Artificial Intelligence as a Driver of Business Excellence: A Protocol for Scoping Review
Olaleye Banji Rildwan
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Olaleye Banji Rildwan: North West University
A chapter in Embracing Technological Agility in Accounting and Business – Vol. 2, 2026, pp 105-113 from Springer
Abstract:
Abstract Artificial intelligence (AI) has become a vital force driving companies toward excellence and transformation since its emergence as a distant idea. According to the current study, businesses are experiencing a paradigm shift toward sustainability and excellence, emphasizing the merging of AI and its expanding role in the information technology (IT) sector. However, enhancing businesses to automate decision-making and predict market changes in real time offers unprecedented resilience and foresight through emerging technologies like AI. This chapter aims to establish a foundation for a scoping review of the related literature, systematically developing a more comprehensive definition and terminology that captures artificial intelligence for business excellence, while mapping and integrating concepts through their theoretical lens. The methodological approach initially suggested by Arksey and O’Malley and later refined by Levac and colleagues will guide this scoping review. Search strategies will be developed in collaboration with a local librarian and management practitioner. The search will be conducted across electronic databases such as Web of Science and Scopus. By analyzing a challenging sample of 317 academic publications from 2000 to 2025, this chapter systematically examines artificial intelligence and its causal relationship within a business context, providing an overview of this emerging research area and proposing clear, precise, scientific, and operational terminology to improve future research design.
Keywords: Artificial intelligence; Business excellence; Databases; Protocol; Scoping review (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-032-13384-7_8
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DOI: 10.1007/978-3-032-13384-7_8
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