Modelling the Attitudes Regarding Acceptance and Use of AI-driven Decision-Making by Business Managers: A Narrative Review
Bosede Olatomi Ige ORCID ↗
International Journal of Research in Business and Social Science · 2026 · Vol. 15 / Issue 6 · 136-148
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Abstract
Artificial Intelligence (AI) is increasingly reshaping strategic decision-making by enabling predictive analytics, pattern recognition, and organizational responsiveness. While global evidence highlights AI’s transformative potential, research on managerial acceptance remains uneven, particularly in emerging economies such as South Africa. This gap is essential, as socio-economic and organizational conditions strongly influence adoption outcomes. This narrative literature review synthesizes the existing knowledge on managerial acceptance of AI-driven decision-making, evaluates the applicability of established technology acceptance frameworks, and identifies challenges and future research needs. The review adopts a narrative approach and emphasizes conceptual synthesis rather than systematic database searching. It focuses on the Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT), Theory of Planned Behavior (TPB), and emerging AI-specific approaches such as the AI Acceptance–Avoidance Model (IAAAM), with emphasis on managerial decision-making in organizational contexts. Findings indicate that determinants such as perceived usefulness, ease of use, trust, transparency, and organizational support strongly influence adoption, while inhibitors include opacity, ethical concerns, and perceived loss of autonomy. The review highlights theoretical gaps in applying traditional acceptance frameworks to AI’s socio-technical complexities and underscores the absence of empirical validation of the emerging AI-specific Model in South Africa. Practical implications stress the importance of organizational readiness, leadership support, and ethical governance. Future research should prioritize longitudinal, context-sensitive studies to advance both theory and practice in AI adoption.
Keywords: {'name': 'Artificial Intelligence (AI), Technology Acceptance Models, Managerial Acceptance, AI-Driven Decision-Making, South Africa.'}
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Bosede Olatomi Ige
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