The impact of SME access to finance and performance on exporting behaviour at firm level: A case of furniture manufacturing SMEs in Zimbabwe
Bibliographic record
Abstract
Orientation: Globally, the majority of Small and Medium-sized entities (SMEs) are resource constrained. As a result, not all SMEs are able to fully exploit the benefits associated with international trade as they face challenges when exporting their produce.Research purpose: This article presents an investigation into the impact of access to finance on firm performance and exporting behaviour of SMEs in Harare, Zimbabwe.Motivation for the study: The article stems from the observation that although there is a growing importance and contribution of SMEs worldwide, research has shown that only a few of these SMEs are involved in international trade.Research design, approach and method: A cross-sectional study was employed with quantitative methods being utilised. The collected data were analysed using a structural equation modelling technique, which employed the Smart partial least squares software (version 2.0).Main findings: The key findings reveal that a significant positive relationship between access to finance and SMEs exporting behaviour does exist. Furthermore, the study’s findings challenge the notion that firm performance has a significant impact on exporting behaviour and show a negative impact of access to finance on SME firm performance.Practical/managerial implications: There is a need to put systems in place in Zimbabwe that that will (1) prioritise the need to have clear routes to market and increase awareness among SME owners, and (2) help SMEs overcome high costs associated with participating in export of goods and services.Contribution/value-add: The article provides a unique empirical analysis of the relationship that exists between access to finance, firm performance and export behaviour of SME firms in Zimbabwe, and thereby makes a valid contribution to SME literature.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".