by Chandon Bezuidenhout
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 work. The projected change in workforce structures is higher 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.
Supervisor: Prof. Albert Wöcke
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