Strategic value and electronic commerce adoption among small and medium‐sized enterprises in a transitional economy
Bibliographic record
Abstract
Purpose This paper sets out to examine the relationship between the perceptions of the strategic value of e‐commerce and e‐commerce adoption among 107 owners/managers of small and medium‐sized enterprises (SMEs) in a transitional economy, Ghana. Design/methodology/approach The membership of the top 100 Ghanaian businesses, called the Ghana Club 100 (GC 100), and non‐traditional exporters (NTEs) was surveyed using a structured questionnaire in face‐to‐face interviews. Principal axis factoring with varimax rotation was employed to identify and estimate the constructs in the model, followed by an exploratory factor analysis to test for the inclusion of all items in the model. Finally, canonical analysis was employed to study the interrelationships among the sets of multiple dependent and multiple independent variables. By so doing control for moderator effects existing among various variables was effected. Findings Organizational support was the strongest predictor on the perceived strategic value (PSV) construct, followed by managerial productivity, and decision aids. Perceived usefulness, compatibility, external pressure and organizational pressure were found to be statistically significant determinants of e‐commerce adoption. These findings are consistent with prior studies. Additionally, it was found that ease of use was also influential in the e‐commerce adoption decision of Ghanaian SMEs. Originality/value The study shows the value that SME owners/managers in a transitional economy place on e‐commerce, and the role envisaged for it. The study also provides an insight into the e‐commerce adoption in a transitional economy context. Owners/managers of SMEs in other developing countries may find the study useful in making decisions relating to e‐commerce adoption. The impact of the perceptions of e‐commerce and e‐commerce adoption on firm performance in transitional economies is a worthy area for future research. Replication of the study in other transitional economies is therefore warranted.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".