Barriers to the Effective Integration of Developed ICT for SMEs in Rural NIGERIA
Bibliographic record
Abstract
This study investigated three key factors (technological-related, organisational-related and environmental-related barriers) affecting the adaptation to or integration of developed ICT. It also examined how SMEs in less developed countries can explore the different stages of developed ICT by moving from one stage to the other. The integration of ICT in SMEs is important as technologies have become competitive tools in contemporary business practices. This study is based on a survey of 322 Nigerian SMEs which was successfully validated using the SmartPLS3 software. The quantitative analysis centred on the three hypothesised barriers to measure the extent to which SMEs’ internal and external variables could limit their competitiveness in relation to business expansion and organisational growth. The analysis helped explain some of the critical challenges faced by rural SMEs in an emerging economy such as Nigeria despite the literature’s previous emphasis on the impacts of ICT on the SMEs’ growth and expansion. A major contribution of the study is the development of a distinct model to help SMEs identify the significance of developed ICT and propose a strategy for SMEs to navigate the stages of developed ICT.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".