Abstract
Background: Over the past 30 years of democracy, South Africa has undergone a significant technological transformation, with an increasing integration of artificial intelligence (AI) in government operations. Local governments remain reliant on manual systems, resulting in inadequate service delivery, community protests, and corruption. Aim: This study assessed municipal employees’ perceptions of using AI in their work, highlighting a research gap in reliance on manual systems that contribute to poor service delivery, community unrest, and corruption. The findings underscore the need for electronic management systems and a deeper understanding of employee perceptions of AI. Setting: The study was conducted in the Buffalo City Metropolitan Municipality (BCMM) in the Eastern Cape Province of South Africa. Method: A quantitative, hypothetical-deductive study employed structured questionnaires to assess the perceptions of 255 employees in BCMM’s corporate services and finance divisions, utilising a cross-sectional, purposive sampling design. Results: Employees generally view AI positively, recognising its advantages. Structural Equation Modelling results indicate that a low perceived ease of use hinders performance, while positive attitudes and perceived usefulness enhance it, highlighting the challenges in experience and capacity-building for effective AI use. Conclusion: Municipalities can enhance employee performance and service delivery by implementing user-friendly systems and cultivating a positive attitude towards AI. Furthermore, strategic investment in employee retention and institutional capacity building is crucial for the effective and efficient adoption of AI. Contribution: This study contributes to the limited literature on electronic management systems and provides insights to improve employee perceptions and adoption of AI within municipal contexts.
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Bunyula, L., Lungisa, S., & Mathentamo, Q. (2025). Municipal employee perceptions on the use of artificial intelligence to perform their work. South African Journal of Economic and Management Sciences, 28(1). https://doi.org/10.4102/sajems.v28i1.6203
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