Izugboekwe, Chimezie Seth and Joshua, Sonia Sim and Gambo, Nasamu and Olubodun, Sijibomi Victor and Ameh, Blessing Onyemowo (2024) Artificial Intelligence and Business Security among SMEs in Abuja Metropolis. International Journal of Management Technology, 11 (3). pp. 17-41. ISSN 2055-0847(Print) ,2055-0855(Online)
![[thumbnail of Artificial Intelligence.pdf]](https://tudr.org/style/images/fileicons/text.png)
Text
Artificial Intelligence.pdf - Published Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.
Download (822kB)
Abstract
This study investigates the impact of Artificial Intelligence (AI) on business security among Small and Medium Enterprises (SMEs) in Abuja, Federal Capital Territory (FCT), Nigeria. The primary objectives are to assess the influence of AI security protocols, employee AI training, customer data privacy measures, and automated threat detection on enhancing business security. Anchored in the Socio-Technical Systems (STS) Theory, which emphasizes the interplay between social and technical elements within organizations, this research explores how these AI-driven measures collectively contribute to securing SMEs. Utilizing a cross-sectional survey design, data was collected from a representative sample of 379 employees within the Information and Communication sector, derived from an estimated population of 24,832 employees according to SMEDAN (2021). Multiple regression analysis revealed that AI security protocols, customer data privacy measures, and automated threat detection significantly enhance business security, while employee AI training showed no substantial impact. These findings underscore the necessity for integrating advanced technological measures with robust social frameworks to optimize business security. The study's results align with STS Theory, highlighting the importance of a balanced approach that incorporates both technical and social components for effective security management in SMEs.
| Item Type: | Article |
|---|---|
| Subjects: | T Technology > T Technology (General) |
| Depositing User: | Professor Mark T. Owen |
| Date Deposited: | 23 Jul 2024 20:33 |
| Last Modified: | 23 Jul 2024 20:33 |
| URI: | https://tudr.org/id/eprint/3206 |
Actions (login required)
- View Item
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)