Version 1
: Received: 11 September 2024 / Approved: 12 September 2024 / Online: 12 September 2024 (09:18:21 CEST)
How to cite:
Kgakatsi, M.; Galeboe, O.; Molelekwa, K.; Thango, B. The Impact of Big Data on SME Performance: A Systematic Review. Preprints2024, 2024090985. https://doi.org/10.20944/preprints202409.0985.v1
Kgakatsi, M.; Galeboe, O.; Molelekwa, K.; Thango, B. The Impact of Big Data on SME Performance: A Systematic Review. Preprints 2024, 2024090985. https://doi.org/10.20944/preprints202409.0985.v1
Kgakatsi, M.; Galeboe, O.; Molelekwa, K.; Thango, B. The Impact of Big Data on SME Performance: A Systematic Review. Preprints2024, 2024090985. https://doi.org/10.20944/preprints202409.0985.v1
APA Style
Kgakatsi, M., Galeboe, O., Molelekwa, K., & Thango, B. (2024). The Impact of Big Data on SME Performance: A Systematic Review. Preprints. https://doi.org/10.20944/preprints202409.0985.v1
Chicago/Turabian Style
Kgakatsi, M., Kopo Molelekwa and Bonginkosi Thango. 2024 "The Impact of Big Data on SME Performance: A Systematic Review" Preprints. https://doi.org/10.20944/preprints202409.0985.v1
Abstract
In recent years, Big Data (BD) has become a crucial tool for Small and Medium-sized Enterprises (SMEs), impacting their performance and growth in significant ways. This systematic literature review aims to analyze the effects of BD on SMEs by examining 93 research papers published from 2014 to 2024. Employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, the review focuses on key drivers and barriers to BD adoption, including business improvement, economic performance, and revenue growth. The methodology involved a comprehensive analysis of research methodologies used in the studies, addressing biases, gaps, and the need for diverse approaches. The findings reveal that while BD has led to notable enhancements in operational efficiency and revenue for many SMEs, challenges such as limited resources and technical expertise remain. A significant reporting bias was observed, with 47% of the literature comprising quantitative studies, followed by 28% case studies, while mixed-methods and qualitative studies were underrepresented (22% and 17%, respectively). This imbalance suggests a potential overemphasis on quantitative approaches, limiting the diversity of insights available. Addressing these biases is essential to fully harness the potential of BD for SMEs to drive innovation, enhance competitiveness, and achieve better performance in the increasingly data-driven business environment.
Keywords
big data; small and medium-sized enterprises (SMEs); impact; performance
Subject
Business, Economics and Management, Business and Management
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
