An evaluation of contemporary leadership in industry 4.0: a survey of IT project management practitioners in the Gauteng Province

Abstract

Technological advancements that bridge the physical and biological worlds have resulted from the fourth industrial revolution (4IR), radically altering entire sectors, pushing the boundaries and opening up new avenues for business. Therefore, 4IR is becoming a more relevant subject in project management. Social inclusion and economic development are aided significantly by information technology (IT). Project management practitioners should re-evaluate their approach to project management considering the gradual but steady advancements in digital technology and innovation. To benefit fully from 4IR, project managers should adopt a strong leadership style that is different from the traditional controlling role. Project delivery is impacted by an organisation’s leadership quality. Situation-based leadership approaches are required, considering the revolution in technology and the millennial generation. The important role that leadership plays and contributes to project management is examined in this study. This study examines the increasing significance of 4IR in adding value to projects by closely examining the methods that project managers can use to encourage agility in projects. This study aimed to investigate how project managers can improve their leadership skills to handle 4IR and digital transformation, using the quantitative paradigm. The managers in Gauteng province IT industry, including first-line, middle- and top managers, were the target population of the study. The researcher used a convenience sampling strategy, which involved gathering samples from a group of people who were readily available or contactable. The sample size in this study was 150 participants. To gather consistent data from a sizeable population, the study made use of semi-structured questionnaires. The researcher entered the data from the semi-structured questionnaire into a separate spreadsheet and cross-referenced it with the data from the SPSS 27 spreadsheet to ensure accuracy. Before using regression analysis to further analyse the data, descriptive statistics for each variable, such as frequency, means and standard deviations, were applied.