The weather report for the supply chain: a longitudinal analysis of the ISM/Forrester Research Reports on Technology in Supply Management, 2001‐2003
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article presents an analysis of three years results from the quarterly Report on Technology in Supply Management, conducted through a joint effort of the Institute for Supply Management (ISM) (formerly the National Association of Purchasing Managers) and Forrester Research. This report provides the best snapshot on the growth of e‐procurement in the United States. However, the sponsors do not publicly provide any analysis on the trends the data show from quarter‐to‐quarter. Now, with three years of available data from the twelve quarterly surveys conducted to date, there is an opportunity to analyze the adoption rates of e‐procurement tools, techniques and protocols in the American marketplace. The author of this study has conducted just such a longitudinal analysis of the ISM/Forrester data, examining the trends for organizations across the U.S. marketplace. What is demonstrated is that overall, both in manufacturing and service‐oriented firms and in large and small purchasing organizations, e‐procurement methods are rising and reaching “critical mass” in most areas with the “e‐way” fast becoming “the way”. However, important differences due exist between the groups and their specific needs, motivations, and results in their shift to an electronic acquisition environment. These are highlighted and discussed in this article.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.017 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it