AN EMPIRICAL ANALYSIS OF FACTORS INFLUENCING INTERNET/E-BUSINESS TECHNOLOGIES ADOPTION BY SMES IN CANADA
Bibliographic record
Abstract
Small and medium enterprises (SMEs) around the world engage in e-commerce and e-business to support business operations as well as to enhance revenue generation from nontraditional sources. Internet and e-business technologies (IEBT) are the pillars of e-commerce and e-business. Despite the universal appeal of IEBT, it has been reported that the adoption of such technologies by SMEs is influenced by contextual imperatives. The objective of this research is to investigate factors impacting the adoption of IEBT in SMEs based in the Maritime region of Canada. A research model based on the diffusion of innovation (DIT) and the Technology–Organization–Environment (TOE) frameworks was used to guide the discourse. Such factors as relative advantage, compatibility, complexity, management support, organizational readiness, external pressure, and government support were used to develop relevant hypotheses. Questionnaires were mailed to key informants in SMEs. Data analysis was performed using the partial least squares (PLS) technique. Predictions related to relative advantage, management support, and competition's pressure were confirmed. The study did not support the constructs of compatibility, complexity, government support, customers' and partners' pressures as significant predictors of IEBT adoption by the SMEs in the region.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| 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".