An Empirical Study of Factors Influencing E-Commerce Adoption/Non-Adoption in Slovakian SMEs
Bibliographic record
Abstract
While the global emergence of e-commerce has led businesses to take advantage of the technologies available to them to enhance their use of e-commerce, small and medium-sized enterprises (SMEs) are known for not adopting information technology to create jobs. Due to the dearth of studies of SME adoption of technology in transitioning European countries such as Slovakia, (Saffu, Walker, and Mazurek 2012 Saffu, K., J. H. Walker, and M. Mazurek. 2012. Perceived strategic value and e-commerce adoption among SMEs in Slovakia. Journal of Internet Commerce 11 (1):1–23. doi:10.1080/15332861.2012.650986.[Taylor & Francis Online] , [Google Scholar]), it is imperative to understand the factors that differentiate SME e-commerce adopters from non-adopters. Adoption and non-adoption factors of 230 Slovakian SMEs were empirically examined using logistic regression. Compatibility and Organizational Readiness, Decision and Operational Aids, and External Pressure were significant for discerning e-commerce adoption. Practical, policy, and research implications are presented. This study goes beyond the determinants of e-commerce adoption and contributes to the understanding of factors that distinguish between adopters and non-adopters of e-commerce for Slovakian SMEs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".