Internet/e‐business technologies acceptance in Canada's SMEs: an exploratory investigation
Bibliographic record
Abstract
Purpose This study aims at contributing to the discussion related to what causes Canadian small and medium‐size enterprises (SMEs) to be reticent about accepting internet and e‐business technologies (IEBT) in their operations. The research also seeks to gain an understanding of the relative importance of each of the selected factors in the research setting. Design/methodology/approach A survey was conducted in the Atlantic region of Canada. Questionnaires were mailed to key SMEs' informants. Data analysis was performed using the partial least squares (PLS) approach. A research framework based on the technology‐organization‐environment (TOE) frameworks was used to guide the research effort. Such contingent factors as perceived benefits, management commitment/support, organizational IT competence, external pressure, information systems (IS) vendor support, and availability of financial support, were used to develop relevant hypotheses. Findings The study's findings indicated that perceived benefits, management commitment/support, and external pressure are significant predictors of IEBT acceptance in the sampled SMEs; the results did not show that organizational IT competence, IS vendor support, and availability of financial support positively influence IEBT acceptance in the sampled SMEs. Practical implications Policy makers, industry leaders, and small business operators wishing to understand some of the reasons why certain SMEs in the country lag in the adoption of IEBT and related technologies can benefit from the information provided in this study. The study also alerted the attention of local IS vendors and financial institutions to what can be done to strengthen IS adoption in Canadian small businesses. Originality/value A handful of previous research in Canada has researched IEBT adoption; however, some of these studies are dated. A such, this current investigation of IEBT acceptance in a less endowed part of the country is timely and welcoming; it also serves to complement other prior studies in the country and elsewhere. A scan of the extant literature indicates that no previous study in the country has modeled some of the factors (e.g. the availability of financial support) as were used herein. The inclusion of such a factor enriches insight in this area of study.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 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) | 0.002 | 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".