Igun, Iyobosa (2025) Implementing Business Intelligence Tools in SMES: Challenges and benefits for strategic planning. Masters thesis, York St John University.

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Abstract

This study investigates the implementation of Business Intelligence (BI) tools in Small and Medium-sized Enterprises (SMEs), with a particular focus on the challenges and benefits for strategic planning. Anchored in the Technology–Organisation–Environment (TOE) framework and the Resource-Based View (RBV), the research employs a sequential explanatory mixed-methods approach, combining quantitative survey data from 88 SMEs with qualitative interviews from six industry stakeholders across multiple sectors in Nigeria. Quantitative analysis, including regression modelling, reveals that leadership support, frequency of BI usage, employee training, and data integration quality significantly predict strategic planning effectiveness (R² = 0.62, p < 0.001). The qualitative findings deepen this understanding, highlighting cultural resistance, skills gaps, sector-specific constraints, and adaptive workarounds as critical factors shaping BI adoption outcomes. While the study confirms that BI enhances decision-making, operational efficiency, and strategic forecasting, it also finds that financial gains and cultural transformation often lag behind initial adoption, suggesting a “BI maturity curve” in SMEs. Sector-specific use cases demonstrate that tailored BI solutions yield greater strategic value than generic deployments. The research concludes with practical recommendations for SME leaders, policymakers, and BI vendors on fostering adoption through targeted training, leadership engagement, sector-specific tool design, and infrastructural support. These findings contribute to the academic discourse on digital transformation in SMEs and provide actionable strategies for enhancing BI’s role in long-term business competitiveness.

Keywords: Business Intelligence, SMEs, Strategic Planning, Technology–Organisation–Environment Framework, Resource-Based View, Data-Driven Decision-Making, Digital Transformation, BI Adoption Challenges, AI-Driven Analytics.

Item Type:	Thesis (Masters)
Status:	Unpublished
School/Department:	London Campus
URI:	https://ray.yorksj.ac.uk/id/eprint/13742

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