Business Intelligence Adoption in Diverse Economies: A Clustering Study of Five European Countries

Objective: This paper explores the factors that influences Business Intelligence (henceforth, BI) tool adoption and its impact on Armenian IT enterprises' performance. Theoretical Framework: Grounded in organizational shift, technology adoption (e.g., TOE framework), and competitive advantage theories, the research employs SWOT analysis, K-Means clustering, Logistic Regression, and the Elbow method to examine competitive pressures and BI's performance impact. Method: A mixed-methods approach was used, combining surveys of Armenian tech companies with logistic regression analysis to evaluate BI adoption's relation to performance indicators such as revenue growth, market share, and customer satisfaction. K-Means clustering compared Armenia's BI adoption levels to Lithuania, Romania, Estonia, and Denmark, based on digital infrastructure and regulatory environments. Results and Discussion: Findings disclosed low BI adoption rates in Armenia in comparison to Estonia and Denmark. Logistic regression connected strategic planning with advanced efficiency and customer needs, while K-Means clustering grouped Armenia with Romania in lower adoption clusters. Obstacles such as the digital divide, skills gaps, and regulations were discussed. Research Implications: The study highlights the need for Armenian policymakers to enhance digital infrastructure, foster data analysis expertise, and create favorable regulations. For businesses, it emphasizes investing in training, data governance, and aligning BI strategies with objectives. Originality/Value: This research paper uniquely benchmarks BI adoption in Armenia against European peers, offering actionable insights for improving adoption in emerging economies and enriching BI literature