4376. Big Data Analytics and Sustainable SME Performance in Emerging Economies: A Comparative Evaluation of Traditional Econometric Models and Advanced MLTechniques
Invited abstract in session Digital twins and Industry 5.0: Building Resilience, stream OR for Development.
Authors (first author is the speaker)
| 1. | Sharon Mandizha
|
| ENTREPRENEURIAL STUDIES AND MANAGEMENT, DURBAN UNIVERSITY OF TECHNOLOGY | |
| 2. | Fulufhelo Netswera
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| 3. | Helper Zhou
|
| School of Accounting, Economics and Finance, University of KwaZulu Natal |
Abstract
SMEs are vital to South Africa’s economy but face persistent sustainability challenges. Big Data Analytics (BDA) offers a strategic capability to enhance decision-making, efficiency, and competitiveness. This study investigates the impact of BDA on SME sustainable performance through a comparative analysis of traditional statistical models and advanced machine learning techniques. Using SME data across sectors, the study evaluates how BDA influences financial, operational, and competitive outcomes and identifies effective analytical approaches to explain complex sustainability drivers.
Keywords
- Big Data
- Machine Learning
- Artificial Intelligence
Status: accepted
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