An investigation into the viability of bringing emerging technology into the DVLA as a way of streamlining the recruitment process

Irumba, Mohammed Kiibi (2022) An investigation into the viability of bringing emerging technology into the DVLA as a way of streamlining the recruitment process. Masters thesis, University of Wales Trinity Saint David.

[img]
Preview
Text
Irumba, Mohammed (2022) MBA An investigation into the viablility of bringing emerging technology.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (1MB) | Preview

Abstract

The research focuses on recruitment and section within the Driver and Vehicle Licensing Agency (DVLA), and if this process can be streamlined by using technology such as artificial intelligence for some straightforward tasks, leaving more time for Human Resources staff to focus on other more important parts of their role. The research methodology is qualitative, deductive research, undertaken by interviewing willing DVLA staff, particularly those working in Human Resources (HR). Extensive secondary research is also used to gain context into the subject of emerging technology. The main findings reveal that emerging technology such as Artificial Intelligence (AI) are not appropriate for the DVLA, and most of the staff interviewed would not want to include AI into the recruitment process, although some can see how AI might be useful with certain tasks such as filtering applications and much more as required. The conclusion is that the DVLA should not use AI at this point but should consider it for the future. It is recommended that the DVLA to continue to provide alternative options for applications for people who are uncomfortable with technology, or do not have access to it. If the DVLA can consider AI in the future, the recommendations are to limit AI use to screening applications and answering basic queries, consider the cost of AI and how it weighs up against staff costs, and to use change management theory to help guide HR staff through changing to working with AI.

Item Type: Thesis (Masters)
Subjects: H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
T Technology > T Technology (General)
Divisions: Theses and Dissertations > Masters Dissertations
Depositing User: Natalie Williams
Date Deposited: 24 Apr 2023 10:18
Last Modified: 24 Apr 2023 10:18
URI: https://repository.uwtsd.ac.uk/id/eprint/2378

Administrator Actions (login required)

Edit Item - Repository Staff Only Edit Item - Repository Staff Only