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Job Classification for the Purpose of Making Optimal Decisions Concerning Management Control
Author(s) -
PerisOrtiz Marta,
RuedaArmengot Carlos,
Pechuán Ignacio Gil
Publication year - 2012
Publication title -
canadian journal of administrative sciences / revue canadienne des sciences de l'administration
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.347
H-Index - 48
eISSN - 1936-4490
pISSN - 0825-0383
DOI - 10.1002/cjas.1223
Subject(s) - macro , micro level , control (management) , multidisciplinary approach , human resources , macro level , human resource management , computer science , contrast (vision) , fuzzy logic , simple (philosophy) , operations research , knowledge management , operations management , management science , artificial intelligence , management , mathematics , economics , microeconomics , sociology , economic system , social science , philosophy , epistemology , economic impact analysis , programming language
The job classification literature has been dominated by a micro‐level orientation reflective of the needs of specialists in human resources. In contrast, we take a multidisciplinary approach involving both micro‐ and macro‐level variables to propose a relatively simple system comprised of four job classes. Our model isintended to inform management as to the type and mix of controls (e.g., centralization, formalization, and various human resources policies) that could be applied to different job types to optimally promote the overall goals of the organization. Fuzzy numbers analysis is employed to illustrate the application of the system using four jobs in a car dealership in Spain. Copyright © 2012 ASAC. Published by John Wiley & Sons, Ltd.