Business Intelligence for SMEs: a hybrid review of models, barriers, and future directions

This study examines how Business Intelligence (BI) is conceptualised, adopted, and implemented within Small and Medium-sized Enterprises (SMEs), a context where digital transformation is essential yet insufficiently explored. Although BI is widely recognised as a strategic driver of informed decision-making and competitiveness, existing research remains fragmented and often sector-specific. Using a hybrid methodology that combines bibliometric analysis with a qualitative systematic literature review from 2015 to 2024, the study traces a shift from early semantic and dashboard-driven models to cloud-based, predictive solutions. It also identifies key limitations and proposes future research on empirical validation, adaptable governance, and innovation-resource trade-offs.

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Publication Details

Journal
Enterprise Information Systems
Published
2025-11-21
DOI
https://doi.org/10.1080/17517575.2025.2588755
Primary Topic
Big Data and Business Intelligence
Type
article
Field-Weighted Citation Impact
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article

Business Intelligence for SMEs: a hybrid review of models, barriers, and future directions

Pierluigi Toma, Giuseppe Dammacco, Alessandra Tafuro, Chiara Colamartino
Enterprise Information Systems
Big Data and Business Intelligence
article

Business Intelligence for SMEs: a hybrid review of models, barriers, and future directions

Pierluigi Toma, Giuseppe Dammacco, Alessandra Tafuro, Chiara Colamartino
article en

Abstract

This study examines how Business Intelligence (BI) is conceptualised, adopted, and implemented within Small and Medium-sized Enterprises (SMEs), a context where digital transformation is essential yet insufficiently explored. Although BI is widely recognised as a strategic driver of informed decision-making and competitiveness, existing research remains fragmented and often sector-specific. Using a hybrid methodology that combines bibliometric analysis with a qualitative systematic literature review from 2015 to 2024, the study traces a shift from early semantic and dashboard-driven models to cloud-based, predictive solutions. It also identifies key limitations and proposes future research on empirical validation, adaptable governance, and innovation-resource trade-offs.

Enterprise Information SystemsVol. 20(1)
University of Salento (IT), Ospedale San Giuseppe (IT), University of Bari Aldo Moro (IT)
Openalex Percentile: Top 46%
Big Data and Business Intelligence
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