E-commerce Adoption in Nigerian Businesses
Bibliographic record
Abstract
Business organizations around the world engage in e-commerce (EC) and e-business to support business operations and enhance revenue generation from non-traditional sources. Studies focusing on EC adoption in Sub Saharan Africa (SSA) are just beginning to emerge in the extant information systems (IS) literature. The objective of this current study is to investigate factors impacting the acceptance of EC in small businesses in SSA with Nigeria as an example. A research model based on the Diffusion of Innovation (DIT) and the Technology–Organization–Environment (TOE) frameworks were used to guide this discourse. Such factors as relative advantage, compatibility, complexity, management support, organizational readiness, external pressure, and IS vendor support were used to develop relevant hypotheses. Questionnaires were administered to respondents in Nigeria and data analysis was performed using the Partial Least Squares (PLS) technique. Predictions related to relative advantage, management support, and IS vendor support were confirmed; the other hypotheses were unsupported by the data. The study’s implications for research and practice are discussed in the chapter.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.004 |
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".