Determinants and Performance Outcome of SMEs' Use of Vertical B-to-B e-Marketplaces to Sell Products
Bibliographic record
Abstract
This study measures the influence of four key determinants on SMEs' use of vertical B‐to‐B marketplaces to sell products and assesses whether the use of these electronic intermediaries can have a positive impact on SMEs' operational performance. The theoretical model is tested on data collected from 148 SMEs operating in one Canadian province. Results show that SMEs technological readiness, external pressure and support from technology experts positively influence SMEs use of B‐to‐B e‐marketplaces to sell products. The structural model also demonstrates that the characteristics of B‐to‐B e‐marketplaces, external pressure and support from technology experts are antecedents to SMEs technological readiness. Moreover, findings show that SMEs technological readiness completely mediates the relationship between the characteristics of B‐to‐B e‐marketplaces and SMEs use of B‐to‐B e‐marketplaces to sell products while partially mediating the relationship between the support from technology experts and B‐to‐B e‐marketplaces to sell products. Finally, the use of B‐to‐B e‐marketplaces to sell products can provide operational benefits to 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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".