Information Systems Effectiveness in Small Businesses: Extending a Singaporean Model in Canada
Bibliographic record
Abstract
In this study, a model of information systems (IS)effectiveness is tested. Developed by James Y. L. Thong and Chee-Sing Yap andtested originally in Singapore, the model is evaluated in the context ofCanadian small businesses. The other aim of this study is to extend theSingaporean model by introducing the additional construct of intention of ISexpansion. Following a discussion of Thong's and Yap's study, the new researchmodel is presented, and several hypotheses are proposed. Data from a cross-sectional survey of 105 Canadian small businesses, all ofwhich used IS, are used to test the hypotheses. These data reveal that theSingaporean model is, on the whole, applicable to small businesses in Canada.In both models, managerial and vendor support are predictors of ISeffectiveness. A few differences do exist between the Singaporean and Canadianstudies, however: unlike the original study, the new model does not support thepositive relation between consultant effectiveness and IS effectiveness.Finally, the Canadian data support only one of the hypotheses regarding theintention of IS expansion. (SAA)
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".