Yes, This Is A Puff Piece? A Comparative Analysis of the Vendor Defences of Puffery, Statements of Future Intent and Disclaimers – Part 2 — How far does the divergence between promised and actual capabilities of an ERP implementation stretch?
Bibliographic record
Abstract
Abstract One of the common themes among various failed ERP implementations and outsourcing transactions is the divergence between the representations made by technology vendor sales teams as to promised skills, expertise and delivery, and the actually provided skills, expertise and delivery. Part 1 (Beardwood, CRi 2024, 85) began by providing an overview of the law of misrepresentation, and then the common vendor defences of puffery and opinion, statements of future intent, and contractual disclaimers, in Canada (I) and the United States (II). Part 2 continues by providing an overview of the law of misrepresentation, and then the common vendor defences of puffery and opinion, statements of future intent, and contractual disclaimers, in the European Union (III). The analysis then assesses how these defences were raised by vendors in two recent ERP failure lawsuits (IV), before concluding with lessons learned for vendors and customers (V).
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 | 0.044 | 0.152 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.012 | 0.022 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".