Author
Listed:
- Bianca Ifeoma Chigbu
- Fhulu Hastings Nekhwevha
Abstract
Through the lens of the evolutionary economic theory, this study aimed to investigate what drives the implementation of technologies in the South African automobile industry and to understand the experiences employees have had with the introduction of technologies to the sector with regard to work collaboration, safety, and job satisfaction. Using a qualitative methodology, data were collected through a semi-structured in-depth interview, which induced its information from three automotive companies with a total of 30 participants that were purposively chosen as the sample size. Findings revealed that robots and human employees work efficiently together in the automobile sector. In an attempt to minimize product imperfection due to human inconsistencies and to increase productivity, the automobile industry will adopt more technologies to meet the needs of its customers. Findings further revealed that the human-robot collaborative work experiences are negatively impacting on the job satisfaction and confidence of autoworkers and resulting in underutilized skills of the autoworkers. The recommendation is that it will be best to pair robots with human employees in ways that autoworkers’ job satisfaction and job security are not constrained. This research contributes to the ongoing study of human-machine collaborative work in the global manufacturing industry and, for the most part, to the study of labour processes and technical advances in the automotive industry worldwide.
Suggested Citation
Bianca Ifeoma Chigbu & Fhulu Hastings Nekhwevha, 2022.
"The collaborative work experience of robotics and human workers in the automobile industry in South Africa,"
African Journal of Science, Technology, Innovation and Development, Taylor & Francis Journals, vol. 14(1), pages 280-287, January.
Handle:
RePEc:taf:rajsxx:v:14:y:2022:i:1:p:280-287
DOI: 10.1080/20421338.2020.1837446
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:rajsxx:v:14:y:2022:i:1:p:280-287. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/rajs .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.