iranian research center using structural equation modeling, therefore this is a descriptive study based on co relational and regression analysis. Results proved that among the various appraisal criteria, innovation is the most important and significant factor which is equal to 4.35 in average from 5 and collaborate on research projects as well as attending seminars are the least important criteria according to the respondents point of view.

Contribution/ Originality
This study originates a new model for knowledge worker's performance evaluation for an Iranian research center. The proposed model investigates the researcher performance through four aspects including executive, educational, scientific and research works. This model contributes in the existing literature of KWs performance modeling.