Significant advancement in technologies such as artificial intelligence, machine-learning,
and robotics has sparked broad debate amongst economists, futurists, and current
business leaders regarding the future of jobs. The purpose of this research was to
determine the impact of technological change on jobs and workforce structures. The
study involved a structured collection, classification, and analysis of secondary data. It
aimed to (i) determine a relationship between futures and labour economics literature,
(ii) identify occupational groups with higher susceptibility to job automation, and (iii)
project changes in workforce structure for various industries.
This study found that there is alignment between predicted probabilities of job automation
and parameters of task routineness and task complexity from the routine-task-biased
and complex-task biased technological change models. Routine-simple occupations are
more susceptible to job automation, followed closely by nonroutine-simple occupations.
Complex occupations are least susceptible. Stratum I occupations were more
susceptible to job automation than occupations in higher strata of workThe projected
change in workforce structures is highest for large hierarchical industries such as
machine bureaucracies and divisionalised forms (Type 1 and 2 industries).
Technological change will bring about both productivity improvements and technological
anxiety. Business in affected industries must develop appropriate innovation and
workforce strategies to manage this disruption. / Mini Dissertation (MBA)--University of Pretoria, 2017. / Gordon Institute of Business Science (GIBS) / MBA / Unrestricted
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:up/oai:repository.up.ac.za:2263/64525 |
| Date | January 2017 |
| Creators | Bezuidenhout, Chandon |
| Contributors | Wocke, Albert, ichelp@gibs.co.za |
| Publisher | University of Pretoria |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Mini Dissertation |
| Rights | © 2018 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
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