EURO 2025 Leeds
Abstract Submission

2233. Decommmoditised SMEs: The role of big data in driving nuanced innovation among SMEs in South Africa

Invited abstract in session WA-23: Data Analytics for Business Resilience and Sustainability - Measuring SME Performance , stream OR for Societal Development.

Wednesday, 8:30-10:00
Room: Esther Simpson 3.01

Authors (first author is the speaker)

1. Sharon Mandizha
ENTREPRENEURIAL STUDIES AND MANAGEMENT, DURBAN UNIVERSITY OF TECHNOLOGY
2. Fulufhelo Godfrey Netswera
FACULTY OF MANAGEMENT SCIENCES, DURBAN UNIVERSITY OF TECHNOLOGY
3. Helper Zhou
School of Accounting, Economics and Finance, University of KwaZulu Natal

Abstract

In today's data-driven economy, Small and Medium-sized Enterprises (SMEs) face increasing pressure to escape commodity traps through strategic differentiation. This study investigates how big data analytics enables SMEs to implement nuanced innovation strategies that foster decommoditisation and sustainable competitive advantage. The study utilised mixed methods approach combining systematic literature review with empirical analysis to develop a comprehensive understanding of this phenomenon. The study employed advanced R programming techniques to analyse bibliometric data from major academic databases including Scopus and Web of Science; and performs quantitative analysis of unstructured internet data from SME digital footprints. This research established a new framework that classifies big data applications into four key dimensions: customer insight generation, operational optimisation, product development acceleration, and market positioning refinement. The study found that SMEs leveraging big data analytics demonstrated higher innovation diffusion rates and improved margin retention compared to non-adopters. The study contributes to operational research literature by proposing a decision support model that enables resource-constrained SMEs to prioritise big data investments aligned with specific decommoditisation objectives. This study offers practical implications for SME leaders seeking to leverage data analytics for strategic differentiation.

Keywords

Status: accepted

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