Nexus between Exchange Rate Fluctuation and the Investment Decision of Small and Medium-sized Enterprises (SMEs)
Bibliographic record
Abstract
This study examines the impact of exchange rate fluctuation on the investment decisions of Small and Medium-sized Enterprises (SMEs) in Nigeria’s South-west geopolitical zone. The objective is to examine how exchange volatility influences SME investment decision behavior, particularly in a developing economy where external shocks significantly affect business sustainability. The research adopted a descriptive survey design and employed a mixed-methods approach that integrates both quantitative and qualitative data to provide a holistic analysis. The study population consists of 149,317 SMEs registered with the Small and Medium Enterprises Development Agency of Nigeria (SMEDAN), distributed across Ekiti, Ondo, Osun, Oyo, Ogun, and Lagos States. The study employed stratified random and purposive sampling techniques. Data collected were analysed using the Statistical Package for the Social Sciences (SPSS), applying both descriptive and inferential statistics. Findings revealed that volatility in exchange rates introduces a high level of uncertainty into the business environment, directly impacting SMEs’ ability to make sound, long-term investment decisions. The study concludes that exchange rate fluctuation introduces uncertainty that impairs strategic investment decisions. It recommends that government and financial institutions should make hedging instruments and foreign exchange risk management tools more accessible and affordable for SMEs to reduce uncertainty in investment planning.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".