Psychomotor ability and learning potential as predictors of driver and machine operator performance in a road construction company

Abstract

The changing nature of work and its competitive characteristics are global phenomena and are mainly fuelled by ongoing technological advancement. This creates unique challenges for talent attraction and the retention of high performing individuals. In addition, the global workforce is becoming more diverse due to demographic, societal and cultural changes and companies are placing greater demands on employee competency and performance. Managing the human factor as a strategic asset in organisations remains a primary challenge in securing a competitive advantage. The road construction industry in South Africa is no different. There is growing competition between civil engineering contractors to secure tenders and to maximise profitability. This is only possible with a sufficient and sustainable labour force. Valid selection processes are therefore required to ensure that the most productive individuals are selected for the most suitable jobs. Reliable and valid performance predictors will assist employers in making appropriate selection decisions. Selecting high performing individuals will support and enhance overall organisational performance. ix In this study the investigation focused on whether psychomotor ability and learning potential are statistically significant predictors of work performance - with specific reference to drivers and machine operators in a road construction company. A quantitative approach was followed to investigate the relationships between variables, or then the prediction of one dependent variable (driver and machine operator performance) by means of two independent variables (psychomotor ability and learning potential). Results from the study did not indicate any statistically significant relationships between the variables. Only scientifically validated assessment instruments were used in the study - which means the findings led to a renewed focus on the importance of performance measurement and the psychometric quality (reliability and validity) of performance data.