Information technology sourcing changes in an SME: <i>Ça Va de Soi</i> in the cloud with diamonds
Bibliographic record
Abstract
This case study presents the information technology (IT) sourcing decisions made by a Canadian small and medium enterprise specializing in knitwear, called Ça Va de Soi ( CVDS), during the deployment of the second phase of their two-phase organizational strategy based on a “Bricks and Clicks” business model. CVDS has 30 employees and 5 stores with annual sales of around $CDN 5 million (2015). The case focuses on phase two, the “Clicks,” where an IT project, divided into two parallel subprojects, was realized: (1) the custom development of an ERP system, and (2) the creation of an online e-commerce. The project was based on an “on-premises” sourcing strategy where the information systems were developed “in-house” by external service providers. After several months of efforts, the subprojects were abandoned and CVDS’ activities were rolled back to their legacy systems (Part A). Pulling the plug on the IT project was a tough decision for CVDS who still needed the online store to be implemented in order to support its stores’ activities. However, CVDS’ management team considered this failure as an opportunity to learn from their mistakes, review, and transform its IT sourcing strategy (Part B).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".