* Corresponding author. E-mail: heidaryd@aut.ac.ir (J. Heidary Dahooie) © 2013 Growing Science Ltd. All rights reserved. doi: 10.5267/j.ijiec.2013.06.003 International Journal of Industrial Engineering Computations 4 (2013) 517–534 Contents lists available at GrowingScience International Journal of Industrial Engineering Computations homepage: www.GrowingScience.com/ijiec Applying fuzzy integral for evaluating intensity of knowledge work in jobs Jalil Heidary Dahooie a* and Mohammad Reza Ghezel Arsalan b a Department of Industrial Engineering, Amirkabir University of Technology, Hafez Ave.,No.424, Tehran, Iran b Department of Industrial Engineering, School of Engineering, University of Tehran, Tehran, Iran C H R O N I C L E A B S T R A C T Article history: Received February 2 2013 Received in revised format May 28 2013 Accepted May 30 2013 Available online June 4 2013 In this article, a framework is proposed to define and identify knowledge work intensity in jobs, quantitatively. For determining the Knowledge Work Intensity Score (KWIS) of a job, it is supposed that the job comprises some tasks and KWIS of the job is determined based on knowledge intensity of these tasks. Functional Job Analysis (FJA) method is applied to determine tasks of jobs and then Task’s Knowledge Intensity Score (TKIS) is computed by using Fuzzy integral method. Besides, importance weight and time weight of tasks are determined by utilizing appropriate methods. Finally, KWIS is calculated by a formula composed of tasks’ TKISs and the weights. For evaluating applicability of the framework, it is applied to calculate KWISs of two jobs (Deputy of Finance and service, Laboratory technician). © 2013 Growing Science Ltd. All rights reserved Keywords: Knowledge work Knowledge worker Fuzzy integral Job analysis 1. Introduction In today's world, work in organizations has become complex and knowledge-intensive, considerably (Eppler et al., 1999). The growing trend towards knowledge workers in the labor market is, indeed, one of the primary features of the economy and society (Drucker, 1995; Lavoie et al, 2003; Overbeek, 2007). Measuring and increasing the productivity of knowledge workers are the biggest management challenges during the 21 st century (Drucker, 1991, 1999). In order to improve the performance of knowledge workers in a systematic manner, it is necessary to have a clear understanding of knowledge work and knowledge workers in the first place (Ramirez, 2008). Up to now, there is still no effective way to define knowledge work, which is the primary requirement of knowledge work productivity (Shi-You, 2008). Ramirez (2006) states “Some researchers (Helton, 1998; Drucker, 1999; Agarwalet al., 2011) argue that knowledge workers account for roughly 75% of the workforce. Although a lack of a clear definition of what constitutes a knowledge worker creates doubts on the reliability of that figure, we can assume that the number is high enough that, even if it is overestimated, it is significantly high.”