EXPLORING THE DETERMINANTS OF WEB-BASED E-BUSINESS EVOLUTION IN CANADA
Bibliographic record
Abstract
Canadian businesses are increasingly adopting Internet based e-business technologies such as websites, web-based procurement, and e-commerce.While there have been many studies in various countries of the determinants of e-business adoption, the newness of ebusiness has precluded much understanding of the factors that influence the evolution of e-business within companies.There is therefore considerable interest in stages of ebusiness and the factors that cause organizations to move between e-business stages.Using panel data for approximately 4,700 business enterprises for the period 2001-2003 collected by Statistics Canada, we perform logistic analyses to better understand which technological, environmental, and organizational factors proposed in the theoretical literature appear to be associated with changes in e-business stages.Our findings provide some support for the TOE model as a framework for understanding e-business evolution, although coefficient signs were in a number of cases opposite of what was predicted.We also find differences in how the TOE components influence small and medium versus large enterprises, with the latter more influenced by environmental factors relative to small and medium enterprises.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".