Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/338773 
Year of Publication: 
2024
Citation: 
[Journal:] Businesses [ISSN:] 2673-7116 [Volume:] 4 [Issue:] 4 [Year:] 2024 [Pages:] 632-695
Publisher: 
MDPI, Basel
Abstract: 
Big Data (BD) has emerged as a pivotal tool for small and medium-sized enterprises (SMEs), offering substantial benefits in enhancing business performance and growth. This review investigates the impact of BD on SMEs, specifically focusing on business improvement, economic performance, and revenue growth. The objective of this systematic review is to evaluate the drivers and barriers of BD adoption in SMEs and assess its overall impact on operational efficiency and business outcomes. A comprehensive systematic review of 93 research papers published between 2014 and 2024 was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. The methodology included detailed analysis of research approaches, addressing biases and gaps in the literature. BD adoption in SMEs led to significant improvements in operational efficiency, revenue generation, and competitiveness. However, the studies reveal persistent challenges, such as limited financial resources and technical expertise. The review identified a reporting bias, with 47% of studies using quantitative methods, 28% employing case studies, and mixed-method and qualitative studies underrepresented (22% and 17%, respectively). This imbalance highlights a potential overreliance on quantitative approaches, which may limit the depth of insights gained. While BD offers considerable potential for driving innovation and enhancing competitiveness in SMEs, addressing the current methodological biases and resource-related barriers is crucial to fully harness its benefits. Future research should focus on diverse approaches to provide a holistic understanding of BD's impact on SMEs.
Subjects: 
Big Data
small and medium-sized enterprises (SMEs)
business performance
economic growth
data-driven innovation
systematic review
Persistent Identifier of the first edition: 
Creative Commons License: 
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Document Type: 
Article
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