Role of artificial intelligence on small and medium-sized enterprises (SMES) management in southwest, Nigeria
Bibliographic record
Abstract
he study examined the application of artificial intelligence (AI) in small and medium-sized enterprises (SMEs) in Southwest Nigeria. Specifically, the study assessed the level of awareness and understanding of Artificial Intelligence (AI) among the SMEs operators including the factors hindering the adoption and implementation of AI technologies in SMEs operations in the study area. Primary data was used for the study with the aid of a questionnaire on 355 respondents. The data gathered was analyzed using percentage, mean and standard deviation, while hypothesis was tested using t-test inferential statistics. The findings revealed that 75% of respondents are aware of AI technologies, but only 55-63% deeply understands industry-specific applications. Also revealed are the major barriers to the adoption, which include financial constraints, inadequate technological infrastructure, insufficient technical manpower skills, cultural and organizational resistance, regulatory challenges, data privacy and security concerns. The study recommended among others that targeted educational initiatives, financial support, and improved infrastructure to bridge the knowledge gap and enhance AI integration are key. Also that organizational culture, leadership openness, and employee readiness are crucial for successful AI adoption. Therefore the creation and implementation of comprehensive educational programs, financial incentives and policies to create a supportive environment for AI technologies should highly encouraged by all stakeholders.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".