A Survey Of Electronic Data Interchange (Edi) In The Top Public Companies In Canada
Bibliographic record
Abstract
This paper reports on management view’s of the implementation, use, and benefits of EDI systems among the top 400 public companies in Canada. The market penetration of EDI is still growing linearly. Our best estimate is that 64% of the top public companies in Canada had an EDI system as of 1999.The most popular reasons for adopting EDI in decreasing order are improved customer service, improved supplier relationship, reduced clerical error, and competitive advantage. With the exception of the latter, the first three of these are viewed also as important impacts of EDI after implementation. There were no differences between smaller and larger companies (defined as having annual sales less than or greater than $500 million, respectively). Respondents generally felt that the benefits of EDI outweighed the costs of implementation.In-house staff rather than external consultants were used predominantly in the design and implementation of EDI systems. The most common problems encountered were inadequacy of resources to implement the systems, deciding which activities to include, and training. Much less common were difficulties in selling the concept to different parts of the organization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".