4554. AI-Driven ESD Impact Assessment of SMEs in Emerging Markets: Evidence from South Africa
Invited abstract in session Business Management in Dynamic Emerging Markets, stream Selected Aspects of International Finance and OR.
Authors (first author is the speaker)
| 1. | Rudzani Ratshitanga
|
| School of Commerce, UKZN | |
| 2. | Helper Zhou
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| School of Accounting, Economics and Finance, University of KwaZulu Natal | |
| 3. | Gordon Dash
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| Finance and Decision Sciences, University of Rhode Island | |
| 4. | Nina Kajiji
|
| Computer Science and Statistics, University of Rhode Island, and The NKD Group, Inc. |
Abstract
Developing countries prioritise SME development during the pandemic, yet empirical evidence on initiative impact remains scarce. South African studies often use cross-sectional data and linear models, overlooking non-linearities in the heterogeneous SME sector. Applying K4-RANN to longitudinal data from 212 entrepreneurs in South Africa (2019-2021), we find incubated SMEs outperformed peers (MSE=0.024; AIC=-1045), but benefits were size-dependent as small firms gained most, micro/medium saw minimal/negative effects. Findings inform targeted policy for millions of SMEs in developing countries.
Keywords
- Artificial Intelligence
- Developing Countries
- Sustainable Development
Status: accepted
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