Adoption of artificial intelligence (AI) and job satisfaction in South African SMMEs
| dc.contributor.advisor | Werbeloff, Merle | |
| dc.contributor.email | ichelp@gibs.co.za | |
| dc.contributor.postgraduate | Pasipanodya, Tawanda | |
| dc.date.accessioned | 2026-03-23T09:36:01Z | |
| dc.date.available | 2026-03-23T09:36:01Z | |
| dc.date.created | 2026-05-05 | |
| dc.date.issued | 2025 | |
| dc.description | Mini Dissertation (MBA)--University of Pretoria, 2025. | |
| dc.description.abstract | The study investigates the relationship between the theoretically derived determinants of Artificial Intelligence (AI) adoption Perceived ease of use (PEOU) and Perceived Usefulness (PU) and employee Job satisfaction within South African Small, Medium and Micro Enterprises (SMMEs). While Artificial Intelligence (AI) is globally recognised as a driver of efficiency and innovation, its human and organisational implications in resource-constrained environments remain underexplored. Grounded in the Technology Acceptance Model (TAM) and extended by the inclusion of Perceived Organisational Support (POS), this study examines whether Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) the core determinants of technology acceptance (or adoption) directly or indirectly, influence employee job satisfaction in South African Small, Medium and Micro Enterprises (SMMEs), and whether POS moderates these relationships. A quantitative, cross-sectional design was employed, gathering survey data from employees across diverse SMMEs utilising AI-driven systems. Data were analysed using regression-based and moderation techniques. The findings suggest that having a supportive organisational climate containing training, communication and recognition, leads to employees being more inclined to engage with AI positively, which elevates job satisfaction with their work and contributes to organisational resilience. The study contributes theoretically by expanding TAM with a different perspective of psychosocial factors, and practically, by providing ways for SMME managers to implement AI responsibly in developing economies. | |
| dc.description.availability | Unrestricted | |
| dc.description.degree | MBA | |
| dc.description.department | Gordon Institute of Business Science (GIBS) | |
| dc.description.faculty | Gordon Institute of Business Science (GIBS) | |
| dc.description.sdg | SDG-09: Industry, innovation and infrastructure | |
| dc.identifier.citation | * | |
| dc.identifier.other | A2025 | |
| dc.identifier.uri | http://hdl.handle.net/2263/109166 | |
| dc.language.iso | en | |
| dc.publisher | University of Pretoria | |
| dc.rights | © 2025 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. | |
| dc.subject | UCTD | |
| dc.subject | Artificial Intelligence | |
| dc.subject | Technology acceptance model | |
| dc.subject | Perceived organisational support | |
| dc.subject | Job satisfaction | |
| dc.subject | SMMEs | |
| dc.subject | South Africa | |
| dc.title | Adoption of artificial intelligence (AI) and job satisfaction in South African SMMEs | |
| dc.type | Mini Dissertation |
